6-plotting-data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An excellent way for us to get a better understanding of our data is by plotting our data.  In this lesson, we'll see how matplotlib functionality is built directly into pandas, and how we can quickly create plots with the integration of this library into pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading our Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So let's again load up our houston claims dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "url = \"https://raw.githubusercontent.com/jigsawlabs-student/pandas-free-curriculum/master/houston_claims.csv\"\n",
    "claims_df = pd.read_csv(url, index_col = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And again, we'll look at the columns available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['reportedCity', 'dateOfLoss', 'elevatedBuildingIndicator', 'floodZone',\n",
       "       'latitude', 'longitude', 'lowestFloodElevation',\n",
       "       'amountPaidOnBuildingClaim', 'amountPaidOnContentsClaim', 'yearofLoss',\n",
       "       'reportedZipcode', 'id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "claims_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now pandas builds allows us to plot both our dataframes and series objects.  For example, imagine that we want to get a sense of the distributions of our different features.  Doing so is pretty simple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x12395ac40>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x124071160>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1240ad580>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1240d8a00>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x124106e50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x12413e220>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "claims_df.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So this is a nice way to get an overview of our data.  But as we can see, it's a little squished.  Instead, let's select a column of data that we want to focus in on and then call `hist` on the selected series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117093940>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "claims_df['amountPaidOnContentsClaim'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, we can see that our amount paid is frequently zero, and that most of the data is between $0$ and $100,000$.  Let's scope our data down and then plot again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x124ff97c0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_claims = claims_df[claims_df['amountPaidOnContentsClaim'] < 100000]\n",
    "\n",
    "selected_claims['amountPaidOnContentsClaim'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the plot method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above looks pretty good, but let's say that we want to add a title to this.  We can do so by using the `plot` method, and specifying the values of a couple keyword arguments. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1264928b0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_claims['amountPaidOnContentsClaim'].plot(kind = 'hist', \n",
    "                                                  title = 'Distribution of Content Claims', \n",
    "                                                  colormap = 'summer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that with the plot method, we specify the type of graph, `kind = 'hist'`, and add a title with `title = 'Distribution of Content Claims'`.  Finally, we changed the color of the plot by specifying a colormap. \n",
    "\n",
    "> A list of available color maps is available [here](https://matplotlib.org/3.1.1/gallery/color/colormap_reference.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `plot` method, we can decide to only display data within a specific range.  For example, here we'll set the limit of our xaxis to be between $10,000$ and $100,000$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12a08e400>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_claims['amountPaidOnContentsClaim'].plot(kind = 'hist', \n",
    "                                                  title = 'Distribution of Content Claims', xlim = [10000, 100000],\n",
    "                                                  colormap = 'summer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's set the ylim to be between $0$ and $2000$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12a279ac0>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_claims['amountPaidOnContentsClaim'].plot(kind = 'hist', \n",
    "                                                  title = 'Distribution of Content Claims', xlim = [10000, 100000],\n",
    "                                                  ylim = [0, 2000],\n",
    "                                                  colormap = 'summer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with Scatter Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The other main plot to work with is the scatter plot, which shows the relationship between two different kinds of data. \n",
    "\n",
    "For example, let's see if there is a relationship between the `amountPaidOnContentsClaim` and the `amountPaidOnBuildingClaim`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='amountPaidOnContentsClaim', ylabel='amountPaidOnBuildingClaim'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "claims_df.plot(kind = 'scatter', x = 'amountPaidOnContentsClaim', y = 'amountPaidOnBuildingClaim')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> So to plot the two kinds of data, we specify the `kind` as `scatter`, and then specify the data plotted on the x and y axes.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the data, we see that there is a cutoff around 500,000 dollars, for the amount paid on building claim.  And it looks like there is another cutoff for amount paid on building around 250,000 dollars.  \n",
    "> Perhaps there are different caps for different kinds of buildings (like residential vs commercial buildings).  \n",
    "\n",
    "Unfortunately, it's still difficult to get a sense of the relationship between contents and building claims.  Looking at the bottom left of the above plot, we appear to have too many samples.  So let's sample a subset of our `selected_claims` data, and then plot this subset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x125061d60>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_sample = selected_claims.sample(1000, random_state = 1)\n",
    "\n",
    "selected_sample.plot(kind = 'scatter', x = 'amountPaidOnContentsClaim', y = 'amountPaidOnBuildingClaim')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By randomly selecting 1000 observations before plotting, it looks like that **there is** an association between the two columns of data, and that we run up against the `amountPaidOnBuildingClaim` cap more often than the `amountPaidOnContentsClaim`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Bonus: If we want to plot claim amounts by longitude and latitude we can do so with the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12984ebb0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_claims.plot(kind = 'scatter', x = 'longitude', y = 'latitude', c = 'amountPaidOnContentsClaim',\n",
    "                     ylim = [29, 30.25], xlim = [-95.8, -95], colormap = 'Reds')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lesson we saw how we can use pandas to create different plots of our data.  We plotted the distribution of each series of data using the `claims_df.hist()` function.  And also used the `hist` method to plot the distribution of a single column of data.\n",
    "\n",
    "For a more granular control over our plots, we moved to the `plot` method, using the keyword arguments of `kind` to specify a `hist` or `scatter` plot, and `xlim` or `ylim` to only display data within a specific range.  We also provided the `title` and `colormap` arguments to plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "selected_claims['amountPaidOnContentsClaim'].plot(kind = 'hist', \n",
    "                                                  title = 'Distribution of Content Claims', xlim = [10000, 100000],\n",
    "                                                  ylim = [0, 2000],\n",
    "                                                  colormap = 'summer')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To display a scatter plot, we also specified the column for the x values and the column for the y values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "claims_df.plot(kind = 'scatter', x = 'amountPaidOnContentsClaim', y = 'amountPaidOnBuildingClaim')\n",
    "```"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
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 },
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}