Notebook 01 - Intro to Pandas.ipynb

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    "# Notebook 1\n",
    "## Introduction to Pandas\n",
    "\n",
    "Inspired to the tutorial available [here](https://pandas.pydata.org/pandas-docs/stable/tutorials.html).\n",
    "\n",
    "Let's import pandas!"
   ]
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   "source": [
    "import pandas as pd"
   ]
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   "source": [
    "## Table of content\n",
    "* [Populating a DataFrme](#Populating-a-DataFrme)\n",
    "* [Manipulating a DataFrme](#Manipulating-a-DataFrme)\n",
    "* [Groupby](#Groupby)"
   ]
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    "## Populating a DataFrme\n",
    "\n",
    "There are sveral possibilities to **create a DataFrame**, but we focus on the two simplest ones.\n",
    "\n",
    "### From a dictionary\n",
    "\n",
    "It is possible to transofrm into a DataFrame a **dictionary** where each key is associated to a list of values. \n",
    "The keys becomes the name of the columns, the lists the columns themeselves."
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
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       "      <th>0</th>\n",
       "      <td>John</td>\n",
       "      <td>40</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jane</td>\n",
       "      <td>19</td>\n",
       "      <td>170</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Judy</td>\n",
       "      <td>38</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jessica</td>\n",
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       "      Name  Age  Height\n",
       "0     John   40     160\n",
       "1     Jane   19     170\n",
       "2     Judy   38     162\n",
       "3  Jessica   43     157\n",
       "4      Joe   25     181"
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    "example = {'Name': ['John', 'Jane', 'Judy', 'Jessica', 'Joe'],\n",
    "           'Age': [40, 19, 38, 43, 25],\n",
    "           'Height': [160, 170, 162, 157, 181]}\n",
    "\n",
    "data = pd.DataFrame(example)\n",
    "data"
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    "### Reading a CSV\n",
    "We can read directly a csv file! \n"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
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       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>setosa</td>\n",
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       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
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       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa"
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    "data = pd.read_csv('data/iris.csv')\n",
    "data.head(3) #Print the first few rows"
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   "source": [
    "***\n",
    "## Manipulating a DataFrame"
   ]
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    "### Looking into a DataFrame\n",
    "\n",
    "Beside **head()**, there are several useful function to see the content of a DataFrame.\n",
    "Some of the must useful are illustrated below."
   ]
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       "      <td>0.100000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
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       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
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       "      <th>50%</th>\n",
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       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
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       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
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       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
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   "source": [
    "data = pd.read_csv('data/iris.csv')\n",
    "data.describe() # Shows different statistic values on the data attributes."
   ]
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      "text/plain": [
       "        sepal_length  sepal_width  petal_length  petal_width    species\n",
       "count     150.000000   150.000000    150.000000   150.000000        150\n",
       "unique           NaN          NaN           NaN          NaN          3\n",
       "top              NaN          NaN           NaN          NaN  virginica\n",
       "freq             NaN          NaN           NaN          NaN         50\n",
       "mean        5.843333     3.054000      3.758667     1.198667        NaN\n",
       "std         0.828066     0.433594      1.764420     0.763161        NaN\n",
       "min         4.300000     2.000000      1.000000     0.100000        NaN\n",
       "25%         5.100000     2.800000      1.600000     0.300000        NaN\n",
       "50%         5.800000     3.000000      4.350000     1.300000        NaN\n",
       "75%         6.400000     3.300000      5.100000     1.800000        NaN\n",
       "max         7.900000     4.400000      6.900000     2.500000        NaN"
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   "source": [
    "data.describe(include='all') # Get also unique, top, freq values"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal_length  150 non-null    float64\n",
      " 1   sepal_width   150 non-null    float64\n",
      " 2   petal_length  150 non-null    float64\n",
      " 3   petal_width   150 non-null    float64\n",
      " 4   species       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
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   "source": [
    "data.info()"
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          species\n",
       "count         150\n",
       "unique          3\n",
       "top     virginica\n",
       "freq           50"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(exclude=['float64'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1139,
     "status": "ok",
     "timestamp": 1589716959614,
     "user": {
      "displayName": "Daniele Loiacono",
      "photoUrl": "",
      "userId": "03206643914008722225"
     },
     "user_tz": -120
    },
    "id": "bc2cgGpA-tBK",
    "outputId": "739e3338-3cc8-42f3-d502-ea1b080dd935"
   },
   "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",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(3) #show the last three rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QOuzY9Nz-tBM"
   },
   "source": [
    "### Selection\n",
    "\n",
    "It is possible to select columns and or rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1136,
     "status": "ok",
     "timestamp": 1589716959614,
     "user": {
      "displayName": "Daniele Loiacono",
      "photoUrl": "",
      "userId": "03206643914008722225"
     },
     "user_tz": -120
    },
    "id": "GcF8_BdW-tBN",
    "outputId": "f29492d5-b51c-4e46-c187-0bc47e6cdb32"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    setosa\n",
       "1    setosa\n",
       "2    setosa\n",
       "3    setosa\n",
       "4    setosa\n",
       "Name: species, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/iris.csv')\n",
    "data['species'].head() #select only the column 'species'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1134,
     "status": "ok",
     "timestamp": 1589716959615,
     "user": {
      "displayName": "Daniele Loiacono",
      "photoUrl": "",
      "userId": "03206643914008722225"
     },
     "user_tz": -120
    },
    "id": "sRxL-W9j-tBP",
    "outputId": "9a3efbcd-efd1-4b58-e168-20f2e7f44719"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['species'].unique() #once selected a col, I can see unique values..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1131,
     "status": "ok",
     "timestamp": 1589716959615,
     "user": {
      "displayName": "Daniele Loiacono",
      "photoUrl": "",
      "userId": "03206643914008722225"
     },
     "user_tz": -120
    },
    "id": "wE3Y-at2-tBR",
    "outputId": "9ab07bbe-53c9-47a7-a32c-8aa1abf13905"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.9"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['sepal_length'].max() #... and print max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1129,
     "status": "ok",
     "timestamp": 1589716959616,
     "user": {
      "displayName": "Daniele Loiacono",
      "photoUrl": "",
      "userId": "03206643914008722225"
     },
     "user_tz": -120
    },
    "id": "HLnCufgx-tBV",
    "outputId": "671c4906-1e76-4ef3-8480-22a9906fa019"
   },
   "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",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width species\n",
       "0           5.1          3.5  setosa\n",
       "1           4.9          3.0  setosa\n",
       "2           4.7          3.2  setosa\n",
       "3           4.6          3.1  setosa\n",
       "4           5.0          3.6  setosa"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_data = data[['sepal_length','sepal_width','species']] #I can select more cols\n",
    "sel_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1127,
     "status": "ok",
     "timestamp": 1589716959616,
     "user": {
      "displayName": "Daniele Loiacono",
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      "userId": "03206643914008722225"
     },
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    },
    "id": "1PTWoxc0-tBX",
    "outputId": "55496f16-75b4-4714-9a4c-4f710b311c51"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>species</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
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       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
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       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>virginica</td>\n",
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       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>virginica</td>\n",
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      ],
      "text/plain": [
       "     sepal_length    species\n",
       "140           6.7  virginica\n",
       "141           6.9  virginica\n",
       "142           5.8  virginica\n",
       "143           6.8  virginica\n",
       "144           6.7  virginica\n",
       "145           6.7  virginica"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_data = data[['sepal_length','species']][140:146] #I can also select rows\n",
    "sel_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JY2XGeRV-tBa"
   },
   "source": [
    "You can also select by value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1125,
     "status": "ok",
     "timestamp": 1589716959617,
     "user": {
      "displayName": "Daniele Loiacono",
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     },
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    },
    "id": "oWM0el3H-tBa",
    "outputId": "02706893-4cf4-4b43-e7b4-c4f7901fccaa"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.9</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length     species\n",
       "50           7.0  versicolor\n",
       "51           6.4  versicolor\n",
       "52           6.9  versicolor\n",
       "53           5.5  versicolor\n",
       "54           6.5  versicolor"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_data = data[data.species == 'versicolor'] #select all the rows where species is versicolor\n",
    "sel_data = sel_data[['sepal_length', 'species']]\n",
    "sel_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1122,
     "status": "ok",
     "timestamp": 1589716959617,
     "user": {
      "displayName": "Daniele Loiacono",
      "photoUrl": "",
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     },
     "user_tz": -120
    },
    "id": "aILGAWHo-tBc",
    "outputId": "a2cad631-5c0b-45a4-d46e-af0a23df404b"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>species</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.9</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   sepal_length     species\n",
       "0           7.0  versicolor\n",
       "1           6.4  versicolor\n",
       "2           6.9  versicolor\n",
       "3           5.5  versicolor\n",
       "4           6.5  versicolor"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_data = sel_data.reset_index(drop=True) # Drop means that we reset the index (now we start from 0 and not from 50!)\n",
    "sel_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
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    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1120,
     "status": "ok",
     "timestamp": 1589716959618,
     "user": {
      "displayName": "Daniele Loiacono",
      "photoUrl": "",
      "userId": "03206643914008722225"
     },
     "user_tz": -120
    },
    "id": "auLEucQy-tBf",
    "outputId": "35a3b30f-4831-4c7a-f8dc-cb894a15c6ea"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width     species\n",
       "0           7.0          3.2           4.7          1.4  versicolor\n",
       "1           6.4          3.2           4.5          1.5  versicolor\n",
       "2           6.9          3.1           4.9          1.5  versicolor\n",
       "3           5.5          2.3           4.0          1.3  versicolor\n",
       "4           6.5          2.8           4.6          1.5  versicolor"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_data = data[data.species == 'versicolor'] #select all the rows where species is versicolor\n",
    "sel_data = sel_data.reset_index(drop=True)\n",
    "sel_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "u8WGr6Ja-tBh"
   },
   "source": [
    "### Adding computed columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1402,
     "status": "ok",
     "timestamp": 1589716959903,
     "user": {
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      "photoUrl": "",
      "userId": "03206643914008722225"
     },
     "user_tz": -120
    },
    "id": "k7cRv1NG-tBi",
    "outputId": "0ea8ece1-7986-4abb-b9db-6b102778e16a"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>sl_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0.194444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species   sl_norm\n",
       "0           5.1          3.5           1.4          0.2  setosa  0.222222\n",
       "1           4.9          3.0           1.4          0.2  setosa  0.166667\n",
       "2           4.7          3.2           1.3          0.2  setosa  0.111111\n",
       "3           4.6          3.1           1.5          0.2  setosa  0.083333\n",
       "4           5.0          3.6           1.4          0.2  setosa  0.194444"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/iris.csv')\n",
    "sl_min = data['sepal_length'].min()\n",
    "sl_max = data['sepal_length'].max()\n",
    "# we add a sl_norm attribute to each line of the dataframe\n",
    "data['sl_norm'] = (data['sepal_length']-sl_min)/(sl_max-sl_min)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i-F2rtop-tBk"
   },
   "source": [
    "## Groupby\n",
    "\n",
    "It is also possible to aggregate rows with the same value of a categorical attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "colab_type": "code",
    "executionInfo": {
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    "id": "8fX17zwK-tBk",
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.006</td>\n",
       "      <td>3.418</td>\n",
       "      <td>1.464</td>\n",
       "      <td>0.244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td>5.936</td>\n",
       "      <td>2.770</td>\n",
       "      <td>4.260</td>\n",
       "      <td>1.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.588</td>\n",
       "      <td>2.974</td>\n",
       "      <td>5.552</td>\n",
       "      <td>2.026</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "            sepal_length  sepal_width  petal_length  petal_width\n",
       "species                                                         \n",
       "setosa             5.006        3.418         1.464        0.244\n",
       "versicolor         5.936        2.770         4.260        1.326\n",
       "virginica          6.588        2.974         5.552        2.026"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/iris.csv')\n",
    "data.groupby('species').mean() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "56udnYaP-tBn"
   },
   "source": [
    "It is possible to specify different aggregation function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
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    "colab_type": "code",
    "executionInfo": {
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    "id": "cy3vHncX-tBn",
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   "outputs": [
    {
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       "      <th>sepal_width</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td>7.0</td>\n",
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       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.5</td>\n",
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      "text/plain": [
       "            sepal_length  sepal_width  petal_length  petal_width\n",
       "species                                                         \n",
       "setosa               5.8          4.4           1.9          0.6\n",
       "versicolor           7.0          3.4           5.1          1.8\n",
       "virginica            7.9          3.8           6.9          2.5"
      ]
     },
     "execution_count": 20,
     "metadata": {
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   ],
   "source": [
    "data.groupby('species').max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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    "colab_type": "code",
    "executionInfo": {
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    },
    "id": "rOC9mLuY-tBp",
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    {
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       "      <th>setosa</th>\n",
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       "      <td>50</td>\n",
       "      <td>50</td>\n",
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       "      <th>versicolor</th>\n",
       "      <td>50</td>\n",
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       "      <td>50</td>\n",
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       "      <th>virginica</th>\n",
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      "text/plain": [
       "            sepal_length  sepal_width  petal_length  petal_width\n",
       "species                                                         \n",
       "setosa                50           50            50           50\n",
       "versicolor            50           50            50           50\n",
       "virginica             50           50            50           50"
      ]
     },
     "execution_count": 21,
     "metadata": {
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   ],
   "source": [
    "data.groupby('species').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MWwtffLN-tBr"
   },
   "source": [
    "In the result, the attribute used to group become the new index of the DataFrame.\n",
    "If this is not the goal, it can be avioded:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
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    "colab_type": "code",
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    "id": "SwQg9Pzq-tBs",
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    {
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       "      species  sepal_length  sepal_width  petal_length  petal_width\n",
       "0      setosa         5.006        3.418         1.464        0.244\n",
       "1  versicolor         5.936        2.770         4.260        1.326\n",
       "2   virginica         6.588        2.974         5.552        2.026"
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   "source": [
    "data.groupby('species',as_index=False).mean()"
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  }
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