pandas_assignments_solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python pandas exercises with solutions\n",
    "\n",
    "\n",
    "## Series exercises\n",
    "\n",
    "Following are a couple of exercises working with pandas Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create and display a one-dimensional array-like object containing an array of data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     2\n",
      "1     4\n",
      "2     6\n",
      "3     8\n",
      "4    10\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "s = pd.Series([2, 4, 6, 8, 10])\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert a Panda module Series to Python list and it's type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "[2, 4, 6, 8, 10]\n",
      "[2, 4, 6, 8, 10]\n",
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "l = s.tolist()\n",
    "print(type(s))\n",
    "print(l)\n",
    "print(list(s))\n",
    "print(type(l))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add, subtract, multiple and divide two Pandas Series.\n",
    "Sample Series: [2, 4, 6, 8, 10], [1, 3, 5, 7, 9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     3\n",
      "1     7\n",
      "2    11\n",
      "3    15\n",
      "4    19\n",
      "dtype: int64\n",
      "0    1\n",
      "1    1\n",
      "2    1\n",
      "3    1\n",
      "4    1\n",
      "dtype: int64\n",
      "0     2\n",
      "1    12\n",
      "2    30\n",
      "3    56\n",
      "4    90\n",
      "dtype: int64\n",
      "0    2.000000\n",
      "1    1.333333\n",
      "2    1.200000\n",
      "3    1.142857\n",
      "4    1.111111\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "s_l = pd.Series([2, 4, 6, 8, 10])\n",
    "s_r = pd.Series([1, 3, 5, 7, 9])\n",
    "print(s_l+s_r)\n",
    "print(s_l-s_r)\n",
    "print(s_l*s_r)\n",
    "print(s_l/s_r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the largest integer smaller or equal to the division of the inputs.  \n",
    "Sample Series: [2, 4, 6, 8, 10], [1, 3, 5, 7, 9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    2.000000\n",
      "1    1.333333\n",
      "2    1.200000\n",
      "3    1.142857\n",
      "4    1.111111\n",
      "dtype: float64\n",
      "0    2.0\n",
      "1    1.0\n",
      "2    1.0\n",
      "3    1.0\n",
      "4    1.0\n",
      "dtype: float64\n",
      "0    2\n",
      "1    1\n",
      "2    1\n",
      "3    1\n",
      "4    1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "s_l = pd.Series([2, 4, 6, 8, 10])\n",
    "s_r = pd.Series([1, 3, 5, 7, 9])\n",
    "res = s_l / s_r\n",
    "print(res)\n",
    "print(res.apply(np.floor))\n",
    "print(s_l // s_r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations\n",
    "\n",
    "- I learned the '//' notation (from Steven), which is probably more correct, as it actually returns integers :-)\n",
    "- Some used \"python\" for this. Which is not incorrect, but try to make use of pandas for performance reasons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DateFrame exercises\n",
    "\n",
    "Following are a couple of exercises working with pandas DataFrames."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create and display a DataFrame from a specified dictionary data which has the index labels.\n",
    "Sample Python dictionary data and list labels:\n",
    "\n",
    "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempts</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>Anastasia</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>Dima</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>Katherine</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>James</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>Emily</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Michael</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>Laura</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>Jonas</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name  score  attempts qualify\n",
       "a  Anastasia   12.5         1     yes\n",
       "b       Dima    9.0         3      no\n",
       "c  Katherine   16.5         2     yes\n",
       "d      James    NaN         3      no\n",
       "e      Emily    9.0         2      no\n",
       "f    Michael   20.0         3     yes\n",
       "g    Matthew   14.5         1     yes\n",
       "h      Laura    NaN         1      no\n",
       "i      Kevin    8.0         2      no\n",
       "j      Jonas   19.0         1     yes"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exam_data = {\n",
    "    'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "    'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "    'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "    'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']\n",
    "}\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
    "\n",
    "df = pd.DataFrame(exam_data, index=labels)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations\n",
    "\n",
    "- Not: df = pd.DataFrame(exam_data.values(), columns = list(labels), index = exam_data.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display a summary of the basic information about a specified DataFrame and its data.\n",
    "Sample Python dictionary data and list labels:\n",
    "\n",
    "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>score</th>\n",
       "      <th>attempts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.562500</td>\n",
       "      <td>1.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.693746</td>\n",
       "      <td>0.875595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17.125000</td>\n",
       "      <td>2.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           score   attempts\n",
       "count   8.000000  10.000000\n",
       "mean   13.562500   1.900000\n",
       "std     4.693746   0.875595\n",
       "min     8.000000   1.000000\n",
       "25%     9.000000   1.000000\n",
       "50%    13.500000   2.000000\n",
       "75%    17.125000   2.750000\n",
       "max    20.000000   3.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the first 3 rows of a given DataFrame. \n",
    "Sample Python dictionary data and list labels:\n",
    "\n",
    "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        name  score  attempts qualify\n",
      "a  Anastasia   12.5         1     yes\n",
      "b       Dima    9.0         3      no\n",
      "c  Katherine   16.5         2     yes\n",
      "        name  score  attempts qualify\n",
      "a  Anastasia   12.5         1     yes\n",
      "b       Dima    9.0         3      no\n",
      "c  Katherine   16.5         2     yes\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[0:3])\n",
    "print(df.head(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations\n",
    "\n",
    "- Not print df[1:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select the 'name' and 'score' columns from the following DataFrame. \n",
    "Sample Python dictionary data and list labels:\n",
    "\n",
    "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>name</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>Anastasia</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>Dima</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>Katherine</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>James</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>Emily</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Michael</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>Laura</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>Jonas</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name  score\n",
       "a  Anastasia   12.5\n",
       "b       Dima    9.0\n",
       "c  Katherine   16.5\n",
       "d      James    NaN\n",
       "e      Emily    9.0\n",
       "f    Michael   20.0\n",
       "g    Matthew   14.5\n",
       "h      Laura    NaN\n",
       "i      Kevin    8.0\n",
       "j      Jonas   19.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['name', 'score']]\n",
    "df.loc[:,['name', 'score']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select the specified columns and rows from a given data frame. \n",
    "Select 'name' and 'score' columns in rows 1, 3, 5, 6 from the following data frame.\n",
    "\n",
    "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>name</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>Dima</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>James</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Michael</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name  score\n",
       "b     Dima    9.0\n",
       "d    James    NaN\n",
       "f  Michael   20.0\n",
       "g  Matthew   14.5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['name', 'score']].iloc[[1,3,5,6]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations\n",
    "- Not df.loc[['a','c','e','f'],['name', 'score']]\n",
    "- Not exam_data.iloc[[1,3,5,6,],[1,3]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select the rows where the number of attempts in the examination is greater than 2.\n",
    "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempts</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>Dima</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>James</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Michael</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name  score  attempts qualify\n",
       "b     Dima    9.0         3      no\n",
       "d    James    NaN         3      no\n",
       "f  Michael   20.0         3     yes"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.attempts > 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Count the number of rows and columns of a DataFrame.\n",
    "Sample data:\n",
    "\n",
    "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "4\n",
      "10\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "# columns\n",
    "print(len(df.columns))\n",
    "print(len(df.axes[1]))\n",
    "\n",
    "# rows\n",
    "print(len(df.axes[0]))\n",
    "print(len(df.index))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select the rows where the score is missing, i.e. is NaN. \n",
    "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempts</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>James</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>Laura</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  score  attempts qualify\n",
       "d  James    NaN         3      no\n",
       "h  Laura    NaN         1      no"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.score.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select the rows the score is between 15 and 20 (inclusive). \n",
    "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempts</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>Katherine</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Michael</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>Jonas</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name  score  attempts qualify\n",
       "c  Katherine   16.5         2     yes\n",
       "f    Michael   20.0         3     yes\n",
       "j      Jonas   19.0         1     yes"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.score > 15) & (df.score <= 20)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select the rows where number of attempts in the examination is less than 2 and score greater than 15. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempts</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>Jonas</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  score  attempts qualify\n",
       "j  Jonas   19.0         1     yes"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.attempts < 2) & (df.score > 15)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change the score in row 'd' to 11.5.\n",
    "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.at['d','score'] = 11.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the sum of the examination attempts by the students. \n",
    "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.attempts.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the mean score for each different student in DataFrame. \n",
    "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.333333333333334"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.score.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Append a new row 'k' to data frame with given values for each column. Now delete the new row and return the original DataFrame. \n",
    "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
    "\n",
    "Values for each column will be:\n",
    "\n",
    "name : \"Suresh\", score: 15.5, attempts: 1, qualify: \"yes\", label: \"k\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        name  score  attempts qualify\n",
      "a  Anastasia   12.5         1     yes\n",
      "b       Dima    9.0         3      no\n",
      "c  Katherine   16.5         2     yes\n",
      "d      James   11.5         3      no\n",
      "e      Emily    9.0         2      no\n",
      "f    Michael   20.0         3     yes\n",
      "g    Matthew   14.5         1     yes\n",
      "h      Laura    NaN         1      no\n",
      "i      Kevin    8.0         2      no\n",
      "j      Jonas   19.0         1     yes\n",
      "        name  score  attempts qualify\n",
      "a  Anastasia   12.5         1     yes\n",
      "b       Dima    9.0         3      no\n",
      "c  Katherine   16.5         2     yes\n",
      "d      James   11.5         3      no\n",
      "e      Emily    9.0         2      no\n",
      "f    Michael   20.0         3     yes\n",
      "g    Matthew   14.5         1     yes\n",
      "h      Laura    NaN         1      no\n",
      "i      Kevin    8.0         2      no\n",
      "j      Jonas   19.0         1     yes\n",
      "k     Suresh   15.5         1     yes\n",
      "        name  score  attempts qualify\n",
      "a  Anastasia   12.5         1     yes\n",
      "b       Dima    9.0         3      no\n",
      "c  Katherine   16.5         2     yes\n",
      "d      James   11.5         3      no\n",
      "e      Emily    9.0         2      no\n",
      "f    Michael   20.0         3     yes\n",
      "g    Matthew   14.5         1     yes\n",
      "h      Laura    NaN         1      no\n",
      "i      Kevin    8.0         2      no\n",
      "j      Jonas   19.0         1     yes\n"
     ]
    }
   ],
   "source": [
    "print(df)\n",
    "new_row = {'name': \"Suresh\", 'score': 15.5, 'attempts': 1, 'qualify': \"yes\"}\n",
    "label = \"k\"\n",
    "\n",
    "s = pd.Series(new_row, name=label)\n",
    "df2 = df.append(s)\n",
    "print(df2)\n",
    "\n",
    "df3 = df2.drop('k')\n",
    "print(df3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sort the DataFrame first by 'name' in descending order, then by 'score' in ascending order. \n",
    "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
    "\n",
    "Values for each column will be:\n",
    "\n",
    "name : \"Suresh\", score: 15.5, attempts: 1, qualify: \"yes\", label: \"k\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempts</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>Emily</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>Dima</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>James</td>\n",
       "      <td>11.5</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>Anastasia</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>Katherine</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>Jonas</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Michael</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>Laura</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name  score  attempts qualify\n",
       "i      Kevin    8.0         2      no\n",
       "e      Emily    9.0         2      no\n",
       "b       Dima    9.0         3      no\n",
       "d      James   11.5         3      no\n",
       "a  Anastasia   12.5         1     yes\n",
       "g    Matthew   14.5         1     yes\n",
       "c  Katherine   16.5         2     yes\n",
       "j      Jonas   19.0         1     yes\n",
       "f    Michael   20.0         3     yes\n",
       "h      Laura    NaN         1      no"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='name', ascending=False).sort_values(by='score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replace the 'qualify' column contains the values 'yes' and 'no' with True and False. \n",
    "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']"
   ]
  },
  {
   "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",
       "    }\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>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempts</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>Anastasia</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>Dima</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>Katherine</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>James</td>\n",
       "      <td>11.5</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>Emily</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Michael</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>Laura</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>Jonas</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name  score  attempts  qualify\n",
       "a  Anastasia   12.5         1     True\n",
       "b       Dima    9.0         3    False\n",
       "c  Katherine   16.5         2     True\n",
       "d      James   11.5         3    False\n",
       "e      Emily    9.0         2    False\n",
       "f    Michael   20.0         3     True\n",
       "g    Matthew   14.5         1     True\n",
       "h      Laura    NaN         1    False\n",
       "i      Kevin    8.0         2    False\n",
       "j      Jonas   19.0         1     True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "df2['qualify'] = df2['qualify'].map({'yes': True, 'no': False})\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change the name 'James' to 'Suresh' in name column of the DataFrame. \n",
    "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        name  score  attempts qualify\n",
      "a  Anastasia   12.5         1     yes\n",
      "b       Dima    9.0         3      no\n",
      "c  Katherine   16.5         2     yes\n",
      "d     Suresh   11.5         3      no\n",
      "e      Emily    9.0         2      no\n",
      "f    Michael   20.0         3     yes\n",
      "g    Matthew   14.5         1     yes\n",
      "h      Laura    NaN         1      no\n",
      "i      Kevin    8.0         2      no\n",
      "j      Jonas   19.0         1     yes\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "a    Anastasia\n",
       "b         Dima\n",
       "c    Katherine\n",
       "d       Suresh\n",
       "e        Emily\n",
       "f      Michael\n",
       "g      Matthew\n",
       "h        Laura\n",
       "i        Kevin\n",
       "j        Jonas\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.name == 'James', 'name'] = 'Suresh'\n",
    "print(df)\n",
    "# or\n",
    "df.name.replace('James', 'Suresh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete the 'attempts' column from the DataFrame. \n",
    "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>name</th>\n",
       "      <th>score</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>Anastasia</td>\n",
       "      <td>12.5</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>Dima</td>\n",
       "      <td>9.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>Katherine</td>\n",
       "      <td>16.5</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>Suresh</td>\n",
       "      <td>11.5</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>Emily</td>\n",
       "      <td>9.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Michael</td>\n",
       "      <td>20.0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>14.5</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>Laura</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>8.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>Jonas</td>\n",
       "      <td>19.0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name  score qualify\n",
       "a  Anastasia   12.5     yes\n",
       "b       Dima    9.0      no\n",
       "c  Katherine   16.5     yes\n",
       "d     Suresh   11.5      no\n",
       "e      Emily    9.0      no\n",
       "f    Michael   20.0     yes\n",
       "g    Matthew   14.5     yes\n",
       "h      Laura    NaN      no\n",
       "i      Kevin    8.0      no\n",
       "j      Jonas   19.0     yes"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('attempts', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insert a new column in existing DataFrame. \n",
    "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempts</th>\n",
       "      <th>qualify</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>Anastasia</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>Dima</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>Katherine</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>Orange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>Suresh</td>\n",
       "      <td>11.5</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>Emily</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Michael</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>Laura</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>Green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>Green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>Jonas</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name  score  attempts qualify   color\n",
       "a  Anastasia   12.5         1     yes     Red\n",
       "b       Dima    9.0         3      no    Blue\n",
       "c  Katherine   16.5         2     yes  Orange\n",
       "d     Suresh   11.5         3      no     Red\n",
       "e      Emily    9.0         2      no   White\n",
       "f    Michael   20.0         3     yes   White\n",
       "g    Matthew   14.5         1     yes    Blue\n",
       "h      Laura    NaN         1      no   Green\n",
       "i      Kevin    8.0         2      no   Green\n",
       "j      Jonas   19.0         1     yes     Red"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "color = ['Red','Blue','Orange','Red','White','White','Blue','Green','Green','Red']\n",
    "df['color'] = color\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get list from DataFrame column headers. \n",
    "Sample data:\n",
    "\n",
    "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "\n",
    "'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "\n",
    "'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "\n",
    "'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['name', 'score', 'attempts', 'qualify', 'color']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations"
   ]
  }
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