EX_10/ex_10_pandas_advancedIndexing.ipynb

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      "source": [
        "print('Hello, Advanced Pandas: Hierarchical Index & Cross-section!')\n",
        "import numpy as np\n",
        "import pandas as pd \n",
        "from numpy.random import randn\n",
        "np.random.seed(101)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hello, Advanced Pandas: Hierarchical Index & Cross-section!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X79WRrsUmNEb"
      },
      "source": [
        "## Pandas - MultiIndex & Advanced Index\n",
        "\n",
        "As a convenience, we can pass a list of arrays directly into a special method below to construct a MultiIndex automatically:"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "outside=['G1','G1','G1','G2','G2','G2']\n",
        "inside=[1,2,3,1,2,3]\n",
        "hier_index=list(zip(outside, inside))\n",
        "hier_index=pd.MultiIndex.from_tuples(hier_index)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GqzxroR-nmSn"
      },
      "source": [
        "df=pd.DataFrame(randn(6,2), hier_index, ['A', 'B'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "source": [
        "df\n"
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">G1</th>\n",
              "      <th>1</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">G2</th>\n",
              "      <th>1</th>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "             A         B\n",
              "G1 1  2.706850  0.628133\n",
              "   2  0.907969  0.503826\n",
              "   3  0.651118 -0.319318\n",
              "G2 1 -0.848077  0.605965\n",
              "   2 -2.018168  0.740122\n",
              "   3  0.528813 -0.589001"
            ]
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      "cell_type": "markdown",
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      "source": [
        "The reason that the MultiIndex matters is that it can allow you to do grouping, selection, and reshaping operations such as:\n",
        "##Calling Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O-7Qq5mgqYxK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "66028d1f-e73a-4cbe-f064-8d518e3c7f5b"
      },
      "source": [
        "# If we want everything that is under 'G1', type df.loc():\n",
        "# We will get a sub-set 'G1' of the DataFrame; check it out:\n",
        "df.loc['G1']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "          A         B\n",
              "1  2.706850  0.628133\n",
              "2  0.907969  0.503826\n",
              "3  0.651118 -0.319318"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yA8kbm_wq1Ym",
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          "height": 68
        },
        "outputId": "db28694f-b19e-41d2-bb68-50c3d90db041"
      },
      "source": [
        "# We can continue to indexing off this, going deeper...\n",
        "df.loc['G1'].loc[1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A    2.706850\n",
              "B    0.628133\n",
              "Name: 1, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oHfdBpYerocb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5a3788d9-9419-44ac-8d1d-c4b87ac4d74f"
      },
      "source": [
        "# ...and deeper, and deeper...\n",
        "# The basic idea we can from the outside index continue calling inside deeper!\n",
        "df.loc['G1'].loc[1][0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.706849839399938"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "skNd-k4ksoPT"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sWl6gMZCySP2"
      },
      "source": [
        "## Naming Columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SecBwWw2ygqu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3f9fe5e2-0928-4ffe-8971-f6f4f99e88cd"
      },
      "source": [
        "# Indexes do not have any names\n",
        "df.index.names"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FrozenList([None, None])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TOD1ttXLyJQs"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Odo0zN2Hy2hU"
      },
      "source": [
        "# But you can pass a list of names, for instance:\n",
        "df.index.names = ['Groups', 'Nums']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c7-7xleyzF5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "485149eb-a225-4f4a-8785-1715874b566a"
      },
      "source": [
        "# Now when we call it we have the outside label as 'Groups' and inside 'Nums'\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\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></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Groups</th>\n",
              "      <th>Nums</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">G1</th>\n",
              "      <th>1</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">G2</th>\n",
              "      <th>1</th>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "                    A         B\n",
              "Groups Nums                    \n",
              "G1     1     2.706850  0.628133\n",
              "       2     0.907969  0.503826\n",
              "       3     0.651118 -0.319318\n",
              "G2     1    -0.848077  0.605965\n",
              "       2    -2.018168  0.740122\n",
              "       3     0.528813 -0.589001"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZOfR9u8mzy5y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "52f2b33a-099c-4547-8741-dfcfd8ad9dea"
      },
      "source": [
        "  # Location and  grab the group 'G2', nuns 2, 'B' column like this:\n",
        "  df.loc['G2'].loc[2]['B']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7401220570561068"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QXxn1Wux06Ha"
      },
      "source": [
        "##Cross Section - Multi Level Index"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Na_FoXJ-0yeH"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cc0xTfZK1ASx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "b71a2569-301e-4acb-dd4b-d9e19a25eb2e"
      },
      "source": [
        "# Let's say we want to grab everything under 'Nums' = 1 with both groups;\n",
        "# What differentiates it from the loc method is that we can skip or go inside the multi-level index\n",
        "# This is to say: grab a cross-section where the level is equal 1 and level is equal 'Nums'\n",
        "df.xs(1, level='Nums')\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Groups</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>G1</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G2</th>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               A         B\n",
              "Groups                    \n",
              "G1      2.706850  0.628133\n",
              "G2     -0.848077  0.605965"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p8DoXxA32O2L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8e410aaa-38b3-4ccc-f47e-a3c86512043b"
      },
      "source": [
        "# TODO[THE LINK TO THE WEB PAGE GOES HERE!]\n",
        "print(\"Thank you for Reading this Post! See you in the next PySeries Episode o/\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Thank you for Reading this Post! See you in the next PySeries Episode o/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qVOTJeee3JDR"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}