reduce_dimensions_and_cluster_asthma_patients.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dimensionality Reduction and Clustering of UK Biobank Asthma Patients\n",
    "\n",
    "This notebook does the following:\n",
    "\n",
    "* Reads in a sample of UKBB Asthma patients\n",
    "* Performs dimensionality reduction on UKBB Asthma Patients with PCA and finds the optimal number of principal components\n",
    "* Performs dimensionality reduction on UKBB Asthma patients wit tSNE, using the otpimal number of principal components\n",
    "* Performs kMeans clustering to find any clusters of UKBB asthma patients\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "from matplotlib import rc\n",
    "rc('font', **{'family':'sans-serif','sans-serif':['Helvetica']})\n",
    "#rc('text', usetex=False)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize']=(12,6)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn import decomposition\n",
    "from sklearn import manifold\n",
    "from sklearn.cluster import KMeans "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1: Read in UKBB asthma patients and subselect data\n",
    "\n",
    "Fields in UKBB phenotype data come in three types:\n",
    "\n",
    "1) 'QUANTITY' fields - these fields are continuous variables. Integers and Decimal numbers\n",
    "2) 'CATEGORY' fields - these fields are not continuous. There are a limited number of integers which label specfic category values\n",
    "3) 'BIN' fields - these are binary fields. They only have two possible values. They only label a field as being 'TRUE' or 'FALSE' \n",
    "\n",
    "For summary statistics and finding the most correlated variables, we only selected 'QUANTITY' fields.  For dimensionality reduction for further clustering, it's not so clear whether we have to select only 'QUANTITY' fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Read in 5K sample\n",
    "#asthma_df=pd.read_csv(\"ukbb_asthma_sample_5k.csv\",sep='\\t')\n",
    "#asthma_df=pd.read_csv(\"ukbb_asthma_sample_5k_new.csv\",sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "asthma_df=pd.read_csv(\"ukbb_asthma_full_filtered.csv\",sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'age', u'BMI', u'asthma',\n",
       "       u'f_20006_0_0_f_QUANT_Interpolated_Year_when_cancer_first_diagnosed',\n",
       "       u'f_20008_0_0_f_QUANT_Interpolated_Year_when_non_cancer_illness_first_diagnosed',\n",
       "       u'f_20010_0_0_f_QUANT_Interpolated_Year_when_operation_took_place',\n",
       "       u'f_20018_0_0_f_QUANT_Prospective_memory_result',\n",
       "       u'f_22508_0_0_f_QUANT_Amount_of_tobacco_currently_smoked',\n",
       "       u'f_1160_0_0_f_QUANT_Sleep_duration',\n",
       "       u'f_1269_0_0_f_QUANT_Exposure_to_tobacco_smoke_at_home',\n",
       "       ...\n",
       "       u'specialRequest_ageSquared_QUANT_age_squared',\n",
       "       u'specialRequest_QUANT_years_observed_in_primary_HES',\n",
       "       u'specialRequest_QUANT_type_of_asthma_exacerbation',\n",
       "       u'specialRequest_QUANT_asthma_hospital_exacerbations_raw',\n",
       "       u'specialRequest_QUANT_asthma_hospital_exacerbations_annual_rate',\n",
       "       u'specialRequest_QUANT_asthma_HES_based_age_of_onset',\n",
       "       u'specialRequest_QUANT_asthma_HES_or_NI_based_age_of_onset_youngest',\n",
       "       u'specialRequest_QUANT_OA_HES_based_age_of_onset',\n",
       "       u'f_20016_20191_0_0_f_QUANT_Fluid_intelligence_score',\n",
       "       u'f_4282_20240_0_0_f_QUANT_Maximum_digits_remembered_correctly'],\n",
       "      dtype='object', length=968)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asthma_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Select columns to filter on\n",
    "cols = [col for col in asthma_df.columns if 'HES_p' not in col and 'PC' not in col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#asthma_df=asthma_df[cols].drop('Unnamed: 0',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#select only 'QUANTITY' fields\n",
    "asthma_df_quant=asthma_df.loc[:, asthma_df.columns.str.contains('QUANT|age|BMI')].fillna(0.0)\n",
    "#select only fields with 10 or more unique values\n",
    "for col in asthma_df_quant.columns:\n",
    "    if len(asthma_df_quant[col].unique()) < 20 :\n",
    "        asthma_df_quant.drop(col,inplace=True,axis=1)\n",
    "\n",
    "        \n",
    "#select only 'QUANTITY' and \"CATEGORY\" fields\n",
    "#asthma_df_quant_cat=asthma_df.loc[:, asthma_df.columns.str.contains('QUANT|age|BMI|CAT|sex')].fillna(0.0)\n",
    "#asthma_df_quant_cat=asthma_df_quant_cat.drop(columns=['f_22182_0_0_f_CAT_HLA_imputation_values_and_quality'])\n",
    "#selects all features. \n",
    "\n",
    "#asthma_df_all=asthma_df.loc[:,~asthma_df.columns.str.contains('ID|random|chip|asthma')].fillna(0.0)\n",
    "#asthma_df_all=asthma_df_all.drop(columns=['f_22182_0_0_f_CAT_HLA_imputation_values_and_quality'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61171, 779)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asthma_df_quant.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2: Reduce dimensions with PCA\n",
    "\n",
    "The next step is to perform PCA to reduce the dimensions in the data set. PCA is straightforward with Python. It's not straightforward to identify the ideal number of Principal Components to use and which data to include. We have three data frames in this notebook:\n",
    "\n",
    "* asthma_df_quant - this data frame only has 'QUANTITY' fields. This is exaclty the same as the exploratory analysis phase finding summary statistics and correlated/uncorrelated variables.\n",
    "* asthma_df_quant_cat - this data frame has both 'QUANTITY' fields and 'CATEGORY' fields. This totals about 1500 features\n",
    "* asthma_df_all - this data frame has everything - 'QUANTITY', 'CATEGORY', and 'BINARY' fields.\n",
    "\n",
    "\n",
    "In this step we have to:\n",
    "\n",
    "* Perform PCA in two configurations: Two principal components and the optimal number of principal components\n",
    "* We need to perform this PCA on all three data frames listed above. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Scaling\n",
    "Before dimensionality reduction and further clustering with kMeans, data has to be scaled. The standard scaler scales the data to an average value of 0 and a unit variance of 1. This is appropriate for the 'QUANTITY' features. We can't use the standard scaler for the other features, so we do 'min/max' scaling, which scales the data in the range from 0 to 1.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Scale the quantity features only\n",
    "data_quant=StandardScaler().fit_transform(asthma_df_quant.values)\n",
    "#Scale the quantity AND CATEGORY features\n",
    "#data_quant_cat=StandardScaler().fit_transform(asthma_df_quant_cat.applymap(lambda x: float(x)).values)\n",
    "#Scale ALL THE features\n",
    "#data_all=StandardScaler().fit_transform(asthma_df_all.applymap(lambda x:float(x)).values)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/wakari/anaconda/envs/default/lib/python2.7/site-packages/matplotlib/font_manager.py:1328: UserWarning: findfont: Font family [u'sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca_2d = decomposition.PCA(n_components=2)\n",
    "pca_quant_2d=pca_2d.fit_transform(data_quant)\n",
    "\n",
    "plt.scatter(pca_quant_2d[:,0],pca_quant_2d[:,1])\n",
    "\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.title('QUANTITY FEATURES ONLY two principal components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding the optimal number of principal components for each three data frames\n",
    "\n",
    "It's easier to talk about in person, but the best way to determine the optimal number of principal components is to plot the 'cummulative eigenvalue fraction' vs the number of eigenvalues. The code to do this is below. The example code is for 500 principal components - this is way too many. I'll explain why below:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PCA for 500 components\n",
    "pca_500d = decomposition.PCA(n_components=500)\n",
    "pca_quant_500d=pca_500d.fit_transform(data_quant)\n",
    "\n",
    "# Calculate the cummalative eigenvalue fraction\n",
    "eigen_fraction_500d=np.cumsum(pca_500d.explained_variance_)/(np.sum(pca_500d.explained_variance_))\n",
    "#Plot the Results\n",
    "plt.scatter(range(1,len(pca_500d.explained_variance_)+1),eigen_fraction_500d)\n",
    "plt.xlabel('Number of Principal Components')\n",
    "plt.ylabel('Cummatlive Eigenvalue Fraction')\n",
    "plt.title('QUANTITY FEATURES ONLY 500 principal components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do you see how the plot is flat for most of it? It means 500 is way too many principal components. The optimal number of Principal components is when the above plot only starts to get flat. Copy and paste the code below to find the optimal number of Principal Components for all three data frames:\n",
    "\n",
    "* Quantitative Features Only\n",
    "* Quantitative + Categorical Features\n",
    "* All Features\n",
    "\n",
    "#### Hint\n",
    "\n",
    "This is not as hard as it looks. You can reduce the number of principal components in the above plot (to say 150 or so) and pick out the optimal number of components by eye. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PCA for 300 components\n",
    "\n",
    "pca_300d = decomposition.PCA(n_components=300)\n",
    "pca_quant_300d=pca_300d.fit_transform(data_quant)\n",
    "\n",
    "# Calculate the cummalative eigenvalue fraction\n",
    "eigen_fraction_300d=np.cumsum(pca_300d.explained_variance_)/(np.sum(pca_300d.explained_variance_))\n",
    "#Plot the Results\n",
    "plt.scatter(range(1,len(pca_300d.explained_variance_)+1),eigen_fraction_300d)\n",
    "plt.xlabel('Number of Principal Components')\n",
    "plt.ylabel('Cummatlive Eigenvalue Fraction')\n",
    "plt.xlim(5,60)\n",
    "plt.ylim(0.1,0.8)\n",
    "plt.title('QUANTITY FEATURES ONLY 300 principal components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca_55d = decomposition.PCA(n_components=55)\n",
    "pca_quant_55d=pca_55d.fit_transform(data_quant)\n",
    "\n",
    "plt.scatter(pca_quant_55d[:,0],pca_quant_55d[:,1])\n",
    "\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.title('QUANTITY FEATURES ONLY 55 principal components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3: Dimensionality Reduction with tSNE\n",
    "\n",
    "This part is straightforward. You will have to run the same code below and replace the number of dimensions with the optimal number of principal components you found above. You will have to do so again for all three data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"tsne_2d = manifold.TSNE(n_components=2, init='pca', random_state=0, perplexity=20,\\n                     learning_rate=300, n_iter=400)\\n    #tsne = manifold.TSNE(n_components=2, init='pca')\\ntsne_quant_2d=tsne_2d.fit_transform(data_quant)\\n\\nplt.scatter(tsne_quant_2d[:,0],tsne_quant_2d[:,1])\\n\\nplt.xlabel('Dimension 1')\\nplt.ylabel('Dimension 2')\\nplt.title('QUANTITY FEATURES ONLY tSNE with two components')\\nplt.show()\\n\""
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''tsne_2d = manifold.TSNE(n_components=2, init='pca', random_state=0, perplexity=20,\n",
    "                     learning_rate=300, n_iter=400)\n",
    "    #tsne = manifold.TSNE(n_components=2, init='pca')\n",
    "tsne_quant_2d=tsne_2d.fit_transform(data_quant)\n",
    "\n",
    "plt.scatter(tsne_quant_2d[:,0],tsne_quant_2d[:,1])\n",
    "\n",
    "plt.xlabel('Dimension 1')\n",
    "plt.ylabel('Dimension 2')\n",
    "plt.title('QUANTITY FEATURES ONLY tSNE with two components')\n",
    "plt.show()\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"tsne_2d = manifold.TSNE(n_components=2, init='pca', random_state=0, perplexity=20,\\n                     learning_rate=300, n_iter=400)\\n    #tsne = manifold.TSNE(n_components=2, init='pca')\\ntsne_quant_2d=tsne_2d.fit_transform(pca_quant_55d)\\n\\nplt.scatter(tsne_quant_2d[:,0],tsne_quant_2d[:,1])\\n\\nplt.xlabel('Dimension 1')\\nplt.ylabel('Dimension 2')\\nplt.title('QUANTITY FEATURES ONLY tSNE with two components')\\nplt.show()\\n\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"tsne_2d = manifold.TSNE(n_components=2, init='pca', random_state=0, perplexity=20,\n",
    "                     learning_rate=300, n_iter=400)\n",
    "    #tsne = manifold.TSNE(n_components=2, init='pca')\n",
    "tsne_quant_2d=tsne_2d.fit_transform(pca_quant_55d)\n",
    "\n",
    "plt.scatter(tsne_quant_2d[:,0],tsne_quant_2d[:,1])\n",
    "\n",
    "plt.xlabel('Dimension 1')\n",
    "plt.ylabel('Dimension 2')\n",
    "plt.title('QUANTITY FEATURES ONLY tSNE with two components')\n",
    "plt.show()\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4 - kMeans Clustering\n",
    "\n",
    "We will have to do kMeans clustering on the data frame with quantitative features only, on the PCA transformed data. We will do kMeans on two different inputs:\n",
    "\n",
    "* Two principal components \n",
    "* The optimal number of principal components (55)\n",
    "\n",
    "\n",
    "## 2 principal component kMeans\n",
    "\n",
    "From the PCA plots above, it looks like there are three clusters, possibly 4. A tSNE visualization on the PCA output would give us a better idea how many clusters there are likely to be. To perform kMeans clustering, you must perform the following steps:\n",
    "\n",
    "* Run kMeans with the parameters n_init=15 and max_iter=400 on the 2-dimensional PCA output to find 3 clusters. Look at the documentation in http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html and https://jakevdp.github.io/PythonDataScienceHandbook/05.11-k-means.html\n",
    "* Visualize the resulsting clusters on the 2-dimensional PCA scatter plot above. We want to just reproduce that plot and color the points with which cluster they belong to. Cell #4 in https://jakevdp.github.io/PythonDataScienceHandbook/05.11-k-means.html demonstrates how to do this\n",
    "* We next have to optimize the number of clusters. There are many ways to do this, but using the Silhouette Score to select the best number of clusters is probably the most direct and the fastest: http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html and http://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html#sklearn.metrics.silhouette_score Basically, we want the number of clusters such that the silhouette score (a measure of cluster separation) is as close to 1.0 as possible. Start with 2 clusters, Don't go over 8 clusters. \n",
    "\n",
    "## Optimal number (55) principal components\n",
    "\n",
    "Repeat the above steps, but with 55 principal components. You can't visualize 55 dimensions of course, so just plot the first two principal components of the 55. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f7abf6b8090>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = pca_quant_55d\n",
    "kmeans = KMeans(n_clusters=2, n_init=15, max_iter=400).fit(X)\n",
    "y_pred = kmeans.predict(X)\n",
    "\n",
    "plt.scatter(X[:,0], X[:,1], c=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61171"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8003236106145404\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = pca_quant_55d\n",
    "kmeans = KMeans(n_clusters=2, n_init=15, max_iter=400)\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "cluster_labels = kmeans.fit_predict(X)\n",
    "\n",
    "silhouette_avg = silhouette_score(X, cluster_labels)\n",
    "print(silhouette_avg)\n",
    "plt.scatter(X[:,0], X[:,1], c=cluster_labels, cmap=\"coolwarm\")\n",
    "plt.title(\"Kmeans Clustering for 55 Principal Components\")\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.show()\n",
    "#plt.savefig('kmeans_55d_scatterplot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.cluster import MeanShift, estimate_bandwidth\n",
    "\n",
    "X = pca_quant_55d\n",
    "n=2\n",
    "#clusterer = KMeans(n_clusters=n, random_state=1\n",
    "clusterer = AgglomerativeClustering(n_clusters=n,linkage='average',affinity='l1')\n",
    "\n",
    "cluster_labels = clusterer.fit_predict(X)\n",
    "\n",
    "silhouette_avg = silhouette_score(X, cluster_labels)\n",
    "print(silhouette_avg)\n",
    "plt.scatter(X[:,0], X[:,1], c=cluster_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Step 5 - find which features contribute the most to PCA. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## which variables change the data the most/ have the highest variance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/local/apps/wakari/anaconda/envs/default/lib/python2.7/site-packages/ipykernel/__main__.py:4: RuntimeWarning: invalid value encountered in log\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.rcParams['figure.figsize']=(12,6)\n",
    "\n",
    "sns.heatmap(np.log(pca_55d.inverse_transform(np.eye(pca_quant_55d.shape[1])))[:,0:100], cmap=\"hot\", cbar=True)\n",
    "plt.ylabel('Principal Component')\n",
    "plt.xlabel('Quantitative Feature index')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## pale colors are most important "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The above plot shows a heat map of feature importances. Because we have over 600 quantitative features, I only show 100. There are 55 principal components. The more yellow the heatmap is, the more important the feature is. Let's average over the principal components to get the most important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/local/apps/wakari/anaconda/envs/default/lib/python2.7/site-packages/ipykernel/__main__.py:1: RuntimeWarning: invalid value encountered in log\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "log_importances=np.log(pca_55d.inverse_transform(np.eye(pca_quant_55d.shape[1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "779\n"
     ]
    }
   ],
   "source": [
    "mean_log_importances=np.apply_along_axis(np.mean,0,np.nan_to_num(log_importances))\n",
    "print(len(mean_log_importances))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mean_log_importances_df=pd.DataFrame(data=mean_log_importances,columns=['importance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    index  importance\n",
      "0       0   -2.676006\n",
      "1       1   -2.482571\n",
      "2       2   -2.187262\n",
      "3       3   -2.694750\n",
      "4       4   -2.201851\n",
      "5       5   -2.663930\n",
      "6       6   -2.677477\n",
      "7       7   -2.518011\n",
      "8       8   -2.651156\n",
      "9       9   -2.210778\n",
      "10     10   -1.657518\n",
      "11     11   -2.125427\n",
      "12     12   -2.321709\n",
      "13     13   -1.783814\n",
      "14     14   -3.041993\n"
     ]
    }
   ],
   "source": [
    "mean_log_importances_df=mean_log_importances_df.reset_index()\n",
    "print(mean_log_importances_df.head(15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The numpy array above is an array of importances, of length 638. They map back to the original data frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('age', 'f_20157_0_0_f_QUANT_Duration_to_complete_alphanumeric_path_trail_2_residualizedRelated')\n"
     ]
    }
   ],
   "source": [
    "print(asthma_df_quant.columns[0],asthma_df_quant.columns[637])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## every entry in array, can get variabe name "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find the ten most important features to start, including their names, to focus on. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## labels 0-638: have value associated. \n",
    "## output array as csv file -- find 10 highest values in the array and associated array index number \n",
    "\n",
    "np.savetxt(\"Asthma Important Features.csv\", mean_log_importances_df, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     index  importance\n",
      "109    109   -1.229521\n",
      "584    584   -1.312077\n",
      "357    357   -1.334944\n",
      "113    113   -1.353731\n",
      "41      41   -1.362257\n",
      "228    228   -1.376823\n",
      "449    449   -1.391536\n",
      "97      97   -1.417307\n",
      "411    411   -1.419085\n",
      "56      56   -1.450074\n",
      "60      60   -1.457951\n",
      "61      61   -1.470489\n",
      "57      57   -1.470791\n",
      "617    617   -1.474340\n",
      "608    608   -1.488014\n"
     ]
    }
   ],
   "source": [
    "asthma_df_important_features = mean_log_importances_df.sort_values(by=\"importance\", ascending=False)\n",
    "print(asthma_df_important_features.head(15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 6: ANOVA of the ten most important features between the two clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Add the group label to the data frame:\n",
    "asthma_df_quant['Cluster Label']=y_pred\n",
    "asthma_df_quant['Cluster Label']=asthma_df_quant['Cluster Label'].apply(str).apply(lambda x: x.replace('0','A')).apply(lambda x: x.replace('1','B'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61171, 780)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asthma_df_quant.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### adding the cluster labels allows you to visualize box plots broken down by cluster easily. For example, for age:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## include box plots for top 10 variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b387beb90>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[0],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "v = asthma_df_important_features['index'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b38528fd0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[449],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b3845b390>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[584],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b38414610>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtoAAAGTCAYAAAAWUbUKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3XmcJHV9//HXey/R5RZcbvDOClE0q9GIyQImarzQeC3ebCTGiBoPQFajJm4UTbxjEAPxXoi3EeMVdlSCYkDxgFV/RjkWAZFLdjl39/P7o2qgd5xrd7qmmdnX8/GYx0x3Vdf3U1XdPe/+1reqU1VIkiRJ6q85gy5AkiRJmo0M2pIkSVIHDNqSJElSBwzakiRJUgcM2pIkSVIHDNqSJElSBwzakma0JB9K8uZB1zFo422HJC9IctY01TFtbU2XJBclefR0P1bSzGfQltQXbaC4Kcm6JNcmOSPJvoOuq1eSSnKfQdcx0yV5TJJvJrkhyVVJvpHkSX1uo+/7yv0vaboZtCX10xOrantgT+BK4L0DrqczaWxz76FJngZ8EvgIsA+wCPg74ImDrKtXknmDrkGSwKAtqQNVdTPwKeABw/cl2SnJR9oe0IuTvG44qCb51ySf6pn3xCT/3YbZpUnWJjkhyW/anvNnj9V2khcl+XmSa5J8Icle7f3fbGf5Qdvr/sxRHjs3yT+37fwyyUvbXtB57fShJCuT/A9wI3CvJHu17VzTtvuinuVtNpxjeF16bl+U5LVJLmyPAvx7ku16pj8hyflJrktydpIH9kx7cJLvtb3KpwO3P27sTZP3Jrk+yU+SHN7e+fQk542Y8VVJPjfaAoB3AP9QVf9WVddX1aaq+kZVvWiU+Q/o3X492/Av27/v0/aGX99u89Pb+0fdVxNsj4uSHJfkh8D6LQnbSe6d5MwkV7d1fDzJziNme+jW7CdJ2zaDtqS+S3I34JnAd3rufi+wE3Av4E+A5wEvbKe9CnhgmvG9jwKWA8+vqmqn7wHsBuwNPB84Ocn9R2n3MOAtwDNoetUvBk4DqKo/bmd7UFVtX1Wnj1L6i4DHAQcDDwGOGGWe5wJHAzu0y18FrAX2Ap4G/ONwiJ2kZwOPAe4N3A94XbsuDwFOBf4KuDvwAeALSe6SZAHwOeCjwK40Pcx/MUE7fwj8gmY7vgH4TJJdgS8A90yyuGfe57TLHun+wL40H6L64R+ArwK70PSOvxdG31fjbY+e5S0DHg/sXFUbtqCO0Dxv9gIW06zjG0fMs8X7aQvalzRLGbQl9dPnklwH/Bb4U+Dt0PQU0wTv11bVDVV1EfDPNKGVqrqRJty9A/gYcExVrR2x7NdX1S1V9Q3gDJowPdKzgVOr6ntVdQvwWuARSQ6YZP3PAN5dVWur6lrgraPM86GquqANcnsAhwDHVdXNVXU+8G/D6zVJ76uqS6vqGmAlTViEJvR/oKrOqaqNVfVh4Bbg4e3PfOBdVXVbVX0K+N8J2vl1z/ynAz8FHt9up9Nptj9JDgQOAL44yjLu3v6+fAvWbzy3AfsDe7Xbb7yTKMfbHsPe027Lm7akiKr6eVV9rX1+XUXzPPyTEbNtzX6StI0zaEvqpyOqamfgLsBLgW8kGe6NXkDTAzzsYpoeagCq6rs0Pa4B/mPEcq+tqvUjHrvXKO3v1dtGVa0Dru5tZwJ7AZf23L50lHl679sLuKaqbhhR22TbG7m83vXaH3hVOxzhuvYDzL7t9L2Ay3p6/IcfO57R5h9u68PAke3QkOcC/9EG8JGubn/vOdFKTdKxNPv7u0kuSHLUOPOOtz2Gjba/JpTkHklOS3JZkt/SfNjbbcRsW7OfJG3jDNqS+q7t2fsMsJGmx/c33NF7OWw/4LLhG0n+hiag/4omgPXaJcnCEY/91ShN/6q3jfYxd+9tZwKX0wxhGDbaVVN6w+qvgF2T7DCituH21gN365m2xyjL622jd70uBVZW1c49P3erqlVtnXu3wbj3seMZbf5fAVTVd4BbgUcBRzL6sBFoesEvZeJhKsOGPxyNug2q6oqqelFV7UUz9OL9GfuqIONtj9sXOcm6RnpL+9gHVtWONL37GTHP1uwnSds4g7akvkvjyTRjb9dU1UaaXuqVSXZIsj/wSpqeQ5LcD3gzTcB5LnBskoNHLPZNSRa0Y7ifQDMueaRPAC9McnA7RvYfgXPaoSrQXAnlXuOU/h/Ay5Ps3Z4Md9x461lVlwJnA29Jsl17Etxy4OPtLOcDf55k17Zn/xWjLOZvkuzTjpc+gWYYB8AHgRcn+cN2ey5M8vg21H8b2AC8LMm8JE8FHjZercA92vnnJ3k6zVjkL/VM/wjwPmDDWEM42h7xVwKvT/LCJDsmmZPkkCQnjzL/VTQfOp6T5kTTo2jGOAO3n4g5/MHmWpqwu7G9PXJfjbc9tsSCdl8N/8ylGW+/Drguyd7Aa0Z53NbsJ0nbOIO2pH76zyTraMZor6Q5ofGCdtoxND2cvwDOognFp6a5OsTHgBOr6gdV9f9ogsxHe04ou4ImiP2KJsS+uKp+MrLxqvpv4PXAp2l6fe8NPKtnljcCH24P8Y82xvuDNCfn/RD4Pk0Q3cAd4W80y2jGNP8K+Czwhqr6Wjvto8APgIva5Y52AuYn2mm/aH/e3K7LuTTjf9/XrvvPgRe0024FntrevpZm/PtnxqkR4BzgvjRHF1YCT6uqq3umfxQ4iLF7s2nb/lTb3lHtOl/Z1vz5MR7yIprgejVwIM0Hk2EPBc5pnzNfAF5eVb9sp72Rnn013vbYQhcAN/X8vBB4E83Jr9fTjP8fbVtu8X6SpGw+ZE+S7lySLAU+VlX7TDRvB20/DjipqvafcOatW/5FwF9W1de7WP4W1nJXmhMmH9J+2JEkTZE92pLUSnLXJH/eDsfYm+YyeJ8ddF3T5K+B/zVkS1L/+O1ZknSH0AwjOJ1mWMEZNN96OKu1Peth9OuGS5K2kkNHJEmSpA44dESSJEnqgEFbkiRJ6oBBW5IkSeqAQVuSJEnqgEFbkiRJ6oBBWwOT5P5Jvp/khiQvG3Q9XdqW1nUyklzQfhHNdLb5oSRvbv9+VJKfTmFZL0gy6teUt9OHkvzl1i5/nOXevg7atiQ5Icm/jTP9oiSP7kM7mz23k6xLcq+pLndEG317fXT1WhujrTcm+dh0tKXZw6CtQToWGKqqHarqPePNmGRBkp8kWTvi/oOTnJfkxvb3wT3T/qv9JzH8c2uSH/VM/6Mk323D7w+THDKZopMcmeTiJOuTfC7Jrn1e1z9KcmZb1/VJvpDk93qmjxryRvtH2/5jqCQPG3H/C9r7XzPi/rVJliY5acR2u63n9n+NU/sB7XKH570yyReT/GnvfFV1YFUNjbcdJjKV0FlV36qq+0+lfWk6VdU/VtW0BMoR7W5fVb/oavnte9HG9v3it0l+kOQJHbXVlw8j0pYwaGuQ9gcumOS8r6H5eujbJVkAfB74GLAL8GHg8+39VNXj2n8S21fV9sDZwCfbx+4KfAF4O7Az8DbgP5PsMl4RSQ4EPgA8F1gE3Ai8fxL1T2pdkzwC+Gq7XnsB9wR+CPxPkgMm0U7vstLWeQ3w/FFmuQY4LsmOIydU1Yt7tts/Aqf3bMvHTaL5ndvHPgj4GvDZJC/Ykvo1PZLMHXD728wXp21L67qFvt2+X+xM8356WpKdB1yT1BcGbQ1EkjOBQ4H3tT0Z9xtn3nsCzwHeMmLSUppvN31XVd3S9hQHOGyUZRwAPAr4aHvXHwFXVtUnq2pjVX0MuAp46gSlPxv4z6r6ZlWtA14PPDXJDv1YV5rA/5GqendV3VBV11TV64Dv0nwd+JZ4FE1YfznwrOEPID3WAN8G/nYLlztpVXVFVb0beCNwYpI5sHnPUpKHJfl2kuuSXJ7kfcO1pvHOJL9ue/d/mOSgJEfT7Itj2236n+38i9tDydelGZ7ypNHqanvt1/bc3jfJZ5JcleTqJO+bxOolyXvbun6S5PAxZpqT5HXtUZBfJ/lIkp16pj+prfW6tvbFPdMenOR77dGN04HtJlHU0vbIxAlJftNu62f3TP9Qkn9N8qUk64FDk9wlyT8luaQ9CnFSkru28+/WHpW4Lsk1Sb7Vsx+PS3JZW99Ph7fByKMNo2zvi9rH/hBYn+Yr7/dK8ul2H/wykxhileaIzSeTfKyt4UdJ7pfkte22vjTJn/XMv1OSU9rn2WVJ3jz8QSPJvdMcSbq63W4f7w17bc2vbp+D1yc5Pcm4+6NnXxyX5Arg39v7n5Dk/Habnp3kgT2PGWubbjZsIclz2+fU1UlWjGh3ou1/fJL/a9u4MMlTxlmHSnKfdv/0HiG8MUn1zHdUkjVJrk3ylST790z70zSvkevTvLYyWltVtYnmPXohcN+exz+83U7XpenxXjpGrWPuwyQfBfaj6VBZl+TYiZad5J5JvtFup68Bu421naSxGLQ1EFV1GPAt4KVtL+nPxpn9vcAJNF+J3etA4Ie1+deb/rC9f6TnAd+qql+2t8PvvtkHOGiC0g8EftCzHv8H3AqMGZ4nu65J7kbzAeCTo0z+D+DPRrl/PM8H/pPm68QBRjsc+3rgbzO54S9T8RngHsBowzU20oT93YBHAIcDL2mn/RnwxzTbd2fgmcDVVXUy8HHgbe02fWKS+TTr+9W2rWOAjycZd4hIG7S+CFwMHADsDZw2iXX6Q+AXbd1vAD4zxnZ8QftzKHAvYHvgfW3b9wNWAa8Adge+RBMEFqT5sPE5muCxK83z4i8mURfAHm1de9M8D04esR2OBFYCOwBnASfSbOODgfu0jxv+6vlXAWvb+hbRvBarXd5LgYdW1Q7AY4CLJlkfwDLg8TT7dRPNvvtB2/bhwCuSPGYSy3kizTbaBfg+8BWa/217A39PcwRq2IeBDe06Ppjm+TU8HCM0H+b3AhYD+9J8QOz1DOCxNEeaHkizXyeyB83+2x84OslDgFOBvwLu3tb3hTQfdia1TZM8APhXmiNWe7XL2WcStQz7P5oP4jsBbwI+lmTP8R5QVb8acYTws7SvkyRH0DwvnkrzPPkWzfOaJLsBnwZeR/Oc/D/gkaO10b4WXwjcRvN6JMnewBnAm2m246uBTyfZfbRFMMY+rKrnApcAT2zX4W2TWPYngPPauv+B0Y8MSuMyaOtOre1pmVdVnx1l8vbA9SPuu54mPIz0POBDPbfPBvZKsizJ/CTPB+4N3G2CkrakzS21K81r8vJRpl1O8w9sUtrQ/nTgE1V1G/ApRvknUVXn0wTT47am4C3wq/b37wTRqjqvqr5TVRuq6iKa4PEn7eTbaLbt7wGpqjVVNdr2AXg4zf55a1XdWlVn0gToZRPU9jCaf8yvqar1VXVzVY15omOPX9McTbmtqk4HfkoTHEd6NvCOqvpFexTktTRHGObRfHA4o6q+1u6nfwLuSvOB6+HA/J42PgX87yTqGvb69kjPN2jCxDN6pn2+qv6n7UG8BXgR8LftEZQbaIYLPaud9zZgT2D/to5vtR9uNwJ3AR6QZH5VXdR+8Jys91TVpVV1E/BQYPeq+vt23/0C+GBPDeP5VlV9pao20HwY2Z3mOXAbTRA8IMnOSRYBjwNe0e7nXwPvHG6jqn7e7odbquoq4B3c8TzsrflXVXUNzQeDg5nYJuAN7XJvotnWH6iqc9qjaR+m2QcPZ/Lb9GnAF9sja7fQfGDeNIlaaNf1k+16bGqfu/+P5nUwKUmOo3lNHtXe9VfAW9rX5waa58/Bba/2nwMXVtWn2n3yLuCKEYt8eJLrgJtpXgPPafcPNEczv1RVX2rr/Rpwbrvckes1mX3Ya8xlJ9mP5nk5/Dr6Js0+l7aIQVt3WkkW0gylOGaMWdYBI8cX7wjcMGI5h9D0Kn1q+L6quhp4MvBK4EqaXqqv0/TcjWdSbW6la2n+WY7Ws7QnzdAWaHrk5o8yz3yaUATwlHa+L7W3Pw48boxeoL8D/jrJHltZ92Ts3f6+ZuSENIf6v5jkiiS/pfknvRtAG5bfB/wLcGWSkzPKmPLWXsClbXgcdnFP22PZF7i4DQhb4rIRR1MubmsYra6LR8w3j6Z3eLNpbe2XtjXvNUYbk3FtVa0fp7ZLe/7eneYD5nnt4fPrgC9zxwe7twM/B76a5BdJjm9r/TlNT/wbgV8nOS3JaOs/lt4a9qf54HtdTw0n0GyjiVzZ8/dNwG+qamPPbWg+gO1P8xq5vKeND9Ac/SDJPdp1uKx9Hn6M3x0q0BsQb2yXO5Grqurmntv7A68asa77AnttwTbdi57t1+7rqydRCwBJnpc7hq5cR3Mkb1LDIpI8jmY42hHtB4fhdXp3z/KuoeldHn4e99ZabL7vAb5TVTvTHJX4Ak1v+7D9gaeP2F6HMMr75CT3Ya/xlr0Xo7+OpC1i0Nad2X1pDuV/K834xs8Ae7aB7ACakwsfmKR3CMgD+d2TDp8PfKbtTbxdVX2jqh5aVbvSHIK9P81Y6PFcQHOCHwBpLnt1F2C8oS+T0r6hf5umJ3qkZwDfaP++BNivd73bHux7cMc/gufThIBL2m33SZqQ8Tu9u1X1E5pte8JU12EcT6HpAR7tknr/CvwEuG9V7djWcfu6VdV7quoPaIbt3I/mxFiAGrGcXwH7ph0/3NoPuGyC2i6l2Z5beqLa3iOee/txR8/9yLr2HzHfBpqAuNm0dnn7tjVfPkYbk7FL+0F1rNp6t91vaALpgVW1c/uzUzs8gGrOFXhVVd2LZpjGK9OOG66qT1TVIe06FM0QFID1bH50aLQPcb01XAr8sqf9nau5Qs/v9FpOwaU0Pce79bSxY1UNDzV7S1vTA9vn4XMYYyzxFhr5PL0UWDliXe9WVatg3G3a63Ka5wlw++v/7j3Tx9z+bS/zB2mGqNy9Dbg/ZhLr2g5t+TDwjKrqDcuXAn81Yp3uWlVnj1Jrem/3at+jXwI8N8mDe5b90RHLXlhVbx1lERPtw9H2xVjLvpzRX0fSFjFo687sxzRvyAe3P39JE04OpnmDHKI51PqydnzjS9vHnTm8gDQndD2dzYeNDE97cDtsZEeaw5Vrq+orE9T0ceCJaa7DvJBmDOhnqjnc3g/HA89P8rIkOyTZJc1JTX/MHSeDnkNziPX4JNu1dbyV5pDnxe24w8NpxmQPb7sH0fzDHmuM4Ztoxkb29Uz/JIva/fIG4LUjepuH7QD8FliX5jKGf93z+Icm+cM046/X06z3cG/llTRjnoed085zbLtfl9IEw4nGW3+X5p/qW5MsbLfpqGNIR7gHzXNvfpKn04wJ/dIo862iGQd/zyS9V3HZQDP2/vFJDm/X8VU0YfBsmg9dG9o25iV5KltweB94U5qx3o+ieS6MNvZ/uBf9g8A7kwz37u6ddnx0mhP37tMGpN/SbP+Naa4Nf1iSu9Dsl5u4Y9+cT3P4fdf2SMkrJqj1u8Bv05wIeNckc9Oc9PrQLVjfcVUz5OirwD8n2THNSar3TjI8tGAHmiNW17WvodeMtawp+iDw4vZ5nfY59/j29T7eNu31KeAJSQ5JM5b/79n8//l4238hTeC8CiDJC5n43BTa98nPA6+r3x1adRLw2jRXZRo+6XS4w+AM4MAkT20/zL6M0T94Abcfbfw37jhH4GM077mPaZ8X26U5uXO0MekT7cOR7xljLruqLqZ5Tx1+HR1C834ibRGDtu60qhmze8XwD83hyE3t7Y1VdStwBM346+toxgse0d4/7AiaMdSrR2niWJrevEtpDhWOeeZ9T00XAC+mCdy/pnljf8m4D9oC7T+wx9CcVHQ5d1ya77Cq+lE7zy00Y4GX0gx1+QXNYc5ntIdlnwucX1VfHbH93kNzBOB3/qlWc5Lo8Nn+/XBdmita/IhmLOXTq+rUMeZ9Nc3JeTfQhJDTe6bt2N53LU1v/dU0H4oATqEZy3pdks+1+/1JNONwf0NzmbDntT32Y2qHGTyR5gS5S2i26TMnsY7n0Bx1+Q3NiYVPa0PCSKfSbNtvAr+kCVDHtG3/lKbX7b3tcp5Ic7LWre36PJXmhLtr25o+M4m6oBnicC1NL/bHgRdPsB2Ooxke8p00h9y/zh0nrt63vb2OJvy/v5proN+F5gPeb9r27sEdR0U+SnNi40U04bZ3n/6Onn1wMM02+g1N2NppvMdthecBC4ALabbPp7hjCMKbgIfQvF+cweS39RapqnNpxmm/r63h59xxUuV427R3GRcAf0Nzst7l7XJ6h72Nuf2r6kLgn2n25ZXA7wP/M4nSH0LznHhHeq4+0i7zszQf5E9rnz8/pnkdUlW/oenseCvN6/e+k2jvXTQfFB7Y9pw/ud0OV9G8X7+G0fPLRPvwLcDr2veMV09i2UfSnPR8DU1nwUcmqFv6Hdl8+J+kO5MkD6LpoT9yEr3tEm1P/seqakuuQiFJ6oA92tKdWFX9gKZX/ve3YgyxJEkaIIO2Bi7Nl3WsG+Xn2RM/upN6ThqjnpPGecx+YzxmXZrLRPXOu0XrW83l1P5pK66K0Ykkzx6j/sl+y+eMsDXPg2ms7YQxavuvQdfWb0n+a4x17fLk3UnblvaFpC3n0BFJkiSpA/ZoS5IkSR0YyJjP3XbbrQ444IBBNC1tlfXr17NwYb8uyCFJ6uV7rGaa88477zdVNeE3Ng8kaB9wwAGce+65g2ha2ipDQ0MsXbp00GVI0qzke6xmmiST+qZQh45IkiRJHTBoS5IkSR0waEuSJEkdMGhLkiRJHTBoS5IkSR0waEuSJEkdMGhLkiRJHTBoS5IkSR0waEuSJEkdMGhLkiRJHTBoS+NYtWoVBx10EIcffjgHHXQQq1atGnRJkiRphpg36AKkO6tVq1axYsUKTjnlFDZu3MjcuXNZvnw5AMuWLRtwdZIk6c7OHm1pDCtXruSUU07h0EMPZd68eRx66KGccsoprFy5ctClSZKkGcCgLY1hzZo1HHLIIZvdd8ghh7BmzZoBVSRJkmaSvgXtJHOTfD/JF/u1TGmQFi9ezFlnnbXZfWeddRaLFy8eUEWSJGkm6WeP9ssBu/o0a6xYsYLly5ezevVqNmzYwOrVq1m+fDkrVqwYdGmSJGkG6MvJkEn2AR4PrARe2Y9lSoM2fMLjMcccw5o1a1i8eDErV670REhJkjQpqaqpLyT5FPAWYAfg1VX1hFHmORo4GmDRokV/cNppp025XWm6rFu3ju23337QZUjSrOR7rGaaQw899LyqWjLRfFPu0U7yBODXVXVekqVjzVdVJwMnAyxZsqSWLh1zVulOY9WqVaxcufL2Hu0VK1bYoy1JfTY0NIS5QLNRP4aOPBJ4UpI/B7YDdkzysap6Th+WLQ2M19GWJElTMeWTIavqtVW1T1UdADwLONOQrdnA62hLkqSp8Dra0hi8jrYkSZqKvgbtqhoa7URIaSbyOtqSJGkq+nJ5P2k2WrFiBU9+8pO5+eabue2225g/fz7bbbcdH/jABwZdmiRJmgEcOiKN4eyzz2bdunVs2rQJgE2bNrFu3TrOPvvsAVcmSZJmAoO2NIaTTjqJu93tbuy7777MmTOHfffdl7vd7W6cdNJJgy5NkiTNAA4dkcawYcMG7n73u3Pqqafefnm/ZcuWsX79+kGXJkmSZgCDtjSOAw88cLOvYD/wwAO58sorB12WJEmaAQza0jjOPPNM5syZw6ZNm1izZg0XXHDBoEuSJEkzhGO0pTEk2aL7JUmSehm0pTFUFQsXLmS//fYjCfvttx8LFy6kqgZdmiRJmgEM2tI4XvKSl7Bw4UKSsHDhQl7ykpcMuiRJkjRDGLSlMcybN4/3v//9t19lZP369bz//e9n3jxPbZAkSRMzaEtjOOyww1i/fj2XXHIJmzZt4pJLLmH9+vUcdthhgy5NkiTNAAZtaQwXXnjh7VccgeabIefMmcOFF1444MokSdJMYNCWxrB27Vo2bdrELrvsAsAuu+zCpk2bWLt27YArkyRJM4FBW5rAtddeu9lvSZKkyTBoSxMYvm6218+WJElbwqAtTcCgLUmStoZBW5pA78mQkiRJk2XQliRJkjpg0JYkSZI6YNCWJEmSOmDQliRJkjpg0JYkSZI6YNCWJEmSOmDQliRJkjpg0JYkSZI6YNCWJEmSOmDQliRJkjpg0JYkSZI6MOWgnWS7JN9N8oMkFyR5Uz8KkyRJkmayfvRo3wIcVlUPAg4GHpvk4X1YriRJmsVWrVrFQQcdxOGHH85BBx3EqlWrBl2S1FfzprqAqipgXXtzfvtTU12uJEmavVatWsWKFSs45ZRT2LhxI3PnzmX58uUALFu2bMDVSf0x5aANkGQucB5wH+BfquqcUeY5GjgaYNGiRQwNDfWjaWkgfP5K0tSccMIJvOxlLyMJN998M9tvvz3HHHMMJ5xwAnvuueegy5P6Ik2HdJ8WluwMfBY4pqp+PNZ8S5YsqXPPPbdv7UpdSDLmtH6+biRpWzR37lxuvvlm5s+fz9DQEEuXLuW2225ju+22Y+PGjYMuTxpXkvOqaslE8/X1qiNVdR0wBDy2n8uVJEmzy+LFiznrrLM2u++ss85i8eLFA6pI6r9+XHVk97YnmyR3BR4N/GSqy5UkSbPXihUrWL58OatXr2bDhg2sXr2a5cuXs2LFikGXJvVNP8Zo7wl8uB2nPQf4j6r6Yh+WK0mSZqnhEx6POeYY1qxZw+LFi1m5cqUnQmpW6esY7clyjLZmAsdoS9L0GB6jLc0UAxmjLUmSNFleR1uzXV8u7ydJkrQlvI62tgX2aEuSpGm3cuVKTjnlFA499FDmzZvHoYceyimnnMLKlSsHXZrUNwZtSZI07dasWcMhhxyy2X2HHHIIa9asGVBFUv8ZtCVJ0rTzOtraFhi0JUnStPM62toWeDKkJEmadl5HW9sCe7QlSZKkDhi0JUnStFu1ahUvf/nLWb9+PVXF+vXrefnLX+61tDWrGLQlSdK0O/bYY7n11luBO76J99Zbb+XYY48dZFlSXzlGW9uc8b5avd/L8KvaJWl0a9euZY899uDUU0+9/QtrjjzySNauXTvo0qS+sUdb25yqmtRP18uQpG3dK1/5ys2+sOaVr3zloEuS+soebUmSNBDveMc7WLJkCRs3bmT16tW84x3vGHRJUl8ZtKUxVNWoQ0TZKJP3AAATQklEQVTsqZakqdtnn3244YYbOOqoo7jkkkvYb7/9uOmmm9hnn30GXZrUNw4dkcYxPARk/+O+6HAQSeqjt73tbSxYsAC4owNjwYIFvO1tbxtkWVJfGbQlSdK0W7ZsGe9+97tZuHAhSVi4cCHvfve7/cIazSoOHZEkSQOxbNkyli1bxtDQEEuXLh10OVLfGbQlSVLf9OMSqpPlcD7d2Tl0RJIk9c1kL3/a+9N7HsyW/Eh3dgZtSZIkqQMGbUmSJKkDBm1JkiSpAwZtSZIkqQMGbUmSJKkDBm1JkiSpAwZtSZIkqQMGbUmSJKkDUw7aSfZNsjrJmiQXJHl5PwqTJEmSZrJ+fAX7BuBVVfW9JDsA5yX5WlVd2IdlS5IkSTPSlHu0q+ryqvpe+/cNwBpg76kuV5IkSZrJ+tGjfbskBwAPBs4ZZdrRwNEAixYtYmhoqJ9NS53zOStJ3fE9VrNR34J2ku2BTwOvqKrfjpxeVScDJwMsWbKkli5d2q+mpe59+Qx8zkpSR3yP1SzVl6uOJJlPE7I/XlWf6ccyJUmSpJmsH1cdCXAKsKaq3jH1kiRJkqSZrx892o8EngscluT89ufP+7BcSZIkacaa8hjtqjoLSB9qkSRJkmYNvxlSkiRJ6oBBW5IkSeqAQVuSJEnqgEFbkiRJ6oBBW5IkSeqAQVuSJEnqgEFbkiRJ6oBBW5IkSeqAQVuSJEnqgEFbkiRJ6oBBW5IkSeqAQVuSJEnqgEFbkiRJ6oBBW5IkSeqAQVuSJEnqgEFbkiRJ6oBBW5IkSeqAQVuSJEnqgEFbkiRJ6oBBW5IkSeqAQVuSJEnqgEFbkiRJ6oBBW5IkSeqAQVuSJEnqgEFbkiRJ6oBBW5IkSeqAQVuSJEnqgEFbkiRJ6kBfgnaSU5P8OsmP+7E8SZIkaabrV4/2h4DH9mlZkiRJ0ozXl6BdVd8ErunHsiRJkqTZYN50NZTkaOBogEWLFjE0NDRdTUt94XNWkrrje6xmo2kL2lV1MnAywJIlS2rp0qXT1bQ0dV8+A5+zktQR32M1S3nVEUmSJKkDBm1JkiSpA/26vN8q4NvA/ZOsTbK8H8uVJEmSZqq+jNGuqmX9WI4kSZI0Wzh0RJIkSeqAQVuSJEnqgEFbkiRJ6oBBW5IkSeqAQVuSJEnqgEFbkiRJ6sC0fQW71IUHvemrXH/TbdPS1gHHn9F5GzvddT4/eMOfdd6OJEnqnkFbM9r1N93GRW99fOftDA0NsXTp0s7bmY4wL0mSpodDRyRJkqQOGLQlSZKkDhi0JUmSpA44RluSJI3KE86lqTFoS5KkUXnCuTQ1Dh2RJEmSOmDQliRJkjpg0JYkSZI6YNCWJEmSOmDQliRJkjpg0JYkSZI6YNCWJEmSOmDQliRJkjpg0JYkSZI6YNCWJEmSOmDQliRJkjpg0JYkSZI6YNCWJEmSOmDQliRJkjrQl6Cd5LFJfprk50mO78cyJUmSpJlsykE7yVzgX4DHAQ8AliV5wFSXK0mSJM1k/ejRfhjw86r6RVXdCpwGPLkPy5UkSZJmrHl9WMbewKU9t9cCfzhypiRHA0cDLFq0iKGhoT40rW3dDouP5/c/PE2jlT7cfRM7LIahoYXdNyRJkzQd/6/XrVs3bbnA/KHp1I+gnVHuq9+5o+pk4GSAJUuW1NKlS/vQtLZ1Nxz/Vi566+M7b2doaIjpeM4ecPwZLH1+9+1I0qR8+Yxpee+brvfY6VofaVg/gvZaYN+e2/sAv+rDciVJ0gDNxqOG0H3njDSsH0H7f4H7JrkncBnwLODIPixXkiQN0A1rZt9RQ2k6TTloV9WGJC8FvgLMBU6tqgumXJkkSZI0g/WjR5uq+hLwpX4sS5IkSZoN/GZISZIkqQMGbUmSJKkDBm1JkiSpAwZtSZIkqQMGbUmSJKkDBm1JkiSpAwZtSZIkqQMGbUmSJKkDBm1JkiSpAwZtSZIkqQMGbUmSJKkDBm1JkiSpAwZtSZIkqQMGbUmSJKkDBm1JkiSpAwZtSZIkqQPzBl2ANFUHHH/G9DT05e7b2emu8ztvQ5IkTQ+Dtma0i976+Glp54Djz5i2tiRJ0uzg0BFJkiSpAwZtSZIkqQMOHZEkSWPyPBhp6xm0JUnSqDwPRpoah45IkiRJHTBoS5IkSR0waEuSJEkdMGhLkiRJHTBoS5IkSR0waEuSJEkdmFLQTvL0JBck2ZRkSb+KkiRJkma6qfZo/xh4KvDNPtQiSZIkzRpT+sKaqloDkKQ/1UiSJEmzxLR9M2SSo4GjARYtWsTQ0NB0NS31hc9ZSeqO77GajSYM2km+DuwxyqQVVfX5yTZUVScDJwMsWbKkli5dOtmHSoP35TPwOStJHfE9VrPUhEG7qh49HYVIkiRJs4mX95MkSZI6MNXL+z0lyVrgEcAZSb7Sn7IkSZKkmW2qVx35LPDZPtUiSZIkzRoOHZEkSZI6YNCWJEmSOmDQliRJkjpg0JYkSZI6YNCWJEmSOmDQliRJkjpg0JYkSZI6YNCWJEmSOmDQliRJkjpg0JYkSZI6YNCWJEmSOmDQliRJkjpg0JYkSZI6YNCWJEmSOmDQliRJkjpg0JYkSZI6YNCWJEmSOmDQliRJkjpg0JYkSZI6YNCWJEmSOmDQliRJkjpg0JYkSZI6YNCWJEmSOmDQliRJkjpg0JYkSZI6YNCWJEmSOmDQliRJkjowpaCd5O1JfpLkh0k+m2TnfhUmSZIkzWRT7dH+GnBQVT0Q+Bnw2qmXJEmSJM18UwraVfXVqtrQ3vwOsM/US5IkSZJmvnl9XNZRwOljTUxyNHA0wKJFixgaGupj01L3fM5KUnd8j9VsNGHQTvJ1YI9RJq2oqs+386wANgAfH2s5VXUycDLAkiVLaunSpVtTrzQYXz4Dn7OS1BHfYzVLTRi0q+rR401P8nzgCcDhVVX9KkySJEmayaY0dCTJY4HjgD+pqhv7U5IkSZI08031qiPvA3YAvpbk/CQn9aEmSZIkacabUo92Vd2nX4VIkiRJs4nfDClJkiR1wKAtSZIkdcCgLUmSJHXAoC1JkiR1oJ/fDClJkrZxSbbucSdu+WP8+g7d2dmjLUmS+qaqtvhn9erVW/U46c7OoC1JkiR1wKAtSZIkdcCgLUmSJHXAkyGlcfSe1DN8oo7jAiVJ0mTYoy2NYawz57f2jHpJkrRtMWhLkiRJHXDoiLY5/eiRnuwyHGYiSdK2y6Ctbc5kw+94YdoALUmSJuLQEUmSJKkDBm1JkiSpAwZtSZIkqQMGbUmSJKkDBm1JkiSpAwZtSZIkqQMGbUmSJKkDBm1JkiSpAwZtSZIkqQMGbUmSJKkDBm1JkiSpAwZtSZIkqQMGbUmSJKkDBm1JkiSpA1MK2kn+IckPk5yf5KtJ9upXYZIkSdJMNtUe7bdX1QOr6mDgi8Df9aEmSZIkacabUtCuqt/23FwI1NTKkSRJkmaHeVNdQJKVwPOA64FDx5nvaOBogEWLFjE0NDTVpqWB8fkrSf2zbt0631c1K6Vq/E7oJF8H9hhl0oqq+nzPfK8FtquqN0zU6JIlS+rcc8/d0lqlaZVkzGkTvW4kSZM3NDTE0qVLB12GNGlJzquqJRPNN2GPdlU9epJtfgI4A5gwaEuSJEmz3VSvOnLfnptPAn4ytXIkSZKk2WGqY7TfmuT+wCbgYuDFUy9JkiRJmvmmFLSr6i/6VYgkSZI0m/jNkJIkSVIHDNqSJElSBwzakiRJUgcM2pIkSVIHDNqSJElSBwzakiRJUgcM2pIkSVIHDNqSJElSBwza0gTmzJmz2W9JkqTJMDlIY5g7dy4AmzZt2uz38P2SJEnjMWhLYxgZrEcGb0mSpPEYtKUxLFiwgEc+8pHMmzcPgHnz5vHIRz6SBQsWDLgySZI0E8wbdAHSndUtt9zCOeecw4knnsgDHvAALrzwQo477jg2bNgw6NIkSdIMYNCWxnCXu9yF/fffn1e/+tVUFUm4733vy8UXXzzo0iRJ0gzg0BFpDLfccgs/+9nP2HnnnZkzZw4777wzP/vZz7jlllsGXZokSZoBDNrSOObOncu1117Lpk2buPbaa73iiCRJmjSDtjSOjRs3sssuuwCwyy67sHHjxgFXJEmSZgqDtjSOefPmsdNOOzFnzhx22mmn269AIkmSNBGDtjSOjRs3cswxx3DGGWdwzDHH2KMtSZImze45aRwLFizg+OOP57bbbmP+/PksWLDAkyElSdKk2KMtjWHXXXfllltuoaoAqCpuueUWdt111wFXJkmSZgKDtjSGI488EuD2L6gZ/j18vyRJ0ngM2tIYPvGJT5CERYsWbfb7E5/4xKBLkyRJM4BBWxrDNddcw4knnsgVV1zBmWeeyRVXXMGJJ57INddcM+jSJEnSDGDQlsZx0EEHjXtbkiRpLF51RBrDvHnzePrTn87uu+/OJZdcwn777cdVV13ltbQlSdKk2KMtjeGwww5j/fr1XH/99VQV119/PevXr+ewww4bdGmSJGkGMGhLY7jssss44ogjuPHGG6kqbrzxRo444gguu+yyQZcmSZJmgL4cA0/yauDtwO5V9Zt+LFMatDVr1vD973+f+fPnMzQ0xNKlS7ntttvYbrvtBl2aJEmaAabco51kX+BPgUumXo5057F48WLOOuusze4766yzWLx48YAqkiRJM0k/ho68EzgWqD4sS7rTWLFiBcuXL2f16tVs2LCB1atXs3z5clasWDHo0iRJ0gwwpaEjSZ4EXFZVP0gy0bxHA0cDLFq0iKGhoak0LXVuzz335NnPfjZHHXXU7Vcdec5znsOee+7p81eS+mjdunW+r2pWStX4HdFJvg7sMcqkFcAJwJ9V1fVJLgKWTGaM9pIlS+rcc8/dinKlwRgeoy1J6j/fYzXTJDmvqpZMNN+EPdpV9egxGvh94J7AcG/2PsD3kjysqq7YwnolSZKkWWWrh45U1Y+Aewzf3pIebUmSJGm28zrakiRJUgf69l3SVXVAv5YlSZIkzXT2aEuSJEkdMGhLkiRJHTBoS5IkSR0waEuSJEkdMGhLkiRJHTBoS5IkSR2Y8CvYO2k0uQq4eNoblrbeboBfxiRJ3fA9VjPN/lW1+0QzDSRoSzNNknOrasmg65Ck2cj3WM1WDh2RJEmSOmDQliRJkjpg0JYm5+RBFyBJs5jvsZqVHKMtSZIkdcAebUmSJKkDBm1JkiSpAwZtaRxJnpKkkvzeoGuRpNkmycYk5yf5QZLvJfmjQdck9ZNBWxrfMuAs4FmDLkSSZqGbqurgqnoQ8FrgLYMuSOong7Y0hiTbA48ElmPQlqSu7QhcO+gipH6aN+gCpDuxI4AvV9XPklyT5CFV9b1BFyVJs8hdk5wPbAfsCRw24HqkvrJHWxrbMuC09u/T2tuSpP4ZHjrye8BjgY8kyaCLkvrF62hLo0hyd2At8GuggLnt7/3LF40k9UWSdVW1fc/tK4Hfr6pfD7AsqW/s0ZZG9zTgI1W1f1UdUFX7Ar8EDhlwXZI0K7VXd5oLXD3oWqR+cYy2NLplwFtH3Pdp4EjgW9NfjiTNSsNjtAECPL+qNg6yIKmfHDoiSZIkdcChI5IkSVIHDNqSJElSBwzakiRJUgcM2pIkSVIHDNqSJElSBwzaktSBJHskOS3J/yW5MMmXktwvyQFJfryVy3xBkr2mWNcLkrxvC+a/KMluXS1fkmYzg7Yk9Vn7FdKfBYaq6t5V9QDgBGDRFBf9AmCLgnYSvy9BkgbEoC1J/XcocFtVnTR8R1WdX1WbfdnRyN7fJF9MsjTJ3CQfSvLjJD9K8rdJngYsAT6e5Pwkd03yB0m+keS8JF9Jsme7nKEk/5jkG8DLJ1Nwkn9Ncm6SC5K8acTk1yT5bvtzn3b+3ZN8Osn/tj+P3KotJUmzmD0dktR/BwHnTeHxBwN7V9VBAEl2rqrrkrwUeHVVnZtkPvBe4MlVdVWSZwIrgaPaZexcVX+yBW2uqKprkswF/jvJA6vqh+2031bVw5I8D3gX8ATg3cA7q+qsJPsBXwEWT2GdJWnWMWhL0p3PL4B7JXkvcAbw1VHmuT9NoP9aM1KFucDlPdNP38I2n5HkaJr/C3sCDwCGg/aqnt/vbP9+NPCAtm2AHZPssIVtStKsZtCWpP67AHjaJObbwOZD+LYDqKprkzwIeAzwN8AzuKOneliAC6rqEWMse/1ki01yT+DVwEPbtj80XEurRvl7DvCIqrppxLIm26wkzXqO0Zak/jsTuEuSFw3fkeShSUYO5bgIODjJnCT7Ag9r590NmFNVnwZeDzyknf8GYLjX+KfA7kke0T5mfpIDt7LeHWmC+fVJFgGPGzH9mT2/v93+/VXgpT3rd/BWti1Js5Y92pLUZ1VVSZ4CvCvJ8cDNNKH6FSNm/R/gl8CPgB8D32vv3xv49yTDnSGvbX9/CDgpyU3AI2h6zd+TZCea9/N30fSmT+QFSY7ouf1w4PvtY3/R1tXrLknOoemcWdbe9zLgX5L8sG37m8CLJ9G2JG0zUlUTzyVJkiRpizh0RJIkSeqAQVuSJEnqgEFbkiRJ6oBBW5IkSeqAQVuSJEnqgEFbkiRJ6oBBW5IkSerA/wfmDgSH4PcdKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[659],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b383c2410>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[617],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b38283d50>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[453],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b3821d1d0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[357],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b382e3b50>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[10],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b3822b2d0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[597],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b38844910>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[411],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b3888bc10>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[612],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b388504d0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[109],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7abf4c5d50>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtoAAAGTCAYAAAAWUbUKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3Xt8XFW9/vHnaVOhtuUOldJC8YKmVqj8iorWYyoqInpEDyIpKpRIwaMVPZ5DgYiIGqEqKl6OgAYrCgEVRBRRARs89YaAgC3xCoVWoAIt0FZuCd/fH3tPmQ4zySSZlemkn/frlVeS2Xuv9Z09k8mz116zxxEhAAAAALU1pt4FAAAAAKMRQRsAAABIgKANAAAAJEDQBgAAABIgaAMAAAAJELQBAACABAjaABqa7SW2P1XvOuqtv/1g+xjby0aojhHra6TYXmn7dSO9LYDGR9AGUBN5oHjU9gbb62xfZXtavesqZjtsP7/edTQ62wfb/qXt9bbvt3297X+vcR81f6x4/AGMNII2gFp6S0RMlLS7pDWSvlznepJxZqt7DbV9uKTvSbpQ0lRJkyV9TNJb6llXMdtN9a4BACSCNoAEIuIxSd+XNKNwm+3tbV+Yj4DeZfujhaBq+2u2v1+07mLb1+VhtsX2atun2n4gHzk/qlLfto+z/Tfba21faXtKfvsv81VuzUfd31lm27G2z877udP2B/JR0KZ8ebftDtu/kvQvSc+1PSXvZ23e73FF7W02naNwX4p+X2n7FNu352cBvml726Llb7Z9i+2HbP/a9r5Fy15q++Z8VPlSSZu2q7xr/GXbD9v+k+2D8hvfYfumkhU/YvuKcg1I+rykT0bENyLi4Yh4KiKuj4jjyqw/vXj/Fe3D9+Y/Pz8fDX843+eX5reXfawG2B8rbS+yfZukjYMJ27afZ/sXth/M67jI9g4lqx0wlMcJwNaNoA2g5mw/W9I7Jf226OYvS9pe0nMlvUbSeyTNz5d9RNK+zub3vlpSm6SjIyLy5c+RtIukPSQdLel82y8s0+9rJZ0p6Qhlo+p3SbpEkiLi3/LV9ouIiRFxaZnSj5N0iKRZkvaXdFiZdd4taYGkSXn7XZJWS5oi6XBJny6E2CodJelgSc+TtI+kj+b3ZX9JF0g6XtLOks6TdKXtbWw/S9IVkr4taSdlI8z/MUA/L5d0h7L9eLqky23vJOlKSXvbbi5a911526VeKGmasoOoWvikpJ9L2lHZ6PiXpfKPVX/7o6i9VkmHStohInoHUYeVPW+mSGpWdh8/XrLOoB+nQfQPYJQiaAOopStsPyTpEUmvl/RZKRspVha8T4mI9RGxUtLZykKrIuJfysLd5yV9R9LCiFhd0vZpEfF4RFwv6SplYbrUUZIuiIibI+JxSadIOtD29CrrP0LSORGxOiLWSTqrzDpLImJFHuSeI2mOpEUR8VhE3CLpG4X7VaWvRMSqiFgrqUNZWJSy0H9eRPwuIvoi4luSHpf0ivxrnKQvRsSTEfF9Sb8foJ9/Fq1/qaQ/Szo030+XKtv/sv1iSdMl/bhMGzvn3+8dxP3rz5OS9pI0Jd9//b2Jsr/9UfClfF8+OpgiIuJvEXFN/vy6X9nz8DUlqw3lcQKwlSNoA6ilwyJiB0nbSPqApOttF0ajn6VsBLjgLmUj1JKkiLhB2YirJX23pN11EbGxZNspZfqfUtxHRGyQ9GBxPwOYImlV0e+ryqxTfNsUSWsjYn1JbdX2V9pe8f3aS9JH8ukID+UHMNPy5VMk/aNoxL+wbX/KrV/o61uS5uVTQ94t6bt5AC/1YP5994HuVJVOUvZ432B7he1j+1m3v/1RUO7xGpDt3WxfYvsfth9RdrC3S8lqQ3mcAGzlCNoAai4f2btcUp+yEd8H9PToZcGekv5R+MX2+5UF9HuUBbBiO9qeULLtPWW6vqe4j3ybnYv7GcC9yqYwFJS7akpxWL1H0k62J5XUVuhvo6RnFy17Tpn2ivsovl+rJHVExA5FX8+OiK68zj3yYFy8bX/KrX+PJEXEbyU9IenVkuap/LQRKRsFX6WBp6kUFA6Oyu6DiLgvIo6LiCnKpl78rytfFaS//bGpySrrKnVmvu2+EbGdstF9l6wzlMcJwFaOoA2g5px5q7K5tz0R0adslLrD9iTbe0n6L2Ujh7K9j6RPKQs475Z0ku1ZJc2eYftZ+RzuNyubl1zqYknzbc/K58h+WtLv8qkqUnYllOf2U/p3JZ1oe4/8zXCL+rufEbFK0q8lnWl72/xNcG2SLspXuUXSm2zvlI/sf6hMM++3PTWfL32qsmkckvR1SSfYfnm+PyfYPjQP9b+R1Cvpg7abbL9d0sv6q1XSbvn642y/Q9lc5J8ULb9Q0lck9VaawpGPiP+XpNNsz7e9ne0xtufYPr/M+vcrO+h4l7M3mh6rbI6zpE1vxCwc2KxTFnb78t9LH6v+9sdgPCt/rApfY5XNt98g6SHbe0j6nzLbDeVxArCVI2gDqKUf2d6gbI52h7I3NK7Ily1UNsJ5h6RlykLxBc6uDvEdSYsj4taI+KuyIPPtojeU3acsiN2jLMSeEBF/Ku08Iq6TdJqky5SN+j5P0pFFq3xc0rfyU/zl5nh/Xdmb826T9AdlQbRXT4e/clqVzWm+R9IPJJ0eEdfky74t6VZJK/N2y70B8+J82R3516fy+3Kjsvm/X8nv+98kHZMve0LS2/Pf1ymb/355PzVK0u8kvUDZ2YUOSYdHxINFy78taaYqj2Yr7/v7eX/H5vd5TV7zDytscpyy4PqgpBcrOzApOEDS7/LnzJWSToyIO/NlH1fRY9Xf/hikFZIeLfqaL+kMZW9+fVjZ/P9y+3LQjxMAePMpewCwZbHdIuk7ETF1oHUT9H2IpHMjYq8BVx5a+yslvTcirk3R/iBrGa/sDZP75wc7AIBhYkQbAHK2x9t+Uz4dYw9ll8H7Qb3rGiHvk/R7QjYA1A6fngUAT7OyaQSXKptWcJWyTz0c1fKRdav8dcMBAEPE1BEAAAAgAaaOAAAAAAkQtAEAAIAECNoAAABAAgRtAAAAIAGCNgAAAJAAQRsAAABIgKANDIPtF9r+g+31tj9Y73oaDfsvYztsP7/R2h5EDRtsP7eO/e+Z1zC2wvKP2/5OjfratL9tn2v7tFq0W9T+MbaXbWltVdHX9Hzf8Pkd2KoQtIHhOUlSd0RMiogvlVvB9ods32H7Edv32P5C8T+b/B/QUtv/sv0n268r2f7Dtu+z/bDtC2xvU7TslbZvyIPqbbbnlGw7z/ZdtjfavsL2TgPdIds72f5Bvs1dtudVsyOG0peq2H9F7b/S9i/y+/qw7Sttv6hoednQYHtlmX368fyf/stKbj8mv/1/Sm5fbbslD04b8q8nbD9Z9PvV/dReCBmFdVfaPnmAfdNfOzUPK/n9W13rdiUpIiZGxB15P0tsfypFP/30f3deQ98I93tCRHwyZR/582Fj/rz6h+3PVzqgGGY/NTsYAbYmBG1gePaStGKAdX4kaf+I2E7STEn7SSoeve2S9AdJO0tql/R927tKku2DJZ0s6SBJ0yU9V9knFyoPsldK+qykHSR9RtKPbO+YL3+xpPMkvVvSZEn/kvS/Vdynr0p6It/mKElfy9uqaBh9VbP/ZPtAST+X9ENJUyTtLek2Sb+yPb2Kforbcl7nWklHl1llraRFtrcrXZAHp4kRMVHSpyVdWvg9Ig6povsd8m1bJX3M9hsHU3ujqeUBASOh/dovf169RtI7JR1b53oA5AjawBDZ/oWkuZK+ko8m7VNuvYj4e0Q8VNhM0lOSCqeW95G0v6TTI+LRiLhM0h8l/Ue+/tGSOiNiRUSsk/RJScfky14paU1EfC8i+iLiO5Lul/T2fPlRkn4UEb+MiA2STpP0dtuT+rlPE/K+T4uIDRGxTFmYf/cAu2MofVW1/3KfkXRhRJwTEesjYm1EfFTSDZJOH6C2Uq9WFtZPlHSk7WeVLO+R9BtJHx5ku1WLiN8oO8CYWbrM9qHOptM8YnuV7Y8XLf5l/v2hfJ8dmG9zrO0e2+ts/8z2XuX6tb2N7c/Zvtv2mnyEfnz+uF8taUrRqPuUSvXbHmv7VNt/z88w3GR7Wr4sbL/f9l8l/bXotufbXqDsuXJS3seP+ttP+cj/Itu3Sdpou8n2FNuX2b7f9p0umnJk+2W2b8z33Rrbn89v3+xMgO29bV+f136NpF2K2njGyH7xWZG8j9/Yfsj2vba/UuY5VNhu0+i97R8V7dsNtp+yfUy+7EW2r7G91vafbR9R1MbOzs7ePGL7BknPq7S/IuJvkn4laVbR9tvb7sxr/YftT1Ua8bZ9Tv6ceyR/TF+d3/5GSadKemde+60DtZ0/Rz5n+wHbd0g6tFLdwGhG0AaGKCJeK+n/JH0gH9H8S6V1nU2reETSA8pGtM/LF71Y0h0Rsb5o9Vvz2wvLby1ZNtn2zspCu0u70tPhbbNtI+Lvykaq+wu0+0jqK7kvxfVUMui+qt1/tp+t7KDie2UWf1fSGwaordTRys4yXJr//uYy65wm6cOubvrLoDjzKmX77A9lVtko6T3KzlIcKul9tg/Ll/1b/n2HfJ/9Jl92qrIDrF2V7dOuCt0vVvaYzFJ2sLeHpI9FxEZJh0i6p2iE/p5+7sZ/KRuVf5Ok7ZSNoP6raPlhkl4uaUbxRhFxvqSLJH0m7+Mt/fRR0KpsP+yg7CD1R8qea3soO9PzIWdnfiTpHEnn5GePnqfs+VHOxZJuUhawP6nyZzYq6VN2ELaLpAPzGv5zoI0i4i1FZ0MOl3SfpOvyg5xr8pp2y+/v//rps0hflfSYpN2V7eeKo9XOplK9WtLfim7+lqReZY/3S5X9vby3QhO/V/bc2Cmv53u2t42In2rzMzj7VdH2ccr+tl4qaXZ+n4GtDkEbGAERcXH+z38fSedKWpMvmijp4ZLVH5Y0qcLyws+TJP1a2Qhkq+1xto9WFi6eXWXb5Qxlm+FsV42dlL1W3Vtm2b3KwmVV8tD+DkkXR8STkr6vMiErIm5RNlVl0VAK7scDyqamfEPSyRFxXZm+uyPijxHxVETcpiw0v6afNo+XdGZE9EREr7JANKt0VNu2lYWfD+dnBNbn6x45hPvxXkkfjYg/R+bWiHiwaPmZeR+PDqHtUl+KiFV5WwdI2jUiPhERT+Tzvr9edB+elPR827vkZ2R+W9qY7T3zdk6LiMcj4pfKwntVIuKmiPhtRPRGxEplB839PT6l/e8j6UJJ74yIVcrC6MqI+Gbe5s2SLpN0eD46/B/KD4YiYrmycFvqZtsblZ2N6VY+bcv2ZGUHUB/Kt/+npC+owmMeEd+JiAfzOs6WtI2kF1a4HwO1fYSkL+aP3VpJZ1a7j4DRhDlvwAiKiL/aXqHsH+HbJW1QNiJYbDtJhRHu0uWFn9dHxIO23yrpc8pGvX4m6VpJqytsW9p2OUPZZjjbVWOdspHM3SX9qWTZ7sqmy0jZyNq4MtuPUxbAJOlt+Xo/yX+/SNK1tneNiPtLtvuYpBtsf2F45W9mlzwMV2T75ZLOUnZm4lnKwk650fyCvSSdY/vs4maUjfjeVXTbrsoOwm7KMvem9Ybyxrlpkv7ez/JVQ2izmrb2UnZw+VDRbWOVjeJLUpukT0j6k+07JZ0RET8uaW+KpHX5KH7BXcru04DyoPx5ZaO0z1b2f/SmKrfdXtn7DE6LiELNe0l6ecl9apL0bWWPWZM23wfFj2nB/soej3coe+5MkPR43vY4SfcWPeZjVOHxsf0RZQdRUySFsr/hXcqtW0XbU6qoGxj1GNEGRl6Tnp5nuULSc735XOb99PQbBFfkvxcvW1MYPYyI6yPigIjYSdk86hcqm7f8jG2dXV5tG0kVp7jky5psv6BCPZUMpa+q5IHoN8pCRKkjJF2f/3y3pD1dnCKzEezd9PQ/+aOVjb7fbfs+ZQF2nLLT9aX9/knS5cqmZYyki5XNi58WEdsrOwNSuE9RZv1Vko6PiB2KvsZHxK9L1ntA0qOSXly03vb5VIZKbVeySv3MFR6grcH0U7r+Kkl3ltzXSRHxJik7kI2IVmWP+WJlbyyeUNLevZJ2LLl9z6KfN+rps0LKR5WLz5p8TdkB3wvys1Sn6plTuJ7B9hhlj+3SiDivaNEqSdeX3KeJEfE+ZQeRvdr8IKC41k3yMwvfVfa38rGith9XdoBXaHu7iHjGVLB8PvYiZX9TO0bEDsrOSlV67g3U9r3V1A2MdgRtIDHb77W9W/7zDEmnSLpOkvJ5ybdIOt32trbfJmlfZaeOpewUc5vtGc6uJvJRSUuK2n5pPm1kO2Uj26sj4mf54oskvcX2q/NQ8QlJl8fm88E3k4fayyV9wvaEfC7xW5WNrvVn0H0N0smSjrb9QduTbO+Yv8ns3/T0KenfKZvLenK+LycoG927UdJdtgtzet+sbB7qLGUHB4tVeY7uGZLmK5sfPFImSVobEY85u/xg8eUV71c2ul98TepzJZ1SmNObv0HtGQclEfGUsmkWXyh6Pu5RNL95jaSd81HXgXxD0idtvyCfc75v/r6BaqwpqX8wbpD0iLM3SI7P33A30/YBkmT7XfnZiackFUaIN7ukX0Tcpew5cYbtZzm7JGbxXPG/SNrW2ZtSxyn7m9umaPkkSY9I2pDPiX5flbV3KBtpPrHk9h9L2sf2u/O/5XG2D7DdHNnlCC+X9HHbz85fPwaaT36WpAW2nxMR9yqbAnW27e1sj7H9PNvlprpMUhbq71d2sP0xbX6Wao2k6fkBg6po+7uSPmh7av7aNejLWQKjAUEbSO9Vkv6Yz6H8Sf5VPEp6pLLT0OuU/ZM8vDCNIX8T0mckLVU2KnuXNr/KxknKRipXKZtG8bbCgohYIekEZSH4n8r+kQ74pq18nfH5Nl2S3pe3VdEw+qpKZFc/OVjZdJt79fSl+V4bEX/M13lc2ZvmWpRNn7lD2enrIyIilI343xIRP4+I+wpfkr4kaV/bz7gCSETcqewgo3RUNKX/VHags17ZyOSmN/RFxL+UBbZfObvqxSsi4gfKDhYucfaG2+XK5s6Ws0jZG+V+m697rfI5uPkIfpekO/K2K151RNnUie8qC1qPSOpU9pypRqekGXkfV1S5jfIa+5SF4lmS7lT23P+GpMLBwRslrbC9QdkbI4+MiMfKNDVP2Zs11yr7e7qwqI+HlT0G35D0D2Uj3MVXIfnvfPv1yg5cLlV1WiW9QtI6P33lkaPyg9E3KHsduEfZmyQX6+lw/wFlZ2HuU3aQ/c3+Osn/Hq6XVLgW/HuUTUG6XdlrzPeVvVaU+pmyK8/8RdnrzGPafOpHYfrSg7ZvrqLtr+dt3irpZmUHDMBWx9n/HwBoHLb3k/QLSfOKRvABANiiMKINoOFExK3KLiH3EvNBJgCALRRBG6gB2yu8+YdRbDo1XO/aStnes0KtG5xd+qzSdudW2ObcWvQ12H0YEf8XEZ8b6CoeI8X2URXqH/CTL7dEtq+ucH9q9ubQoT4XAaBRMHUEAAAASKAup1x32WWXmD59ej26BoZk48aNmjBhJN8PBwBbD15j0WhuuummByJiwA9Mq0vQnj59um688cZ6dA0MSXd3t1paWupdBgCMSrzGotHYrupDmJijDQAAACRA0AYAAAASIGgDAAAACRC0AQAAgAQI2gAAAEACBG0AAAAgAYI2AAAAkABBGwAAAEiAoA0AAAAkQNAGAAAAEiBoA/3o6urSzJkzddBBB2nmzJnq6uqqd0kAAKBBNNW7AGBL1dXVpfb2dnV2dqqvr09jx45VW1ubJKm1tbXO1QEAgC0dI9pABR0dHers7NTcuXPV1NSkuXPnqrOzUx0dHfUuDQAANACCNlBBT0+P5syZs9ltc+bMUU9PT50qAgAAjYSgDVTQ3NysZcuWbXbbsmXL1NzcXKeKAABAIyFoAxW0t7erra1NS5cuVW9vr5YuXaq2tja1t7fXuzQAANAAeDMkUEHhDY8LFy5UT0+Pmpub1dHRwRshAQBAVQjaQD9aW1vV2tqq7u5utbS01LscAADQQJg6AgAAACRA0Ab6wQfWAACAoWLqCFABH1gDAACGgxFtoAI+sAYAAAwHQRuogA+sAQAAw0HQBirgA2sAAMBw1Cxo2x5r+w+2f1yrNoF64gNrAADAcNTyzZAnSuqRtF0N2wTqhg+sAQAAw1GTEW3bUyUdKukbtWgP2FK0trZq+fLluu6667R8+XJCNgAAqFqtRrS/KOkkSZMqrWB7gaQFkjR58mR1d3fXqGsgvQ0bNvCcBYBEeI3FaDXsoG37zZL+GRE32W6ptF5EnC/pfEmaPXt28HHWaCR8BDsApMNrLEarWkwdeZWkf7e9UtIlkl5r+zs1aBcAAABoWMMO2hFxSkRMjYjpko6U9IuIeNewKwO2AHwEOwAAGCquow1U0NXVpRNPPFEbN25URGjjxo068cQTCdsAAKAqjogR73T27Nlx4403jni/wGBMmzZNvb29uvjii9XX16exY8dq3rx5ampq0qpVq+pdHgCMGszRRqOxfVNEzB5oPUa0gQpWr16t+fPna+HChTr44IO1cOFCzZ8/X6tXr653aQAAoAHU8gNrgFHnm9/85jNGtAEAAKrBiDZQQVNTk5544onNbnviiSfU1MTxKQAAGBiJAaigr69PY8aM0bHHHqu7775be+65p8aMGaO+vr56lwYAABoAI9pABTNmzNDxxx+vCRMmSJImTJig448/XjNmzKhzZQAAoBEwog1U0N7ervb2dnV2dm6ao93W1qaOjo56lwYAABoAQRuooLW1VUuWLNFBBx2kiJBtvf71r1dra2u9SwMAAA2AqSNABQsXLtS1116r3XbbTba122676dprr9XChQvrXRoAAGgABG2ggnPPPVcRoTVr1mz2/dxzz613aQAAoAEQtIEKent7FREaP368bGv8+PGKCPX29ta7NAAA0AAI2kA/bG8K3L29vbJd75IAAECD4M2QQD8iQk8++aQkbfoOAABQDUa0AQAAgAQI2gAAAEACBG0AAAAgAYI2AAAAkABBGwAAAEiAoA0AAAAkQNAGAAAAEiBoAwAAAAkQtAEAAIAECNoAAABAAgRtAAAAIAGCNgAAAJAAQRsAAABIgKANAAAAJEDQBgAAABIgaAMAAAAJELQBAACABAjaAAAAQAIEbQAAACABgjYAAACQAEEbAAAASICgDQAAACRA0AYAAAASIGgDAAAACTTVuwBgpNkesTYiYth9AQCAxkTQxlan2vDbX5gmQANAebUYzKgWr8XY0jF1BAAA1ExEDPprr0U/HtJ2wJaOoA1UUOlFnBd3AABQDYI20I9yoy0AAADVIGgDAAAACRC0AQAAgAQI2gAAAEACBG0AAAAgAYI2AAAAkABBGwAAAEiAoA0AAAAkQNAGAAAAEiBoAwAAAAkQtAEAAIAECNoAAABAAgRtAAAAIAGCNgAAAJAAQRsAAABIgKANAAAAJEDQBgAAABIYdtC2Pc32Uts9tlfYPrEWhQEAAACNrKkGbfRK+khE3Gx7kqSbbF8TEbfXoG0AAACgIQ17RDsi7o2Im/Of10vqkbTHcNsFAAAAGlktRrQ3sT1d0ksl/a7MsgWSFkjS5MmT1d3dXcuugeR4zgJAOrzGYjSqWdC2PVHSZZI+FBGPlC6PiPMlnS9Js2fPjpaWllp1DaT306vEcxYAEuE1FqNUTa46YnucspB9UURcXos2AQAAgEZWi6uOWFKnpJ6I+PzwSwIAAAAaXy1GtF8l6d2SXmv7lvzrTTVoFwAAAGhYw56jHRHLJLkGtQAAAACjBp8MCQAAACRA0AYAAAASIGgDAAAACRC0AQAAgAQI2gAAAEACBG0AAAAgAYI2AAAAkABBGwAAAEiAoA0AAAAkQNAGAAAAEiBoAwAAAAkQtAEAAIAECNoAAABAAgRtAAAAIAGCNgAAAJAAQRsAAABIgKANAAAAJEDQBgAAABIgaAMAAAAJELQBAACABAjaAAAAQAIEbQAAACABgjYAAACQAEEbAAAASICgDQAAACRA0AYAAAASIGgDAAAACRC0AQAAgAQI2gAAAEACBG0AAAAgAYI2AAAAkABBGwAAAEiAoA0AAAAkQNAGAAAAEiBoAwAAAAkQtAEAAIAEmupdADAc+53xcz386JMj0tf0k69K3sf248fp1tPfkLwfAACQHkEbDe3hR5/UyrMOTd5Pd3e3WlpakvczEmEeAACMDKaOAAAAAAkwog0AAMpieh4wPARtAABQFtPzgOFh6ggAAACQAEEbAAAASICgDQAAACRA0AYAAAASIGgDAAAACRC0AQAAgAQI2gAAAEACBG0AAAAgAYI2AAAAkABBGwAAAEiAoA0AAAAkQNAGAAAAEiBoAwAAAAkQtAEAAIAECNoAAABAAgRtAAAAIAGCNgAAAJBATYK27Tfa/rPtv9k+uRZtAgAAAI2sabgN2B4r6auSXi9ptaTf274yIm4fbtvAQCY1n6yXfGuEju2+lb6LSc2SdGj6jgAAQHLDDtqSXibpbxFxhyTZvkTSWyURtJHc+p6ztPKs9MG0u7tbLS0tyfuZfvJVyfsAAAAjoxZBew9Jq4p+Xy3p5aUr2V4gaYEkTZ48Wd3d3TXoGtCIPJc2bNgwYs9Z/jYAbClG41nD7u4J6TsCcrUI2i5zWzzjhojzJZ0vSbNnz46RGB3EVuCnV43ISPNIjWiP1P0BgGqsP3n0nTVsOTp9P0BBLd4MuVrStKLfp0q6pwbtAgAAAA2rFkH795JeYHtv28+SdKSkK2vQLgAAANCwhj11JCJ6bX9A0s8kjZV0QUSsGHZlAAAAQAOrxRxtRcRPJP2kFm0BAAAAowGfDAkAAAAkQNAGAAAAEiBoAwAAAAkQtAEAAIAECNoAAABAAgRtAAAAIAGCNgAAAJAAQRsAAABIgKANAAAAJEDQBgAAABIgaAMAAAAJELQBAACABJrqXQAwXNNPvmpkOvpp+n62Hz8ueR8AAGBkELTR0FaedeiI9DP95KtGrC8AADA6MHUEAAAASICgDQAAACRA0AYAAAASIGgDAAAACRC0AQAAgAS46ggAAKiIS6gCQ0fQBgAAZXEJVWB4mDoCAAAAJEDDqi33AAAN00lEQVTQBgAAABIgaAMAAAAJELQBAACABAjaAAAAQAIEbQAAACABgjYAAACQAEEbAAAASICgDQAAACRA0AYAAAASIGgDAAAACRC0AQAAgAQI2gAAAEACBG0AAAAgAYI2AAAAkABBGwAAAEiAoA0AAAAkQNAGAAAAEiBoAwAAAAkQtAEAAIAECNoAAABAAgRtAAAAIAGCNgAAAJAAQRsAAABIgKANAAAAJEDQBgAAABIgaAMAAAAJELQBAACABAjaAAAAQAIEbQAAACABgjYAAACQAEEbAAAASICgDQAAACRA0AYAAAASIGgDAAAACRC0AQAAgASGFbRtf9b2n2zfZvsHtneoVWEAAABAIxvuiPY1kmZGxL6S/iLplOGXBAAAADS+YQXtiPh5RPTmv/5W0tThlwQAAAA0vqYatnWspEsrLbS9QNICSZo8ebK6u7tr2DWQHs9ZAEiH11iMRgMGbdvXSnpOmUXtEfHDfJ12Sb2SLqrUTkScL+l8SZo9e3a0tLQMpV6gPn56lXjOAkAivMZilBowaEfE6/pbbvtoSW+WdFBERK0KAwAAABrZsKaO2H6jpEWSXhMR/6pNSQAAAEDjG+5VR74iaZKka2zfYvvcGtQEAAAANLxhjWhHxPNrVQgAAAAwmvDJkAAAAEACBG0AAAAgAYI2AAAAkABBGwAAAEiAoA0AAAAkQNAGAAAAEiBoAwAAAAkQtAEAAIAECNoAAABAAsP6ZEhgtNt55521du1aSZIXSzvttJMefPDBOlcFAAAaASPaQAXFIbtg7dq12nnnnetUEQAAaCQEbaCC0pA90O0AAADFmDqCrY7tEWsjIobdFwAAaEwEbWx1qg2//YVpAjQAABgIU0cAAACABAjaAAAAQAIEbQAAACABgjYAAACQAEEbAAAASICgDQAAACRA0AYAAAAS4DraAACgZob6oWBePPht+EwDbOkY0QYAADUTEYP+Wrp06ZC2A7Z0BG0AAFAXXV1dmjlzpg466CDNnDlTXV1d9S4JqCmmjgAAgBHX1dWl9vZ2dXZ2qq+vT2PHjlVbW5skqbW1tc7VAbXBiDYAABhxHR0d6uzs1Ny5c9XU1KS5c+eqs7NTHR0d9S4NqBmCNgAAGHE9PT2aM2fOZrfNmTNHPT09daoIqD2mjgAAgBHX3NysM844Q1dccYV6enrU3Nysww47TM3NzfUuDagZgjYAABhxc+fO1eLFi7V48WLNmDFDt99+uxYtWqQTTjih3qUBNUPQBgAAI27p0qVatGiRLrjggk0j2osWLdIVV1xR79KAmnE9rkM5e/bsuPHGG0e8X2Aw+vvQBa7fCgDDM3bsWD322GMaN26curu71dLSoieffFLbbrut+vr66l0e0C/bN0XE7IHW482QAABgxDU3N2vZsmWb3bZs2TLmaGNUIWgDAIAR197erra2Ni1dulS9vb1aunSp2tra1N7eXu/SgJphjjYAABhxhQ+lWbhw4aY52h0dHXxYDUYV5mgDFTBHGwBGRmGONtAomKMNAAAA1BFBGwAAAEiAoA0AAAAkQNAGAAAAEiBoAwAAAAkQtAEAAIAECNoAAABAAgRtAAAAIAGCNgAAAJAAQRsYwJgxYzb7DgAAUA2SAzCAp556arPvAAAA1SBoAwAAAAkQtAEAAIAECNrAAHbccUfZ1o477ljvUgAAQAMhaAP9aGpq0rp16xQRWrdunZqamupdEgAAaBAEbaAffX19Ovvss3X11Vfr7LPPVl9fX71LAgAADYLhOaAfEaGTTjpJfX19Gjt2rCKi3iUBAIAGwYg2AAAAkABBG6igqalJEydO1LRp02Rb06ZN08SJE5mnDQAAqkLQBiro6+vT+PHjJUm2JUnjx49nnjYAAKgKQRuoYMaMGVqwYIEmTJggSZowYYIWLFigGTNm1LkyAADQCDgHDlTQ3t6u9vZ2dXZ2bnozZFtbmzo6OupdGgAAaAAEbaCC1tZW/frXv9Yhhxyixx9/XNtss42OO+44tba21rs0AADQAGoStG3/t6TPSto1Ih6oRZtAvXV1demqq67S1VdfvdmI9itf+UrCNgAAGNCwg7btaZJeL+nu4ZcDbDk6Ojo0b948LVy4UD09PWpubta8efPU0dFB0AYAAAOqxYj2FySdJOmHNWgL2GLcfvvtWrNmjSZOnKiI0MaNG3XeeefpwQcfrHdpAACgAQwraNv+d0n/iIhbC5c/62fdBZIWSNLkyZPV3d09nK6B5MaMGaPHH39cp556qvbee2/deeedOv300zVmzBievwBQQxs2bOB1FaPSgEHb9rWSnlNmUbukUyW9oZqOIuJ8SedL0uzZs6OlpaX6KoE66Ovr04QJEzRr1iz19fVp1qxZmjBhgtavXy+evwBQO93d3byuYlQaMGhHxOvK3W77JZL2llQYzZ4q6WbbL4uI+2paJVAn8+fP32yO9vz583XmmWfWuywAANAAhjx1JCL+KGm3wu+2V0qazVVHMFpMnTpVS5Ys0UUXXbTpqiNHHXWUpk6dWu/SAABAA+A62kAFn/nMZ3TiiSfq2GOP1d13360999xTvb29Ovvss+tdGgAAaAA1+wj2iJjOaDZGk9bWVp1zzjmbfQT7Oeecw6X9AABAVRjRBvrR2tqq1tZW3qgDAAAGrWYj2gAAAACeRtAG+tHV1aWZM2fqoIMO0syZM9XV1VXvkgAAQINg6ghQQVdXl9rb29XZ2bnpqiNtbW2SxDxtAAAwIEa0gQo6OjrU2dmpuXPnqqmpSXPnzlVnZ6c6OjrqXRoAAGgABG2ggp6eHq1evXqzqSOrV69WT09PvUsDAAANgKkjQAVTpkzRSSedpIsvvnjT1JF58+ZpypQp9S4NAAA0AEa0gX7Y7vd3AACAShjRBiq45557tGTJEi1cuFA9PT1qbm7W4sWLdcwxx9S7NAAA0AAY0QYqaG5u1tSpU7V8+XJdd911Wr58uaZOnarm5uZ6lwYAABoAQRuooL29XW1tbVq6dKl6e3u1dOlStbW1qb29vd6lAQCABsDUEaCCwrWyi6eOdHR0cA1tAABQFYI20I/W1la1traqu7tbLS0t9S4HAAA0EKaOAAAAAAkQtAEAAIAECNoAAABAAgRtAAAAIAGCNgAAAJAAQRsAAABIgKANAAAAJEDQBgAAABIgaAMAAAAJELQBAACABBwRI9+pfb+ku0a8Y2DodpH0QL2LAIBRitdYNJq9ImLXgVaqS9AGGo3tGyNidr3rAIDRiNdYjFZMHQEAAAASIGgDAAAACRC0geqcX+8CAGAU4zUWoxJztAEAAIAEGNEGAAAAEiBoAwAAAAkQtIF+2H6b7bD9onrXAgCjje0+27fYvtX2zbZfWe+agFoiaAP9a5W0TNKR9S4EAEahRyNiVkTsJ+kUSWfWuyCglgjaQAW2J0p6laQ2EbQBILXtJK2rdxFALTXVuwBgC3aYpJ9GxF9sr7W9f0TcXO+iAGAUGW/7FknbStpd0mvrXA9QU4xoA5W1Srok//mS/HcAQO0Upo68SNIbJV1o2/UuCqgVrqMNlGF7Z0mrJf1TUkgam3/fK/ijAYCasL0hIiYW/b5G0ksi4p91LAuoGUa0gfIOl3RhROwVEdMjYpqkOyXNqXNdADAq5Vd3GivpwXrXAtQKc7SB8lolnVVy22WS5kn6v5EvBwBGpcIcbUmypKMjoq+eBQG1xNQRAAAAIAGmjgAAAAAJELQBAACABAjaAAAAQAIEbQAAACABgjYAAACQAEEbABKw/Rzbl9j+u+3bbf/E9j62p9tePsQ2j7E9ZZh1HWP7K4NYf6XtXVK1DwCjGUEbAGos/wjpH0jqjojnRcQMSadKmjzMpo+RNKigbZvPSwCAOiFoA0DtzZX0ZEScW7ghIm6JiM0+7Kh09Nf2j2232B5re4nt5bb/aPvDtg+XNFvSRbZvsT3e9v+zfb3tm2z/zPbueTvdtj9t+3pJJ1ZTsO2v2b7R9grbZ5Qs/h/bN+Rfz8/X39X2ZbZ/n3+9akh7CgBGMUY6AKD2Zkq6aRjbz5K0R0TMlCTbO0TEQ7Y/IOm/I+JG2+MkfVnSWyPiftvvlNQh6di8jR0i4jWD6LM9ItbaHivpOtv7RsRt+bJHIuJltt8j6YuS3izpHElfiIhltveU9DNJzcO4zwAw6hC0AWDLc4ek59r+sqSrJP28zDovVBbor8lmqmispHuLll86yD6PsL1A2f+F3SXNkFQI2l1F37+Q//w6STPyviVpO9uTBtknAIxqBG0AqL0Vkg6vYr1ebT6Fb1tJioh1tveTdLCk90s6Qk+PVBdY0oqIOLBC2xurLdb23pL+W9IBed9LCrXkoszPYyQdGBGPlrRVbbcAMOoxRxsAau8XkraxfVzhBtsH2C6dyrFS0izbY2xPk/SyfN1dJI2JiMsknSZp/3z99ZIKo8Z/lrSr7QPzbcbZfvEQ691OWTB/2PZkSYeULH9n0fff5D//XNIHiu7frCH2DQCjFiPaAFBjERG23ybpi7ZPlvSYslD9oZJVfyXpTkl/lLRc0s357XtI+qbtwmDIKfn3JZLOtf2opAOVjZp/yfb2yl7Pv6hsNH0gx9g+rOj3V0j6Q77tHXldxbax/TtlgzOt+W0flPRV27flff9S0glV9A0AWw1HxMBrAQAAABgUpo4AAAAACRC0AQAAgAQI2gAAAEACBG0AAAAgAYI2AAAAkABBGwAAAEiAoA0AAAAk8P8Bx7uKI88zPJEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[605],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7abeb6d2d0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[56],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7abeba63d0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[603],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7abeb7a310>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[60],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### which means the Data is not clustering by age. \n",
    "\n",
    "1) Visualize the data in box plots for all ten most important features\n",
    "2) Run ANOVA on the ten most important features above to derive p-values. Good resources for ANOVA in Python include: https://www.marsja.se/four-ways-to-conduct-one-way-anovas-using-python/ (first section with SciPy only) \n",
    "\n",
    "and\n",
    "\n",
    "https://docs.scipy.org/doc/scipy-0.16.1/reference/generated/scipy.stats.f_oneway.html\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# ANOVA - analyzes groups of data (group A vs B) to quantify how different they are \n",
    "## do each variable separately - 15 different ANOVA's\n",
    "## lower the better p-value. we define the cutoff ( < 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "asthma_df_quant_group1 = asthma_df_quant[asthma_df_quant['Cluster Label'].str.contains('A')]\n",
    "\n",
    "asthma_df_quant_group2 = asthma_df_quant[asthma_df_quant['Cluster Label'].str.contains('B')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['f_3786_0_0_f_QUANT_Age_asthma_diagnosed_residualizedRelated']"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[asthma_df_quant.columns[381]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 7.160612893392889 and p-value 0.007454157476362064\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "feature = asthma_df_quant.columns[449]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))\n",
    "## if pvalue < 0.05:\n",
    "\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 993.5124228226186 and p-value 2.5189051910182446e-216\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[584]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 4.683874271915652 and p-value 0.03045086381438937\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[659]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 0.337180446810969 and p-value 0.5614633996534308\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[617]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['f_5265_0_0_f_QUANT_Corneal_resistance_factor_left_residualizedRelated']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[asthma_df_quant.columns[453]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 3.165118373022105 and p-value 0.07523194670522179\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[453]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 3.6864334910717877 and p-value 0.05486144288856012\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[357]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 1.0555423528730155 and p-value 0.3042381398745969\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[10]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 0.8042892843346484 and p-value 0.36981755971151353\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[597]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['f_4526_0_0_f_QUANT_Happiness_residualizedRelated']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[asthma_df_quant.columns[411]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 0.008086018973845324 and p-value 0.9283492591964536\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[411]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['f_30160_0_0_f_QUANT_Basophill_count_residualizedRelated']"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[asthma_df_quant.columns[612]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 16.573210910414602 and p-value 4.6866341322968534e-05\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[612]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 8.963074207463185 and p-value 0.0027560101552165576\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[109]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 13.017113242979384 and p-value 0.0003089031488269694\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[605]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 0.23363755466667072 and p-value 0.6288413862495658\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[56]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 4.548635059889868 and p-value 0.03294896910125065\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[603]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 0.15151546384867673 and p-value 0.6970926682787828\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[60]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'age', u'BMI', u'asthma',\n",
       "       u'f_20006_0_0_f_QUANT_Interpolated_Year_when_cancer_first_diagnosed',\n",
       "       u'f_20008_0_0_f_QUANT_Interpolated_Year_when_non_cancer_illness_first_diagnosed',\n",
       "       u'f_20010_0_0_f_QUANT_Interpolated_Year_when_operation_took_place',\n",
       "       u'f_20018_0_0_f_QUANT_Prospective_memory_result',\n",
       "       u'f_22508_0_0_f_QUANT_Amount_of_tobacco_currently_smoked',\n",
       "       u'f_1160_0_0_f_QUANT_Sleep_duration',\n",
       "       u'f_1269_0_0_f_QUANT_Exposure_to_tobacco_smoke_at_home',\n",
       "       ...\n",
       "       u'specialRequest_ageSquared_QUANT_age_squared',\n",
       "       u'specialRequest_QUANT_years_observed_in_primary_HES',\n",
       "       u'specialRequest_QUANT_type_of_asthma_exacerbation',\n",
       "       u'specialRequest_QUANT_asthma_hospital_exacerbations_raw',\n",
       "       u'specialRequest_QUANT_asthma_hospital_exacerbations_annual_rate',\n",
       "       u'specialRequest_QUANT_asthma_HES_based_age_of_onset',\n",
       "       u'specialRequest_QUANT_asthma_HES_or_NI_based_age_of_onset_youngest',\n",
       "       u'specialRequest_QUANT_OA_HES_based_age_of_onset',\n",
       "       u'f_20016_20191_0_0_f_QUANT_Fluid_intelligence_score',\n",
       "       u'f_4282_20240_0_0_f_QUANT_Maximum_digits_remembered_correctly'],\n",
       "      dtype='object', length=968)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asthma_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p_values=[]\n",
    "features=list(asthma_df_quant_group1.columns.values[:-1])\n",
    "anova_dict=dict()\n",
    "\n",
    "for feature in asthma_df_quant_group1.columns.values[:-1]:\n",
    "    (statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    p_values.append(pvalue)\n",
    "anova_dict['feature']=features\n",
    "anova_dict['pvalue']=p_values\n",
    "anova_df=pd.DataFrame.from_dict(anova_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "anova_df=anova_df.sort_values(by='pvalue')\n",
    "threshold=0.01\n",
    "anova_df_significant=anova_df.loc[(anova_df.pvalue < threshold) & (anova_df.pvalue > 0)].sort_values(by='pvalue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>f_23123_0_0_f_QUANT_Arm_fat_percentage_left</td>\n",
       "      <td>0.002216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>f_21002_0_0_f_QUANT_Weight_residualizedRelated</td>\n",
       "      <td>0.002225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>f_23115_0_0_f_QUANT_Leg_fat_percentage_left_re...</td>\n",
       "      <td>0.002278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>f_22677_0_0_f_QUANT_Mean_carotid_IMT_intima_me...</td>\n",
       "      <td>0.002663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>f_3786_0_0_f_QUANT_Age_asthma_diagnosed</td>\n",
       "      <td>0.002745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>f_23119_0_0_f_QUANT_Arm_fat_percentage_right</td>\n",
       "      <td>0.002756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>f_23111_0_0_f_QUANT_Leg_fat_percentage_right</td>\n",
       "      <td>0.002863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>f_20021_0_0_f_QUANT_Speech_reception_threshold...</td>\n",
       "      <td>0.003170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>f_23128_0_0_f_QUANT_Trunk_fat_mass_residualize...</td>\n",
       "      <td>0.003299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>f_20107_20110_code3_QUANT_Lung_cancer_residual...</td>\n",
       "      <td>0.003438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>specialRequest_QUANT_FEV1_FVC_ratio_martin_tob...</td>\n",
       "      <td>0.003604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>specialRequest_QUANT_FEV1_FVC_ratio_strict</td>\n",
       "      <td>0.003604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>specialRequest_QUANT_FEV1_FVC_ratio_martin_tob...</td>\n",
       "      <td>0.003711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>f_20009_0_dxAgeCode1111_QUANT_asthma</td>\n",
       "      <td>0.003770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>f_20154_0_0_f_QUANT_Forced_expiratory_volume_i...</td>\n",
       "      <td>0.004118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>f_20154_0_0_f_QUANT_Forced_expiratory_volume_i...</td>\n",
       "      <td>0.004118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>f_23099_0_0_f_QUANT_Body_fat_percentage_residu...</td>\n",
       "      <td>0.004476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>f_20010_0_0_f_QUANT_Interpolated_Year_when_ope...</td>\n",
       "      <td>0.004492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>f_4080_0_f_QUANT_systolic_blood_pressure_mean_...</td>\n",
       "      <td>0.004951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>f_23099_0_0_f_QUANT_Body_fat_percentage</td>\n",
       "      <td>0.005266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>f_3894_0_0_f_QUANT_Age_heart_attack_diagnosed</td>\n",
       "      <td>0.005369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>f_23114_0_0_f_QUANT_Leg_predicted_mass_right_r...</td>\n",
       "      <td>0.005819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>f_5255_0_0_f_QUANT_Intra_ocular_pressure_Goldm...</td>\n",
       "      <td>0.006042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>f_23113_0_0_f_QUANT_Leg_fat_free_mass_right_re...</td>\n",
       "      <td>0.006206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>f_4012_0_0_f_QUANT_Age_deep_vein_thrombosis_DV...</td>\n",
       "      <td>0.006347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>f_23125_0_0_f_QUANT_Arm_fat_free_mass_left_res...</td>\n",
       "      <td>0.006699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>f_22503_0_0_f_QUANT_Years_of_cough_on_most_day...</td>\n",
       "      <td>0.006818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>f_3063_0_f_QUANT_FEV1_maximumValue_forFEV1_Pre...</td>\n",
       "      <td>0.007066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>f_3063_0_f_QUANT_FEV1_maximumValue_strict_resi...</td>\n",
       "      <td>0.007066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>f_20150_0_0_f_QUANT_Forced_expiratory_volume_i...</td>\n",
       "      <td>0.007066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>f_30080_0_0_f_QUANT_Platelet_count_residualize...</td>\n",
       "      <td>0.007067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>f_3143_0_0_f_QUANT_Ankle_spacing_width_residua...</td>\n",
       "      <td>0.007104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>f_23126_0_0_f_QUANT_Arm_predicted_mass_left_re...</td>\n",
       "      <td>0.007149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>f_23115_0_0_f_QUANT_Leg_fat_percentage_left</td>\n",
       "      <td>0.007183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>specialRequest_QUANT_FEV1_never_smoked_only</td>\n",
       "      <td>0.007422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>f_5257_0_0_f_QUANT_Corneal_resistance_factor_r...</td>\n",
       "      <td>0.007454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>f_30190_0_0_f_QUANT_Monocyte_percentage</td>\n",
       "      <td>0.007691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>f_23121_0_0_f_QUANT_Arm_fat_free_mass_right_re...</td>\n",
       "      <td>0.007818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>f_3581_0_0_f_QUANT_Age_at_menopause_last_menst...</td>\n",
       "      <td>0.007958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>f_4194_0_0_f_QUANT_Pulse_rate</td>\n",
       "      <td>0.008158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>specialRequest_QUANT_age_stopped_smoking_resid...</td>\n",
       "      <td>0.008173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>f_20107_20110_code1_QUANT_Heart_disease_residu...</td>\n",
       "      <td>0.008178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>f_3148_0_0_f_QUANT_Heel_bone_mineral_density_BMD</td>\n",
       "      <td>0.008463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>specialRequest_QUANT_FEV1_never_smoked_only_st...</td>\n",
       "      <td>0.008468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>f_12702_2_0_f_QUANT_Cardiac_index_during_PWA_r...</td>\n",
       "      <td>0.008620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>specialRequest_QUANT_FEV1_FVC_ratio</td>\n",
       "      <td>0.008787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>f_23101_0_0_f_QUANT_Whole_body_fat_free_mass_r...</td>\n",
       "      <td>0.008864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>f_30250_0_0_f_QUANT_Reticulocyte_count_residua...</td>\n",
       "      <td>0.008982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>f_23269_2_0_f_QUANT_Leg_total_mass_left_residu...</td>\n",
       "      <td>0.009270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>f_40008_0_p_D46_QUANT_Myelodysplastic_syndrome...</td>\n",
       "      <td>0.009687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               feature    pvalue\n",
       "113        f_23123_0_0_f_QUANT_Arm_fat_percentage_left  0.002216\n",
       "478     f_21002_0_0_f_QUANT_Weight_residualizedRelated  0.002225\n",
       "502  f_23115_0_0_f_QUANT_Leg_fat_percentage_left_re...  0.002278\n",
       "492  f_22677_0_0_f_QUANT_Mean_carotid_IMT_intima_me...  0.002663\n",
       "19             f_3786_0_0_f_QUANT_Age_asthma_diagnosed  0.002745\n",
       "109       f_23119_0_0_f_QUANT_Arm_fat_percentage_right  0.002756\n",
       "101       f_23111_0_0_f_QUANT_Leg_fat_percentage_right  0.002863\n",
       "460  f_20021_0_0_f_QUANT_Speech_reception_threshold...  0.003170\n",
       "515  f_23128_0_0_f_QUANT_Trunk_fat_mass_residualize...  0.003299\n",
       "731  f_20107_20110_code3_QUANT_Lung_cancer_residual...  0.003438\n",
       "336  specialRequest_QUANT_FEV1_FVC_ratio_martin_tob...  0.003604\n",
       "333         specialRequest_QUANT_FEV1_FVC_ratio_strict  0.003604\n",
       "334  specialRequest_QUANT_FEV1_FVC_ratio_martin_tob...  0.003711\n",
       "265               f_20009_0_dxAgeCode1111_QUANT_asthma  0.003770\n",
       "726  f_20154_0_0_f_QUANT_Forced_expiratory_volume_i...  0.004118\n",
       "472  f_20154_0_0_f_QUANT_Forced_expiratory_volume_i...  0.004118\n",
       "494  f_23099_0_0_f_QUANT_Body_fat_percentage_residu...  0.004476\n",
       "364  f_20010_0_0_f_QUANT_Interpolated_Year_when_ope...  0.004492\n",
       "660  f_4080_0_f_QUANT_systolic_blood_pressure_mean_...  0.004951\n",
       "97             f_23099_0_0_f_QUANT_Body_fat_percentage  0.005266\n",
       "21       f_3894_0_0_f_QUANT_Age_heart_attack_diagnosed  0.005369\n",
       "501  f_23114_0_0_f_QUANT_Leg_predicted_mass_right_r...  0.005819\n",
       "447  f_5255_0_0_f_QUANT_Intra_ocular_pressure_Goldm...  0.006042\n",
       "500  f_23113_0_0_f_QUANT_Leg_fat_free_mass_right_re...  0.006206\n",
       "23   f_4012_0_0_f_QUANT_Age_deep_vein_thrombosis_DV...  0.006347\n",
       "512  f_23125_0_0_f_QUANT_Arm_fat_free_mass_left_res...  0.006699\n",
       "489  f_22503_0_0_f_QUANT_Years_of_cough_on_most_day...  0.006818\n",
       "658  f_3063_0_f_QUANT_FEV1_maximumValue_forFEV1_Pre...  0.007066\n",
       "655  f_3063_0_f_QUANT_FEV1_maximumValue_strict_resi...  0.007066\n",
       "727  f_20150_0_0_f_QUANT_Forced_expiratory_volume_i...  0.007066\n",
       "604  f_30080_0_0_f_QUANT_Platelet_count_residualize...  0.007067\n",
       "438  f_3143_0_0_f_QUANT_Ankle_spacing_width_residua...  0.007104\n",
       "513  f_23126_0_0_f_QUANT_Arm_predicted_mass_left_re...  0.007149\n",
       "105        f_23115_0_0_f_QUANT_Leg_fat_percentage_left  0.007183\n",
       "340        specialRequest_QUANT_FEV1_never_smoked_only  0.007422\n",
       "449  f_5257_0_0_f_QUANT_Corneal_resistance_factor_r...  0.007454\n",
       "218            f_30190_0_0_f_QUANT_Monocyte_percentage  0.007691\n",
       "508  f_23121_0_0_f_QUANT_Arm_fat_free_mass_right_re...  0.007818\n",
       "378  f_3581_0_0_f_QUANT_Age_at_menopause_last_menst...  0.007958\n",
       "242                      f_4194_0_0_f_QUANT_Pulse_rate  0.008158\n",
       "769  specialRequest_QUANT_age_stopped_smoking_resid...  0.008173\n",
       "729  f_20107_20110_code1_QUANT_Heart_disease_residu...  0.008178\n",
       "48    f_3148_0_0_f_QUANT_Heel_bone_mineral_density_BMD  0.008463\n",
       "341  specialRequest_QUANT_FEV1_never_smoked_only_st...  0.008468\n",
       "456  f_12702_2_0_f_QUANT_Cardiac_index_during_PWA_r...  0.008620\n",
       "332                specialRequest_QUANT_FEV1_FVC_ratio  0.008787\n",
       "496  f_23101_0_0_f_QUANT_Whole_body_fat_free_mass_r...  0.008864\n",
       "621  f_30250_0_0_f_QUANT_Reticulocyte_count_residua...  0.008982\n",
       "560  f_23269_2_0_f_QUANT_Leg_total_mass_left_residu...  0.009270\n",
       "723  f_40008_0_p_D46_QUANT_Myelodysplastic_syndrome...  0.009687"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_df_significant.tail(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, 2)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_df_significant[anova_df_significant['feature'].str.contains('FEV|FVC')].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(256, 2)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_df_significant.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a6ecb2490>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtoAAAGTCAYAAAAWUbUKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3Xt8XHWd//H3u025lQJWtDS0tCpeglXQX8UV648UvFXQZVcFU7Qgsay7S8RdpYLRFXeNCv5Eoeyut1RQIeCiolJQEBN2K94KgpZGV8WW1hQEyq0tt7Sf3x/fM3QyzCSTdk6mk76ej0ceSc7t+5lzJpP3fM/3nHFECAAAAEBtTah3AQAAAMB4RNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AbQ0GxfYvsT9a6j3obbD7ZPtb1ijOoYs7bGiu01tl871usCaHwEbQA1kQWKR21vsv2A7eW2Z9a7rmK2w/ah9a6j0dl+g+3/tv2I7Xtt32T7LTVuo+bHiuMPYKwRtAHU0psjYl9J0yXdI2lpnevJjZPd7jXU9tsk/Zekr0maIWmapH+R9OZ61lXMdlO9awAAiaANIAcR8ZikqyQdVphme3/bX8t6QNfa/kghqNr+T9tXFS17nu0bszDbanu97Q/bvi/rOT+5Utu2F9v+g+2Ntr9nuzmb/t/ZIrdnve4nlVl3ou3PZu38yfYZWS9oUza/z3aX7Z9I2iLpubabs3Y2Zu0uLtrekOEchcdS9Psa2+fYXp2dBfiq7b2K5h9v+zbbD9q+2fZLi+a9zPatWa/ylZKeWq/yrvFS2w/Z/q3tY7OJb7d9S8mCH7B9dbkNSLpA0r9FxFci4qGI2BYRN0XE4jLLzy7ef0X78D3Zz4dmveEPZfv8ymx62WM1wv5YY/tDtn8tafNowrbt59n+se37szous31AyWKv2JHjBGD3RtAGUHO295F0kqSfFU1eKml/Sc+VdLSkRZLenc37gKSXOo3vfY2kdkmnRERk8w+SdKCkgyWdIulLtl9Ypt1jJH1K0olKveprJV0hSRHxf7PFDo+IfSPiyjKlL5a0QNIRkl4u6YQyy7xL0umSpmTb75G0XlKzpLdJ+mQhxFbpZElvkPQ8SS+Q9JHssbxc0jJJfyfpmZK+KOl7tve0vYekqyV9XdJUpR7mt47Qzisl3am0Hz8m6du2p0r6nqTn2G4pWvad2bZLvVDSTKU3UbXwb5Kul/QMpd7xpVL5YzXc/ijaXpuk4yQdEBGDo6jDSs+bZkktSo/x3JJlRn2cRtE+gHGKoA2glq62/aCkhyW9TtJnpNRTrBS8z4mIRyJijaTPKoVWRcQWpXB3gaRvSOqIiPUl2/5oRDweETdJWq4UpkudLGlZRNwaEY9LOkfSq2zPrrL+EyVdGBHrI+IBSZ8us8wlEXFHFuQOkjRP0oci4rGIuE3SVwqPq0oXR8S6iNgoqUspLEop9H8xIn4eEVsj4lJJj0v6q+xrkqTPR8STEXGVpF+O0M5fipa/UtLvJB2X7acrlfa/bL9Y0mxJ15TZxjOz7xtG8fiG86SkWZKas/033EWUw+2PgouyffnoaIqIiD9ExA3Z8+tepefh0SWL7chxArCbI2gDqKUTIuIASXtKOkPSTbYLvdF7KPUAF6xV6qGWJEXEL5R6XC3pmyXbfSAiNpes21ym/ebiNiJik6T7i9sZQbOkdUW/ryuzTPG0ZkkbI+KRktqqba90e8WPa5akD2TDER7M3sDMzOY3S/pzUY9/Yd3hlFu+0NalkhZmQ0PeJembWQAvdX/2ffpID6pKS5SO9y9s32H7tGGWHW5/FJQ7XiOy/WzbV9j+s+2Hld7sHViy2I4cJwC7OYI2gJrLeva+LWmrUo/vfdree1lwiKQ/F36x/Y9KAX1AKYAVe4btySXrDpRpeqC4jWydZxa3M4INSkMYCsrdNaU4rA5Immp7SklthfY2S9qnaN5BZbZX3Ebx41onqSsiDij62icierI6D86CcfG6wym3/IAkRcTPJD0h6TWSFqr8sBEp9YKv08jDVAoKb47K7oOIuDsiFkdEs9LQi/9w5buCDLc/ntpklXWV+lS27ksjYj+l3n2XLLMjxwnAbo6gDaDmnPy10tjb/ojYqtRL3WV7iu1Zkv5ZqedQtl8g6RNKAeddkpbYPqJksx+3vUc2hvt4pXHJpS6X9G7bR2RjZD8p6efZUBUp3QnlucOU/k1JZ9o+OLsY7kPDPc6IWCfpZkmfsr1XdhFcu6TLskVuk/Qm21Oznv33l9nMP9qekY2X/rDSMA5J+rKk99p+ZbY/J9s+Lgv1P5U0KOl9tpts/62kI4erVdKzs+Un2X670ljka4vmf03SxZIGKw3hyHrE/1nSR22/2/Z+tifYnmf7S2WWv1fpTcc7nS40PU1pjLOkpy7ELLyxeUAp7G7Nfi89VsPtj9HYIztWha+JSuPtN0l60PbBks4qs96OHCcAuzmCNoBa+r7tTUpjtLuULmi8I5vXodTDeaekFUqheJnT3SG+Iem8iLg9In6vFGS+XnRB2d1KQWxAKcS+NyJ+W9p4RNwo6aOSvqXU6/s8Se8oWuRcSZdmp/jLjfH+stLFeb+W9CulIDqo7eGvnDalMc0Dkr4j6WMRcUM27+uSbpe0JttuuQswL8/m3Zl9fSJ7LCuVxv9enD32P0g6NZv3hKS/zX5/QGn8+7eHqVGSfi7p+UpnF7okvS0i7i+a/3VJc1S5N1tZ21dl7Z2WPeZ7spq/W2GVxUrB9X5JL1Z6Y1LwCkk/z54z35N0ZkT8KZt3roqO1XD7Y5TukPRo0de7JX1c6eLXh5TG/5fbl6M+TgDgoUP2AGDXYrtV0jciYsZIy+bQ9gJJX4iIWSMuvGPbXyPpPRHxozy2P8pa9la6YPLl2ZsdAMBOokcbADK297b9pmw4xsFKt8H7Tr3rGiN/L+mXhGwAqB0+PQsAtrPSMIIrlYYVLFf61MNxLetZt8rfNxwAsIMYOgIAAADkgKEjAAAAQA4I2gAAAEAOCNoAAABADgjaAAAAQA4I2gAAAEAOCNqoGdsvtP0r24/Yfl+962k0tl9t+/e2N9nmNmuoKdth+9Aab3N2tt2GvlVstfvG9qm2y348fTb/Otun1La6HZe9ljy3wrxhH8so21lj+7XZzx+2/ZVabLdo+6221+9q26qyvZr/3aGxELRRS0sk9UXElIi4qNwCtt9v+07bD9sesP254n/S2T/uXttbbP+28OJdNP+fbN9t+yHby4o+orvwYv9o9s9lk+3rq123kpHqGWa9Ubcl6V8lXRwR+0bE1SNs/3jbv7C92fb9tr+RfcBKYf65tr9RZr2nvejbvsT2oO3mkunnZsu/vWhaUzZtdhYqCvv6SdtPFP3+hWFqb8228e2S6Ydn0/uGe+zArioiFkTEpfWuoyB7LblzjNv8ZES8J882Sl7r785ew/bNoZ2avRnB7ougjVqaJemOEZb5vtJHPO8naY6kwyUV9373SPqVpGdK6pR0le1nSZLtN0g6W9KxkmZLeq7Sh4sUe3P2z2XfiHh9YWKV65ZTsZ5KdqKtavafbL9N0uWSLpR0oKQXS3pC0v/YPqCKdoq3NVnSWyU9JOnkMotslPSvtieWzshCxb4Rsa+kyySdX7Tv3ztC0/dKOsr2M4umnSLpf0dT/67Iybh5bW303upGNt6eSzX25uy15whJL5N0Tp3rAcriDxg1YfvHkuZLujjrZXhBueUi4o8R8WBhNUnbJB2abeMFkl4u6WMR8WhEfEvSb5SCoJSCWHdE3BERD0j6N0mnVlniqNetop5atvVHpUD+/Wz/le0Bt21Jn5X0iYi4LKvrbknvkbRF0pkj1FbqrZIeVOpNL3fK+wdKIf6do9zuSJ6QdLWkd0hSFuRPVArsT7H9Its32N5o+3e2Tyyad5zTUKWHba+zfW7RvMKQhlNs32X7PtudIxVle6LTqe8/Og2BusX2zGzeUbZ/mZ2l+KXto4rW67PdZfsnSsfhudm0T2VnHh6y/V3bU7Pln3b62kNPvx9pe2X22O6xfUHRcvNs32z7wexxn1pUw3uKlqvYG1flvmu3fZekH4+03ySdXG4/Z4/jp1mtG2xfbHuPovnDHd9LbP+Ht585+Yntg2x/3vYDTmeYXla0fEu2Dx60fYfttxTNe6bt72eP95e2PzHMvtnf9tds32t7re2PlIRd216aHdPf2j62aMZTx6Cw/23/v6zeP9leMNKOrPBc2t92d7YP/5zVPzFb/lDbN2X13Gf7yqJtPXUGK9sH38v2wS8kPa9ouacNASp5LM+z/WOns2f32b7MFd7Uu+hsWna8NxV9DRaea7abbX8r289/ctFwQ9t7Z8f/AdurJb2i0v7KXv9+qBS4C+vvme33u7K/ny/Y3rtCvWd7+9/7att/k01vkfQFSa/Kan+wmm3bPis7TgO2T6tUN3YfBG3UREQcI+l/JJ2R9WhW7Jm0vdD2w5LuU+rR/mI268WS7oyIR4oWvz2bXph/e8m8aR7aK3pZ9sJ9ve3Di6ZXs26pkeoZbr1RtRURz5N0l7b3yD9eYdEXSjpE0n+VrL9N0rckvb7cSsM4RanX/gpJL7L98tLSJH1U0sdsTxrltkfyNUmLsp/foNSbP1CY6dTbfoNS7/2zJbVJ+g/bhf2/OVv/AEnHSfp7P31s+zylfXaspH/J/nkO55+zdt4kaT9Jp0na4hSQl0u6SOnsxgWSlpcc03dJOl3SFElrs2mLsm00SxrM1q/GhZIuzM78PE/SNyXJ9iGSrpO0VNKzlMLFbVVus1g1++5oSS1Kx2YklfbzVkn/pHTm5VXZ/H/IHstIx1dKb74+kq3/uKSfSro1+/0qpeOg7Ln5fUnXZ9vqUHoteGG2nX/PHvNBSs/54cZRL5W0v9Ib36OV9tO7i+a/UtKdWQ0fk/Tt7PlRzisl/S5b9nxJ3bY9TNsFpc+lS5WeP4cq9d6+XunNtZTeyF8v6RmSZmT1l/Pvkh6TNF3pOTmaEGhJn1J6HrdIminp3JFWiojC/4N9lZ4jD0j6bvbG5ftKr40HKz0v3u90NlBK+/V52dcbNMzxsj1D0gJJfyiafJ6kFyj9fRyatfEvFTbxR0mvUTrmH5f0DdvTI6Jf0nsl/TR7DIU3FhW3bfuNkj4o6XWSni+pqqGGGN8I2hhzEXF5FiBeoNRjcE82a1+lIQzFHlL6Z1NufuHnwvyTlYZqzJLUK+mHRb0uI61bzkj1VLteNW1V68Ds+4Yy8zYoha+qZKFtvqTLI+IeSTeqzD+0iPie0lCPmo67jIibJU3NwtAipeBd7HhJayLiqxExGBG3Kr2ZeFu2fl9E/CYitkXEr5XeMBxdso2PZ73+tyv9Uz9cw3uPpI9ExO8iuT0i7lcKo7+PiK9ntfRI+q2kNxete0l2FmMwIp7Mpn09IlZFxGalNywnuswwnDKelHSo7QMjYlNE/CybfrKkH0VET0Q8GRH3R8Sog3aV++7ciNgcEY9Wscmy+zkibomIn2X7ZI3Sm+pCO8Me38x3sm08Juk7kh6LiK9FxFZJVyqFTkn6K6W/u09HxBMR8WNJ10hqy/b3W5XOTG2JiNVKwfVpsmVPknRORDyS1fxZpeBb8BdJn8/2/5VKQfq4CvtlbUR8Oav3UqWQO22EfSkVPZckTVUKku/PjsdfJH1O2dkgpefKLEnNEfFYRDytp75oH/xLto1VlfZBORHxh4i4ISIej4h7ld7glD5fKnIabne1pI6I+JVSD/WzIuJfs+N1p6QvFz2mEyV1RcTGiFin8m9Qr7b9iKR1SsfkY1lblrRY0j9l6z8i6ZNF2y59bP8VEQPZ38KVkn4v6cgKj2OkbZ8o6atFf/PnVruPMH4RtFE3EfF7pV7M/8gmbVLqRSy2n6RHKswv/PxItr2fZP/st0TEp5SGRLymmnUrGKmeaterpq1q3Zd9n15m3nSlQCyl3q8hPdBFPdKFEPguSf1FQe0ySQsr9Fx/RGmM+l47WHclX5d0hlLg/07JvFmSXuk0FODB7NTtyUq9krL9SqcLVe+1/ZBS79OBJdu4u+jnLUphbDgzlXq4SjVrey91wVql3qyCdWXWK562VumYlNZYTrvSG9HfOg11OH6E+kalyn1X7vFUUnY/236B7WucLlh7WCmUFNoZ9vhm7in6+dEyvxeOZ7OkddmZnYLC8XmWpKaSx1PpsR0oaQ8NPdalx/nPEREl84dcSFzkqf0SEVuyH6u5aK+4vllKz5sNRfvpi0o991K6CN2SfuE0ZKZcT3W5fVD6fK7I9rNtX+E0bOVhSd9Qdc/jwuvOVUpv6K8oekzNJcf+w9r+JqS5ilpPiIgpklolvaionmdJ2kfSLUXb/oEqdELYXmT7tqJl5wzz2EbadjV1YzdD0Ea9NWn7WME7lMYjFvf8Hq7tFwjeoaE9kodLuifrcSwnlP4B7ci61dQz3Hqjbatav5O0XtLbiydmp2LfKummbNJdSr37xZ6jdCr/z9nvi5Qe392271bqpTpQqfdsiIi4QenU7D/U4DEU+3q2zWuLgkjBOkk3RcQBRV/7RsTfZ/Mvl/Q9STMjYn+lsyPVnJYfzjoVjV0tMqAUDoodou37UkrPt1IzS5Z/UunN0malf9iSnupxfCoIRMTvI6JNKUydp3QR7uRh6lPpNjU0sJaqZt+Vezyj9Z9KPf/Pz85ifbionZGO72gMSJrpoWOpC8fnXqU3njOK5hUfl2L3aXsPcel2Cg4uGf5xiIqGPNVI8b5fpzRs5sCi/bRfRLxYSmOUI2JxRDRL+jul4Telt5Mr7IPS52PB5ux7pefPp7KaXpodx3eq+r+1pUqdDB8peUx/Kjn2UyLiTdn8DcPUOkRE3CTpEkn/L5t0n9KbsBcXbXv/SMNXhrA9S6kn/QxJz4w0PGRV0WMr/RsYadtV143dB0EbY8r2e2w/O/v5MKUrxW+UpEjjum9TGg+8V3ZRykuVTidLaWhBu+3DbD9D6YX7kmxbhzjdh3qPbN2zlELjT0Zat5Iq6qlk1G1VK+tJ+6Ckj2Rj3fe2fZCkryg93sL4zB9IeqHtd9melI0h/aSkqyJi0ParlALbkUpjDY9Q6sm5XJXHQ3Yq9Z7VTET8SekUdLkLFa+R9IKixzDJ9iuKxv9OkbQxIh6zfaSkhTUo6SuS/s328528NBuHfW1Wy0KnWxyeJOmwrMbhvDN7HuyjdMHpVdkwgv+VtJfTRYmTlJ4jxbeqfKftZ2U9tIWLh7cqnXV4re0TszqeabtwEdhtkv7W9j5Z0Gofpq489l2ldh6WtMn2iyQVh+iRju9o/FwpLC7JttOqNKznimx/f1vSudm+eZG2XxswRLbsNyV12Z6SBbF/VurBLXi2pPdl7bxdaczytTtQc1UiYoPSGOzP2t7P9gSnixOPliTbb3capyylMdCh9FwpfVzF++AwFf2dZ8NB/qz0fJ2Y9YoXv6GbonSm7kGn24ieVU3ttv9O6e97YcnZhl9Ietj2h7LXsIm259guXPT4TUnn2H5G9tg6Rmjq85JeZ/uIrJ0vS/pc0f+ag719/HexyUr7695suXcrvQ4W3CNphrMLeKvY9jclnVr0N/+xkfYRxj+CNsbaqyX9xvZmpX9O1yr1chW8Q9JcpX8Yn5b0tuyfgCLiB0oXFPUqnZJbq+0vZFOUes8eUPqH8UZJCwq9yCOsO5yK9VSyE21VJRtH+C6li8zuV+pFeYWko7N/ysrGcb5JqYfrL0q9NA9pe9A5RdJ3I43TvbvwpXQR3vEuc3FXRPxE6R9kTUXEioh4Wo9gNv7x9UrHYEDpNPx52h5I/0Hp1oOPKF2M9M0alHNBtp3rlQJit6S9s+fR8ZI+oLTPl0g6PiLuq7ShzNeV3mTdrTTs5n3ZY3soq/8rSs/XzUpnKgreKOkO25uUjsk7svG3dykd1w8o3XrxNm0/e/I5pbu53KM0/nbIHVxK5LHvyvmgUoh/RCmgPHVHjCqOb9Ui4glJb1E6G3Of0nC0RRHx22yRM5Qudrtb6Zj0KPUSl9OhdDzulLRC6c3nsqL5P1e60O0+SV1Krwm1OFs1nEVKQ1pWK70WXaXtw8deIenn2XPle5LOzN7AljpDadjK3UrPya+WzF+sFKDvV7qg++aieR9XugPTQ0oXBX9b1WlTuqh0wNvvPPLhLPi/WekN/p+U9uVXlI5Rob212bzrlY5ZRdlr8teUroOQpA8pnYH7mdNQlx8pXaxbut5qpTH4P1X6u3mJtnfOSOmOO3dIutt24W+94rYj4jql0P/jbJlq7tiDcc5Dh5oBaDS2X68UHI6NHbgwDvlw+uCdb0RETT8lDzvP9nmSDoqIXeZTHAGMT/RoAw0uIq5Xuk/3X9W5FGCX5HS/7pdmw4GOVBpWU3rxLQDUHEEbNed05fumMl/lPnmwrmy/pkKtm0ZY77oK6314mHWqbqvScrZfU27bEfH9iKj4sedjzelDX8rVf90uUNuoj93uzPbJFfbXiJ9iuguZojTcYbPSMJnPSvpuvYoZ7d83gMbF0BEAAAAgB/RoAwAAADloqkejBx54YMyePbseTQM7ZPPmzZo8eXK9ywCAcYnXWDSaW2655b6IGPHTmOsStGfPnq2VK1fWo2lgh/T19am1tbXeZQDAuMRrLBqN7ao++ZOhIwAAAEAOCNoAAABADgjaAAAAQA4I2gAAAEAOCNoAAABADgjaAAAAQA4I2gAAAEAOCNoAAABADgjaAAAAQA4I2gAAAEAOCNrAMHp6ejRnzhwde+yxmjNnjnp6eupdEgAAaBBN9S4A2FX19PSos7NT3d3d2rp1qyZOnKj29nZJUltbW52rAwAAuzp6tIEKurq61N3drfnz56upqUnz589Xd3e3urq66l0aAABoAARtoIL+/n7NmzdvyLR58+apv7+/ThUBAIBGQtAGKmhpadGKFSuGTFuxYoVaWlrqVBEAAGgkBG2ggs7OTrW3t6u3t1eDg4Pq7e1Ve3u7Ojs7610aAABoAFwMCVTQ1tamm2++WQsWLNDjjz+uPffcU4sXL+ZCSAAAUBV6tIEKenp6dOWVV2r69OmaMGGCpk+friuvvJJb/AEAgKoQtIEKlixZoqamJi1btkw//OEPtWzZMjU1NWnJkiX1Lg0AADQAgjZQwfr163XppZcOub3fpZdeqvXr19e7NAAA0AAI2gAAAEAOCNpABTNmzNCiRYuG3HVk0aJFmjFjRr1LAwAADYC7jgAVnH/++TrzzDN12mmnae3atZo1a5a2bt2qCy64oN6lAQCABkCPNlBBW1ubLrzwQk2ePFm2NXnyZF144YXc3g8AAFSlZkHb9kTbv7J9Ta22CQAAADSqWg4dOVNSv6T9arhNoG56enrU2dmp7u5ubd26VRMnTlR7e7sk0asNAABGVJMebdszJB0n6Su12B6wK+jq6lJ3d/eQ2/t1d3erq6ur3qUBAIAGUKse7c9LWiJpSqUFbJ8u6XRJmjZtmvr6+mrUNJCP/v5+3XDDDTrttNN011136ZBDDlFbW5v6+/t5/gJADW3atInXVYxLjoid24B9vKQ3RcQ/2G6V9MGIOH64debOnRsrV67cqXaBvM2cOVODg4O6/PLLnxo6snDhQjU1NWndunX1Lg8Axo2+vj61trbWuwygarZviYi5Iy1Xi6Ejr5b0FttrJF0h6Rjb36jBdoG6sz3s7wAAAJXsdNCOiHMiYkZEzJb0Dkk/joh37nRlQJ0NDAzohBNO0IIFC/S6171OCxYs0AknnKCBgYF6lwYAABoA99EGKmhubtbll1+u6dOna8KECZo+fbouv/xyNTc317s0AADQAGoatCOib6Tx2UCj2LJlizZt2qSOjg4tX75cHR0d2rRpk7Zs2VLv0gAAQAOgRxuoYOPGjTrrrLO0bNkyHXfccVq2bJnOOussbdy4sd6lAQCABkDQBoZxzDHHaNWqVbrxxhu1atUqHXPMMfUuCQAANAiCNlDBjBkztGjRIvX29mpwcFC9vb1atGiRZsyYUe/SAABAA6jlR7AD48r555+vM888U6eddprWrl2rWbNmaevWrbrgggvqXRoAAGgA9GgDFbS1temkk07Shg0bFBHasGGDTjrpJLW1tdW7NAAA0ADo0QYq6Onp0fLly3Xdddc99cmQ7e3tOuqoowjbAABgRARtoIKuri4tXLhQHR0d6u/vV0tLixYuXKiuri6CNgAAGBFBG6hg9erVuueee7TvvvtKkjZv3qwvfvGLuv/+++tcGQAAaAQEbaCCiRMnauPGjbrvvvskSWvWrNGECRM0ceLEOlcGAAAaAUEbqGBwcPBp07Zt26Zt27bVoRoAANBouOsIAAAAkAOCNjCCCRMmDPkOAABQDZIDAAAAkAOCNjCCPfbYY8h3AACAahC0gRE89thjQ74DAGqjp6dHc+bM0bHHHqs5c+aop6en3iUBNcVdRwAAwJjr6elRZ2enuru7h3z6riQ+FAzjBj3aAABgzHV1dam7u1vz589XU1OT5s+fr+7ubnV1ddW7NKBmCNoAAGDM9ff3a968eUOmzZs3T/39/XWqCKg9gjYAABhzLS0tWrFixZBpK1asUEtLS50qAmqPoA0AAMZcZ2en2tvb1dvbq8HBQfX29qq9vV2dnZ31Lg2oGS6GBAAAY65wwWNHR4f6+/vV0tKirq4uLoTEuOKIGPNG586dGytXrhzzdoHRsF1xXj3+bgBgvOrr61Nra2u9ywCqZvuWiJg70nIMHQEAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAActBU7wIAAMD4YXvM2oqIMWsL2BH0aAMAgJqJiFF/zfrQNTu0HrCrI2gDAAAAOWDoCHY7tTitWe026HEBAGD3RdDGbqfa8DtcmCZAAwCAkTB0BKjgjDPOGNV0AACAYvRoAxUsXbpUkvTlL39Zjz/+uPbcc08tXrz4qekAAADDoUcbGMbSpUv12GOPadaHrtFjjz1GyAYAAFUjaAMAAAA5IGgDAAAAOSBoAwAJ03MJAAATTUlEQVQAADkgaAMAAAA5IGgDAAAAOSBoAwAAADkgaAMAAAA5IGgDAAAAOSBoAwAAADkgaAMAAAA5IGgDAAAAOSBoAwAAADnY6aBte6btXtv9tu+wfWYtCgMAAAAaWVMNtjEo6QMRcavtKZJusX1DRKyuwbYBAACAhrTTPdoRsSEibs1+fkRSv6SDd3a7AAAAQCOrRY/2U2zPlvQyST8vM+90SadL0rRp09TX11fLpoHc8ZwFgPzwGovxqGZB2/a+kr4l6f0R8XDp/Ij4kqQvSdLcuXOjtbW1Vk0D+fvBcvGcBYCc8BqLcaomdx2xPUkpZF8WEd+uxTYBAACARlaLu45YUrek/oi4YOdLAgAAABpfLXq0Xy3pXZKOsX1b9vWmGmwXAAAAaFg7PUY7IlZIcg1qAQAAAMYNPhkSAAAAyAFBGwAAAMgBQRsAAADIAUEbAAAAyAFBGwAAAMgBQRsAAADIAUEbAAAAyAFBGwAAAMgBQRsAAADIAUEbAAAAyAFBGwAAAMgBQRsAAADIAUEbAAAAyAFBGwAAAMgBQRsAAADIAUEbAAAAyAFBGwAAAMgBQRsAAADIAUEbAAAAyAFBGwAAAMgBQRsAAADIAUEbAAAAyAFBGwAAAMgBQRsAAADIAUEbAAAAyAFBGwAAAMgBQRsAAADIAUEbAAAAyAFBGwAAAMgBQRsAAADIAUEbAAAAyAFBGwAAAMgBQRsAAADIAUEbAAAAyAFBGwAAAMgBQRsAAADIAUEbAAAAyEFTvQsAAAC7psM/fr0eevTJMWlr9tnLc29j/70n6faPvT73doACgjYAACjroUef1JpPH5d7O319fWptbc29nbEI80AxgjYaGr0tAABgV0XQRkOjtwUAAOyquBgSAAAAyAFBGwAAAMgBQRsAAADIAUEbAAAAyAFBGwAAAMgBQRsAAADIAUEbAAAAyAFBGwAAAMgBQRsAAADIAUEbAAAAyAFBGwAAAMgBQRsAAADIAUEbAAAAyAFBGwAAAMgBQRsAAADIQU2Ctu032v6d7T/YPrsW2wQAAAAa2U4HbdsTJf27pAWSDpPUZvuwnd0uAAAA0Mhq0aN9pKQ/RMSdEfGEpCsk/XUNtgsAAAA0rKYabONgSeuKfl8v6ZWlC9k+XdLpkjRt2jT19fXVoGlAY/Jc2rRp05g9Z/nbALCrmNJytl5y6RiNCL00/yamtEh9fZPzbwjI1CJou8y0eNqEiC9J+pIkzZ07N1pbW2vQNHZ3U9a+RB1rx6ix+/NvYkqL1Nr6m/wbAoAqPHL2p7Xm08fl3k5fX5/GIhfMPnu5Wk/Jvx2goBZBe72kmUW/z5A0UIPtAiN6pH/8/RMAAADjQy3GaP9S0vNtP8f2HpLeIel7NdguAAAA0LB2ukc7IgZtnyHph5ImSloWEXfsdGUAAABAA6vF0BFFxLWSrq3FtgAAAIDxgE+GBAAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHLQVO8CAADArmv22cvHpqEf5N/O/ntPyr0NoBhBGwAAlLXm08eNSTuzz14+Zm0BY4mgjYZHbwsAANgVEbTR0OhtAQAAuyouhgQAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAABysFNB2/ZnbP/W9q9tf8f2AbUqDAAAAGhkO9ujfYOkORHxUkn/K+mcnS8JAAAAaHw7FbQj4vqIGMx+/ZmkGTtfEgAAAND4mmq4rdMkXVlppu3TJZ0uSdOmTVNfX18Nmwbyx3MWAPLDayzGoxGDtu0fSTqozKzOiPhutkynpEFJl1XaTkR8SdKXJGnu3LnR2tq6I/UC9fGD5eI5CwA54TUW49SIQTsiXjvcfNunSDpe0rEREbUqDAAAAGhkOzV0xPYbJX1I0tERsaU2JQEAAACNb2fvOnKxpCmSbrB9m+0v1KAmAAAAoOHtVI92RBxaq0IAAACA8YRPhgQAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHJA0AYAAAByQNAGAAAAckDQBgAAAHLQVO8CAADA+GF7x9Y7b/TrRMQOtQWMFXq0AQBAzUTEqL96e3t3aD1gV0ePNjCMSZMmaXBwUFLqbWlqatKTTz5Z56oAAEAjoEcbqKA4ZBcMDg5q0qRJdaoIAMaXnp4ezZkzR8cee6zmzJmjnp6eepcE1BQ92kAFpSF7pOkAgOr19PSos7NT3d3d2rp1qyZOnKj29nZJUltbW52rA2qDHm3sdmxX9ZX3NgBgd9bV1aXu7m7Nnz9fTU1Nmj9/vrq7u9XV1VXv0oCaIWhjt1OLi2y4UAcAdk5/f7/Wr18/ZOjI+vXr1d/fX+/SgJohaAMAgDHX3Nysjo4Obd68WRGhzZs3q6OjQ83NzfUuDagZgjYAABhzW7Zs0aZNm9TR0aFrr71WHR0d2rRpk7Zs2VLv0oCaIWgDAIAxt3HjRi1ZskTLli3Tcccdp2XLlmnJkiXauHFjvUsDaoagDQAA6qL0WhaubcF4w+39AADAmJs6dao+85nP6Pzzz9dhhx2m1atXa8mSJZo6dWq9SwNqhqANAADG3D777KNt27Zp6dKluuuuu3TIIYdoypQp2meffepdGlAzDB0BAABjbmBgQBdddJEmT54sSZo8ebIuuugiDQwM1LkyoHYI2gAAYMy1tLRoxowZWrVqlW688UatWrVKM2bMUEtLS71LA2qGoA0AAMZcZ2en2tvb1dvbq8HBQfX29qq9vV2dnZ31Lg2oGcZoAwCAMdfW1iZJ6ujoUH9/v1paWtTV1fXUdGA8cD1upTN37txYuXLlmLcLjIbtivO4BRUA1E5fX59aW1vrXQZQNdu3RMTckZZj6AgAAACQA4I2AAAAkAOCNgAAAJADgjYAAACQA4I2AAAAkAOCNgAAAJADgjYAAACQA4I2AAAAkAOCNgAAAJADgjYAAACQA4I2AAAAkAOCNgAAAJADgjYAAACQA4I2AAAAkAOCNgAAAJADgjYAAACQA4I2AAAAkAOCNgAAAJADgjYAAACQA4I2MALbQ74DAABUg6ANjCAihnwHAACoBkEbqGDChAmyrYMOOkgTJkzQQQcdJNuaMIE/GwAAMDISA1DBtm3bNGXKFO21116SpL322ktTpkzRtm3b6lwZAABoBARtYBitra3asGGDtm3bpg0bNqi1tbXeJQEAgAbRVO8CgF3V1KlTtXz5cp1//vk67LDDtHr1ai1ZskRTp06td2kAAKABELSBCvbZZx9t3bpVS5cu1V133aVDDjlE++67r/bZZ596lwYAABpATYaO2P6g7bB9YC22B+wKBgYGtHDhwiFDRxYuXKiBgYF6lwYAABrATvdo254p6XWS7tr5coBdR3Nzs66++mpdd9112rp1qyZOnKiTTz5Zzc3N9S4NAAA0gFr0aH9O0hJJ3GQY407pvbO5lzYAAKjWTvVo236LpD9HxO0jfWqe7dMlnS5J06ZNU19f3840DeRuYGBAxx9/vN7whjfoySef1KRJk7RgwQJdc801PH8BoIY2bdrE6yrGJY/UQ2f7R5IOKjOrU9KHJb0+Ih6yvUbS3Ii4b6RG586dGytXrtyBcoGxM3PmTA0ODuryyy9/aujIwoUL1dTUpHXr1tW7PAAYN/r6+rh9KhqK7VsiYu5Iy43Yox0Rr63QwEskPUdSoTd7hqRbbR8ZEXePsl5gl1R6pmakMzcAAAAFOzx0JCJ+I+nZhd9H06MNNIKBgQFdcskl6ujoUH9/v1paWnTeeefp1FNPrXdpAACgAfDJkEAFLS0tmjFjhlatWqUbb7xRq1at0owZM9TS0lLv0gAAQAOoWdCOiNn0ZmM86ezsVHt7u3p7ezU4OKje3l61t7ers7Oz3qUBAIAGwCdDAhW0tbXp5ptv1oIFC/T4449rzz331OLFi9XW1lbv0gAAQAMgaAMV9PT0aPny5UM+sKa9vV1HHXUUYRsAAIyIoA1U0NXVpYULFw65GHLhwoXq6uoiaAMAgBERtIEKVq9erc2bN2vZsmVP9WifdtppWrt2bb1LAwAADYC7jgAV7LHHHuro6ND8+fPV1NSk+fPnq6OjQ3vssUe9SwMAAA2AHm2ggieeeEIXX3yxXvayl2nr1q3q7e3VxRdfrCeeeKLepQEAgAZA0AYqOOyww3TCCSc8bYz21VdfXe/SAABAAyBoAxV0dnaqs7NT3d3dQ+460tXVVe/SAABAAyBoAxUU7ixS3KPNHUcAAEC1CNrAMNra2tTW1qa+vj61trbWuxwAANBAuOsIMIyenh7NmTNHxx57rObMmaOenp56lwQAABoEPdpABT09PWXHaEti+AgAABgRPdpABV1dXeru7h5yH+3u7m4uhgQAAFUhaAMV9Pf3a968eUOmzZs3T/39/XWqCAAANBKCNlBBS0uLVqxYMWTaihUr1NLSUqeKAABAIyFoAxV0dnaqvb1dvb29GhwcVG9vr9rb29XZ2Vnv0gAAQAPgYkigAu6jDQAAdgZBGxgG99EGAAA7iqEjAAAAQA4I2gAAAEAOCNoAAABADgjaAAAAQA4I2gAAAEAOCNoAAABADgjaAAAAQA4I2gAAAEAOCNoAAABADgjaAAAAQA4cEWPfqH2vpLVj3jCw4w6UdF+9iwCAcYrXWDSaWRHxrJEWqkvQBhqN7ZURMbfedQDAeMRrLMYrho4AAAAAOSBoAwAAADkgaAPV+VK9CwCAcYzXWIxLjNEGAAAAckCPNgAAAJADgjYAAACQA4I2MAzbf2M7bL+o3rUAwHhje6vt22zfbvtW20fVuyaglgjawPDaJK2Q9I56FwIA49CjEXFERBwu6RxJn6p3QUAtEbSBCmzvK+nVktpF0AaAvO0n6YF6FwHUUlO9CwB2YSdI+kFE/K/tjbZfHhG31rsoABhH9rZ9m6S9JE2XdEyd6wFqih5toLI2SVdkP1+R/Q4AqJ3C0JEXSXqjpK/Zdr2LAmqF+2gDZdh+pqT1kv4iKSRNzL7PCv5oAKAmbG+KiH2Lfr9H0ksi4i91LAuoGXq0gfLeJulrETErImZHxExJf5I0r851AcC4lN3daaKk++tdC1ArjNEGymuT9OmSad+StFDS/4x9OQAwLhXGaEuSJZ0SEVvrWRBQSwwdAQAAAHLA0BEAAAAgBwRtAAAAIAcEbQAAACAHBG0AAAAgBwRtAAAAIAcEbQDIge2DbF9h+4+2V9u+1vYLbM+2vWoHt3mq7eadrOtU2xePYvk1tg/Ma/sAMJ4RtAGgxrKPkP6OpL6IeF5EHCbpw5Km7eSmT5U0qqBtm89LAIA6IWgDQO3Nl/RkRHyhMCEibouIIR92VNr7a/sa2622J9q+xPYq27+x/U+23yZprqTLbN9me2/b/8f2TbZvsf1D29Oz7fTZ/qTtmySdWU3Btv/T9krbd9j+eMnss2z/Ivs6NFv+Wba/ZfuX2derd2hPAcA4Rk8HANTeHEm37MT6R0g6OCLmSJLtAyLiQdtnSPpgRKy0PUnSUkl/HRH32j5JUpek07JtHBARR4+izc6I2Gh7oqQbbb80In6dzXs4Io60vUjS5yUdL+lCSZ+LiBW2D5H0Q0ktO/GYAWDcIWgDwK7nTknPtb1U0nJJ15dZ5oVKgf6GNFJFEyVtKJp/5SjbPNH26Ur/F6ZLOkxSIWj3FH3/XPbzayUdlrUtSfvZnjLKNgFgXCNoA0Dt3SHpbVUsN6ihQ/j2kqSIeMD24ZLeIOkfJZ2o7T3VBZZ0R0S8qsK2N1dbrO3nSPqgpFdkbV9SqCUTZX6eIOlVEfFoybaqbRYAxj3GaANA7f1Y0p62Fxcm2H6F7dKhHGskHWF7gu2Zko7Mlj1Q0oSI+Jakj0p6ebb8I5IKvca/k/Qs26/K1plk+8U7WO9+SsH8IdvTJC0omX9S0fefZj9fL+mMosd3xA62DQDjFj3aAFBjERG2/0bS522fLekxpVD9/pJFfyLpT5J+I2mVpFuz6QdL+qrtQmfIOdn3SyR9wfajkl6l1Gt+ke39lV7PP6/Umz6SU22fUPT7X0n6VbbunVldxfa0/XOlzpm2bNr7JP277V9nbf+3pPdW0TYA7DYcESMvBQAAAGBUGDoCAAAA5ICgDQAAAOSAoA0AAADkgKANAAAA5ICgDQAAAOSAoA0AAADkgKANAAAA5OD/Ayzn/x93Z177AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[601],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a6ea91390>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[270],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features that are lung function-specific"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "anova_df_lung=anova_df_significant[anova_df_significant['feature'].str.contains('FEV|FVC|cough|smok|asthma|tobacco|bronc|Lung|lung|PEF')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45, 2)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_df_lung.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>f_22147_0_0_f_QUANT_Age_asthma_diagnosed_by_do...</td>\n",
       "      <td>3.875854e-71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>f_22508_0_0_f_QUANT_Amount_of_tobacco_currentl...</td>\n",
       "      <td>2.604035e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>f_22503_0_0_f_QUANT_Years_of_cough_on_most_days</td>\n",
       "      <td>3.865365e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>f_20150_0_0_f_QUANT_Forced_expiratory_volume_i...</td>\n",
       "      <td>1.748077e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>f_3063_0_f_QUANT_FEV1_maximumValue_forFEV1_Pred</td>\n",
       "      <td>4.976785e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>f_20150_0_0_f_QUANT_Forced_expiratory_volume_i...</td>\n",
       "      <td>4.976785e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>f_3063_0_f_QUANT_FEV1_maximumValue_strict</td>\n",
       "      <td>4.976785e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>f_20151_0_0_f_QUANT_Forced_vital_capacity_FVC_...</td>\n",
       "      <td>5.417083e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>f_3063_0_f_QUANT_FEV1_maximumValue</td>\n",
       "      <td>5.623551e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>f_20151_0_0_f_QUANT_Forced_vital_capacity_FVC_...</td>\n",
       "      <td>6.571714e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>f_3062_0_f_QUANT_FVC_maximumValue_strict</td>\n",
       "      <td>6.571714e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>f_3064_0_f_QUANT_PEF_maximumValue</td>\n",
       "      <td>9.669316e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>f_3064_0_f_QUANT_PEF_maximumValue_strict</td>\n",
       "      <td>9.746044e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>f_3062_0_f_QUANT_FVC_maximumValue</td>\n",
       "      <td>1.738431e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>f_3062_0_f_QUANT_FVC_maximumValue_residualized...</td>\n",
       "      <td>2.442682e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>f_20151_0_0_f_QUANT_Forced_vital_capacity_FVC_...</td>\n",
       "      <td>3.160241e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>specialRequest_QUANT_FEV1_pred</td>\n",
       "      <td>6.759665e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>f_3062_0_f_QUANT_FVC_maximumValue_strict_resid...</td>\n",
       "      <td>1.402096e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>f_20151_0_0_f_QUANT_Forced_vital_capacity_FVC_...</td>\n",
       "      <td>1.402096e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>specialRequest_QUANT_FEV1_percent_pred_strict</td>\n",
       "      <td>2.095742e-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               feature        pvalue\n",
       "84   f_22147_0_0_f_QUANT_Age_asthma_diagnosed_by_do...  3.875854e-71\n",
       "5    f_22508_0_0_f_QUANT_Amount_of_tobacco_currentl...  2.604035e-14\n",
       "92     f_22503_0_0_f_QUANT_Years_of_cough_on_most_days  3.865365e-11\n",
       "74   f_20150_0_0_f_QUANT_Forced_expiratory_volume_i...  1.748077e-06\n",
       "260    f_3063_0_f_QUANT_FEV1_maximumValue_forFEV1_Pred  4.976785e-06\n",
       "337  f_20150_0_0_f_QUANT_Forced_expiratory_volume_i...  4.976785e-06\n",
       "257          f_3063_0_f_QUANT_FEV1_maximumValue_strict  4.976785e-06\n",
       "75   f_20151_0_0_f_QUANT_Forced_vital_capacity_FVC_...  5.417083e-06\n",
       "256                 f_3063_0_f_QUANT_FEV1_maximumValue  5.623551e-06\n",
       "338  f_20151_0_0_f_QUANT_Forced_vital_capacity_FVC_...  6.571714e-06\n",
       "255           f_3062_0_f_QUANT_FVC_maximumValue_strict  6.571714e-06\n",
       "258                  f_3064_0_f_QUANT_PEF_maximumValue  9.669316e-06\n",
       "259           f_3064_0_f_QUANT_PEF_maximumValue_strict  9.746044e-06\n",
       "254                  f_3062_0_f_QUANT_FVC_maximumValue  1.738431e-05\n",
       "652  f_3062_0_f_QUANT_FVC_maximumValue_residualized...  2.442682e-05\n",
       "470  f_20151_0_0_f_QUANT_Forced_vital_capacity_FVC_...  3.160241e-05\n",
       "348                     specialRequest_QUANT_FEV1_pred  6.759665e-05\n",
       "653  f_3062_0_f_QUANT_FVC_maximumValue_strict_resid...  1.402096e-04\n",
       "728  f_20151_0_0_f_QUANT_Forced_vital_capacity_FVC_...  1.402096e-04\n",
       "351      specialRequest_QUANT_FEV1_percent_pred_strict  2.095742e-04"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_df_lung.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "asthma_df_important_features_lung=asthma_df_important_features[asthma_df_important_features['index'].isin(anova_df_lung.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>358</td>\n",
       "      <td>-1.673128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>75</td>\n",
       "      <td>-1.820294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>728</td>\n",
       "      <td>-1.877459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>653</td>\n",
       "      <td>-1.877459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>258</td>\n",
       "      <td>-1.902931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>652</td>\n",
       "      <td>-1.926907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>470</td>\n",
       "      <td>-1.928365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>254</td>\n",
       "      <td>-1.953793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>332</td>\n",
       "      <td>-2.034474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>334</td>\n",
       "      <td>-2.049321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  importance\n",
       "358    358   -1.673128\n",
       "75      75   -1.820294\n",
       "728    728   -1.877459\n",
       "653    653   -1.877459\n",
       "258    258   -1.902931\n",
       "652    652   -1.926907\n",
       "470    470   -1.928365\n",
       "254    254   -1.953793\n",
       "332    332   -2.034474\n",
       "334    334   -2.049321"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asthma_df_important_features_lung.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45, 2)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asthma_df_important_features_lung.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a6e9d6450>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[358],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a6e95a090>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[652],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a6e8c3350>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[470],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a6e803110>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[92],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a6e793c90>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[654],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features that are blood specific"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "anova_df_blood=anova_df_significant[anova_df_significant['feature'].str.contains('LV|cardiac|Neutrophil|blood|Basophil|Platelet|Pulse|Heart|Cardiac|monocyte|heart|Reticulocyte')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21, 2)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_df_blood.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>f_22420_2_0_f_QUANT_LV_ejection_fraction_resid...</td>\n",
       "      <td>1.042056e-150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>f_22423_2_0_f_QUANT_LV_stroke_volume_residuali...</td>\n",
       "      <td>7.103970e-90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>specialRequest_QUANT_LVEDVi_residualizedRelated</td>\n",
       "      <td>7.915657e-48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>f_22421_2_0_f_QUANT_LV_end_diastolic_volume_re...</td>\n",
       "      <td>5.879051e-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>f_22425_2_0_f_QUANT_Cardiac_index_residualized...</td>\n",
       "      <td>1.380493e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>f_22424_2_0_f_QUANT_Cardiac_output_residualize...</td>\n",
       "      <td>2.977231e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>f_22422_2_0_f_QUANT_LV_end_systolic_volume_res...</td>\n",
       "      <td>5.745267e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>f_30140_0_0_f_QUANT_Neutrophill_count_residual...</td>\n",
       "      <td>5.284077e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>f_30000_0_0_f_QUANT_White_blood_cell_leukocyte...</td>\n",
       "      <td>5.795774e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>f_30160_0_0_f_QUANT_Basophill_count_residualiz...</td>\n",
       "      <td>4.686634e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>f_30140_0_0_f_QUANT_Neutrophill_count</td>\n",
       "      <td>2.854141e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>f_30090_0_0_f_QUANT_Platelet_crit_residualized...</td>\n",
       "      <td>3.089031e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>f_30000_0_0_f_QUANT_White_blood_cell_leukocyte...</td>\n",
       "      <td>7.230893e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>f_4080_0_f_QUANT_systolic_blood_pressure_mean_...</td>\n",
       "      <td>4.951063e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>f_3894_0_0_f_QUANT_Age_heart_attack_diagnosed</td>\n",
       "      <td>5.368761e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>f_4012_0_0_f_QUANT_Age_deep_vein_thrombosis_DV...</td>\n",
       "      <td>6.346556e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>f_30080_0_0_f_QUANT_Platelet_count_residualize...</td>\n",
       "      <td>7.066501e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>f_4194_0_0_f_QUANT_Pulse_rate</td>\n",
       "      <td>8.158414e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>f_20107_20110_code1_QUANT_Heart_disease_residu...</td>\n",
       "      <td>8.178480e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>f_12702_2_0_f_QUANT_Cardiac_index_during_PWA_r...</td>\n",
       "      <td>8.619870e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>f_30250_0_0_f_QUANT_Reticulocyte_count_residua...</td>\n",
       "      <td>8.982012e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               feature         pvalue\n",
       "483  f_22420_2_0_f_QUANT_LV_ejection_fraction_resid...  1.042056e-150\n",
       "486  f_22423_2_0_f_QUANT_LV_stroke_volume_residuali...   7.103970e-90\n",
       "743    specialRequest_QUANT_LVEDVi_residualizedRelated   7.915657e-48\n",
       "484  f_22421_2_0_f_QUANT_LV_end_diastolic_volume_re...   5.879051e-19\n",
       "488  f_22425_2_0_f_QUANT_Cardiac_index_residualized...   1.380493e-16\n",
       "487  f_22424_2_0_f_QUANT_Cardiac_output_residualize...   2.977231e-09\n",
       "485  f_22422_2_0_f_QUANT_LV_end_systolic_volume_res...   5.745267e-09\n",
       "610  f_30140_0_0_f_QUANT_Neutrophill_count_residual...   5.284077e-07\n",
       "596  f_30000_0_0_f_QUANT_White_blood_cell_leukocyte...   5.795774e-07\n",
       "612  f_30160_0_0_f_QUANT_Basophill_count_residualiz...   4.686634e-05\n",
       "213              f_30140_0_0_f_QUANT_Neutrophill_count   2.854141e-04\n",
       "605  f_30090_0_0_f_QUANT_Platelet_crit_residualized...   3.089031e-04\n",
       "199  f_30000_0_0_f_QUANT_White_blood_cell_leukocyte...   7.230893e-04\n",
       "660  f_4080_0_f_QUANT_systolic_blood_pressure_mean_...   4.951063e-03\n",
       "21       f_3894_0_0_f_QUANT_Age_heart_attack_diagnosed   5.368761e-03\n",
       "23   f_4012_0_0_f_QUANT_Age_deep_vein_thrombosis_DV...   6.346556e-03\n",
       "604  f_30080_0_0_f_QUANT_Platelet_count_residualize...   7.066501e-03\n",
       "242                      f_4194_0_0_f_QUANT_Pulse_rate   8.158414e-03\n",
       "729  f_20107_20110_code1_QUANT_Heart_disease_residu...   8.178480e-03\n",
       "456  f_12702_2_0_f_QUANT_Cardiac_index_during_PWA_r...   8.619870e-03\n",
       "621  f_30250_0_0_f_QUANT_Reticulocyte_count_residua...   8.982012e-03"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_df_blood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "asthma_df_important_features_blood=asthma_df_important_features[asthma_df_important_features['index'].isin(anova_df_blood.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>605</td>\n",
       "      <td>-1.540485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>604</td>\n",
       "      <td>-1.575223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>612</td>\n",
       "      <td>-1.665470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>621</td>\n",
       "      <td>-1.885290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>483</td>\n",
       "      <td>-1.920435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>660</td>\n",
       "      <td>-2.009456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>199</td>\n",
       "      <td>-2.009475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>242</td>\n",
       "      <td>-2.145588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>610</td>\n",
       "      <td>-2.146862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>487</td>\n",
       "      <td>-2.151238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  importance\n",
       "605    605   -1.540485\n",
       "604    604   -1.575223\n",
       "612    612   -1.665470\n",
       "621    621   -1.885290\n",
       "483    483   -1.920435\n",
       "660    660   -2.009456\n",
       "199    199   -2.009475\n",
       "242    242   -2.145588\n",
       "610    610   -2.146862\n",
       "487    487   -2.151238"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asthma_df_important_features_blood.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a6e6a2050>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[604],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a6e62d390>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[605],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a6e5ac6d0>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[621],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a6e52a990>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[612],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a6e4aff50>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[483],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other features that are present in healthy patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "anova_df_healthy=anova_df_significant[anova_df_significant['feature'].str.contains('fat|bone|mass|grip|adipose|thigh|Waist|BMI|Hip|Ankle|Corneal|ocular')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(123, 2)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_df_healthy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>f_23247_2_0_f_QUANT_Android_tissue_fat_percent...</td>\n",
       "      <td>1.770227e-318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>f_23268_2_0_f_QUANT_Leg_tissue_fat_percentage_...</td>\n",
       "      <td>5.645307e-277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>f_23255_2_0_f_QUANT_Arm_tissue_fat_percentage_...</td>\n",
       "      <td>1.404960e-274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>f_23264_2_0_f_QUANT_Gynoid_tissue_fat_percenta...</td>\n",
       "      <td>1.959677e-239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>f_23308_2_0_f_QUANT_Pelvis_BMC_bone_mineral_co...</td>\n",
       "      <td>1.452724e-233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>f_23284_2_0_f_QUANT_Trunk_fat_mass_residualize...</td>\n",
       "      <td>3.013261e-224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>f_23307_2_0_f_QUANT_Pelvis_bone_area_residuali...</td>\n",
       "      <td>2.518905e-216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>f_23272_2_0_f_QUANT_Leg_tissue_fat_percentage_...</td>\n",
       "      <td>8.851247e-209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>f_23257_2_0_f_QUANT_Arms_fat_mass_residualized...</td>\n",
       "      <td>1.556919e-208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>f_23205_2_0_f_QUANT_L1_L4_BMD_bone_mineral_den...</td>\n",
       "      <td>6.088499e-208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>f_23278_2_0_f_QUANT_Total_fat_mass_residualize...</td>\n",
       "      <td>6.283599e-207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>f_23276_2_0_f_QUANT_Legs_tissue_fat_percentage...</td>\n",
       "      <td>9.192465e-203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>f_23245_2_0_f_QUANT_Android_fat_mass_residuali...</td>\n",
       "      <td>2.117508e-199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>f_23249_2_0_f_QUANT_Arm_fat_mass_left_residual...</td>\n",
       "      <td>5.795929e-194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>f_23224_2_0_f_QUANT_Arms_BMC_bone_mineral_cont...</td>\n",
       "      <td>3.529769e-193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>f_23248_2_0_f_QUANT_Android_total_mass_residua...</td>\n",
       "      <td>4.964819e-167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>f_23305_2_0_f_QUANT_Head_bone_area_residualize...</td>\n",
       "      <td>1.351594e-165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>f_23230_2_0_f_QUANT_Legs_BMC_bone_mineral_cont...</td>\n",
       "      <td>9.391547e-164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>f_23225_2_0_f_QUANT_Arms_BMD_bone_mineral_dens...</td>\n",
       "      <td>2.824549e-151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>f_23253_2_0_f_QUANT_Arm_fat_mass_right_residua...</td>\n",
       "      <td>2.399473e-150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               feature         pvalue\n",
       "538  f_23247_2_0_f_QUANT_Android_tissue_fat_percent...  1.770227e-318\n",
       "559  f_23268_2_0_f_QUANT_Leg_tissue_fat_percentage_...  5.645307e-277\n",
       "546  f_23255_2_0_f_QUANT_Arm_tissue_fat_percentage_...  1.404960e-274\n",
       "555  f_23264_2_0_f_QUANT_Gynoid_tissue_fat_percenta...  1.959677e-239\n",
       "585  f_23308_2_0_f_QUANT_Pelvis_BMC_bone_mineral_co...  1.452724e-233\n",
       "575  f_23284_2_0_f_QUANT_Trunk_fat_mass_residualize...  3.013261e-224\n",
       "584  f_23307_2_0_f_QUANT_Pelvis_bone_area_residuali...  2.518905e-216\n",
       "563  f_23272_2_0_f_QUANT_Leg_tissue_fat_percentage_...  8.851247e-209\n",
       "548  f_23257_2_0_f_QUANT_Arms_fat_mass_residualized...  1.556919e-208\n",
       "123  f_23205_2_0_f_QUANT_L1_L4_BMD_bone_mineral_den...  6.088499e-208\n",
       "569  f_23278_2_0_f_QUANT_Total_fat_mass_residualize...  6.283599e-207\n",
       "567  f_23276_2_0_f_QUANT_Legs_tissue_fat_percentage...  9.192465e-203\n",
       "536  f_23245_2_0_f_QUANT_Android_fat_mass_residuali...  2.117508e-199\n",
       "540  f_23249_2_0_f_QUANT_Arm_fat_mass_left_residual...  5.795929e-194\n",
       "523  f_23224_2_0_f_QUANT_Arms_BMC_bone_mineral_cont...  3.529769e-193\n",
       "539  f_23248_2_0_f_QUANT_Android_total_mass_residua...  4.964819e-167\n",
       "582  f_23305_2_0_f_QUANT_Head_bone_area_residualize...  1.351594e-165\n",
       "526  f_23230_2_0_f_QUANT_Legs_BMC_bone_mineral_cont...  9.391547e-164\n",
       "524  f_23225_2_0_f_QUANT_Arms_BMD_bone_mineral_dens...  2.824549e-151\n",
       "544  f_23253_2_0_f_QUANT_Arm_fat_mass_right_residua...  2.399473e-150"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_df_healthy.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a6e437190>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[538],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a6e4c5990>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[559],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a6e3473d0>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtoAAAGTCAYAAAAWUbUKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3XucHFWd9/HvlyQESLgoYCAEEhXFYB5EjaIYl0FQQLwtIu6AEiAmukrEfRAEowIroyiPFy7uxrhBQHCEB0SQgIBLBp+ooAQBwSCrXCRyvxNuJuH3/HHOkE7Tc8vU6c5MPu/Xq19Jd1Wdc6q6uubbp05VOyIEAAAAoFrrtboBAAAAwHBE0AYAAAAKIGgDAAAABRC0AQAAgAII2gAAAEABBG0AAACgAII2gCHN9pm2T2x1O1qtt+1g+xDbi5rUjqbV1Sy277K9Z7OXBTD0EbQBVCIHimdtL7P9mO0Ftrdtdbtq2Q7b27e6HUOd7b1s/8r2U7Yfsn2N7Q9UXEfl7xXvP4BmI2gDqNL7I2KspK0lPSDptBa3pxgn69wx1Pb+kv6vpLMlTZA0TtJXJL2/le2qZXtkq9sAABJBG0ABEfGcpAsk7dj9mu1NbZ+de0Dvtv2l7qBq+z9tX1Az7zds/3cOs222l9r+ou2Hc8/5QT3VbXum7b/YftT2JbbH59d/lWe5Kfe6f7TBsiNsfyvXc6ftw3Mv6Mg8vct2h+1fS3pG0qtsj8/1PJrrnVlT3mrDObrXpeb5XbaPtf2nfBbgh7Y3qJn+Pts32n7c9m9s71Qz7Y22b8i9yudJenG5njeNT7P9hO3bbO+RX/yI7cV1Mx5p+2eNCpD0bUlfjYj/iognIuKFiLgmImY2mH9S7far2YafyP/fPveGP5G3+Xn59YbvVR/b4y7bX7B9s6SnBxK2bb/a9tW2H8ntONf2ZnWzvWVN3icA6zaCNoDK2d5I0kclXVvz8mmSNpX0Kkm7STpY0qF52pGSdnIa3/tOSTMkTY+IyNO3krSFpG0kTZc0z/YODep9l6SvSzpAqVf9bkk/kaSI+Kc82xsiYmxEnNeg6TMl7SNpZ0lvkvShBvN8XNIsSRvn8jslLZU0XtL+kr7WHWL76SBJe0l6taTXSvpSXpc3STpD0iclbS7p+5IusT3a9vqSfibpR5JertTD/OE+6tlF0h1K2/E4ST+1/XJJl0h6pe3JNfN+LJddbwdJ2yp9iarCVyVdKellSr3jp0mN36vetkdNee2S9pW0WUSsGEA7rLTfjJc0WWkdj6+bZ8Dv0wDqBzBMEbQBVOlnth+X9KSkd0s6WUo9xUrB+9iIeCoi7pL0LaXQqoh4RincfVvSOZJmR8TSurK/HBHPR8Q1khYohel6B0k6IyJuiIjnJR0r6e22J/Wz/QdIOiUilkbEY5JOajDPmRFxaw5yW0maJukLEfFcRNwo6b+616ufTo+IeyLiUUkdSmFRSqH/+xFxXUSsjIizJD0v6W35MUrSdyNieURcIOn3fdTzYM3850n6s6R983Y6T2n7y/brJU2SdGmDMjbP/943gPXrzXJJEyWNz9uvt4soe9se3U7N2/LZgTQiIv4SEVfl/eshpf1wt7rZ1uR9ArCOI2gDqNKHImIzSaMlHS7pGtvdvdHrK/UAd7tbqYdakhQRv1PqcbWk8+vKfSwinq5bdnyD+sfX1hERyyQ9UltPH8ZLuqfm+T0N5ql9bbykRyPiqbq29be++vJq12uipCPzcITH8xeYbfP08ZL+XtPj371sbxrN313XWZIOzENDPi7p/BzA6z2S/926r5Xqp6OV3u/f2b7V9mG9zNvb9ujW6P3qk+1X2P6J7b/bflLpy94WdbOtyfsEYB1H0AZQudyz91NJK5V6fB/Wqt7LbttJ+nv3E9ufUQro9yoFsFovsz2mbtl7G1R9b20deZnNa+vpw31KQxi6NbprSm1YvVfSy21vXNe27vqelrRRzbStGpRXW0ftet0jqSMiNqt5bBQRnbmd2+RgXLtsbxrNf68kRcS1kv4h6Z2SDlTjYSNS6gW/R30PU+nW/eWo4TaIiPsjYmZEjFcaevEf7vmuIL1tjxeL7Ge76n09L7tTRGyi1LvvunnW5H0CsI4jaAOonJMPKo29XRIRK5V6qTtsb2x7oqT/rdRzKNuvlXSiUsD5uKSjbe9cV+wJttfPY7jfpzQuud6PJR1qe+c8RvZrkq7LQ1WkdCeUV/XS9PMlHWF7m3wx3Bd6W8+IuEfSbyR93fYG+SK4GZLOzbPcKOm9tl+ee/Y/16CYz9iekMdLf1FpGIck/UDSp2zvkrfnGNv75lD/W0krJH3W9kjb+0l6a29tlfSKPP8o2x9RGot8Wc30syWdLmlFT0M4co/4/5b0ZduH2t7E9nq2p9me12D+h5S+dHzM6ULTw5TGOEt68ULM7i82jymF3ZX5ef171dv2GIj183vV/RihNN5+maTHbW8j6agGy63J+wRgHUfQBlCln9tepjRGu0PpgsZb87TZSj2cd0hapBSKz3C6O8Q5kr4RETdFxP8oBZkf1VxQdr9SELtXKcR+KiJuq688Iv5b0pclXajU6/tqSf9SM8vxks7Kp/gbjfH+gdLFeTdL+oNSEF2hVeGvkXalMc33SrpI0nERcVWe9iNJN0m6K5fb6ALMH+dpd+THiXldrlca/3t6Xve/SDokT/uHpP3y88eUxr//tJc2StJ1kl6jdHahQ9L+EfFIzfQfSZqinnuzleu+INd3WF7nB3KbL+5hkZlKwfURSa9X+mLS7S2Srsv7zCWSjoiIO/O041XzXvW2PQboVknP1jwOlXSC0sWvTyiN/2+0LQf8PgGAVx+yBwBrF9ttks6JiAl9zVug7n0kzY2IiX3OvGbl3yXpExHxyxLlD7AtGypdMPmm/GUHADBI9GgDQGZ7Q9vvzcMxtlG6Dd5FrW5Xk/yrpN8TsgGgOvx6FgCsYqVhBOcpDStYoPSrh8Na7lm3Gt83HACwhhg6AgAAABTA0BEAAACgAII2AAAAUABBGwAAACiAoA0AAAAUQNAGAAAACiBoY1Bs72D7D7afsv3ZVrdnqMn3bf657SdsN/pJcRRgezvby/LPb681bL/D9v/ktnGrvbWE7cttT+/nvF22P1G6TQNhe67tL/cyPWxvX0E9x9s+J/+/yGfM9l2291zbyupHXWfaPrEZdWHtQtDGYB0tqSsiNo6IUxvNYPso27fkMH6n7aPqpi+0/ZDtJ23fZPuDNdP2tb0o/wzz/bZ/YHvjmuln2v5HPqAvqz2w256U/4DUTuvxj01Nmf8nh52nbN9m++D+bAjbO9tebPuZ/O/O/Vhsf0njJG0eER/po/wJts+1/Yjtp23/zvZ7a6Z3r+/IuuVecoC33ZbnPbru9e4yFtS9fk7+I3pQzbZ81vYLtdu3H+vbHUQeq/l59eLq/6BGxN8iYmxE9PbT6q3w75JOz237WW8z5vdwaZPa1RRVBb6qRcQ+EXHWYMvp6TNaWkR8KiK+2uQ6i3/G8jFpeT7+PG77N7bfXqiutXLfxNqPoI3Bmijp1j7msaSDJb1M0t6SDrf9LzXTj5C0dURsImmWpHNsb52nbSrpREnjJU2WNEHSyXXlfzMf0Mf2cGDfrGZaf/7YPC3p/bnu6ZJOsb1rrytory/pYknn5PU8S9LF+fXeTJR0e0Ss6KP8l0taJOkfkl4vaQtJ35H0kzXs+Zwu6dH8byNvs/2O+hcj4tzubSlpH0n31m77viq1PUnSOyWFpA/0Me9a1dvcJP35PLVUs0NiKzlZK/5OrkvbfYDOy8eeLSQtlMSZQaxV1ooDCIYm21dL2l3S6blH4bWN5ouIb0bEDRGxIiL+rBRI31Ez/eaaoBmSRknaNk/7cUT8IiKeiYjHJP2gdtkSIuK4iLgtIl6IiOsk/T9JffWStCn90up3I+L53LtvSe/qaQHbJyj96uBH8/ab0Uv5/yZpmaQZEXF/RDwbEZ2SOiR927b7u362N1LqSf+MpNfYntpgtm8qfcGp2sGSrpV0pupCfu55/0/bl9l+WtLu+bX/cDp1v8z2r21vZfu7uVf8Nttv7K1C2z+StJ2kn+cyjq7vWbR9iO07vOqsy0H59e1tX+M0tOdh2+fl11/SM+m6IQO2D7O9JLfzCtsT+2jnXyW9qqado20fmst4Krfvk3neMZIulzS+5ozC+F7KPt72BbbPy2XdYPsNNdPH277Q6czSna4ZBlaz7Dm2n5R0iO0Rtr9o+6+5vMW2t83zv872VbYftf1n2wfUvcffs70gL3ed7Vfnab/Ks92U1+ejtl9m+9Lcrsfy/yfUlPdK27/KZf0yl31OzfS3OfVyPu50tqytt/cgL9Nlu8P2ryU9I+lVte9tXvdv5f3hTtuH1+8LkibmffUp21fa3iK/3r2Oj+d17PG4kvfJX9v+ju1HJR2fX2+4Xzn5ju0H8/56s+0pNdv9xJqyj7J9n+17bR/WYP0/UdeORTXPT7F9j9MZyMW239lD+1/8jNh+u1c/s/ic06+RyvZ6to/J+9Ijts936ljoLufjtu/O0+b0tL3y35BzJW1je8ua5d9n+0av6vHeqYf2vtX2b/N899k+3bmjpNG+2VfZtt/o9Dl7yum4sUFPbccwFxE8eKzxQ1KXpE8MYH5L+oOkT9W9fqmk55SC9i8krdfD8t+V9JOa52cq9cw+KmmxpA/XTJuUy/u7pKWSfihpiwGu34aS7pO0dx/z/Zukyxus05F9LHe8pHP60Y5rJZ3Q4PVX5nV8Tc36jqyb50xJJ9Y8/3hepxGSfi7p1AbbbGzebnvm18+RdHxduW2Slg5we/5F0qclvVnScknj6tr5hNIXqfWU/jCdKenhPP8Gkq6WdKdSYB+h9GVgYT/qvat7XerWc6SkMZKelLRDnra1pNfn/3dKmlPTnmn1yzf6LCj9lPlflM7CjJT0JUm/WYN27ivp1Uqfm92Ugt+bBrr98362XOkL1ihJn8/bcVRet8VKX/rWVwr7d0jaq27ZD+V5N5R0lKQ/Stoht+0NkjbP2/IeSYfm9X5Tfv+6t+eZSp/Vt+bp52r1z3NI2r7m+eaSPixpI0kbK/VW/qxm+m8l/Z/c7mn5fTwnT9tG0iOS3pvb/e78fMs+tlWXpL8pnTkambdR7Xv7KUl/Ujq79jJJv6zdF/K8f5X02rytuiSd1NN+00s7DpG0QtLs3I4N1ct+JWmv/D5ult+TyUpnCru3+4n5/3tLekDSlPx+/bh2u6vumJ7bsajm+cfy+zJS0pGS7pe0Qf3xrKd1rdmeX8/PP6d0fJsgabSk70vqzNN2VOpg+Kc87dt5m+zZoL71JZ2ktL91vxdvkvSgpF2UjhfTlT5jo+s/b0rHmLfl9ZokaYmkz/Wyb/ZYdm7L3Up/F0Ypfe6Wq+Y4zGPdedCjjWY7XumP3g9rX4yI9yn9IX2vpCsi4oX6BW2/W+lg9pWal09VCpmvkPRlSWd61ZCHhyW9Rel0/Jtz+ecOsL1zJd0k6Yo+5hurFBJrPZHrrMIWSuG4XvdrWzaY1pPpSqdbVyr9kW23PapunueUessr69W2PU3pvTg/IhYrhZED62a7OCJ+HelswnP5tYsiYnF+fpGk5yLi7Nz+8yT12qPdTy9ImmJ7w4i4LyK6h28sz20eHxHPRcSinotYzSeVgsSSSD1tX5O0s/vo1a4XEQsi4q+RXCPpSqWhN2ticURcEBHLlQLLBkrB4i1K4fPfI+IfEXGH0pmj2uFdv42In+X35VlJn5D0pYj4c27bTRHxiKT3SborIn4Y6QzWDZIuVAoa3X4aEb+LVT2QPV7LEBGPRMSFkc5oPaW0T+4mpYvtctu/ktu9SNIlNYt/TNJlEXFZbvdVkq5XOsb05cyIuDWvw/K6aQdIOiUilkY6y3ZSg+V/GBG35211fm/r2Id7I+K03I5n1ft+tVzpePM6Sc7zNDpmHJDbd0tEPK3cU95fEXFOfl9WRMS3lILlDgMo4lSl4XndvdOflDQnb8/nc3v2z2cI9pd0aUT8Kk/7stJndbX1sf24pGclzZS0f6w6QzpT0vcj4rqIWBlpnP3zSvt9/Xotjohr83rdpRT4d+tlPXor+21KAfu7EbE8Ii6Q9Pv+byIMJwRtNI3tw5V6IvfNB83V5APS5ZL2sv2BumXfphQK94+I22uWuaHmoH+Z0h/u/fK0ZRFxfZ72gKTDJb3H9ib9bO/JSr0+B0SkLoxeLJNUX+4mkp7qT1398LBST2u97tceUurpkdIBvtYopT/Ccjq9v7tWfeG4WClw7dug7B9IGmf7/Wve7NVMl3RlRDycn/9YLx0jfk+D5R6o+f+zDZ73OTa8NzlsfFSpp/I+p2ENr8uTj1bqHfyd7VvrT7P3YqLS2P7Hcwh4NJezzUDaZnsf29c6DcN4XCkkbtHXcj14cdvmL7JLla59mKg0BOXxmvZ+Ueki3Zcsm22r9EWp3kRJu9SVdZCkrWrmub/m/8+ol/fP9ka2v5+HDjypNPRiM6fx++MlPRoRz/TQzomSPlLXlmlq/Dmq12g/7Da+bnqjefu9jgNsR4/7VURcLel0Sd+T9IDteT0c6+rbf/dAGmT7yDx05Ynchk3Vz33SaehTm6QDazpTJkq6qGadlkhaqbT/rdbW/Fl9pK7Y8yNiszz/LUqdKt0mSjqybh/YNpdb37bXOg1Nuj/va1/rY716K3u8pL/X/d0Y0HbG8EHQRlPkgHKMpD0ioq87JYxUOl3evewblXqqDouI/+5j2VD6w9PTNPUyvba9Jyhd7PeeiHiyr/mVLmDbyV5trPROqu7Ctl9K+rBfemHWAUqB6a9KvdvLlU571nqlVh3kP670uf+57fuVhghsoPQFaDW5J+8ESV9VP7ZZb2xvmNu6W/5Ddr/SadU3uGassFa9R1XrtdyIuCIi3q0Uwm5T+pKhSOPhZ0bEeKWet/9wuvPA03nRjWqKqQ2T90j6ZERsVvPYMCJ+098GO92V5UKloRHjcpi4TKvei4Fuq21ryl5P6VT9vbmtd9a1deOIqO35ra/rHtV8Rutev6aurLER8a8DbGu3I5V6S3eJdLH0P3WvgtL+/nKnaw5eso65LT+qa8uYiGjUA12vt217n9K2a1TnYMrtz/y97lcRcWpEvFlp2MtrlYb41Luvrs3b1U1/Wj3s13k89heUPssvy/vkE+rfMfWdSseSD0ZE7dm/eyTtU7dOG0TE3+vbmt/rzRuVn7/Af1LS8V51Mf09kjrqyt4o0vUt9f5T6bP/mryvfbGP9eqt7PuUxorXLl+/nbGOIGijOKcLy74m6d2RTkvXTntd7rXb0PYo2x9T+mN6TZ4+RWnM9uyI+HmDsve3PTZfUPMepdPFl+Rpuzjd53s925srnbLsqjvIN2rvsUpDGt4d6XR4f3Qp9cJ81ukitsPz61f3c/m+fEeph3y+08WAG9huVzqVelw+Nb5SKZh12N48b892pXGOl+dyDlYKzzvXPD4sad+8jer9SOnU8N6DbP+HlLbPjjX1Tla60LRft08cpAeUxh6/hO1xtj/gdIHh80pnJ1bmaR/xqovvHlMKPisj4iGlMewfc7o47jCtHjznSjrW9utzOZva7vX2jQ2sr7TtH5K0wvY+kt5Tt06b2960n+W92fZ++ZT85/K6Xivpd5KetP2F/DkcYXuK7bf0UtZ/Sfqq7dc42SnvP5dKeq3TBWyj8uMttif3s43179PGSmctHne6QO647gkRcbfSUJDjba/vdGFh7dmXcyS93/ZeeZ02cLolYm1IXhPnSzrC9ja2N1MKnv31kNLQh4b7Yj/0uF/l7byL0zCwp5WGfzW6td75She07piD63F102+UtF8+m7C9pNqLtDdWOnP2kKSRtr+il57JewmnM2nnSTq49oxkzTp1eNVFnVt61S1eL5D0PtvTnC5M/Hf1klsi4jalYX7dty39gaRP5e1i22OcbhnbaEjfxkpj/Jc5ndGq/3JYv2/2VvZvlbbTZ50uBt1P6boErIMI2miGE5V6IX7vVVedz83TrDQm70Glg/cRkj4aaWynlHq0tlQKmN3L1vYSH6EUeB5Xuu3fzIjoytNepRTSn1I6pfi8pPZ+tPdrSr0P/1NT5xd7WyAi/qEUJg/ObTlM0ofy64OWA/80pd7nPymFwbMlfSYizqiZ9dNKp5NvVtqmhysN1XnAafjNJEnfyz213Y9LlC6wesm2yeH9OEkvr582QNOVxoX+rbZupVPdB7n8rcu+LulLTqd4P183bT2l/exepW23m9J2lNIY4Ouc7hF+iaQjIuLOPG2mUo/hI0o9iC/2VkfERZK+oXT7xSeV9r99BtLgSGOSP6sUjB5T+vJ3Sc3025Qu1rwjr1ePdx3JLlYaIvOY0pmN/fJwrZVKAXVnpQskH1YK0r0F+G/ndl2pFE7mS9owt/k9SuO771UaQvENpS8M/XG8pLPy+hygdPHzhrlN1yp9nmsdpHRHoEeUjjPnKX3OFRH3SPqgUs/kQ0o9kEdp8H/3fqC03jcrXdh9mVKo6vN+0XmYS4ekX+d1fMlY4T6W722/2iS37TGlM1iPKJ0NqS/jcqXterXS576+M+A7SrcRfUDpNqW117VcofSl/fZcx3PqfZhNtz2UesYvaHAcP0Vpv77S9lNK7/Muua23Kt0d6cdKvcSPKZ3B683JkmbZfkVEXK/0OT09L/sXpYs7G/m80mfsKaXteF7d9ONVs2/2VnY+7u+Xnz+m9Ln7aR/txjDlvoeeAljbOI29/LXShYJf6Wt+rNtsH690x4SPtbotJTndRu22iKjvpS1Z5z6S5kbEgC50BbBuoEcbGILyuPH3Slppe6u+5geGozxc4tV5eNjeSj3Yvf6iZgV1bmj7vXlIwDZKZ3wuKlkngKGLoI1KON2RYVmDx0GtblsjPbR1mXv48YW8zEE9LNPrBY/93Ta25/Yw39xG5UbEPRFxQh6C0XK2t+tluxa9EKiVdQ+E7Xf21M4Kyr68h7J7HfY0xG2ldH3EMqVrMP41Iv7Q10Jr8vmvXVzpOofHlIaOLNHqtxztt4F+5gEMPQwdAQAAAAqgRxsAAAAoYNBX+tveQOlHBEbn8i7o60KULbbYIiZNmjTYqoGmefrppzVmzJhWNwMAhiWOsRhqFi9e/HBE9PmrzFXcUut5Se+KiGVO9+9cZPvyiLi2pwUmTZqk66+/voKqgebo6upSW1tbq5sBAMMSx1gMNbb79Wufgw7a+SdGuy/kGZUfDPwGAADAOq2SH4mwPULSYknbK/0YxnUN5pklaZYkjRs3Tl1dXVVUDTTFsmXL2GcBoBCOsRiuKr3riNPP0V6k9HPZt/Q039SpU4OhIxhKOK0JAOVwjMVQY3txREzta75K7zoSEY8r3dN07yrLBQAAAIaaQQdt21vmnmzZ3lDSnpJuG2y5AAAAwFBWxRjtrSWdlcdpryfp/Ii4tIJyAQAAgCGriruO3CzpjRW0BQAAABg2+GVIAAAAoACCNgAAAFAAQRsAAAAogKANAABaorOzU1OmTNEee+yhKVOmqLOzs9VNAipVyS9DAgAADERnZ6fmzJmj+fPna+XKlRoxYoRmzJghSWpvb29x64Bq0KMNAACarqOjQ/Pnz9fuu++ukSNHavfdd9f8+fPV0dHR6qYBlSFoAwCApluyZImmTZu22mvTpk3TkiVLWtQioHoEbQAA0HSTJ0/WokWLVntt0aJFmjx5cotaBFSPoA0AAJpuzpw5mjFjhhYuXKgVK1Zo4cKFmjFjhubMmdPqpgGV4WJIAADQdN0XPM6ePVtLlizR5MmT1dHRwYWQGFYI2gAAoCXa29vV3t6urq4utbW1tbo5QOUYOgIAAAAUQNAGAAAACiBoAwAAAAUQtAEAQEvwE+wY7rgYEgAANB0/wY51AT3aAACg6fgJdqwLCNoAAKDp+Al2rAsI2gAAoOn4CXasCwjaAACg6fgJdqwLuBgSAAA0HT/BjnUBQRsAALQEP8GO4Y6gDQAAKmO7aXVFRNPqAtYEY7QBAEBlImLAj4lfuHSNlgPWdgRtAAAAoACCNgAAAFAAQRsAAAAogKANAAAAFEDQBgAAAAogaAMAAAAFELQBAACAAgjaAAAAQAEEbQAAAKAAgjYAAABQAEEbAAAAKICgDQAAABRA0AYAAAAKIGgDAAAABRC0AQAAgAII2gAAAEABBG0AAACgAII2AAAAUABBGwAAACiAoA0AAAAUMOigbXtb2wttL7F9q+0jqmgYAAAAMJSNrKCMFZKOjIgbbG8sabHtqyLiTxWUDQAAAAxJg+7Rjoj7IuKG/P+nJC2RtM1gywUAAACGsip6tF9ke5KkN0q6rsG0WZJmSdK4cePU1dVVZdVAUcuWLWOfBYCCOMZiOKosaNseK+lCSZ+LiCfrp0fEPEnzJGnq1KnR1tZWVdVAcV1dXWKfBYBCfrGAYyyGpUruOmJ7lFLIPjciflpFmQAAAMBQVsVdRyxpvqQlEfHtwTcJAAAAGPqq6NF+h6SPS3qX7Rvz470VlAsAAAAMWYMeox0RiyS5grYAAAAAwwa/DAkAAAAUQNAGAAAACiBoAwAAAAUQtAEAAIACCNoAAABAAQRtAAAAoACCNgAAAFAAQRsAAAAogKANAAAAFEDQBgAAAAogaAMAAAAFELQBAACAAgjaAAAAQAEEbQAAAKAAgjYAAABQAEEbAAAAKICgDQAAABRA0AYAAAAKIGgDAAAABRC0AQAAgAII2gAAAEABBG0AAACgAII2AAAAUABBGwAAACiAoA0AAAAUQNAGAAAACiBoAwAAAAUQtAEAAIACCNoAAABAAQRtAAAAoACCNgAAAFAAQRsAAAAogKANAAAAFEDQBgAAAAogaAMAAAAFELQBAACAAgjaAAAAQAEEbQAAAKAAgjYAAABQAEEbAAAAKICgDQAAABRA0AYAAAAKIGgDAAAABVQStG2fYftB27dUUR4AAAAw1FXVo32mpL0rKgsAAAAY8ioJ2hHxK0mPVlEWAAAAMByMbFZFtmdJmiVJ48aNU1dXV7OqBgZt2bJl7LMAUBDHWAxHTQvaETFP0jxJmjp1arS1tTWramDQurq6xD4LAIX8YgHHWAxL3HUEAAC3S8fNAAAQzklEQVQAKICgDQAAABRQ1e39OiX9VtIOtpfanlFFuQAAAMBQVckY7Yhor6IcAAAAYLhg6AgAAABQAEEbAAAAKICgDQAAABRA0AYAAAAKIGgDAAAABRC0AQAAgAII2gAAAEABBG0AAACggEp+sAYAAAw/bzjhSj3x7PKm1DXpmAXF69h0w1G66bj3FK8H6EbQBgAADT3x7HLdddK+xevp6upSW1tb8XqaEeaBWgwdAQAAAAogaAMAAAAFELQBAACAAgjaAAAAQAEEbQAAAKAAgjYAAABQAEEbAAAAKICgDQAAABRA0AYAAAAKIGgDAAAABRC0AQAAgAII2gAAAEABBG0AAACgAII2AAAAUABBGwAAACiAoA0AAAAUQNAGAAAACiBoAwAAAAUQtAEAAIACCNoAAABAAQRtAAAAoACCNgAAAFAAQRsAAAAogKANAAAAFEDQBgAAAAogaAMAAAAFELQBAACAAgjaAAAAQAEEbQAAAKAAgjYAAABQAEEbAAAAKICgDQAAABRA0AYAAAAKIGgDAAAABVQStG3vbfvPtv9i+5gqygQAAACGskEHbdsjJH1P0j6SdpTUbnvHwZYLAAAADGVV9Gi/VdJfIuKOiPiHpJ9I+mAF5QIAAABD1sgKythG0j01z5dK2qV+JtuzJM2SpHHjxqmrq6uCqrGum3337OZVdlZzqjlt4mnNqQgA+rDx5GP0v85q0ojQJhxjN54sdXWNKV8RkFURtN3gtXjJCxHzJM2TpKlTp0ZbW1sFVWNd90f9sSn1dHV1iX0WwLrmqWNO0l0n7Vu8nmYdYycds0Bt08vXA3SrYujIUknb1jyfIOneCsoFAAAAhqwqgvbvJb3G9ittry/pXyRdUkG5AAAAwJA16KEjEbHC9uGSrpA0QtIZEXHroFsGAAAADGFVjNFWRFwm6bIqygIAAACGA34ZEgAAACiAoA0AAAAUQNAGAAAACiBoAwAAAAUQtAEAAIACCNoAAABAAQRtAAAAoACCNgAAAFAAQRsAAAAogKANAAAAFEDQBgAAAAogaAMAAAAFELQBAACAAgjaAAAAQAEEbQAAAKCAka1uAAAAWHtNOmZBcyr6Rfl6Nt1wVPE6gFoEbQAA0NBdJ+3blHomHbOgaXUBzcTQEQAAAKAAgjYAAABQAEEbAAAAKICgDQAAABRA0AYAAAAKIGgDAAAABRC0AQAAgAII2gAAAEABBG0AAACgAII2AAAAUABBGwAAACiAoA0AAAAUQNAGAAAACiBoAwAAAAUQtAEAAIACCNoAAABAAQRtAAAAoACCNgAAAFAAQRsAAAAogKANAAAAFEDQBgAAAAogaAMAAAAFELQBAACAAgjaAAAAQAEEbQAAAKAAgjYAAABQAEEbAAAAKGBQQdv2R2zfavsF21OrahQAAAAw1A22R/sWSftJ+lUFbQEAAACGjZGDWTgilkiS7WpaAwAAAAwTgwraA2F7lqRZkjRu3Dh1dXU1q2pg0JYtW8Y+CwAFcYzFcNRn0Lb9S0lbNZg0JyIu7m9FETFP0jxJmjp1arS1tfV3UaDlurq6xD4LAIX8YgHHWAxLfQbtiNizGQ0BAAAAhhNu7wcAAAAUMNjb+/2z7aWS3i5pge0rqmkWAAAAMLQN9q4jF0m6qKK2AAAAAMMGQ0cAAACAAgjaAAAAQAEEbQAAAKAAgjYAAABQAEEbAAAAKICgDQAAABRA0AYAAAAKIGgDAAAABRC0AQAAgAII2gAAAEABBG0AAACgAII2AAAAUABBGwAAACiAoA0AAAAUQNAGAAAACiBoAwAAAAUQtAEAAIACCNoAAABAAQRtAAAAoACCNgAAAFAAQRsAAAAogKANAAAAFEDQBgAAAAogaAMAAAAFELQBAACAAgjaAAAAQAEEbQAAAKAAgjYAAABQAEEbAAAAKICgDQAAABRA0AYAAAAKIGgDAAAABRC0AQAAgAII2gAAAEABBG0AAACgAII2AAAAUABBGwAAACiAoA0AAAAUQNAGAAAACiBoAwAAAAUQtAEAAIACCNoAAABAAQRtAAAAoIBBBW3bJ9u+zfbNti+yvVlVDQMAAACGssH2aF8laUpE7CTpdknHDr5JAAAAwNA3qKAdEVdGxIr89FpJEwbfJAAAAGDoG1lhWYdJOq+nibZnSZolSePGjVNXV1eFVQNlLVu2jH0WAAriGIvhqM+gbfuXkrZqMGlORFyc55kjaYWkc3sqJyLmSZonSVOnTo22trY1aS/QEl1dXWKfBYBCfrGAYyyGpT6DdkTs2dt029MlvU/SHhERVTUMAAAAGMoGNXTE9t6SviBpt4h4ppomAQAAAEPfYO86crqkjSVdZftG23MraBMAAAAw5A2qRzsitq+qIQAAYOizvWbLfWPgyzBiFWs7fhkSAABUJiIG/Fi4cOEaLQes7QjaAAAAQAEEbQAAAKAAgjYAAABQAEEbAAC0RGdnp6ZMmaI99thDU6ZMUWdnZ6ubBFSqyp9gBwAA6JfOzk7NmTNH8+fP18qVKzVixAjNmDFDktTe3t7i1gHVIGgDAICm6+jo0IEHHqjZs2dryZIlmjx5sg488EB1dHQQtDFsELQBAEDT/elPf9KDDz6oMWPGKCL09NNPa968eXr44Ydb3TSgMgRtAADQdCNGjNDKlSt1xhlnvDh0ZP/999eIESNa3TSgMlwMCQAAmm7FihUaNWrUaq+NGjVKK1asaFGLgOoRtAEAQEsceuihmj17tvbaay/Nnj1bhx56aKubBFSKoSMAAKDpJkyYoFNPPVXLly/XCy+8oNtvv12nnnqqJkyY0OqmAZWhRxsAADTdjjvuqGeeeUZjx46VbY0dO1bPPPOMdtxxx1Y3DagMQRsAADTdNddco4MOOkjjx4+XbY0fP14HHXSQrrnmmlY3DagMQ0cAAEDTPf/885o3b5422mgjdXV1qa2tTc8884zOPffcVjcNqAw92gAAoOlGjx6tuXPnrvba3LlzNXr06Ba1CKgePdoAAKDpZs6cqaOOOkonn3yyHnzwQb3iFa/Qgw8+qE9/+tOtbhpQGXq0AQBA0+26664aPXq07r//fr3wwgu6//77NXr0aO26666tbhpQGYI2AABouqOPPlqbbrqprr76al111VW6+uqrtemmm+roo49uddOAyhC0AQBA0y1dulRnn322dt99d40cOVK77767zj77bC1durTVTQMqQ9AGAAAACiBoAwCAppswYYKmT5+uhQsXasWKFVq4cKGmT5/OL0NiWOGuIwAAoOm++c1v6ogjjtBhhx2mv/3tb9puu+20YsUKfetb32p104DK0KMNAACarr29XaeccorGjBkjSRozZoxOOeUUtbe3t7hlQHXo0QYAAC3R3t6u9vb2F38ZEhhu6NEGAAAACiBoAwAAAAUQtAEAAIACCNoAAKAlOjs7NWXKFO2xxx6aMmWKOjs7W90koFJcDAkAAJqus7NTc+bM0fz587Vy5UqNGDFCM2bMkCTuPIJhgx5tAADQdB0dHZo/f/5qP8E+f/58dXR0tLppQGUI2gAAoOmWLFmiadOmrfbatGnTtGTJkha1CKgeQRsAADTd5MmTtWjRotVeW7RokSZPntyiFgHVI2gDAICmmzNnjmbMmKGFCxdqxYoVWrhwoWbMmKE5c+a0umlAZbgYEgAANF17e7t+85vfaJ999tHzzz+v0aNHa+bMmVwIiWGFoA0AAJqus7NTCxYs0OWXX77aXUd23XVXwjaGDYaOAACApuOuI1gXELQBAEDTcdcRrAsI2gAAoOm46wjWBQRtAADQdNx1BOsCLoYEAABN133B4+zZs7VkyRJNnjxZHR0dXAiJYYWgDQAAWqK9vV3t7e3q6upSW1tbq5sDVI6hIwAAAEABBG0AAACggEEFbdtftX2z7RttX2l7fFUNAwAAAIaywfZonxwRO0XEzpIulfSVCtoEAAAADHmDCtoR8WTN0zGSYnDNAQAAAIaHQd91xHaHpIMlPSFp917mmyVpliSNGzdOXV1dg60aaJply5axzwJAIRxjMVw5ovdOaNu/lLRVg0lzIuLimvmOlbRBRBzXV6VTp06N66+/fqBtBVqGW08BQDkcYzHU2F4cEVP7mq/PHu2I2LOfdf5Y0gJJfQZtAAAAYLgb7F1HXlPz9AOSbhtccwAAAIDhYbBjtE+yvYOkFyTdLelTg28SAAAAMPT1OUa7SKX2Q0rBHBgqtpD0cKsbAQDDFMdYDDUTI2LLvmZqSdAGhhrb1/fnogcAwMBxjMVwxU+wAwAAAAUQtAEAAIACCNpA/8xrdQMAYBjjGIthiTHaAAAAQAH0aAMAAAAFELQBAACAAgjaQC9s/7PtsP26VrcFAIYb2ytt32j7Jts32N611W0CqkTQBnrXLmmRpH9pdUMAYBh6NiJ2jog3SDpW0tdb3SCgSgRtoAe2x0p6h6QZImgDQGmbSHqs1Y0AqjSy1Q0A1mIfkvSLiLjd9qO23xQRN7S6UQAwjGxo+0ZJG0jaWtK7WtweoFL0aAM9a5f0k/z/n+TnAIDqdA8deZ2kvSWdbdutbhRQFe6jDTRge3NJSyU9KCkkjcj/Tgw+NABQCdvLImJszfMHJP2viHiwhc0CKkOPNtDY/pLOjoiJETEpIraVdKekaS1uFwAMS/nuTiMkPdLqtgBVYYw20Fi7pJPqXrtQ0oGS/l/zmwMAw1L3GG1JsqTpEbGylQ0CqsTQEQAAAKAAho4AAAAABRC0AQAAgAII2gAAAEABBG0AAACgAII2AAAAUABBGwAKsL2V7Z/Y/qvtP9m+zPZrbU+yfcsalnmI7fGDbNchtk8fwPx32d6iVPkAMJwRtAGgYvknpC+S1BURr46IHSV9UdK4QRZ9iKQBBW3b/F4CALQIQRsAqre7pOURMbf7hYi4MSJW+7Gj+t5f25fabrM9wvaZtm+x/Ufb/2Z7f0lTJZ1r+0bbG9p+s+1rbC+2fYXtrXM5Xba/ZvsaSUf0p8G2/9P29bZvtX1C3eSjbP8uP7bP829p+0Lbv8+Pd6zRlgKAYYyeDgCo3hRJiwex/M6StomIKZJke7OIeNz24ZI+HxHX2x4l6TRJH4yIh2x/VFKHpMNyGZtFxG4DqHNORDxqe4Sk/7a9U0TcnKc9GRFvtX2wpO9Kep+kUyR9JyIW2d5O0hWSJg9inQFg2CFoA8Da5w5Jr7J9mqQFkq5sMM8OSoH+qjRSRSMk3Vcz/bwB1nmA7VlKfxe2lrSjpO6g3Vnz73fy//eUtGOuW5I2sb3xAOsEgGGNoA0A1btV0v79mG+FVh/Ct4EkRcRjtt8gaS9Jn5F0gFb1VHezpFsj4u09lP10fxtr+5WSPi/pLbnuM7vbkkWD/68n6e0R8WxdWf2tFgCGPcZoA0D1rpY02vbM7hdsv8V2/VCOuyTtbHs929tKemuedwtJ60XEhZK+LOlNef6nJHX3Gv9Z0pa2356XGWX79WvY3k2UgvkTtsdJ2qdu+kdr/v1t/v+Vkg6vWb+d17BuABi26NEGgIpFRNj+Z0nftX2MpOeUQvXn6mb9taQ7Jf1R0i2SbsivbyPph7a7O0OOzf+eKWmu7WclvV2p1/xU25sqHc+/q9Sb3pdDbH+o5vnbJP0hL3tHblet0bavU+qcac+vfVbS92zfnOv+laRP9aNuAFhnOCL6ngsAAADAgDB0BAAAACiAoA0AAAAUQNAGAAAACiBoAwAAAAUQtAEAAIACCNoAAABAAQRtAAAAoID/D1iUxnGVkk6xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[546],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a6e2d9250>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[555],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a6e206f10>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[585],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other features that may be specific to asthma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "anova_df_other=anova_df_significant[~anova_df_significant['feature'].str.contains('fat|bone|mass|grip|adipose|thigh|Waist|BMI|Hip|Ankle|Corneal|ocular|LV|cardiac|Neutrophil|blood|Basophil|Platelet|Pulse|Heart|Cardiac|monocyte|heart|Reticulocyte|FEV|FVC|cough|smok|asthma|tobacco|bronc|Lung|lung|PEF')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(67, 2)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_df_other.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>f_22200_0_0_f_QUANT_Year_of_birth</td>\n",
       "      <td>5.095021e-127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>f_90012_0_0_f_QUANT_Overall_acceleration_average</td>\n",
       "      <td>9.937321e-117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>f_20159_0_0_f_QUANT_Number_of_symbol_digit_mat...</td>\n",
       "      <td>1.776397e-106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>f_12338_2_0_f_QUANT_P_duration_residualizedRel...</td>\n",
       "      <td>1.497283e-95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>f_20156_0_0_f_QUANT_Duration_to_complete_numer...</td>\n",
       "      <td>3.322968e-78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>f_20157_0_0_f_QUANT_Duration_to_complete_alpha...</td>\n",
       "      <td>9.090970e-71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>f_12336_2_0_f_QUANT_Ventricular_rate_residuali...</td>\n",
       "      <td>4.119037e-65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>f_22680_0_0_f_QUANT_Mean_carotid_IMT_intima_me...</td>\n",
       "      <td>2.245636e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>f_22200_0_0_f_QUANT_Year_of_birth_residualized...</td>\n",
       "      <td>1.684329e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>f_41142_0_0_f_QUANT_Episodes_containing_Diagno...</td>\n",
       "      <td>1.927940e-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               feature         pvalue\n",
       "241                  f_22200_0_0_f_QUANT_Year_of_birth  5.095021e-127\n",
       "238   f_90012_0_0_f_QUANT_Overall_acceleration_average  9.937321e-117\n",
       "78   f_20159_0_0_f_QUANT_Number_of_symbol_digit_mat...  1.776397e-106\n",
       "454  f_12338_2_0_f_QUANT_P_duration_residualizedRel...   1.497283e-95\n",
       "240  f_20156_0_0_f_QUANT_Duration_to_complete_numer...   3.322968e-78\n",
       "239  f_20157_0_0_f_QUANT_Duration_to_complete_alpha...   9.090970e-71\n",
       "592  f_12336_2_0_f_QUANT_Ventricular_rate_residuali...   4.119037e-65\n",
       "493  f_22680_0_0_f_QUANT_Mean_carotid_IMT_intima_me...   2.245636e-17\n",
       "639  f_22200_0_0_f_QUANT_Year_of_birth_residualized...   1.684329e-15\n",
       "634  f_41142_0_0_f_QUANT_Episodes_containing_Diagno...   1.927940e-10"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_df_other.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "asthma_df_important_features_other=asthma_df_important_features[asthma_df_important_features['index'].isin(anova_df_other.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>633</td>\n",
       "      <td>-1.679608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>661</td>\n",
       "      <td>-1.773184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>436</td>\n",
       "      <td>-1.876800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>634</td>\n",
       "      <td>-1.885420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>417</td>\n",
       "      <td>-1.905691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>414</td>\n",
       "      <td>-1.911386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>218</td>\n",
       "      <td>-1.914577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>736</td>\n",
       "      <td>-1.963451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>-1.964872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>460</td>\n",
       "      <td>-1.966358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  importance\n",
       "633    633   -1.679608\n",
       "661    661   -1.773184\n",
       "436    436   -1.876800\n",
       "634    634   -1.885420\n",
       "417    417   -1.905691\n",
       "414    414   -1.911386\n",
       "218    218   -1.914577\n",
       "736    736   -1.963451\n",
       "45      45   -1.964872\n",
       "460    460   -1.966358"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asthma_df_important_features_other.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Step 7: Write out Asthma Group Labels for different cohorts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cohort_labels_df=pd.get_dummies(asthma_df_quant['Cluster Label']).rename(columns={'A':'Asthma_CohortA','B':'Asthma_CohortB'})\n",
    "cohort_labels_df['Asthma_CohortAnotB']=cohort_labels_df['Asthma_CohortA'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asthma_CohortA</th>\n",
       "      <th>Asthma_CohortB</th>\n",
       "      <th>Asthma_CohortAnotB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Asthma_CohortA  Asthma_CohortB  Asthma_CohortAnotB\n",
       "0               1               0                   1\n",
       "1               1               0                   1\n",
       "2               1               0                   1\n",
       "3               1               0                   1\n",
       "4               1               0                   1"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohort_labels_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "asthma_df_full_gwas=pd.read_csv('ukbb_asthma_full.csv',sep='\\t',usecols=['IID','PC1','PC2','PC3','PC4','PC5','PC6','PC7',\n",
    "                                                           'PC8','PC9','PC10','sex','age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IID</th>\n",
       "      <th>sex</th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>PC5</th>\n",
       "      <th>PC6</th>\n",
       "      <th>PC7</th>\n",
       "      <th>PC8</th>\n",
       "      <th>PC9</th>\n",
       "      <th>PC10</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000149</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.0012</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>-0.0009</td>\n",
       "      <td>-0.0006</td>\n",
       "      <td>0.0024</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000306</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.0011</td>\n",
       "      <td>-0.0025</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>-0.0021</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>-0.0013</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>-0.0022</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000418</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000607</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>-0.0010</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000984</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.0009</td>\n",
       "      <td>-0.0012</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>-0.0009</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0021</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       IID  sex     PC1     PC2     PC3     PC4     PC5     PC6     PC7  \\\n",
       "0  1000149    2 -0.0012  0.0002 -0.0003 -0.0002 -0.0005 -0.0009 -0.0006   \n",
       "1  1000306    2 -0.0011 -0.0025  0.0006 -0.0021  0.0000  0.0021 -0.0013   \n",
       "2  1000418    1 -0.0001 -0.0004  0.0011  0.0002 -0.0003 -0.0003  0.0016   \n",
       "3  1000607    2 -0.0001  0.0007  0.0003 -0.0007  0.0002  0.0002  0.0000   \n",
       "4  1000984    2 -0.0009 -0.0012  0.0004 -0.0009  0.0011  0.0005 -0.0021   \n",
       "\n",
       "      PC8     PC9    PC10  age  \n",
       "0  0.0024  0.0013 -0.0004   45  \n",
       "1 -0.0002 -0.0022  0.0029   69  \n",
       "2  0.0022  0.0005  0.0001   67  \n",
       "3  0.0011 -0.0003 -0.0010   47  \n",
       "4 -0.0001 -0.0007 -0.0003   67  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asthma_df_full_gwas.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asthma_CohortA</th>\n",
       "      <th>Asthma_CohortB</th>\n",
       "      <th>Asthma_CohortAnotB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Asthma_CohortA  Asthma_CohortB  Asthma_CohortAnotB\n",
       "0               1               0                   1\n",
       "1               1               0                   1\n",
       "2               1               0                   1\n",
       "3               1               0                   1\n",
       "4               1               0                   1"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohort_labels_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "asthma_df_full_gwas=asthma_df_full_gwas.join(cohort_labels_df)\n",
    "asthma_df_full_gwas.to_csv('ukbb_asthma_cohort_annotation.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "asthma_df_full_not_asthma=pd.read_csv('ukbb_not_asthma_full_gwas.csv',sep='\\t').drop(columns=['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "asthma_df_full_not_asthma['Asthma_CohortA']=0\n",
    "asthma_df_full_not_asthma['Asthma_CohortB']=0\n",
    "asthma_df_full_not_asthma['Asthma_CohortAnotB']=np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ukbb_df_full_gwas=pd.concat([asthma_df_full_gwas,asthma_df_full_not_asthma]).reset_index().drop(columns='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ukbb_df_full_gwas=ukbb_df_full_gwas[['IID','PC1','PC2','PC3','PC4','PC5','PC6','PC7',\n",
    "'PC8','PC9','PC10','sex','age','Asthma_CohortA','Asthma_CohortB','Asthma_CohortAnotB']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ukbb_df_full_gwas.to_csv('ukbb_asthma_cohorts_gwas_annotation.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>beta</th>\n",
       "      <th>se</th>\n",
       "      <th>pval</th>\n",
       "      <th>pval_raw</th>\n",
       "      <th>rsq</th>\n",
       "      <th>freq</th>\n",
       "      <th>entity</th>\n",
       "      <th>feature</th>\n",
       "      <th>analysis</th>\n",
       "      <th>chrom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ukb_ibd_ni</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ENSG00000259384</td>\n",
       "      <td>ENSG00000259384</td>\n",
       "      <td>qtl_protein_blood_folkersen2017</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61719070.0</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.999500</td>\n",
       "      <td>ENSG00000149968</td>\n",
       "      <td>ENSG00000149968</td>\n",
       "      <td>qtl_protein_blood_folkersen2017</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>675.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ENSG00000117525</td>\n",
       "      <td>ENSG00000117525</td>\n",
       "      <td>qtl_protein_blood_folkersen2017</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48868528.0</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.866300</td>\n",
       "      <td>ENSG00000117525</td>\n",
       "      <td>ENSG00000117525</td>\n",
       "      <td>qtl_protein_blood_folkersen2017</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ENSG00000101017</td>\n",
       "      <td>ENSG00000101017</td>\n",
       "      <td>qtl_protein_blood_folkersen2017</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>67660548.0</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.991100</td>\n",
       "      <td>ENSG00000262655</td>\n",
       "      <td>ENSG00000262655</td>\n",
       "      <td>qtl_protein_blood_folkersen2017</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314821</td>\n",
       "      <td>0.000741399</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.998848</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ukb_ibd_ni</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ukb_ibd_ni</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>311862.0</td>\n",
       "      <td>G</td>\n",
       "      <td>GGAGA</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ENSG00000266171</td>\n",
       "      <td>ENSG00000266171</td>\n",
       "      <td>gtex7_brain_hypothalamus</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>216869.0</td>\n",
       "      <td>T</td>\n",
       "      <td>TA</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ENSG00000266171</td>\n",
       "      <td>ENSG00000266171</td>\n",
       "      <td>gtex7_brain_hypothalamus</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>201384.0</td>\n",
       "      <td>A</td>\n",
       "      <td>AGT</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ENSG00000266456</td>\n",
       "      <td>ENSG00000266456</td>\n",
       "      <td>gtex7_brain_hypothalamus</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>144463.0</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ENSG00000273355</td>\n",
       "      <td>ENSG00000273355</td>\n",
       "      <td>gtex7_brain_hypothalamus</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>243219.0</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ENSG00000264514</td>\n",
       "      <td>ENSG00000264514</td>\n",
       "      <td>gtex7_brain_hypothalamus</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>120267.0</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ENSG00000158270</td>\n",
       "      <td>ENSG00000158270</td>\n",
       "      <td>gtex7_brain_hypothalamus</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>429629.0</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ENSG00000176890</td>\n",
       "      <td>ENSG00000176890</td>\n",
       "      <td>gtex7_brain_hypothalamus</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>311239.0</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ENSG00000265179</td>\n",
       "      <td>ENSG00000265179</td>\n",
       "      <td>gtex7_brain_hypothalamus</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>186471.0</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ENSG00000266171</td>\n",
       "      <td>ENSG00000266171</td>\n",
       "      <td>gtex7_brain_hypothalamus</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>42409965.0</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>2.219400e-08</td>\n",
       "      <td>0.007790</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.832147</td>\n",
       "      <td>0.000370</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>canelaxandri17_sr_1348</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>18002534.0</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>2.933500e-09</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.911651</td>\n",
       "      <td>0.090500</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>canelaxandri17_sr_1348</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pos       ref          alt          beta        se  pval  pval_raw  \\\n",
       "0          NaN                                  NaN       NaN   NaN       NaN   \n",
       "1          NaN        NA           NA           NaN       NaN   NaN       NaN   \n",
       "2   61719070.0         T            C           NaN       NaN   NaN       NaN   \n",
       "3        675.0        NA           NA           NaN       NaN   NaN       NaN   \n",
       "4   48868528.0         C            T           NaN       NaN   NaN       NaN   \n",
       "5          NaN        NA           NA           NaN       NaN   NaN       NaN   \n",
       "6   67660548.0         G            A           NaN       NaN   NaN       NaN   \n",
       "7          NaN  0.314821  0.000741399           NaN       NaN   NaN       NaN   \n",
       "8          NaN                                  NaN       NaN   NaN       NaN   \n",
       "9     311862.0         G        GGAGA  0.000000e+00       NaN   1.0       1.0   \n",
       "10    216869.0         T           TA  0.000000e+00       NaN   1.0       1.0   \n",
       "11    201384.0         A          AGT  0.000000e+00       NaN   1.0       1.0   \n",
       "12    144463.0         A            C  0.000000e+00       NaN   1.0       1.0   \n",
       "13    243219.0         T            C  0.000000e+00       NaN   1.0       1.0   \n",
       "14    120267.0         C            T  0.000000e+00       NaN   1.0       1.0   \n",
       "15    429629.0         C            T  0.000000e+00       NaN   1.0       1.0   \n",
       "16    311239.0         T            C  0.000000e+00       NaN   1.0       1.0   \n",
       "17    186471.0         G            T  0.000000e+00       NaN   1.0       1.0   \n",
       "18  42409965.0         C            T  2.219400e-08  0.007790   1.0       1.0   \n",
       "19  18002534.0         G            A  2.933500e-09  0.000498   1.0       1.0   \n",
       "\n",
       "         rsq      freq           entity          feature  \\\n",
       "0        NaN       NaN             None             None   \n",
       "1        NaN       NaN  ENSG00000259384  ENSG00000259384   \n",
       "2        NaN  0.999500  ENSG00000149968  ENSG00000149968   \n",
       "3        NaN       NaN  ENSG00000117525  ENSG00000117525   \n",
       "4        NaN  0.866300  ENSG00000117525  ENSG00000117525   \n",
       "5        NaN       NaN  ENSG00000101017  ENSG00000101017   \n",
       "6        NaN  0.991100  ENSG00000262655  ENSG00000262655   \n",
       "7   0.520000  0.998848             None             None   \n",
       "8        NaN       NaN             None             None   \n",
       "9        NaN       NaN  ENSG00000266171  ENSG00000266171   \n",
       "10       NaN       NaN  ENSG00000266171  ENSG00000266171   \n",
       "11       NaN       NaN  ENSG00000266456  ENSG00000266456   \n",
       "12       NaN       NaN  ENSG00000273355  ENSG00000273355   \n",
       "13       NaN       NaN  ENSG00000264514  ENSG00000264514   \n",
       "14       NaN       NaN  ENSG00000158270  ENSG00000158270   \n",
       "15       NaN       NaN  ENSG00000176890  ENSG00000176890   \n",
       "16       NaN       NaN  ENSG00000265179  ENSG00000265179   \n",
       "17       NaN       NaN  ENSG00000266171  ENSG00000266171   \n",
       "18  0.832147  0.000370             None             None   \n",
       "19  0.911651  0.090500             None             None   \n",
       "\n",
       "                           analysis chrom  \n",
       "0                        ukb_ibd_ni     1  \n",
       "1   qtl_protein_blood_folkersen2017    13  \n",
       "2   qtl_protein_blood_folkersen2017    11  \n",
       "3   qtl_protein_blood_folkersen2017    15  \n",
       "4   qtl_protein_blood_folkersen2017    12  \n",
       "5   qtl_protein_blood_folkersen2017    12  \n",
       "6   qtl_protein_blood_folkersen2017    15  \n",
       "7                        ukb_ibd_ni     4  \n",
       "8                        ukb_ibd_ni     2  \n",
       "9          gtex7_brain_hypothalamus    18  \n",
       "10         gtex7_brain_hypothalamus    18  \n",
       "11         gtex7_brain_hypothalamus    18  \n",
       "12         gtex7_brain_hypothalamus    18  \n",
       "13         gtex7_brain_hypothalamus    18  \n",
       "14         gtex7_brain_hypothalamus    18  \n",
       "15         gtex7_brain_hypothalamus    18  \n",
       "16         gtex7_brain_hypothalamus    18  \n",
       "17         gtex7_brain_hypothalamus    18  \n",
       "18           canelaxandri17_sr_1348    21  \n",
       "19           canelaxandri17_sr_1348    21  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyodbc\n",
    "import pandas as pd\n",
    "\n",
    "conn = pyodbc.connect(\"DSN=impaladsn\", autocommit=True) \n",
    "data_df = pd.read_sql(\"SELECT * FROM gene_gwas.gwas_results ORDER BY pval DESC  LIMIT 20;\", conn) \n",
    "data_df.head(20)\n",
    " \n",
    "# order by wpval, having specific value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:anaconda-default]",
   "language": "python",
   "name": "conda-env-anaconda-default-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}