Data Analysis With Python/Data Analysis - Pandas In-Depth Review (Day 8).ipynb

{
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      "source": [
        "<a href=\"https://colab.research.google.com/github/harshaljanjani/everything-ml/blob/main/Data%20Analysis%20With%20Python/Data%20Analysis%20-%20Pandas%20In-Depth%20Review%20(Day%208).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "DJcYhJox4RsO"
      },
      "source": [
        "# **Pandas: Data Science Python Package**\n",
        "\n",
        "### 1) **Pandas:** Series\n",
        "### 2) **Pandas:** Dataframes\n",
        "### 3) **Pandas:** Reading CSV And Basic Plotting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "ss3daXU04KLp"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7drLzuOT5UpY"
      },
      "source": [
        "# **Pandas Series**\n",
        "**Example Dataset Used:** `\"The Group of Seven\"` is a political forum formed by Canada, France, Germany, Italy, Japan, the United Kingdom and the United States. Analyze the population, and for that, use a `pandas.Series` object."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sheb42om4wd_",
        "outputId": "481f90d5-652c-4244-8433-35a71055d1e7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0     35.467\n",
            "1     63.951\n",
            "2     80.940\n",
            "3     60.665\n",
            "4    127.061\n",
            "5     64.511\n",
            "6    318.523\n",
            "Name: G7 Population In Millions, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Population Of The Group Of Seven In Millions\n",
        "g7_pop = pd.Series([35.467, 63.951, 80.940, 60.665, 127.061, 64.511, 318.523])\n",
        "g7_pop.name = \"G7 Population In Millions\"\n",
        "print(g7_pop)\n",
        "# Series - Ordered And Indexed Sequence Of Elements. Underlying Data Structure -> NumPy Array"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "N4mDhcaa6l1g",
        "outputId": "fe1b6df1-2a8a-4e6f-dcf5-0a33c385db99"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'>\n",
            "RangeIndex(start=0, stop=7, step=1)\n"
          ]
        }
      ],
      "source": [
        "print(type(g7_pop.values))\n",
        "print(g7_pop.index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CnSeKYdy64D3",
        "outputId": "5ef92c65-2723-4674-f9ac-23ea9f4cb7a4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Canada             35.467\n",
            "France             63.951\n",
            "Germany            80.940\n",
            "Italy              60.665\n",
            "Japan             127.061\n",
            "United Kingdom     64.511\n",
            "United States     318.523\n",
            "Name: G7 Population In Millions, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Customized Indexes In A Pandas Series (g7_population.index)\n",
        "g7_pop.index = [\n",
        "    'Canada',\n",
        "    'France',\n",
        "    'Germany',\n",
        "    'Italy',\n",
        "    'Japan',\n",
        "    'United Kingdom',\n",
        "    'United States',\n",
        "]\n",
        "print(g7_pop)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qPN1q9877JQZ",
        "outputId": "f6404090-8c82-4f37-f14a-51d9d8b69c80"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "Canada             35.467\n",
              "France             63.951\n",
              "Germany            80.940\n",
              "Italy              60.665\n",
              "Japan             127.061\n",
              "United Kingdom     64.511\n",
              "United States     318.523\n",
              "Name: G7 Population in millions, dtype: float64"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create a Pandas Series From A Dictionary. Also Called \"Ordered Dictionaries\" (Dictionaries Before Python 3.7 Are Unordered)\n",
        "pd.Series({\n",
        "    'Canada': 35.467,\n",
        "    'France': 63.951,\n",
        "    'Germany': 80.94,\n",
        "    'Italy': 60.665,\n",
        "    'Japan': 127.061,\n",
        "    'United Kingdom': 64.511,\n",
        "    'United States': 318.523\n",
        "}, name='G7 Population in millions')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rrlgESBa7f2f"
      },
      "source": [
        "## **Indexing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LnJyq4rO7j1H",
        "outputId": "eba8b260-b90f-4b26-c374-b12bc4e58c4e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "35.467 127.061\n",
            "318.523 35.467\n",
            "Italy     60.665\n",
            "France    63.951\n",
            "Name: G7 Population In Millions, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Indexing works similarly to lists and dictionaries, you use the index of the element you're looking for:\n",
        "print(g7_pop['Canada'], g7_pop['Japan'])\n",
        "# Numeric positions can also be used, with the 'iloc' attribute:\n",
        "print(g7_pop.iloc[-1], g7_pop.iloc[0]) # (Locate By Sequential Position in the \"Ordered Dictionary\")\n",
        "# Multiple Elements Selection / Sequential Multi-Indexing\n",
        "print(g7_pop[['Italy', 'France']])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iVWQsetF8F3j",
        "outputId": "5c31b490-cb17-4a06-f8ed-53c8608299f2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Canada     35.467\n",
            "France     63.951\n",
            "Germany    80.940\n",
            "Italy      60.665\n",
            "Name: G7 Population In Millions, dtype: float64 \n",
            "\n",
            "Canada    35.467\n",
            "France    63.951\n",
            "Name: G7 Population In Millions, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print(g7_pop['Canada': 'Italy'], \"\\n\") # Upper Limit Is Also Included In The Slicing, Unlike Vanilla Python\n",
        "print(g7_pop.iloc[[0, 1]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dGiEMfmQ8eXd"
      },
      "source": [
        "## **Conditional Selection (Boolean Series)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NvYpKnmT8e_S",
        "outputId": "6089a3a5-3757-450a-c36d-53e75f620476"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Japan            127.061\n",
            "United States    318.523\n",
            "Name: G7 Population In Millions, dtype: float64 \n",
            "\n",
            "Germany           80.940\n",
            "Japan            127.061\n",
            "United States    318.523\n",
            "Name: G7 Population In Millions, dtype: float64 \n",
            "\n",
            "Canada             35467000.0\n",
            "France             63951000.0\n",
            "Germany            80940000.0\n",
            "Italy              60665000.0\n",
            "Japan             127061000.0\n",
            "United Kingdom     64511000.0\n",
            "United States     318523000.0\n",
            "Name: G7 Population In Millions, dtype: float64 \n",
            "\n",
            "107.30257142857144\n"
          ]
        }
      ],
      "source": [
        "print(g7_pop[g7_pop > g7_pop.mean()], \"\\n\")\n",
        "print(g7_pop[g7_pop > 70], \"\\n\")\n",
        "print(g7_pop * 1_000_000, \"\\n\")\n",
        "print(g7_pop.mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tk3M9RB8819T",
        "outputId": "48357d59-23ec-4744-b9a5-703f3e204a3f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "68.51866666666666 \n",
            "\n",
            "Canada            35.467\n",
            "Germany           80.940\n",
            "Japan            127.061\n",
            "United States    318.523\n",
            "Name: G7 Population In Millions, dtype: float64 \n",
            "\n",
            "Germany     80.940\n",
            "Japan      127.061\n",
            "Name: G7 Population In Millions, dtype: float64 \n",
            "\n",
            "France             63.951\n",
            "Germany            80.940\n",
            "Italy              60.665\n",
            "Japan             127.061\n",
            "United Kingdom     64.511\n",
            "United States     318.523\n",
            "Name: G7 Population In Millions, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print(g7_pop['France': 'Italy'].mean(), \"\\n\")\n",
        "print(g7_pop[(g7_pop > 80) | (g7_pop < 40)], \"\\n\")\n",
        "print(g7_pop[(g7_pop > 80) & (g7_pop < 200)], \"\\n\")\n",
        "\n",
        "print(g7_pop[(g7_pop > g7_pop.mean() - g7_pop.std() / 2) | (g7_pop > g7_pop.mean() + g7_pop.std() / 2)])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xae67CkL9TsY"
      },
      "source": [
        "## **Series Modification**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iq521l-T9Xa4",
        "outputId": "44390f14-d935-4685-a42a-e175e9bc61ad"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Canada             40.500\n",
            "France             99.990\n",
            "Germany            80.940\n",
            "Italy              99.990\n",
            "Japan             127.061\n",
            "United Kingdom     99.990\n",
            "United States     500.000\n",
            "Name: G7 Population In Millions, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "g7_pop[g7_pop < 70] = 99.99\n",
        "g7_pop.iloc[-1] = 500\n",
        "g7_pop['Canada'] = 40.5\n",
        "print(g7_pop)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qJ4iPMuZ_-aK"
      },
      "source": [
        "# **Pandas Dataframes**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "n56PcXFQAJHI"
      },
      "outputs": [],
      "source": [
        "# Usually Created From \".csv\" Files Or By Scraping Data From The Web\n",
        "# Pandas Dataframes -> Collection Of Pandas Series'\n",
        "df = pd.DataFrame({\n",
        "    'Population': [35.467, 63.951, 80.94 , 60.665, 127.061, 64.511, 318.523],\n",
        "    'GDP': [\n",
        "        1785387,\n",
        "        2833687,\n",
        "        3874437,\n",
        "        2167744,\n",
        "        4602367,\n",
        "        2950039,\n",
        "        17348075\n",
        "    ],\n",
        "    'Surface Area': [\n",
        "        9984670,\n",
        "        640679,\n",
        "        357114,\n",
        "        301336,\n",
        "        377930,\n",
        "        242495,\n",
        "        9525067\n",
        "    ],\n",
        "    'HDI': [\n",
        "        0.913,\n",
        "        0.888,\n",
        "        0.916,\n",
        "        0.873,\n",
        "        0.891,\n",
        "        0.907,\n",
        "        0.915\n",
        "    ],\n",
        "    'Continent': [\n",
        "        'America',\n",
        "        'Europe',\n",
        "        'Europe',\n",
        "        'Europe',\n",
        "        'Asia',\n",
        "        'Europe',\n",
        "        'America'\n",
        "    ]\n",
        "}, columns=['Population', 'GDP', 'Surface Area', 'HDI', 'Continent'])"
      ]
    },
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              "      <th></th>\n",
              "      <th>Population</th>\n",
              "      <th>GDP</th>\n",
              "      <th>Surface Area</th>\n",
              "      <th>HDI</th>\n",
              "      <th>Continent</th>\n",
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              "      <td>35.467</td>\n",
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              "      <td>0.913</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>63.951</td>\n",
              "      <td>2833687</td>\n",
              "      <td>640679</td>\n",
              "      <td>0.888</td>\n",
              "      <td>Europe</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>80.940</td>\n",
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              "      <td>0.916</td>\n",
              "      <td>Europe</td>\n",
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              "      <td>60.665</td>\n",
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              "      <td>64.511</td>\n",
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              "      <td>0.907</td>\n",
              "      <td>Europe</td>\n",
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              "    <tr>\n",
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              "      <td>318.523</td>\n",
              "      <td>17348075</td>\n",
              "      <td>9525067</td>\n",
              "      <td>0.915</td>\n",
              "      <td>America</td>\n",
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            "text/plain": [
              "   Population       GDP  Surface Area    HDI Continent\n",
              "0      35.467   1785387       9984670  0.913   America\n",
              "1      63.951   2833687        640679  0.888    Europe\n",
              "2      80.940   3874437        357114  0.916    Europe\n",
              "3      60.665   2167744        301336  0.873    Europe\n",
              "4     127.061   4602367        377930  0.891      Asia\n",
              "5      64.511   2950039        242495  0.907    Europe\n",
              "6     318.523  17348075       9525067  0.915   America"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0dy1N13SI5Gy",
        "outputId": "8a58f421-e5f3-470a-eb2a-224f72029540"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RangeIndex(start=0, stop=7, step=1) \n",
            "\n",
            "Index(['Population', 'GDP', 'Surface Area', 'HDI', 'Continent'], dtype='object') \n",
            "\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7 entries, 0 to 6\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   Population    7 non-null      float64\n",
            " 1   GDP           7 non-null      int64  \n",
            " 2   Surface Area  7 non-null      int64  \n",
            " 3   HDI           7 non-null      float64\n",
            " 4   Continent     7 non-null      object \n",
            "dtypes: float64(2), int64(2), object(1)\n",
            "memory usage: 408.0+ bytes\n",
            "None \n",
            "\n",
            "35\n",
            "(7, 5) \n",
            "\n",
            "       Population           GDP  Surface Area       HDI\n",
            "count    7.000000  7.000000e+00  7.000000e+00  7.000000\n",
            "mean   107.302571  5.080248e+06  3.061327e+06  0.900429\n",
            "std     97.249970  5.494020e+06  4.576187e+06  0.016592\n",
            "min     35.467000  1.785387e+06  2.424950e+05  0.873000\n",
            "25%     62.308000  2.500716e+06  3.292250e+05  0.889500\n",
            "50%     64.511000  2.950039e+06  3.779300e+05  0.907000\n",
            "75%    104.000500  4.238402e+06  5.082873e+06  0.914000\n",
            "max    318.523000  1.734808e+07  9.984670e+06  0.916000 \n",
            "\n",
            "Population      float64\n",
            "GDP               int64\n",
            "Surface Area      int64\n",
            "HDI             float64\n",
            "Continent        object\n",
            "dtype: object \n",
            "\n",
            "float64    2\n",
            "int64      2\n",
            "object     1\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Utility Functions And Useful Attributes\n",
        "print(df.index, \"\\n\")\n",
        "print(df.columns, \"\\n\")\n",
        "print(df.info(), \"\\n\")\n",
        "print(df.size)\n",
        "print(df.shape, \"\\n\")\n",
        "print(df.describe(), \"\\n\") # Statistical Analysis Method\n",
        "print(df.dtypes, \"\\n\")\n",
        "print(df.dtypes.value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kUawt4BGJ4QZ"
      },
      "source": [
        "## **Indexing, Selection And Slicing In Dataframes**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "TlSNesuDKBcx"
      },
      "outputs": [],
      "source": [
        "df.index = [\n",
        "    'Canada',\n",
        "    'France',\n",
        "    'Germany',\n",
        "    'Italy',\n",
        "    'Japan',\n",
        "    'United Kingdom',\n",
        "    'United States',\n",
        "]"
      ]
    },
    {
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      "execution_count": 18,
      "metadata": {
        "colab": {
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        },
        "id": "r1h3cpfjKOWx",
        "outputId": "6a82788b-16c3-4242-9f5e-806d6ee872c1"
      },
      "outputs": [
        {
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            "text/plain": [
              "                Population       GDP  Surface Area    HDI Continent\n",
              "Canada              35.467   1785387       9984670  0.913   America\n",
              "France              63.951   2833687        640679  0.888    Europe\n",
              "Germany             80.940   3874437        357114  0.916    Europe\n",
              "Italy               60.665   2167744        301336  0.873    Europe\n",
              "Japan              127.061   4602367        377930  0.891      Asia\n",
              "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
              "United States      318.523  17348075       9525067  0.915   America"
            ]
          },
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        "df"
      ]
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    {
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      "execution_count": 19,
      "metadata": {
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        },
        "id": "0liYpiSfKnjI",
        "outputId": "3cec7179-3351-4adf-c236-52cd01933875"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Population       35.467\n",
            "GDP             1785387\n",
            "Surface Area    9984670\n",
            "HDI               0.913\n",
            "Continent       America\n",
            "Name: Canada, dtype: object \n",
            "\n",
            "Population       318.523\n",
            "GDP             17348075\n",
            "Surface Area     9525067\n",
            "HDI                0.915\n",
            "Continent        America\n",
            "Name: United States, dtype: object \n",
            "\n",
            "Canada             35.467\n",
            "France             63.951\n",
            "Germany            80.940\n",
            "Italy              60.665\n",
            "Japan             127.061\n",
            "United Kingdom     64.511\n",
            "United States     318.523\n",
            "Name: Population, dtype: float64 \n",
            "\n",
            "         Population      GDP  Surface Area    HDI Continent\n",
            "France       63.951  2833687        640679  0.888    Europe\n",
            "Germany      80.940  3874437        357114  0.916    Europe\n",
            "Italy        60.665  2167744        301336  0.873    Europe \n",
            "\n",
            "France     63.951\n",
            "Germany    80.940\n",
            "Italy      60.665\n",
            "Name: Population, dtype: float64 \n",
            "\n",
            "               Population       GDP  Surface Area    HDI Continent\n",
            "Canada             35.467   1785387       9984670  0.913   America\n",
            "France             63.951   2833687        640679  0.888    Europe\n",
            "United States     318.523  17348075       9525067  0.915   America \n",
            "\n",
            "France     0.888\n",
            "Germany    0.916\n",
            "Name: HDI, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Return Value Of The Utilities .loc[] and .iloc[] -> Pandas Series (Index Of The Series Is Extracted As The \"Name\")\n",
        "# Such Operations Are Deemed \"Immutable\" -> Create New DataFrame, Not Modify Underlying DataFrame\n",
        "\n",
        "print(df.loc['Canada'], \"\\n\") # Select By Index -> df.loc[] (Horizontal)\n",
        "print(df.iloc[-1], \"\\n\") # Select By Sequential Position -> df.iloc[] (Horizontal)\n",
        "print(df['Population'], \"\\n\") # Give The Entire Column Data (Vertical)\n",
        "\n",
        "print(df.loc['France': 'Italy'], \"\\n\")\n",
        "print(df.loc['France': 'Italy', 'Population'], \"\\n\")\n",
        "print(df.iloc[[0, 1, -1]], \"\\n\") #Selects Sequential Positions 0, 1 and -1\n",
        "\n",
        "print(df.iloc[1:3, 3])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "_YBIgRWkMO_v",
        "outputId": "e6130f1d-e52c-48df-a6cf-31a46fbcbdb1"
      },
      "outputs": [
        {
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "         Population      GDP\n",
              "France       63.951  2833687\n",
              "Germany      80.940  3874437\n",
              "Italy        60.665  2167744"
            ]
          },
          "execution_count": 20,
          "metadata": {},
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        }
      ],
      "source": [
        "df.loc['France': 'Italy', ['Population', 'GDP']] # Selection Of Columns By Index/Name"
      ]
    },
    {
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      "execution_count": 21,
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        },
        "id": "xsRkAWEKMnVa",
        "outputId": "422f9e85-5300-484f-f18d-201bafbbda02"
      },
      "outputs": [
        {
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "         Population    HDI\n",
              "France       63.951  0.888\n",
              "Germany      80.940  0.916"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
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        "df.iloc[1:3, [0, 3]] # Selection Of Columns By Sequential Position"
      ]
    },
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      "cell_type": "code",
      "execution_count": 22,
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        },
        "id": "hNw9QzvtMuuT",
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        {
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              "      <th></th>\n",
              "      <th>GDP</th>\n",
              "      <th>Surface Area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>France</th>\n",
              "      <td>2833687</td>\n",
              "      <td>640679</td>\n",
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              "      <th>Germany</th>\n",
              "      <td>3874437</td>\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
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            "text/plain": [
              "             GDP  Surface Area\n",
              "France   2833687        640679\n",
              "Germany  3874437        357114"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.iloc[1:3, 1:3] # Upper Limit Is Included"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cFr6g-W9N3QY"
      },
      "source": [
        "## **Conditional Selection (Boolean Arrays)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
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        },
        "id": "04B-O4hyOQBG",
        "outputId": "b5ed1440-fab0-4170-8ed5-250112fe21bd"
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        {
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              "\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
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              "  "
            ],
            "text/plain": [
              "                Population       GDP  Surface Area    HDI Continent\n",
              "Canada              35.467   1785387       9984670  0.913   America\n",
              "France              63.951   2833687        640679  0.888    Europe\n",
              "Germany             80.940   3874437        357114  0.916    Europe\n",
              "Italy               60.665   2167744        301336  0.873    Europe\n",
              "Japan              127.061   4602367        377930  0.891      Asia\n",
              "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
              "United States      318.523  17348075       9525067  0.915   America"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7OtAzx1VOaia",
        "outputId": "3e892f6a-ee30-4556-ef15-24ff91cdddec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Canada            False\n",
            "France            False\n",
            "Germany            True\n",
            "Italy             False\n",
            "Japan              True\n",
            "United Kingdom    False\n",
            "United States      True\n",
            "Name: Population, dtype: bool \n",
            "\n",
            "               Population       GDP\n",
            "Germany            80.940   3874437\n",
            "Japan             127.061   4602367\n",
            "United States     318.523  17348075\n"
          ]
        }
      ],
      "source": [
        "print(df['Population'] > 70, \"\\n\") # Mask Series\n",
        "print(df.loc[df['Population'] > 70, [\"Population\", \"GDP\"]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kRpMfimiPD9N",
        "outputId": "52313c02-e466-4945-9876-1f1bbde5bdf6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                Population       GDP  Surface Area    HDI Continent\n",
            "France              63.951   2833687        640679  0.888    Europe\n",
            "Germany             80.940   3874437        357114  0.916    Europe\n",
            "Italy               60.665   2167744        301336  0.873    Europe\n",
            "Japan              127.061   4602367        377930  0.891      Asia\n",
            "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
            "United States      318.523  17348075       9525067  0.915   America \n",
            "\n",
            "                Population       GDP  Surface Area    HDI Continent\n",
            "France              63.951   2833687        640679  0.888    Europe\n",
            "Germany             80.940   3874437        357114  0.916    Europe\n",
            "Italy               60.665   2167744        301336  0.873    Europe\n",
            "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
            "United States      318.523  17348075       9525067  0.915   America \n",
            "\n",
            "                Population       GDP  Surface Area    HDI Continent\n",
            "France              63.951   2833687        640679  0.888    Europe\n",
            "Germany             80.940   3874437        357114  0.916    Europe\n",
            "Japan              127.061   4602367        377930  0.891      Asia\n",
            "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
            "United States      318.523  17348075       9525067  0.915   America\n"
          ]
        }
      ],
      "source": [
        "# Delete Existing Entries By Index Name/Value\n",
        "print(df.drop('Canada'), \"\\n\") # Return Value Is The Modified Dataframe Without The Deleted Row/Record\n",
        "print(df.drop(['Canada', 'Japan']), \"\\n\")\n",
        "# Method-2\n",
        "print(df.drop(['Italy', 'Canada'], axis=0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pxFFRxnCPlxF",
        "outputId": "026b2c10-0a30-4c96-e986-ea330645a10b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                     GDP  Surface Area Continent\n",
            "Canada           1785387       9984670   America\n",
            "France           2833687        640679    Europe\n",
            "Germany          3874437        357114    Europe\n",
            "Italy            2167744        301336    Europe\n",
            "Japan            4602367        377930      Asia\n",
            "United Kingdom   2950039        242495    Europe\n",
            "United States   17348075       9525067   America \n",
            "\n",
            "                     GDP  Surface Area Continent\n",
            "Canada           1785387       9984670   America\n",
            "France           2833687        640679    Europe\n",
            "Germany          3874437        357114    Europe\n",
            "Italy            2167744        301336    Europe\n",
            "Japan            4602367        377930      Asia\n",
            "United Kingdom   2950039        242495    Europe\n",
            "United States   17348075       9525067   America\n"
          ]
        }
      ],
      "source": [
        "# Delete Attributes/Columns By Column Name\n",
        "print(df.drop(columns=['Population', 'HDI']), \"\\n\")\n",
        "print(df.drop(['Population', 'HDI'], axis=1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "6lXHnVwTPpjG",
        "outputId": "32af82e3-1398-445d-b04a-5e9a9947cb77"
      },
      "outputs": [
        {
          "data": {
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                     GDP  Surface Area Continent\n",
              "Canada           1785387       9984670   America\n",
              "France           2833687        640679    Europe\n",
              "Germany          3874437        357114    Europe\n",
              "Italy            2167744        301336    Europe\n",
              "Japan            4602367        377930      Asia\n",
              "United Kingdom   2950039        242495    Europe\n",
              "United States   17348075       9525067   America"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Method-3: Delete Attributes/Columns By Column Name\n",
        "df.drop(['Population', 'HDI'], axis='columns')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "JJ6Xq3s5QIy_",
        "outputId": "1727fb38-4d1c-4b49-a242-72901639df54"
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        {
          "data": {
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              "      <th></th>\n",
              "      <th>Population</th>\n",
              "      <th>GDP</th>\n",
              "      <th>Surface Area</th>\n",
              "      <th>HDI</th>\n",
              "      <th>Continent</th>\n",
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              "\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-673c8641-8a68-45ba-b472-f5474d6f5d05');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                Population       GDP  Surface Area    HDI Continent\n",
              "France              63.951   2833687        640679  0.888    Europe\n",
              "Italy               60.665   2167744        301336  0.873    Europe\n",
              "Japan              127.061   4602367        377930  0.891      Asia\n",
              "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
              "United States      318.523  17348075       9525067  0.915   America"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Method-3: Delete Existing Entries By Index Name/Value\n",
        "df.drop(['Canada', 'Germany'], axis='rows')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OrVV3HyjQYYo"
      },
      "source": [
        "All these drop methods return a new DataFrame. If it is to be modified  `\"in place\"`, you can use the `inplace` attribute of the `.drop()` function/method.\n",
        "\n",
        "Operations with Pandas Series work at a `column level`, `broadcasting down the rows`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "G5FVbPH8Q0uE",
        "outputId": "8a31fa98-d17b-4aff-ce27-fcce0eada40d"
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        {
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              "\n",
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              "      <div>\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Population</th>\n",
              "      <th>GDP</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>Canada</th>\n",
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              "    <tr>\n",
              "      <th>Germany</th>\n",
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              "      <td>38744.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Italy</th>\n",
              "      <td>0.60665</td>\n",
              "      <td>21677.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Japan</th>\n",
              "      <td>1.27061</td>\n",
              "      <td>46023.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United Kingdom</th>\n",
              "      <td>0.64511</td>\n",
              "      <td>29500.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United States</th>\n",
              "      <td>3.18523</td>\n",
              "      <td>173480.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-48025d36-731d-4328-aa07-4b88dbb27db6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-48025d36-731d-4328-aa07-4b88dbb27db6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                Population        GDP\n",
              "Canada             0.35467   17853.87\n",
              "France             0.63951   28336.87\n",
              "Germany            0.80940   38744.37\n",
              "Italy              0.60665   21677.44\n",
              "Japan              1.27061   46023.67\n",
              "United Kingdom     0.64511   29500.39\n",
              "United States      3.18523  173480.75"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[['Population', 'GDP']] / 100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QhQy3b9KRGLU",
        "outputId": "d338e8fd-064e-4166-c177-584a38e6c8d8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "GDP   -1000000.0\n",
              "HDI         -0.3\n",
              "dtype: float64"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Broadcasting Operations Between Series And Dataframes\n",
        "crisis = pd.Series([-1_000_000, -0.3], index=['GDP', 'HDI'])\n",
        "crisis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "kInhC6sqRJZM",
        "outputId": "77921371-c44b-484a-b23f-0dc30c9ca0cc"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>GDP</th>\n",
              "      <th>HDI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Canada</th>\n",
              "      <td>785387.0</td>\n",
              "      <td>0.613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>France</th>\n",
              "      <td>1833687.0</td>\n",
              "      <td>0.588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Germany</th>\n",
              "      <td>2874437.0</td>\n",
              "      <td>0.616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Italy</th>\n",
              "      <td>1167744.0</td>\n",
              "      <td>0.573</td>\n",
              "    </tr>\n",
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              "      <th>Japan</th>\n",
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              "      <td>0.607</td>\n",
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              "      <th>United States</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "                       GDP    HDI\n",
              "Canada            785387.0  0.613\n",
              "France           1833687.0  0.588\n",
              "Germany          2874437.0  0.616\n",
              "Italy            1167744.0  0.573\n",
              "Japan            3602367.0  0.591\n",
              "United Kingdom   1950039.0  0.607\n",
              "United States   16348075.0  0.615"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[['GDP', 'HDI']] + crisis # Operations With Pandas Series Work At A Column-Level, Broadcasting Down The Rows"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q7OmtDCSSYEl"
      },
      "source": [
        "## **Modifying Dataframes**\n",
        "\n",
        "## 1) Adding A New Column To A Dataframe\n",
        "## 2) Replacing Values Per Column\n",
        "## 3) Renaming Columns\n",
        "## 4) Dropping Columns\n",
        "## 5) Adding Rows/Entries\n",
        "## 6) More Radical Index Changes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IDo29fASScs9",
        "outputId": "03fef72d-8445-4b73-a7a6-8a9f0f299e32"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "France      French\n",
              "Germany     German\n",
              "Italy      Italian\n",
              "Name: Language, dtype: object"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Task: Add Column 'Languages' To The Existing Dataframe, With Values Assigned For France, Germany And Italy\n",
        "langs = pd.Series(\n",
        "    ['French', 'German', 'Italian'],\n",
        "    index=['France', 'Germany', 'Italy'],\n",
        "    name='Language'\n",
        ")\n",
        "langs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
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        },
        "id": "iobS8UjvS7pM",
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      },
      "outputs": [
        {
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d6eb2932-358d-4164-b01a-18aca63b4cba');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                Population       GDP  Surface Area    HDI Continent Language\n",
              "Canada              35.467   1785387       9984670  0.913   America      NaN\n",
              "France              63.951   2833687        640679  0.888    Europe   French\n",
              "Germany             80.940   3874437        357114  0.916    Europe   German\n",
              "Italy               60.665   2167744        301336  0.873    Europe  Italian\n",
              "Japan              127.061   4602367        377930  0.891      Asia      NaN\n",
              "United Kingdom      64.511   2950039        242495  0.907    Europe      NaN\n",
              "United States      318.523  17348075       9525067  0.915   America      NaN"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Language'] = langs\n",
        "df # Unassigned Countries Default to NaN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "vpY0WhYCTMqn",
        "outputId": "d451964b-9909-4255-c9ba-e6050645d49f"
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              "                Population       GDP  Surface Area    HDI Continent Language\n",
              "Canada              35.467   1785387       9984670  0.913   America  English\n",
              "France              63.951   2833687        640679  0.888    Europe  English\n",
              "Germany             80.940   3874437        357114  0.916    Europe  English\n",
              "Italy               60.665   2167744        301336  0.873    Europe  English\n",
              "Japan              127.061   4602367        377930  0.891      Asia  English\n",
              "United Kingdom      64.511   2950039        242495  0.907    Europe  English\n",
              "United States      318.523  17348075       9525067  0.915   America  English"
            ]
          },
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          "metadata": {},
          "output_type": "execute_result"
        }
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      "source": [
        "df['Language'] = 'English'\n",
        "df"
      ]
    },
    {
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        "id": "W4UcTefMTc7M",
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            "text/plain": [
              "         Population       GDP  Surface Area  Human Development Index  \\\n",
              "Canada       35.467   1785387       9984670                    0.913   \n",
              "France       63.951   2833687        640679                    0.888   \n",
              "Germany      80.940   3874437        357114                    0.916   \n",
              "Italy        60.665   2167744        301336                    0.873   \n",
              "Japan       127.061   4602367        377930                    0.891   \n",
              "UK           64.511   2950039        242495                    0.907   \n",
              "USA         318.523  17348075       9525067                    0.915   \n",
              "\n",
              "        Continent Language  \n",
              "Canada    America  English  \n",
              "France     Europe  English  \n",
              "Germany    Europe  English  \n",
              "Italy      Europe  English  \n",
              "Japan        Asia  English  \n",
              "UK         Europe  English  \n",
              "USA       America  English  "
            ]
          },
          "execution_count": 35,
          "metadata": {},
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      "source": [
        "df.rename(\n",
        "    columns={\n",
        "        'HDI': 'Human Development Index', # Present\n",
        "        'Annual Popcorn Consumption': 'APC' # Absent\n",
        "    }, \n",
        "    index={\n",
        "        'United States': 'USA', # Present\n",
        "        'United Kingdom': 'UK', # Present\n",
        "        'Argentina': 'AR' # Absent\n",
        "    }\n",
        ") # All Indexes/Columns That Are Not Present, Would Not Be Added/Modified"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
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        "id": "FhWLo7KoT7nY",
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      },
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        {
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              "      <td>Europe</td>\n",
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              "      <td>0.915</td>\n",
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            "text/plain": [
              "                Population       GDP  Surface Area    HDI Continent Language\n",
              "CANADA              35.467   1785387       9984670  0.913   America  English\n",
              "FRANCE              63.951   2833687        640679  0.888    Europe  English\n",
              "GERMANY             80.940   3874437        357114  0.916    Europe  English\n",
              "ITALY               60.665   2167744        301336  0.873    Europe  English\n",
              "JAPAN              127.061   4602367        377930  0.891      Asia  English\n",
              "UNITED KINGDOM      64.511   2950039        242495  0.907    Europe  English\n",
              "UNITED STATES      318.523  17348075       9525067  0.915   America  English"
            ]
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        "df.rename(index=str.upper)"
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    {
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              "                Population       GDP  Surface Area    HDI Continent Language\n",
              "canada              35.467   1785387       9984670  0.913   America  English\n",
              "france              63.951   2833687        640679  0.888    Europe  English\n",
              "germany             80.940   3874437        357114  0.916    Europe  English\n",
              "italy               60.665   2167744        301336  0.873    Europe  English\n",
              "japan              127.061   4602367        377930  0.891      Asia  English\n",
              "united kingdom      64.511   2950039        242495  0.907    Europe  English\n",
              "united states      318.523  17348075       9525067  0.915   America  English"
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        "df.rename(index = lambda x: x.lower())"
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        "id": "f9NhXiqkUEwR"
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        "df.drop(columns='Language', inplace=True)"
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        "id": "_wYIDG6cUNIA",
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              "  "
            ],
            "text/plain": [
              "                Population       GDP  Surface Area    HDI Continent\n",
              "Canada              35.467   1785387       9984670  0.913   America\n",
              "France              63.951   2833687        640679  0.888    Europe\n",
              "Germany             80.940   3874437        357114  0.916    Europe\n",
              "Italy               60.665   2167744        301336  0.873    Europe\n",
              "Japan              127.061   4602367        377930  0.891      Asia\n",
              "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
              "United States      318.523  17348075       9525067  0.915   America"
            ]
          },
          "execution_count": 39,
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        }
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            "text/plain": [
              "                Population         GDP  Surface Area    HDI Continent\n",
              "Canada              35.467   1785387.0     9984670.0  0.913   America\n",
              "France              63.951   2833687.0      640679.0  0.888    Europe\n",
              "Germany             80.940   3874437.0      357114.0  0.916    Europe\n",
              "Italy               60.665   2167744.0      301336.0  0.873    Europe\n",
              "Japan              127.061   4602367.0      377930.0  0.891      Asia\n",
              "United Kingdom      64.511   2950039.0      242495.0  0.907    Europe\n",
              "United States      318.523  17348075.0     9525067.0  0.915   America\n",
              "China                3.000         5.0           NaN    NaN       NaN"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.append(pd.Series({\n",
        "    'Population': 3,\n",
        "    'GDP': 5\n",
        "}, name='China')) # All Unassigned Columns Default To NaN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vKUXdgGzUnp0"
      },
      "source": [
        "To `directly set` the new index and values to the existing DataFrame:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
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        },
        "id": "dZkwdLztUyrU",
        "outputId": "25caa7a5-db81-4065-ab44-8d34544f545e"
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        {
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              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  Population         GDP  Surface Area    HDI Continent\n",
              "Canada          3.546700e+01   1785387.0     9984670.0  0.913   America\n",
              "France          6.395100e+01   2833687.0      640679.0  0.888    Europe\n",
              "Germany         8.094000e+01   3874437.0      357114.0  0.916    Europe\n",
              "Italy           6.066500e+01   2167744.0      301336.0  0.873    Europe\n",
              "Japan           1.270610e+02   4602367.0      377930.0  0.891      Asia\n",
              "United Kingdom  6.451100e+01   2950039.0      242495.0  0.907    Europe\n",
              "United States   3.185230e+02  17348075.0     9525067.0  0.915   America\n",
              "China           1.400000e+09         NaN           NaN    NaN      Asia"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.loc['China'] = pd.Series({'Population': 1_400_000_000, 'Continent': 'Asia'})\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
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        "id": "33tFsYeoVGl7",
        "outputId": "f27b738c-4a75-42f6-df0b-0ec5bec70435"
      },
      "outputs": [
        {
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            "text/plain": [
              "                Population         GDP  Surface Area    HDI Continent\n",
              "Canada              35.467   1785387.0     9984670.0  0.913   America\n",
              "France              63.951   2833687.0      640679.0  0.888    Europe\n",
              "Germany             80.940   3874437.0      357114.0  0.916    Europe\n",
              "Italy               60.665   2167744.0      301336.0  0.873    Europe\n",
              "Japan              127.061   4602367.0      377930.0  0.891      Asia\n",
              "United Kingdom      64.511   2950039.0      242495.0  0.907    Europe\n",
              "United States      318.523  17348075.0     9525067.0  0.915   America"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.drop('China', inplace=True)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
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        {
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              "      <td>Europe</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
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              "      <td>17348075.0</td>\n",
              "      <td>9525067.0</td>\n",
              "      <td>0.915</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "            index  Population         GDP  Surface Area    HDI Continent\n",
              "0          Canada      35.467   1785387.0     9984670.0  0.913   America\n",
              "1          France      63.951   2833687.0      640679.0  0.888    Europe\n",
              "2         Germany      80.940   3874437.0      357114.0  0.916    Europe\n",
              "3           Italy      60.665   2167744.0      301336.0  0.873    Europe\n",
              "4           Japan     127.061   4602367.0      377930.0  0.891      Asia\n",
              "5  United Kingdom      64.511   2950039.0      242495.0  0.907    Europe\n",
              "6   United States     318.523  17348075.0     9525067.0  0.915   America"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# More Radical Index Changes -> .reset_index(), .set_index() -> Return Value Is The Modified Dataframe\n",
        "df.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "BWjnwgfDVaTV",
        "outputId": "05696d8d-f019-41c5-8149-381464393b94"
      },
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        {
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              "                   GDP  Surface Area    HDI Continent\n",
              "Population                                           \n",
              "35.467       1785387.0     9984670.0  0.913   America\n",
              "63.951       2833687.0      640679.0  0.888    Europe\n",
              "80.940       3874437.0      357114.0  0.916    Europe\n",
              "60.665       2167744.0      301336.0  0.873    Europe\n",
              "127.061      4602367.0      377930.0  0.891      Asia\n",
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              "318.523     17348075.0     9525067.0  0.915   America"
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              "                Population         GDP  Surface Area    HDI Continent\n",
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              "France              63.951   2833687.0      640679.0  0.888    Europe\n",
              "Germany             80.940   3874437.0      357114.0  0.916    Europe\n",
              "Italy               60.665   2167744.0      301336.0  0.873    Europe\n",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z4mnXZGAV8rg"
      },
      "source": [
        "## **Creating New Columns From Existing Columns**\n",
        "\n",
        "Calculating the `\"GDP Per Capita\"` as part of the `Countries` analysis, which is given by the formula `GDP/Population`."
      ]
    },
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-570cd557-d514-47b5-847c-9a1cb0a07b9f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-570cd557-d514-47b5-847c-9a1cb0a07b9f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                Population         GDP  Surface Area    HDI Continent  \\\n",
              "Canada              35.467   1785387.0     9984670.0  0.913   America   \n",
              "France              63.951   2833687.0      640679.0  0.888    Europe   \n",
              "Germany             80.940   3874437.0      357114.0  0.916    Europe   \n",
              "Italy               60.665   2167744.0      301336.0  0.873    Europe   \n",
              "Japan              127.061   4602367.0      377930.0  0.891      Asia   \n",
              "United Kingdom      64.511   2950039.0      242495.0  0.907    Europe   \n",
              "United States      318.523  17348075.0     9525067.0  0.915   America   \n",
              "\n",
              "                GDP Per Capita  \n",
              "Canada            50339.385908  \n",
              "France            44310.284437  \n",
              "Germany           47868.013343  \n",
              "Italy             35733.025633  \n",
              "Japan             36221.712406  \n",
              "United Kingdom    45729.239975  \n",
              "United States     54464.120330  "
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['GDP Per Capita'] = df['GDP'] / df['Population']\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4GbYEhRfWqSN"
      },
      "source": [
        "## **Statistical Information About The DataFrame**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "614NZAZrWvqF",
        "outputId": "ffb0db2a-a3d4-44a5-fe9b-f8cf2318606d"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-4bd950ab-bf77-4f81-b262-28c9d9f653fe\">\n",
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              "      <th></th>\n",
              "      <th>Population</th>\n",
              "      <th>GDP</th>\n",
              "      <th>Surface Area</th>\n",
              "      <th>HDI</th>\n",
              "      <th>Continent</th>\n",
              "      <th>GDP Per Capita</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Canada</th>\n",
              "      <td>35.467</td>\n",
              "      <td>1785387.0</td>\n",
              "      <td>9984670.0</td>\n",
              "      <td>0.913</td>\n",
              "      <td>America</td>\n",
              "      <td>50339.385908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>France</th>\n",
              "      <td>63.951</td>\n",
              "      <td>2833687.0</td>\n",
              "      <td>640679.0</td>\n",
              "      <td>0.888</td>\n",
              "      <td>Europe</td>\n",
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              "      <td>0.916</td>\n",
              "      <td>Europe</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Italy</th>\n",
              "      <td>60.665</td>\n",
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              "      <td>0.873</td>\n",
              "      <td>Europe</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Japan</th>\n",
              "      <td>127.061</td>\n",
              "      <td>4602367.0</td>\n",
              "      <td>377930.0</td>\n",
              "      <td>0.891</td>\n",
              "      <td>Asia</td>\n",
              "      <td>36221.712406</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4bd950ab-bf77-4f81-b262-28c9d9f653fe')\"\n",
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              "  </svg>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         Population        GDP  Surface Area    HDI Continent  GDP Per Capita\n",
              "Canada       35.467  1785387.0     9984670.0  0.913   America    50339.385908\n",
              "France       63.951  2833687.0      640679.0  0.888    Europe    44310.284437\n",
              "Germany      80.940  3874437.0      357114.0  0.916    Europe    47868.013343\n",
              "Italy        60.665  2167744.0      301336.0  0.873    Europe    35733.025633\n",
              "Japan       127.061  4602367.0      377930.0  0.891      Asia    36221.712406"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head() # First 5 Entries Of The DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "4X1LpG40W0mN",
        "outputId": "791f87a5-049d-4116-e364-5c8bed1fc3ff"
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      "outputs": [
        {
          "data": {
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Population</th>\n",
              "      <th>GDP</th>\n",
              "      <th>Surface Area</th>\n",
              "      <th>HDI</th>\n",
              "      <th>GDP Per Capita</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000e+00</td>\n",
              "      <td>7.000000e+00</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>107.302571</td>\n",
              "      <td>5.080248e+06</td>\n",
              "      <td>3.061327e+06</td>\n",
              "      <td>0.900429</td>\n",
              "      <td>44952.254576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>97.249970</td>\n",
              "      <td>5.494020e+06</td>\n",
              "      <td>4.576187e+06</td>\n",
              "      <td>0.016592</td>\n",
              "      <td>6954.983875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>35.467000</td>\n",
              "      <td>1.785387e+06</td>\n",
              "      <td>2.424950e+05</td>\n",
              "      <td>0.873000</td>\n",
              "      <td>35733.025633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>62.308000</td>\n",
              "      <td>2.500716e+06</td>\n",
              "      <td>3.292250e+05</td>\n",
              "      <td>0.889500</td>\n",
              "      <td>40265.998421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>64.511000</td>\n",
              "      <td>2.950039e+06</td>\n",
              "      <td>3.779300e+05</td>\n",
              "      <td>0.907000</td>\n",
              "      <td>45729.239975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>104.000500</td>\n",
              "      <td>4.238402e+06</td>\n",
              "      <td>5.082873e+06</td>\n",
              "      <td>0.914000</td>\n",
              "      <td>49103.699626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>318.523000</td>\n",
              "      <td>1.734808e+07</td>\n",
              "      <td>9.984670e+06</td>\n",
              "      <td>0.916000</td>\n",
              "      <td>54464.120330</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-54349ecf-5e48-4916-b5be-c2dab8e400d9');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       Population           GDP  Surface Area       HDI  GDP Per Capita\n",
              "count    7.000000  7.000000e+00  7.000000e+00  7.000000        7.000000\n",
              "mean   107.302571  5.080248e+06  3.061327e+06  0.900429    44952.254576\n",
              "std     97.249970  5.494020e+06  4.576187e+06  0.016592     6954.983875\n",
              "min     35.467000  1.785387e+06  2.424950e+05  0.873000    35733.025633\n",
              "25%     62.308000  2.500716e+06  3.292250e+05  0.889500    40265.998421\n",
              "50%     64.511000  2.950039e+06  3.779300e+05  0.907000    45729.239975\n",
              "75%    104.000500  4.238402e+06  5.082873e+06  0.914000    49103.699626\n",
              "max    318.523000  1.734808e+07  9.984670e+06  0.916000    54464.120330"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe() # Statistical Summary Of The DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZCksI4azXACU",
        "outputId": "df5ff72f-17a9-45d2-a974-a6ddcbcdc234"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "35.467 318.523\n",
            "751.118\n",
            "107.30257142857144\n",
            "107.30257142857144\n",
            "97.24996987121581\n",
            "64.511\n"
          ]
        }
      ],
      "source": [
        "# Columnar Analysis Of Data - Population \n",
        "population = df['Population']\n",
        "print(population.min(), population.max()) # In Millions\n",
        "print(population.sum())\n",
        "print(population.sum() / len(population)) # Mean Calculated Manually\n",
        "print(population.mean()) # Mean Using The .mean() Function\n",
        "print(population.std()) # Standard Deviation\n",
        "print(population.median()) # Median Of The Population Column/Attribute"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dBUgJ83OXjYv",
        "outputId": "c5a79dbb-6725-483a-d928-cedcd5ee8a55"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "count      7.000000\n",
              "mean     107.302571\n",
              "std       97.249970\n",
              "min       35.467000\n",
              "25%       62.308000\n",
              "50%       64.511000\n",
              "75%      104.000500\n",
              "max      318.523000\n",
              "Name: Population, dtype: float64"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "population.describe() # Summary Statistics Of Population Attribute"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U02iGN9YXpNB",
        "outputId": "52ea5703-5c09-4771-b5ad-175795d21fdd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "62.308 \n",
            "\n",
            "0.2     61.3222\n",
            "0.4     64.1750\n",
            "0.6     74.3684\n",
            "0.8    117.8368\n",
            "1.0    318.5230\n",
            "Name: Population, dtype: float64\n",
            "318.523\n"
          ]
        }
      ],
      "source": [
        "# Quantiles -> Splits The Data Into Equal Parts / Equal Divisions Of Data Points / Equally Sized Groups\n",
        "# Example: 50% Quantile => Median, Since 50% Of The Data Points Are Above It, And 50% Below.\n",
        "# 25% Quantile For Example => 25% Of The Data Points Are Less Than It\n",
        "# Self-Reminder: 0.25 Quantile Is Also Called 25th Percentile In Practice\n",
        "\n",
        "print(population.quantile(.25), \"\\n\")\n",
        "print(population.quantile([.2, .4, .6, .8, 1]))\n",
        "print(population.max())"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyOuLWKiw6OVJ/WIAJjyxl1r",
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.0 (tags/v3.9.0:9cf6752, Oct  5 2020, 15:34:40) [MSC v.1927 64 bit (AMD64)]"
    },
    "vscode": {
      "interpreter": {
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  "nbformat": 4,
  "nbformat_minor": 0
}