pandas_1-AMF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ROhkJUiJ5QjI"
   },
   "source": [
    "# Introduction to Pandas Lab\n",
    "\n",
    "Complete the following set of exercises to solidify your knowledge of Pandas fundamentals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZqoYtdPt5QjM"
   },
   "source": [
    "### 1. Import Numpy and Pandas and alias them to `np` and `pd` respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "kWz9I8_s5QjN"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8TFJ1uDQ5QjO"
   },
   "source": [
    "### 2. Create a Pandas Series containing the elements of the list below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "iyfx3P735QjP"
   },
   "outputs": [],
   "source": [
    "lst = [5.7, 75.2, 74.4, 84.0, 66.5, 66.3, 55.8, 75.7, 29.1, 43.7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "kL24mCFa5QjP"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     5.7\n",
      "1    75.2\n",
      "2    74.4\n",
      "3    84.0\n",
      "4    66.5\n",
      "5    66.3\n",
      "6    55.8\n",
      "7    75.7\n",
      "8    29.1\n",
      "9    43.7\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series=pd.Series(lst)\n",
    "print(series)\n",
    "type(series) #now we can check it is a series, each value has an index as well!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6EpOhnBs5QjP"
   },
   "source": [
    "### 3. Use indexing to return the third value in the Series above.\n",
    "\n",
    "*Hint: Remember that indexing begins at 0.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "ym-_-1kW5QjQ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74.4"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series[2] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YDDqpk3O5QjQ"
   },
   "source": [
    "### 4. Create a Pandas DataFrame from the list of lists below. Each sublist should be represented as a row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "knKrP9C25QjR"
   },
   "outputs": [],
   "source": [
    "b = [[53.1, 95.0, 67.5, 35.0, 78.4],\n",
    "     [61.3, 40.8, 30.8, 37.8, 87.6],\n",
    "     [20.6, 73.2, 44.2, 14.6, 91.8],\n",
    "     [57.4, 0.1, 96.1, 4.2, 69.5],\n",
    "     [83.6, 20.5, 85.4, 22.8, 35.9],\n",
    "     [49.0, 69.0, 0.1, 31.8, 89.1],\n",
    "     [23.3, 40.7, 95.0, 83.8, 26.9],\n",
    "     [27.6, 26.4, 53.8, 88.8, 68.5],\n",
    "     [96.6, 96.4, 53.4, 72.4, 50.1],\n",
    "     [73.7, 39.0, 43.2, 81.6, 34.7]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "jwgxPg3k5QjR"
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53.1</td>\n",
       "      <td>95.0</td>\n",
       "      <td>67.5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>78.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61.3</td>\n",
       "      <td>40.8</td>\n",
       "      <td>30.8</td>\n",
       "      <td>37.8</td>\n",
       "      <td>87.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.6</td>\n",
       "      <td>73.2</td>\n",
       "      <td>44.2</td>\n",
       "      <td>14.6</td>\n",
       "      <td>91.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>96.1</td>\n",
       "      <td>4.2</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>83.6</td>\n",
       "      <td>20.5</td>\n",
       "      <td>85.4</td>\n",
       "      <td>22.8</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>49.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>31.8</td>\n",
       "      <td>89.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.3</td>\n",
       "      <td>40.7</td>\n",
       "      <td>95.0</td>\n",
       "      <td>83.8</td>\n",
       "      <td>26.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27.6</td>\n",
       "      <td>26.4</td>\n",
       "      <td>53.8</td>\n",
       "      <td>88.8</td>\n",
       "      <td>68.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>96.6</td>\n",
       "      <td>96.4</td>\n",
       "      <td>53.4</td>\n",
       "      <td>72.4</td>\n",
       "      <td>50.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>73.7</td>\n",
       "      <td>39.0</td>\n",
       "      <td>43.2</td>\n",
       "      <td>81.6</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0     1     2     3     4\n",
       "0  53.1  95.0  67.5  35.0  78.4\n",
       "1  61.3  40.8  30.8  37.8  87.6\n",
       "2  20.6  73.2  44.2  14.6  91.8\n",
       "3  57.4   0.1  96.1   4.2  69.5\n",
       "4  83.6  20.5  85.4  22.8  35.9\n",
       "5  49.0  69.0   0.1  31.8  89.1\n",
       "6  23.3  40.7  95.0  83.8  26.9\n",
       "7  27.6  26.4  53.8  88.8  68.5\n",
       "8  96.6  96.4  53.4  72.4  50.1\n",
       "9  73.7  39.0  43.2  81.6  34.7"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df=pd.DataFrame(b)\n",
    "type(my_df) #it is a dataframe\n",
    "my_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 5)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df.shape #it has 10 rows and 5 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   0       10 non-null     float64\n",
      " 1   1       10 non-null     float64\n",
      " 2   2       10 non-null     float64\n",
      " 3   3       10 non-null     float64\n",
      " 4   4       10 non-null     float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 532.0 bytes\n"
     ]
    }
   ],
   "source": [
    "my_df.info() #we have no missing data, all values are float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.00000</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.62000</td>\n",
       "      <td>50.11000</td>\n",
       "      <td>56.950000</td>\n",
       "      <td>47.280000</td>\n",
       "      <td>63.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25.64899</td>\n",
       "      <td>32.12204</td>\n",
       "      <td>30.168278</td>\n",
       "      <td>31.393446</td>\n",
       "      <td>24.562313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>20.60000</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>26.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.95000</td>\n",
       "      <td>29.55000</td>\n",
       "      <td>43.450000</td>\n",
       "      <td>25.050000</td>\n",
       "      <td>39.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.25000</td>\n",
       "      <td>40.75000</td>\n",
       "      <td>53.600000</td>\n",
       "      <td>36.400000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.60000</td>\n",
       "      <td>72.15000</td>\n",
       "      <td>80.925000</td>\n",
       "      <td>79.300000</td>\n",
       "      <td>85.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>96.60000</td>\n",
       "      <td>96.40000</td>\n",
       "      <td>96.100000</td>\n",
       "      <td>88.800000</td>\n",
       "      <td>91.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1          2          3          4\n",
       "count  10.00000  10.00000  10.000000  10.000000  10.000000\n",
       "mean   54.62000  50.11000  56.950000  47.280000  63.250000\n",
       "std    25.64899  32.12204  30.168278  31.393446  24.562313\n",
       "min    20.60000   0.10000   0.100000   4.200000  26.900000\n",
       "25%    32.95000  29.55000  43.450000  25.050000  39.450000\n",
       "50%    55.25000  40.75000  53.600000  36.400000  69.000000\n",
       "75%    70.60000  72.15000  80.925000  79.300000  85.300000\n",
       "max    96.60000  96.40000  96.100000  88.800000  91.800000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JUkoUlzW5QjR"
   },
   "source": [
    "### 5. Rename the data frame columns based on the names in the list below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "dmzmA93O5QjS"
   },
   "outputs": [],
   "source": [
    "c = [[\"Score_1\"],\n",
    "     [\"Score_2\"],\n",
    "     [\"Score_3\"],\n",
    "     [\"Score_4\"],\n",
    "     [\"Score_5\"],\n",
    "     [\"Score_6\"],\n",
    "     [\"Score_7\"],\n",
    "     [\"Score_8\"],\n",
    "     [\"Score_9\"],\n",
    "     [\"Score_10\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "flat_list = []\n",
    "\n",
    "for lists in c:\n",
    "    for i in lists:\n",
    "        flat_list.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Score_1',\n",
       " 'Score_2',\n",
       " 'Score_3',\n",
       " 'Score_4',\n",
       " 'Score_5',\n",
       " 'Score_6',\n",
       " 'Score_7',\n",
       " 'Score_8',\n",
       " 'Score_9',\n",
       " 'Score_10']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "3P5pB_kb5QjS"
   },
   "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_1</th>\n",
       "      <th>Score_2</th>\n",
       "      <th>Score_3</th>\n",
       "      <th>Score_4</th>\n",
       "      <th>Score_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53.1</td>\n",
       "      <td>95.0</td>\n",
       "      <td>67.5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>78.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61.3</td>\n",
       "      <td>40.8</td>\n",
       "      <td>30.8</td>\n",
       "      <td>37.8</td>\n",
       "      <td>87.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.6</td>\n",
       "      <td>73.2</td>\n",
       "      <td>44.2</td>\n",
       "      <td>14.6</td>\n",
       "      <td>91.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>96.1</td>\n",
       "      <td>4.2</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>83.6</td>\n",
       "      <td>20.5</td>\n",
       "      <td>85.4</td>\n",
       "      <td>22.8</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>49.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>31.8</td>\n",
       "      <td>89.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.3</td>\n",
       "      <td>40.7</td>\n",
       "      <td>95.0</td>\n",
       "      <td>83.8</td>\n",
       "      <td>26.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27.6</td>\n",
       "      <td>26.4</td>\n",
       "      <td>53.8</td>\n",
       "      <td>88.8</td>\n",
       "      <td>68.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>96.6</td>\n",
       "      <td>96.4</td>\n",
       "      <td>53.4</td>\n",
       "      <td>72.4</td>\n",
       "      <td>50.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>73.7</td>\n",
       "      <td>39.0</td>\n",
       "      <td>43.2</td>\n",
       "      <td>81.6</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Score_1  Score_2  Score_3  Score_4  Score_5\n",
       "0     53.1     95.0     67.5     35.0     78.4\n",
       "1     61.3     40.8     30.8     37.8     87.6\n",
       "2     20.6     73.2     44.2     14.6     91.8\n",
       "3     57.4      0.1     96.1      4.2     69.5\n",
       "4     83.6     20.5     85.4     22.8     35.9\n",
       "5     49.0     69.0      0.1     31.8     89.1\n",
       "6     23.3     40.7     95.0     83.8     26.9\n",
       "7     27.6     26.4     53.8     88.8     68.5\n",
       "8     96.6     96.4     53.4     72.4     50.1\n",
       "9     73.7     39.0     43.2     81.6     34.7"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df.columns = flat_list[:5]\n",
    "my_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mxCt2sii5QjS"
   },
   "source": [
    "### 6. Create a subset of this data frame that contains only the Score 1, 3, and 5 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "m2UA5-wK5QjS"
   },
   "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_1</th>\n",
       "      <th>Score_3</th>\n",
       "      <th>Score_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53.1</td>\n",
       "      <td>67.5</td>\n",
       "      <td>78.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61.3</td>\n",
       "      <td>30.8</td>\n",
       "      <td>87.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.6</td>\n",
       "      <td>44.2</td>\n",
       "      <td>91.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57.4</td>\n",
       "      <td>96.1</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>83.6</td>\n",
       "      <td>85.4</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>89.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.3</td>\n",
       "      <td>95.0</td>\n",
       "      <td>26.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27.6</td>\n",
       "      <td>53.8</td>\n",
       "      <td>68.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>96.6</td>\n",
       "      <td>53.4</td>\n",
       "      <td>50.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>73.7</td>\n",
       "      <td>43.2</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Score_1  Score_3  Score_5\n",
       "0     53.1     67.5     78.4\n",
       "1     61.3     30.8     87.6\n",
       "2     20.6     44.2     91.8\n",
       "3     57.4     96.1     69.5\n",
       "4     83.6     85.4     35.9\n",
       "5     49.0      0.1     89.1\n",
       "6     23.3     95.0     26.9\n",
       "7     27.6     53.8     68.5\n",
       "8     96.6     53.4     50.1\n",
       "9     73.7     43.2     34.7"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_my_df = list(my_df.columns)[0:5:2]\n",
    "my_df[subset_my_df]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WSTt2ixY5QjS"
   },
   "source": [
    "### 7. From the original data frame, calculate the average Score_3 value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "b2-e0mdt5QjS"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56.95000000000001"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df[\"Score_3\"].mean() #we get the same value as column 2 mean in the df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TrRL5tr85QjT"
   },
   "source": [
    "### 8. From the original data frame, calculate the maximum Score_4 value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "P7U8lnvw5QjT"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88.8"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df[\"Score_4\"].max() #we get the same value as column 3 max value in the df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_tpSq5xn5QjT"
   },
   "source": [
    "### 9. From the original data frame, calculate the median Score 2 value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "KjVYYavK5QjT"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.75"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df[\"Score_2\"].median() #we get the same value as column 1 in the 50% (median) of the df.describe() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JM51DP5D5QjT"
   },
   "source": [
    "### 10. Create a Pandas DataFrame from the dictionary of product orders below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "0zRtNiPU5QjT"
   },
   "outputs": [],
   "source": [
    "orders = {'Description': ['LUNCH BAG APPLE DESIGN',\n",
    "  'SET OF 60 VINTAGE LEAF CAKE CASES ',\n",
    "  'RIBBON REEL STRIPES DESIGN ',\n",
    "  'WORLD WAR 2 GLIDERS ASSTD DESIGNS',\n",
    "  'PLAYING CARDS JUBILEE UNION JACK',\n",
    "  'POPCORN HOLDER',\n",
    "  'BOX OF VINTAGE ALPHABET BLOCKS',\n",
    "  'PARTY BUNTING',\n",
    "  'JAZZ HEARTS ADDRESS BOOK',\n",
    "  'SET OF 4 SANTA PLACE SETTINGS'],\n",
    " 'Quantity': [1, 24, 1, 2880, 2, 7, 1, 4, 10, 48],\n",
    " 'UnitPrice': [1.65, 0.55, 1.65, 0.18, 1.25, 0.85, 11.95, 4.95, 0.19, 1.25],\n",
    " 'Revenue': [1.65, 13.2, 1.65, 518.4, 2.5, 5.95, 11.95, 19.8, 1.9, 60.0]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "jH3Y-RBq5QjU"
   },
   "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>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LUNCH BAG APPLE DESIGN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SET OF 60 VINTAGE LEAF CAKE CASES</td>\n",
       "      <td>24</td>\n",
       "      <td>0.55</td>\n",
       "      <td>13.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RIBBON REEL STRIPES DESIGN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WORLD WAR 2 GLIDERS ASSTD DESIGNS</td>\n",
       "      <td>2880</td>\n",
       "      <td>0.18</td>\n",
       "      <td>518.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PLAYING CARDS JUBILEE UNION JACK</td>\n",
       "      <td>2</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>POPCORN HOLDER</td>\n",
       "      <td>7</td>\n",
       "      <td>0.85</td>\n",
       "      <td>5.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BOX OF VINTAGE ALPHABET BLOCKS</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>11.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PARTY BUNTING</td>\n",
       "      <td>4</td>\n",
       "      <td>4.95</td>\n",
       "      <td>19.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>JAZZ HEARTS ADDRESS BOOK</td>\n",
       "      <td>10</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SET OF 4 SANTA PLACE SETTINGS</td>\n",
       "      <td>48</td>\n",
       "      <td>1.25</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Description  Quantity  UnitPrice  Revenue\n",
       "0              LUNCH BAG APPLE DESIGN         1       1.65     1.65\n",
       "1  SET OF 60 VINTAGE LEAF CAKE CASES         24       0.55    13.20\n",
       "2         RIBBON REEL STRIPES DESIGN          1       1.65     1.65\n",
       "3   WORLD WAR 2 GLIDERS ASSTD DESIGNS      2880       0.18   518.40\n",
       "4    PLAYING CARDS JUBILEE UNION JACK         2       1.25     2.50\n",
       "5                      POPCORN HOLDER         7       0.85     5.95\n",
       "6      BOX OF VINTAGE ALPHABET BLOCKS         1      11.95    11.95\n",
       "7                       PARTY BUNTING         4       4.95    19.80\n",
       "8            JAZZ HEARTS ADDRESS BOOK        10       0.19     1.90\n",
       "9       SET OF 4 SANTA PLACE SETTINGS        48       1.25    60.00"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df2 = pd.DataFrame(orders)\n",
    "my_df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qHLMJYY_5QjU"
   },
   "source": [
    "### 11. Calculate the total quantity ordered and revenue generated from these orders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "FdK1RLL25QjU"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the total quantity generated by these orders is 2978 units\n",
      "the total revenue generated by these orders is 637 €\n"
     ]
    }
   ],
   "source": [
    "total_qty=my_df2[\"Quantity\"].sum()\n",
    "print (f\"the total quantity generated by these orders is {total_qty} units\")\n",
    "\n",
    "total_rev=my_df2[\"Revenue\"].sum()\n",
    "print (f\"the total revenue generated by these orders is {int(total_rev)} €\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "srPZiDid5QjU"
   },
   "source": [
    "### 12. Obtain the prices of the most expensive and least expensive items ordered and print the difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "Yf4GGRwo5QjU"
   },
   "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>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BOX OF VINTAGE ALPHABET BLOCKS</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>11.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PARTY BUNTING</td>\n",
       "      <td>4</td>\n",
       "      <td>4.95</td>\n",
       "      <td>19.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LUNCH BAG APPLE DESIGN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RIBBON REEL STRIPES DESIGN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PLAYING CARDS JUBILEE UNION JACK</td>\n",
       "      <td>2</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SET OF 4 SANTA PLACE SETTINGS</td>\n",
       "      <td>48</td>\n",
       "      <td>1.25</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>POPCORN HOLDER</td>\n",
       "      <td>7</td>\n",
       "      <td>0.85</td>\n",
       "      <td>5.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SET OF 60 VINTAGE LEAF CAKE CASES</td>\n",
       "      <td>24</td>\n",
       "      <td>0.55</td>\n",
       "      <td>13.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>JAZZ HEARTS ADDRESS BOOK</td>\n",
       "      <td>10</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WORLD WAR 2 GLIDERS ASSTD DESIGNS</td>\n",
       "      <td>2880</td>\n",
       "      <td>0.18</td>\n",
       "      <td>518.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Description  Quantity  UnitPrice  Revenue\n",
       "6      BOX OF VINTAGE ALPHABET BLOCKS         1      11.95    11.95\n",
       "7                       PARTY BUNTING         4       4.95    19.80\n",
       "0              LUNCH BAG APPLE DESIGN         1       1.65     1.65\n",
       "2         RIBBON REEL STRIPES DESIGN          1       1.65     1.65\n",
       "4    PLAYING CARDS JUBILEE UNION JACK         2       1.25     2.50\n",
       "9       SET OF 4 SANTA PLACE SETTINGS        48       1.25    60.00\n",
       "5                      POPCORN HOLDER         7       0.85     5.95\n",
       "1  SET OF 60 VINTAGE LEAF CAKE CASES         24       0.55    13.20\n",
       "8            JAZZ HEARTS ADDRESS BOOK        10       0.19     1.90\n",
       "3   WORLD WAR 2 GLIDERS ASSTD DESIGNS      2880       0.18   518.40"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df2.sort_values(by=\"UnitPrice\", ascending=False)\n",
    "#the most expensive item in the dataframe is the box of vintage alphabet block:almost 12€ per unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "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>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WORLD WAR 2 GLIDERS ASSTD DESIGNS</td>\n",
       "      <td>2880</td>\n",
       "      <td>0.18</td>\n",
       "      <td>518.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>JAZZ HEARTS ADDRESS BOOK</td>\n",
       "      <td>10</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SET OF 60 VINTAGE LEAF CAKE CASES</td>\n",
       "      <td>24</td>\n",
       "      <td>0.55</td>\n",
       "      <td>13.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>POPCORN HOLDER</td>\n",
       "      <td>7</td>\n",
       "      <td>0.85</td>\n",
       "      <td>5.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PLAYING CARDS JUBILEE UNION JACK</td>\n",
       "      <td>2</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SET OF 4 SANTA PLACE SETTINGS</td>\n",
       "      <td>48</td>\n",
       "      <td>1.25</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LUNCH BAG APPLE DESIGN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RIBBON REEL STRIPES DESIGN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PARTY BUNTING</td>\n",
       "      <td>4</td>\n",
       "      <td>4.95</td>\n",
       "      <td>19.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BOX OF VINTAGE ALPHABET BLOCKS</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>11.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Description  Quantity  UnitPrice  Revenue\n",
       "3   WORLD WAR 2 GLIDERS ASSTD DESIGNS      2880       0.18   518.40\n",
       "8            JAZZ HEARTS ADDRESS BOOK        10       0.19     1.90\n",
       "1  SET OF 60 VINTAGE LEAF CAKE CASES         24       0.55    13.20\n",
       "5                      POPCORN HOLDER         7       0.85     5.95\n",
       "4    PLAYING CARDS JUBILEE UNION JACK         2       1.25     2.50\n",
       "9       SET OF 4 SANTA PLACE SETTINGS        48       1.25    60.00\n",
       "0              LUNCH BAG APPLE DESIGN         1       1.65     1.65\n",
       "2         RIBBON REEL STRIPES DESIGN          1       1.65     1.65\n",
       "7                       PARTY BUNTING         4       4.95    19.80\n",
       "6      BOX OF VINTAGE ALPHABET BLOCKS         1      11.95    11.95"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df2.sort_values(by=\"UnitPrice\", ascending=True)\n",
    "#the cheapest item in the dataframe is the World War 2Gliders Ssstd Designs :only 0.18€ per unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "diff_price=my_df2[\"UnitPrice\"].max()-my_df2[\"UnitPrice\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.77"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_price #11.95€-0.18€=11.77"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  },
  "toc": {
   "base_numbering": "",
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}