how-to-make-histogram-in-pandas-python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting a Histogram with Pandas\n",
    "In this Jupyter Notebook, you will find the example code for the blog post about [how to plot a histogram with Pandas](https://www.marsja.se/how-to-plot-a-histogram-with-pandas-in-3-simple-steps/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First step is to import pandas as pd:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second step is to load data into a pandas dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Subject ID</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Day</th>\n",
       "      <th>Age</th>\n",
       "      <th>RT</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>John</td>\n",
       "      <td>Sixth</td>\n",
       "      <td>23</td>\n",
       "      <td>0.562719</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Billie</td>\n",
       "      <td>Sixth</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Robert</td>\n",
       "      <td>Sixth</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Don</td>\n",
       "      <td>Sixth</td>\n",
       "      <td>27</td>\n",
       "      <td>0.522961</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Joseph</td>\n",
       "      <td>Sixth</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Subject ID First Name    Day  Age        RT  Gender\n",
       "0           1       John  Sixth   23  0.562719       0\n",
       "1           2     Billie  Sixth   22       NaN       0\n",
       "2           3     Robert  Sixth   20       NaN       0\n",
       "3           4        Don  Sixth   27  0.522961       0\n",
       "4           5     Joseph  Sixth   21       NaN       0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "datafile = Path('SimData', 'play_data.xlsx')\n",
    "\n",
    "df = pd.read_excel(datafile, \n",
    "               index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final step is to use Pandas hist():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002768F210780>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lJOZPAU8fnz7dH236W7D8zdQ6idlm/b153vp7L/DkMK0/4J3A3mbZNwCPABcOuvZu9dcsdw7wPHD2oGvu5qPne+B5ikvuI+I3mvk3Ap8Bfhj4fLMXN5dF7iLWsr9fAj4WEd8HjgK/nM1Wdbpr2V9ZLfu7CvjNiJjj2Pq7epjWX2Y+FhF/AzwM/C9wU2Y+Mriq21vC9nkl8Ld57K+MoeGl9JJUlFdiSlJRBrgkFWWAS1JRBrgkFWWAS1JRBrgkFWWAS1JR/weB8KYBVPe1qwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='RT')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to Group a Histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002768F51D4A8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000002768F576358>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 4,
     "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": [
    "df.hist(by='Gender',\n",
    "       column='RT')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the size of the histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002768F517630>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000002768F675E80>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(by='Gender',\n",
    "       column='RT',\n",
    "       figsize=[10, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002768F874908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002768F89F5F8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002768F74DB70>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002768F78C160>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist()"
   ]
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}