hist_bins.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset('iris')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.displot(data = df, x = 'sepal_length', kind = 'hist');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "h = sns.histplot(data = df, x = 'sepal_length')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rectangle(xy=(4.3, 0), width=0.4, height=9, angle=0)\n"
     ]
    }
   ],
   "source": [
    "print(h.patches[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.3, 4.7, 5.1, 5.5, 5.9, 6.3, 6.7, 7.1, 7.5])"
      ]
     },
     "execution_count": 29,
     "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": [
    "l = [h.xy[0] for h in sns.histplot(df['sepal_length']).patches]\n",
    "np.round(np.array(l),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "counts,bins,_ = plt.hist(df['sepal_length'], edgecolor = 'black');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9., 23., 14., 27., 16., 26., 18.,  6.,  5.,  6.])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.3 , 4.66, 5.02, 5.38, 5.74, 6.1 , 6.46, 6.82, 7.18, 7.54, 7.9 ])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "count, edges = np.histogram(df['sepal_length'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 9, 23, 14, 27, 16, 26, 18,  6,  5,  6], dtype=int64),\n",
       " array([4.3 , 4.66, 5.02, 5.38, 5.74, 6.1 , 6.46, 6.82, 7.18, 7.54, 7.9 ]))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count,edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data = df, x = 'sepal_length', kind = 'hist', bins = 15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data = df, x = 'sepal_length',\n",
    "           kind = 'hist', bins = [4,5,6,7,8]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}