Descriptive Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Descriptive Statistics with Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import pandas (Data Analysis Library) and matplotlib (Plotting Library)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Renaming as 'pd' and 'plt' is not strictly neccesary but can help reduce typing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create sample data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If required we can create sample data driectly using pandas. Below is a list of column names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "columnNames = ['Student Name',\n",
    "              'January Calories',\n",
    "              'February Calories',\n",
    "              'March Calories',\n",
    "              'Total Calories'\n",
    "              ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is a list of lists that represents the individual rows in a table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "calories_per_month = [['Bob',56000, 61000, 55000, 172000],\n",
    "                ['Sue', 49000, 51000, 48000, 148000],\n",
    "                ['Barack', 50000, 51000, 52000, 153000],\n",
    "                ['Boris', 70000, 69000, 75000, 214000],\n",
    "                ['Nancy', 41000, 47000, 43000, 131000]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create a pandas DataFrame using the column names and rows we created above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame = pd.DataFrame(data=calories_per_month, columns=columnNames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>Student Name</th>\n",
       "      <th>January Calories</th>\n",
       "      <th>February Calories</th>\n",
       "      <th>March Calories</th>\n",
       "      <th>Total Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Bob</td>\n",
       "      <td>56000</td>\n",
       "      <td>61000</td>\n",
       "      <td>55000</td>\n",
       "      <td>172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Sue</td>\n",
       "      <td>49000</td>\n",
       "      <td>51000</td>\n",
       "      <td>48000</td>\n",
       "      <td>148000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Barack</td>\n",
       "      <td>50000</td>\n",
       "      <td>51000</td>\n",
       "      <td>52000</td>\n",
       "      <td>153000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Boris</td>\n",
       "      <td>70000</td>\n",
       "      <td>69000</td>\n",
       "      <td>75000</td>\n",
       "      <td>214000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>41000</td>\n",
       "      <td>47000</td>\n",
       "      <td>43000</td>\n",
       "      <td>131000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Student Name  January Calories  February Calories  March Calories  \\\n",
       "0          Bob             56000              61000           55000   \n",
       "1          Sue             49000              51000           48000   \n",
       "2       Barack             50000              51000           52000   \n",
       "3        Boris             70000              69000           75000   \n",
       "4        Nancy             41000              47000           43000   \n",
       "\n",
       "   Total Calories  \n",
       "0          172000  \n",
       "1          148000  \n",
       "2          153000  \n",
       "3          214000  \n",
       "4          131000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student Name</th>\n",
       "      <th>January Calories</th>\n",
       "      <th>February Calories</th>\n",
       "      <th>March Calories</th>\n",
       "      <th>Total Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Bob</td>\n",
       "      <td>56000</td>\n",
       "      <td>61000</td>\n",
       "      <td>55000</td>\n",
       "      <td>172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Sue</td>\n",
       "      <td>49000</td>\n",
       "      <td>51000</td>\n",
       "      <td>48000</td>\n",
       "      <td>148000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Barack</td>\n",
       "      <td>50000</td>\n",
       "      <td>51000</td>\n",
       "      <td>52000</td>\n",
       "      <td>153000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Student Name  January Calories  February Calories  March Calories  \\\n",
       "0          Bob             56000              61000           55000   \n",
       "1          Sue             49000              51000           48000   \n",
       "2       Barack             50000              51000           52000   \n",
       "\n",
       "   Total Calories  \n",
       "0          172000  \n",
       "1          148000  \n",
       "2          153000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student Name</th>\n",
       "      <th>January Calories</th>\n",
       "      <th>February Calories</th>\n",
       "      <th>March Calories</th>\n",
       "      <th>Total Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Boris</td>\n",
       "      <td>70000</td>\n",
       "      <td>69000</td>\n",
       "      <td>75000</td>\n",
       "      <td>214000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>41000</td>\n",
       "      <td>47000</td>\n",
       "      <td>43000</td>\n",
       "      <td>131000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Student Name  January Calories  February Calories  March Calories  \\\n",
       "3        Boris             70000              69000           75000   \n",
       "4        Nancy             41000              47000           43000   \n",
       "\n",
       "   Total Calories  \n",
       "3          214000  \n",
       "4          131000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5 entries, 0 to 4\n",
      "Data columns (total 5 columns):\n",
      "Student Name         5 non-null object\n",
      "January Calories     5 non-null int64\n",
      "February Calories    5 non-null int64\n",
      "March Calories       5 non-null int64\n",
      "Total Calories       5 non-null int64\n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 328.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "data_frame.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>January Calories</th>\n",
       "      <th>February Calories</th>\n",
       "      <th>March Calories</th>\n",
       "      <th>Total Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>53200.000000</td>\n",
       "      <td>55800.000000</td>\n",
       "      <td>54600.000000</td>\n",
       "      <td>163600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>10802.777421</td>\n",
       "      <td>9011.104261</td>\n",
       "      <td>12259.690045</td>\n",
       "      <td>31737.989854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>41000.000000</td>\n",
       "      <td>47000.000000</td>\n",
       "      <td>43000.000000</td>\n",
       "      <td>131000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>49000.000000</td>\n",
       "      <td>51000.000000</td>\n",
       "      <td>48000.000000</td>\n",
       "      <td>148000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>51000.000000</td>\n",
       "      <td>52000.000000</td>\n",
       "      <td>153000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>56000.000000</td>\n",
       "      <td>61000.000000</td>\n",
       "      <td>55000.000000</td>\n",
       "      <td>172000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>69000.000000</td>\n",
       "      <td>75000.000000</td>\n",
       "      <td>214000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       January Calories  February Calories  March Calories  Total Calories\n",
       "count          5.000000           5.000000        5.000000        5.000000\n",
       "mean       53200.000000       55800.000000    54600.000000   163600.000000\n",
       "std        10802.777421        9011.104261    12259.690045    31737.989854\n",
       "min        41000.000000       47000.000000    43000.000000   131000.000000\n",
       "25%        49000.000000       51000.000000    48000.000000   148000.000000\n",
       "50%        50000.000000       51000.000000    52000.000000   153000.000000\n",
       "75%        56000.000000       61000.000000    55000.000000   172000.000000\n",
       "max        70000.000000       69000.000000    75000.000000   214000.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52000.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame['March Calories'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the DataFrame also:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c409850>"
      ]
     },
     "execution_count": 23,
     "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": [
    "data_frame.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not bad! Would be nice if we used the Student Name instead of the index though."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data_frame).set_index('Student Name')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now when we view the DataFrame you'll notice we have used the Student Name as the index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>January Calories</th>\n",
       "      <th>February Calories</th>\n",
       "      <th>March Calories</th>\n",
       "      <th>Total Calories</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Student Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Bob</td>\n",
       "      <td>56000</td>\n",
       "      <td>61000</td>\n",
       "      <td>55000</td>\n",
       "      <td>172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sue</td>\n",
       "      <td>49000</td>\n",
       "      <td>51000</td>\n",
       "      <td>48000</td>\n",
       "      <td>148000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Barack</td>\n",
       "      <td>50000</td>\n",
       "      <td>51000</td>\n",
       "      <td>52000</td>\n",
       "      <td>153000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Boris</td>\n",
       "      <td>70000</td>\n",
       "      <td>69000</td>\n",
       "      <td>75000</td>\n",
       "      <td>214000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Nancy</td>\n",
       "      <td>41000</td>\n",
       "      <td>47000</td>\n",
       "      <td>43000</td>\n",
       "      <td>131000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              January Calories  February Calories  March Calories  \\\n",
       "Student Name                                                        \n",
       "Bob                      56000              61000           55000   \n",
       "Sue                      49000              51000           48000   \n",
       "Barack                   50000              51000           52000   \n",
       "Boris                    70000              69000           75000   \n",
       "Nancy                    41000              47000           43000   \n",
       "\n",
       "              Total Calories  \n",
       "Student Name                  \n",
       "Bob                   172000  \n",
       "Sue                   148000  \n",
       "Barack                153000  \n",
       "Boris                 214000  \n",
       "Nancy                 131000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now when we plot we have the Student Name on the x-xis instead of the index which makes this graph easier to read."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c8be390>"
      ]
     },
     "execution_count": 26,
     "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": [
    "df.plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>January Calories</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Student Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Bob</td>\n",
       "      <td>56000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sue</td>\n",
       "      <td>49000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Barack</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Boris</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Nancy</td>\n",
       "      <td>41000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              January Calories\n",
       "Student Name                  \n",
       "Bob                      56000\n",
       "Sue                      49000\n",
       "Barack                   50000\n",
       "Boris                    70000\n",
       "Nancy                    41000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['January Calories']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>January Calories</th>\n",
       "      <th>Total Calories</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Student Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Bob</td>\n",
       "      <td>56000</td>\n",
       "      <td>172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sue</td>\n",
       "      <td>49000</td>\n",
       "      <td>148000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Barack</td>\n",
       "      <td>50000</td>\n",
       "      <td>153000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Boris</td>\n",
       "      <td>70000</td>\n",
       "      <td>214000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Nancy</td>\n",
       "      <td>41000</td>\n",
       "      <td>131000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              January Calories  Total Calories\n",
       "Student Name                                  \n",
       "Bob                      56000          172000\n",
       "Sue                      49000          148000\n",
       "Barack                   50000          153000\n",
       "Boris                    70000          214000\n",
       "Nancy                    41000          131000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['January Calories', 'Total Calories']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_jan = df['January Calories']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d5c0c90>"
      ]
     },
     "execution_count": 30,
     "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": [
    "df_jan.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Your Turn!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use pandas <strong>'read_csv'</strong> method to read in a .csv file and store in the variable named <strong>'data_file'</strong>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame = pd.read_csv('DATA/Influence of alcohol consumption on anxiety levels.csv',  sep = ',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<strong>TASK:</strong> Use pandas <strong>head</strong> method to show the <strong>top 10</strong> rows of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<strong>TASK:</strong> Use pandas <strong>info</strong> method to get info on <strong>datatypes</strong>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<strong>TASK:</strong> Use pandas <strong>tail</strong> method to show the <strong>last 10</strong> rows of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<strong>TASK:</strong> Get the <strong>number of rows</strong> in the data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<strong>TASK:</strong> Get some basic descriptive statistics using the <strong>describe</strong> method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<strong>TASK:</strong> Get the <strong>median</strong> of the Average Number of Alcoholic units consumed per week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<strong>TASK:</strong> Plot a <strong>line</strong> graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histogram Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d5c5ed0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zrrNnL89k9F/9LwO3dbcL5q0f4KnArV0f24HXddtPBW4Gvg5cCTxq1rWusK9fBK6d1166mr/U3e7Y97s+b6+vsX7OBrZ1r7NrgMcM0YuXKpCkRs37FI0k6QAMeElqlAEvSY0y4CWpUQa8JDXKgJekRhnwktSo/wMt+jcSbhaomAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_frame['Average Number of Alcoholic units consumed per week'].plot(kind='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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}