Notas_Pandas.ipynb

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    "colab": {
      "name": "Notas Pandas.ipynb",
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      "name": "python3",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mailaucq/Data-Science1/blob/master/Notas_Pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "LmeAUlXKI5Mh",
        "colab_type": "text"
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      "cell_type": "markdown",
      "source": [
        "#Escalas\n",
        "\n",
        "Hay cuatro tipos de escalas:\n",
        "\n",
        "\n",
        "1.   Ratio scale: las unidades estan igualmente espaciadas, las operaciones matematicas son validas, e.g height and weight\n",
        "2.   Interval scale: igualmente espaciadas, \n",
        "3.   Ordinal scale: el orden es importante pero no estan igualmente espaciadas\n",
        "4.   Nominal scale: datos categoricos sin algun orden\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "Qt6hlwaCI3Rk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OUtTFV0iUR7a",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Manejar escala nominal con dataframes."
      ]
    },
    {
      "metadata": {
        "id": "FNVAWJKZTs4q",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame([\"A+\", \"A\", \"A-\", \"B+\", \"B\", \"B-\", \"C+\", \"C\", \"C-\", \"D+\", \"D\"], index= [\"excelent\", \"excelent\", \"excelent\", \"good\", \"good\", \"good\", \"ok\", \"ok\", \"ok\", \"poor\", \"poor\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "e4654401-19d3-4181-d8f9-c5e06bda780d"
      },
      "cell_type": "code",
      "source": [
        "df.rename(columns={0: \"Grades\"}, inplace=True)\n",
        "df"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Grades</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>excelent</th>\n",
              "      <td>A+</td>\n",
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              "    <tr>\n",
              "      <th>excelent</th>\n",
              "      <td>A</td>\n",
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              "    <tr>\n",
              "      <th>excelent</th>\n",
              "      <td>A-</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>good</th>\n",
              "      <td>B+</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>good</th>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>good</th>\n",
              "      <td>B-</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ok</th>\n",
              "      <td>C+</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ok</th>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ok</th>\n",
              "      <td>C-</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>poor</th>\n",
              "      <td>D+</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>poor</th>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "         Grades\n",
              "excelent     A+\n",
              "excelent      A\n",
              "excelent     A-\n",
              "good         B+\n",
              "good          B\n",
              "good         B-\n",
              "ok           C+\n",
              "ok            C\n",
              "ok           C-\n",
              "poor         D+\n",
              "poor          D"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
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        },
        "outputId": "88aa2455-dbb0-473b-9644-6d6dbad6fc7e"
      },
      "cell_type": "code",
      "source": [
        "df[\"Grades\"].astype(\"category\").head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "excelent    A+\n",
              "excelent     A\n",
              "excelent    A-\n",
              "good        B+\n",
              "good         B\n",
              "Name: Grades, dtype: category\n",
              "Categories (11, object): [A, A+, A-, B, ..., C+, C-, D, D+]"
            ]
          },
          "metadata": {
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          "execution_count": 4
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        },
        "outputId": "16b0b054-9f93-4247-b5ec-870fbb80f645"
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      "cell_type": "code",
      "source": [
        "grades = df[\"Grades\"].astype(\"category\", categories= [\"D\", \"D+\", \"C-\", \"C\", \"C+\", \"B-\", \"B\", \"B+\", \"A-\", \"A\", \"A+\"], ordered=True)\n",
        "grades.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "excelent    A+\n",
              "excelent     A\n",
              "excelent    A-\n",
              "good        B+\n",
              "good         B\n",
              "Name: Grades, dtype: category\n",
              "Categories (11, object): [D < D+ < C- < C ... B+ < A- < A < A+]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "TO30RV21VIDm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "b61faf84-a1ca-4ad1-ddc0-d2723a822998"
      },
      "cell_type": "code",
      "source": [
        "grades > 'C'"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "excelent     True\n",
              "excelent     True\n",
              "excelent     True\n",
              "good         True\n",
              "good         True\n",
              "good         True\n",
              "ok           True\n",
              "ok          False\n",
              "ok          False\n",
              "poor        False\n",
              "poor        False\n",
              "Name: Grades, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "3mYJWXfYVM3Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "outputId": "47d3b244-71be-4d23-9546-cb61ed2f173e"
      },
      "cell_type": "code",
      "source": [
        "s = pd.Series(['Low', 'Low', 'High', 'Medium', 'Low', 'High', 'Low'])\n",
        "s.astype('category', categories=['Low', 'Medium', 'High'], ordered=True)\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       Low\n",
              "1       Low\n",
              "2      High\n",
              "3    Medium\n",
              "4       Low\n",
              "5      High\n",
              "6       Low\n",
              "dtype: category\n",
              "Categories (3, object): [Low < Medium < High]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "tUabY4cEYenv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Dividir los datos en bins"
      ]
    },
    {
      "metadata": {
        "id": "r-FCbG0_WXqI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "30d1b884-0553-4716-ef4e-b4bff34aacfc"
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      "cell_type": "code",
      "source": [
        "s = pd.Series([168, 180, 174, 190, 170, 185, 179, 181, 175, 169, 182, 177, 180, 171])\n",
        "\n",
        "\n",
        "pd.cut(s, 3)\n",
        "\n",
        "# You can also add labels for the sizes [Small < Medium < Large].\n",
        "pd.cut(s, 3, labels=['Small', 'Medium', 'Large'])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      Small\n",
              "1     Medium\n",
              "2      Small\n",
              "3      Large\n",
              "4      Small\n",
              "5      Large\n",
              "6     Medium\n",
              "7     Medium\n",
              "8      Small\n",
              "9      Small\n",
              "10    Medium\n",
              "11    Medium\n",
              "12    Medium\n",
              "13     Small\n",
              "dtype: category\n",
              "Categories (3, object): [Small < Medium < Large]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "HosMDSqVamB4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Pivot table\n",
        "Resume los datos en un dataframe dado una propuesta en particular."
      ]
    },
    {
      "metadata": {
        "id": "L2JVfuVNYbsc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "6e129cc5-fb8a-4506-e9b1-6481a9826469"
      },
      "cell_type": "code",
      "source": [
        "s = pd.Series([2012,2012,2012,2013,2013,2013])\n",
        "s2 = pd.Series([\"MITSUBISHI\",\"MITSUBISHI\", \"NISSAN\", \"FORD\", \"MITSUBISHI\", \"NISSAN\"])\n",
        "s3 = pd.Series([49, 61, 80,107, 69, 80])\n",
        "df = pd.DataFrame(data={\"YEAR\": s, \"Make\":s2, \"(kW)\":s3})\n",
        "df.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>(kW)</th>\n",
              "      <th>Make</th>\n",
              "      <th>YEAR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>49</td>\n",
              "      <td>MITSUBISHI</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>61</td>\n",
              "      <td>MITSUBISHI</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>80</td>\n",
              "      <td>NISSAN</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>107</td>\n",
              "      <td>FORD</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>69</td>\n",
              "      <td>MITSUBISHI</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   (kW)        Make  YEAR\n",
              "0    49  MITSUBISHI  2012\n",
              "1    61  MITSUBISHI  2012\n",
              "2    80      NISSAN  2012\n",
              "3   107        FORD  2013\n",
              "4    69  MITSUBISHI  2013"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "kU_s0nY9dNh7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yBOBNySocu0f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "cdaaf1a0-ded9-4e0b-e970-1d16142ec372"
      },
      "cell_type": "code",
      "source": [
        "df.pivot_table(values=\"(kW)\", index=\"YEAR\", columns=\"Make\", aggfunc=np.mean)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Make</th>\n",
              "      <th>FORD</th>\n",
              "      <th>MITSUBISHI</th>\n",
              "      <th>NISSAN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YEAR</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>NaN</td>\n",
              "      <td>55.0</td>\n",
              "      <td>80.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>107.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>80.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Make   FORD  MITSUBISHI  NISSAN\n",
              "YEAR                           \n",
              "2012    NaN        55.0    80.0\n",
              "2013  107.0        69.0    80.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "id": "IRHbgvs0dLpR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "e8f57909-015c-401b-e7bd-8c7726813b43"
      },
      "cell_type": "code",
      "source": [
        "df.pivot_table(values=\"(kW)\", index=\"YEAR\", columns=\"Make\", aggfunc=[np.mean, np.max], margins=True)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"4\" halign=\"left\">mean</th>\n",
              "      <th colspan=\"4\" halign=\"left\">amax</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Make</th>\n",
              "      <th>FORD</th>\n",
              "      <th>MITSUBISHI</th>\n",
              "      <th>NISSAN</th>\n",
              "      <th>All</th>\n",
              "      <th>FORD</th>\n",
              "      <th>MITSUBISHI</th>\n",
              "      <th>NISSAN</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YEAR</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>NaN</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>80.0</td>\n",
              "      <td>63.333333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>61.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>107.0</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>80.0</td>\n",
              "      <td>85.333333</td>\n",
              "      <td>107.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>107.0</td>\n",
              "      <td>59.666667</td>\n",
              "      <td>80.0</td>\n",
              "      <td>74.333333</td>\n",
              "      <td>107.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>107</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       mean                                amax                       \n",
              "Make   FORD MITSUBISHI NISSAN        All   FORD MITSUBISHI NISSAN  All\n",
              "YEAR                                                                  \n",
              "2012    NaN  55.000000   80.0  63.333333    NaN       61.0   80.0   80\n",
              "2013  107.0  69.000000   80.0  85.333333  107.0       69.0   80.0  107\n",
              "All   107.0  59.666667   80.0  74.333333  107.0       69.0   80.0  107"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "id": "uuhZQx4B5ngd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Date functionality"
      ]
    },
    {
      "metadata": {
        "id": "yeiwDj8Z45rg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hDc3ddeb5w9g",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Timestamp"
      ]
    },
    {
      "metadata": {
        "id": "p57ytvoU5vrd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "932d5a3e-c8fa-4821-f9c2-45abb0d6b645"
      },
      "cell_type": "code",
      "source": [
        "pd.Timestamp(\"9/1/2016 10:05AM\")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2016-09-01 10:05:00')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "id": "461ooDyI6ARF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Period"
      ]
    },
    {
      "metadata": {
        "id": "tBYcMk1759UB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7c10f81a-d8ba-4a81-f427-879fef520c23"
      },
      "cell_type": "code",
      "source": [
        "pd.Period(\"1/2016\")"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Period('2016-01', 'M')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "metadata": {
        "id": "grGYTkKT6FcN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d05bf616-8066-4e5a-9f20-035466ac6ba3"
      },
      "cell_type": "code",
      "source": [
        "pd.Period(\"3/5/2016\")"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Period('2016-03-05', 'D')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "R24caTrf6QNU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##DatetimeIndex"
      ]
    },
    {
      "metadata": {
        "id": "WnoeTKnI6Oh4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "4cb758bb-581e-4650-9b8b-11b333996258"
      },
      "cell_type": "code",
      "source": [
        "t1 = pd.Series(list(\"abc\"), [pd.Timestamp(\"2016-09-01\"), pd.Timestamp(\"2016-09-02\"), pd.Timestamp(\"2017-09-03\")])\n",
        "t1"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2016-09-01    a\n",
              "2016-09-02    b\n",
              "2017-09-03    c\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "metadata": {
        "id": "3USu-8pL6q-c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "560ae29d-7a32-4753-96c3-5df5fac31f37"
      },
      "cell_type": "code",
      "source": [
        "type(t1.index)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.indexes.datetimes.DatetimeIndex"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "metadata": {
        "id": "42dTEjoo7YQl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##PeriodIndex"
      ]
    },
    {
      "metadata": {
        "id": "JlMu4hP_7WUx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "1c17ef89-6854-467d-b07e-553de301ba8c"
      },
      "cell_type": "code",
      "source": [
        "t2 = pd.Series(list(\"def\"), [pd.Period(\"2016-09\"), pd.Period(\"2016-10\"), pd.Period(\"2016-11\")])\n",
        "t2"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2016-09    d\n",
              "2016-10    e\n",
              "2016-11    f\n",
              "Freq: M, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "id": "gBFo-m9U7xlt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "09b62bfb-bb51-433b-e732-b898f772bfe0"
      },
      "cell_type": "code",
      "source": [
        "type(t2.index)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.indexes.period.PeriodIndex"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "metadata": {
        "id": "KMUxjOHY7-SS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Converting to Datetime"
      ]
    },
    {
      "metadata": {
        "id": "Dt-dJyIj7zqS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "a835d296-1ddc-4231-c4ec-3b8e572c5fac"
      },
      "cell_type": "code",
      "source": [
        "d1 = [\"2 June 2013\", \"Aug 29, 2014\", \"2015-06-26\", \"7/12/16\"]\n",
        "ts3 = pd.DataFrame(np.random.randint(10, 100, (4,2)), index=d1, columns=list(\"ab\"))\n",
        "ts3"
      ],
      "execution_count": 33,
      "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",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2 June 2013</th>\n",
              "      <td>89</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aug 29, 2014</th>\n",
              "      <td>64</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-06-26</th>\n",
              "      <td>94</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7/12/16</th>\n",
              "      <td>12</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               a   b\n",
              "2 June 2013   89  77\n",
              "Aug 29, 2014  64  55\n",
              "2015-06-26    94  66\n",
              "7/12/16       12  96"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "metadata": {
        "id": "a86Hp5zr8rYt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "0b441f37-40af-4107-e14e-c435236f1a95"
      },
      "cell_type": "code",
      "source": [
        "ts3.index = pd.to_datetime(ts3.index)\n",
        "ts3"
      ],
      "execution_count": 34,
      "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",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-06-02</th>\n",
              "      <td>89</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-08-29</th>\n",
              "      <td>64</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-06-26</th>\n",
              "      <td>94</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-07-12</th>\n",
              "      <td>12</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             a   b\n",
              "2013-06-02  89  77\n",
              "2014-08-29  64  55\n",
              "2015-06-26  94  66\n",
              "2016-07-12  12  96"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "metadata": {
        "id": "E177wVbA9KBs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f24c2abe-c0b4-4227-e3a3-9e7c7bda3aed"
      },
      "cell_type": "code",
      "source": [
        "pd.to_datetime(\"4.7.12\", dayfirst=True)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2012-07-04 00:00:00')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "id": "EDgYAevA9gRD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Timedeltas"
      ]
    },
    {
      "metadata": {
        "id": "9YU0Pz4u9dfb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e4fab240-460c-4db3-dd99-d2bac2bbaaff"
      },
      "cell_type": "code",
      "source": [
        "pd.Timestamp(\"9/3/2016\") - pd.Timestamp(\"9/1/2016\")"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timedelta('2 days 00:00:00')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "metadata": {
        "id": "8hjenWz59tEN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4174aaa8-11f4-4c4e-d8ae-38f4a3104d97"
      },
      "cell_type": "code",
      "source": [
        "pd.Timestamp(\"9/2/2016 8:10AM\") + pd.Timedelta(\"12D 3H\")"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2016-09-14 11:10:00')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "metadata": {
        "id": "cq3mD5As-isv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Working with Dates in a DataFrame"
      ]
    },
    {
      "metadata": {
        "id": "5iMZozvx92ud",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "a9fa4f4c-298f-4577-9a20-09199cf862a1"
      },
      "cell_type": "code",
      "source": [
        "dates = pd.date_range(\"03-04-2019\", periods = 9, freq = \"2W-SUN\")\n",
        "dates"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2019-03-10', '2019-03-24', '2019-04-07', '2019-04-21',\n",
              "               '2019-05-05', '2019-05-19', '2019-06-02', '2019-06-16',\n",
              "               '2019-06-30'],\n",
              "              dtype='datetime64[ns]', freq='2W-SUN')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "metadata": {
        "id": "MASqVEGe-waP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "5071c6bd-9f5e-47aa-d72e-ded55bb2a954"
      },
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({\"Count 1\": 100 + np.random.randint(-5, 10, 9).cumsum(), \"Count 2\": 120 + np.random.randint(-5, 10, 9)}, index=dates)\n",
        "df"
      ],
      "execution_count": 46,
      "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>Count 1</th>\n",
              "      <th>Count 2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-03-10</th>\n",
              "      <td>108</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-24</th>\n",
              "      <td>107</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-07</th>\n",
              "      <td>115</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-21</th>\n",
              "      <td>122</td>\n",
              "      <td>115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-05</th>\n",
              "      <td>122</td>\n",
              "      <td>117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-19</th>\n",
              "      <td>128</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-02</th>\n",
              "      <td>128</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-16</th>\n",
              "      <td>133</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-30</th>\n",
              "      <td>130</td>\n",
              "      <td>122</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Count 1  Count 2\n",
              "2019-03-10      108      125\n",
              "2019-03-24      107      121\n",
              "2019-04-07      115      125\n",
              "2019-04-21      122      115\n",
              "2019-05-05      122      117\n",
              "2019-05-19      128      125\n",
              "2019-06-02      128      120\n",
              "2019-06-16      133      121\n",
              "2019-06-30      130      122"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "metadata": {
        "id": "SCezFms8CpJ2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "e990667b-ead5-4fd3-a7ee-277d41cc7bcb"
      },
      "cell_type": "code",
      "source": [
        "df.index.weekday_name"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Sunday', 'Sunday', 'Sunday', 'Sunday', 'Sunday', 'Sunday', 'Sunday',\n",
              "       'Sunday', 'Sunday'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "metadata": {
        "id": "k64xNAfrIIZc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Difference between each row"
      ]
    },
    {
      "metadata": {
        "id": "yEfx3KDIFbMJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "41714f02-a831-4654-f13c-88d75c68e33a"
      },
      "cell_type": "code",
      "source": [
        "df.diff()"
      ],
      "execution_count": 48,
      "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>Count 1</th>\n",
              "      <th>Count 2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-03-10</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-24</th>\n",
              "      <td>-1.0</td>\n",
              "      <td>-4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-07</th>\n",
              "      <td>8.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-21</th>\n",
              "      <td>7.0</td>\n",
              "      <td>-10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-05</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-19</th>\n",
              "      <td>6.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-02</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-16</th>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-30</th>\n",
              "      <td>-3.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Count 1  Count 2\n",
              "2019-03-10      NaN      NaN\n",
              "2019-03-24     -1.0     -4.0\n",
              "2019-04-07      8.0      4.0\n",
              "2019-04-21      7.0    -10.0\n",
              "2019-05-05      0.0      2.0\n",
              "2019-05-19      6.0      8.0\n",
              "2019-06-02      0.0     -5.0\n",
              "2019-06-16      5.0      1.0\n",
              "2019-06-30     -3.0      1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "metadata": {
        "id": "MUBoWaqeIQv3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "9eee5301-af61-41e3-8139-daa5d9353332"
      },
      "cell_type": "code",
      "source": [
        "df.resample(\"M\").mean()"
      ],
      "execution_count": 54,
      "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>Count 1</th>\n",
              "      <th>Count 2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-03-31</th>\n",
              "      <td>107.500000</td>\n",
              "      <td>123.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-30</th>\n",
              "      <td>118.500000</td>\n",
              "      <td>120.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-31</th>\n",
              "      <td>125.000000</td>\n",
              "      <td>121.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-30</th>\n",
              "      <td>130.333333</td>\n",
              "      <td>121.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Count 1  Count 2\n",
              "2019-03-31  107.500000    123.0\n",
              "2019-04-30  118.500000    120.0\n",
              "2019-05-31  125.000000    121.0\n",
              "2019-06-30  130.333333    121.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "metadata": {
        "id": "ViN_en04IdDz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "11879245-1861-43f5-a682-50612f0c729e"
      },
      "cell_type": "code",
      "source": [
        "df[\"2019-05\":]"
      ],
      "execution_count": 55,
      "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>Count 1</th>\n",
              "      <th>Count 2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-05-05</th>\n",
              "      <td>122</td>\n",
              "      <td>117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-19</th>\n",
              "      <td>128</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-02</th>\n",
              "      <td>128</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-16</th>\n",
              "      <td>133</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-30</th>\n",
              "      <td>130</td>\n",
              "      <td>122</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Count 1  Count 2\n",
              "2019-05-05      122      117\n",
              "2019-05-19      128      125\n",
              "2019-06-02      128      120\n",
              "2019-06-16      133      121\n",
              "2019-06-30      130      122"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "metadata": {
        "id": "fITmcih3I5I_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "outputId": "da992685-af65-4e5a-bc9f-52df3b91d71b"
      },
      "cell_type": "code",
      "source": [
        "df.asfreq(\"W\", method=\"ffill\")"
      ],
      "execution_count": 56,
      "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>Count 1</th>\n",
              "      <th>Count 2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-03-10</th>\n",
              "      <td>108</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-17</th>\n",
              "      <td>108</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-24</th>\n",
              "      <td>107</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-31</th>\n",
              "      <td>107</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-07</th>\n",
              "      <td>115</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-14</th>\n",
              "      <td>115</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-21</th>\n",
              "      <td>122</td>\n",
              "      <td>115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-28</th>\n",
              "      <td>122</td>\n",
              "      <td>115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-05</th>\n",
              "      <td>122</td>\n",
              "      <td>117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-12</th>\n",
              "      <td>122</td>\n",
              "      <td>117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-19</th>\n",
              "      <td>128</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-26</th>\n",
              "      <td>128</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-02</th>\n",
              "      <td>128</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-09</th>\n",
              "      <td>128</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-16</th>\n",
              "      <td>133</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-23</th>\n",
              "      <td>133</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-30</th>\n",
              "      <td>130</td>\n",
              "      <td>122</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Count 1  Count 2\n",
              "2019-03-10      108      125\n",
              "2019-03-17      108      125\n",
              "2019-03-24      107      121\n",
              "2019-03-31      107      121\n",
              "2019-04-07      115      125\n",
              "2019-04-14      115      125\n",
              "2019-04-21      122      115\n",
              "2019-04-28      122      115\n",
              "2019-05-05      122      117\n",
              "2019-05-12      122      117\n",
              "2019-05-19      128      125\n",
              "2019-05-26      128      125\n",
              "2019-06-02      128      120\n",
              "2019-06-09      128      120\n",
              "2019-06-16      133      121\n",
              "2019-06-23      133      121\n",
              "2019-06-30      130      122"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "metadata": {
        "id": "fMR1EJdUKarg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Plotting in dataframe\n",
        "La función plot en dataframe nos ayuda a visualizar las series."
      ]
    },
    {
      "metadata": {
        "id": "VlTaM3AmJJwO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "a62ecd39-4efb-4888-f25b-7b5372de308c"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "df.plot()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f38166373c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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g//vaY5iqLMyd1pvYKJlJF0J4ht+Hel2aGvT6hqAPJT0pptVBv+dY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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "E4A0q6dTcHX2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Merge Data\n",
        "\n",
        "\n",
        "1.   outer\n",
        "2.   inner\n",
        "3.   right\n",
        "4.   left\n"
      ]
    },
    {
      "metadata": {
        "id": "QIquJSyGK2GS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "34968577-b8b1-4af4-ab29-e6e5b6fd650e"
      },
      "cell_type": "code",
      "source": [
        "staff_df = pd.DataFrame([{'Name': 'Kelly', 'Role': 'Director of HR'},\n",
        "                         {'Name': 'Sally', 'Role': 'Course liasion'},\n",
        "                         {'Name': 'James', 'Role': 'Grader'}])\n",
        "staff_df = staff_df.set_index('Name')\n",
        "student_df = pd.DataFrame([{'Name': 'James', 'School': 'Business'},\n",
        "                           {'Name': 'Mike', 'School': 'Law'},\n",
        "                           {'Name': 'Sally', 'School': 'Engineering'}])\n",
        "student_df = student_df.set_index('Name')\n",
        "print(staff_df.head())\n",
        "print()\n",
        "print(student_df.head())"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                 Role\n",
            "Name                 \n",
            "Kelly  Director of HR\n",
            "Sally  Course liasion\n",
            "James          Grader\n",
            "\n",
            "            School\n",
            "Name              \n",
            "James     Business\n",
            "Mike           Law\n",
            "Sally  Engineering\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "pBh48ayJagJF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "fbfe04d6-fab3-40d8-d416-dcaf82082b2a"
      },
      "cell_type": "code",
      "source": [
        "pd.merge(staff_df, student_df, how='outer', left_index=True, right_index=True)"
      ],
      "execution_count": 4,
      "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>Role</th>\n",
              "      <th>School</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>James</th>\n",
              "      <td>Grader</td>\n",
              "      <td>Business</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kelly</th>\n",
              "      <td>Director of HR</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mike</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Law</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sally</th>\n",
              "      <td>Course liasion</td>\n",
              "      <td>Engineering</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Role       School\n",
              "Name                              \n",
              "James          Grader     Business\n",
              "Kelly  Director of HR          NaN\n",
              "Mike              NaN          Law\n",
              "Sally  Course liasion  Engineering"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "k6mYuA6vgQLb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "ddca78bd-5d51-4e03-90e5-c5b4c2a11a9d"
      },
      "cell_type": "code",
      "source": [
        "pd.merge(staff_df, student_df, how='inner', left_index=True, right_index=True)"
      ],
      "execution_count": 5,
      "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>Role</th>\n",
              "      <th>School</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Sally</th>\n",
              "      <td>Course liasion</td>\n",
              "      <td>Engineering</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>James</th>\n",
              "      <td>Grader</td>\n",
              "      <td>Business</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Role       School\n",
              "Name                              \n",
              "Sally  Course liasion  Engineering\n",
              "James          Grader     Business"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "Q2vqxUcUgTE0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "129f329a-299a-4c47-fd46-bdca1ca2a32a"
      },
      "cell_type": "code",
      "source": [
        "pd.merge(staff_df, student_df, how='left', left_index=True, right_index=True)"
      ],
      "execution_count": 6,
      "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>Role</th>\n",
              "      <th>School</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Kelly</th>\n",
              "      <td>Director of HR</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sally</th>\n",
              "      <td>Course liasion</td>\n",
              "      <td>Engineering</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>James</th>\n",
              "      <td>Grader</td>\n",
              "      <td>Business</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Role       School\n",
              "Name                              \n",
              "Kelly  Director of HR          NaN\n",
              "Sally  Course liasion  Engineering\n",
              "James          Grader     Business"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "bbhidar-gW8V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "9c12545d-d030-4f7b-f440-243593349289"
      },
      "cell_type": "code",
      "source": [
        "pd.merge(staff_df, student_df, how='right', left_index=True, right_index=True)"
      ],
      "execution_count": 7,
      "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>Role</th>\n",
              "      <th>School</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>James</th>\n",
              "      <td>Grader</td>\n",
              "      <td>Business</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mike</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Law</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sally</th>\n",
              "      <td>Course liasion</td>\n",
              "      <td>Engineering</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Role       School\n",
              "Name                              \n",
              "James          Grader     Business\n",
              "Mike              NaN          Law\n",
              "Sally  Course liasion  Engineering"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "1XPV1v7Tnw7D",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Selecionar un conjunto de columnas: sumar, promediar por filas"
      ]
    },
    {
      "metadata": {
        "id": "BBrtr-cRgYqJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "student_df[\"grades\"] = np.random.randint(0, 100, len(student_df))\n",
        "student_df[\"grades.1\"] = np.random.randint(0, 100, len(student_df))\n",
        "student_df[\"grades.2\"] = np.random.randint(0, 100, len(student_df))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SbLo6gaCpSNN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "4523ad58-b5bb-49d2-f013-db9f0cfae3d9"
      },
      "cell_type": "code",
      "source": [
        "student_df"
      ],
      "execution_count": 17,
      "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>School</th>\n",
              "      <th>grades</th>\n",
              "      <th>grades.1</th>\n",
              "      <th>grades.2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>James</th>\n",
              "      <td>Business</td>\n",
              "      <td>67</td>\n",
              "      <td>51</td>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mike</th>\n",
              "      <td>Law</td>\n",
              "      <td>88</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sally</th>\n",
              "      <td>Engineering</td>\n",
              "      <td>49</td>\n",
              "      <td>57</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            School  grades  grades.1  grades.2\n",
              "Name                                          \n",
              "James     Business      67        51        90\n",
              "Mike           Law      88         1        44\n",
              "Sally  Engineering      49        57        17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "GsMgJEHYpYbW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "ab46dae6-be9b-4fca-ca89-d0349fcb5c00"
      },
      "cell_type": "code",
      "source": [
        "student_df[\"mean grades\"] = student_df[[\"grades\", \"grades.1\", \"grades.2\"]].mean(axis=1)\n",
        "student_df"
      ],
      "execution_count": 19,
      "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>School</th>\n",
              "      <th>grades</th>\n",
              "      <th>grades.1</th>\n",
              "      <th>grades.2</th>\n",
              "      <th>average grades</th>\n",
              "      <th>mean grades</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>James</th>\n",
              "      <td>Business</td>\n",
              "      <td>67</td>\n",
              "      <td>51</td>\n",
              "      <td>90</td>\n",
              "      <td>69.333333</td>\n",
              "      <td>69.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mike</th>\n",
              "      <td>Law</td>\n",
              "      <td>88</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>44.333333</td>\n",
              "      <td>44.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sally</th>\n",
              "      <td>Engineering</td>\n",
              "      <td>49</td>\n",
              "      <td>57</td>\n",
              "      <td>17</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>41.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            School  grades  grades.1  grades.2  average grades  mean grades\n",
              "Name                                                                       \n",
              "James     Business      67        51        90       69.333333    69.333333\n",
              "Mike           Law      88         1        44       44.333333    44.333333\n",
              "Sally  Engineering      49        57        17       41.000000    41.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "ejms7VxL1su8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Query"
      ]
    },
    {
      "metadata": {
        "id": "o7yi3R5oqdbq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "85e21423-c35e-4f06-c058-f3191271bd92"
      },
      "cell_type": "code",
      "source": [
        "student_df.query(\"grades in [67,49]\")"
      ],
      "execution_count": 22,
      "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>School</th>\n",
              "      <th>grades</th>\n",
              "      <th>grades.1</th>\n",
              "      <th>grades.2</th>\n",
              "      <th>average grades</th>\n",
              "      <th>mean grades</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>James</th>\n",
              "      <td>Business</td>\n",
              "      <td>67</td>\n",
              "      <td>51</td>\n",
              "      <td>90</td>\n",
              "      <td>69.333333</td>\n",
              "      <td>69.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sally</th>\n",
              "      <td>Engineering</td>\n",
              "      <td>49</td>\n",
              "      <td>57</td>\n",
              "      <td>17</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>41.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            School  grades  grades.1  grades.2  average grades  mean grades\n",
              "Name                                                                       \n",
              "James     Business      67        51        90       69.333333    69.333333\n",
              "Sally  Engineering      49        57        17       41.000000    41.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "id": "DjEuTuVm2ir9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "d722a1d7-2489-4dd2-dded-f503dac4b5de"
      },
      "cell_type": "code",
      "source": [
        "student_df[student_df[\"School\"].str.startswith(\"L\")]"
      ],
      "execution_count": 25,
      "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>School</th>\n",
              "      <th>grades</th>\n",
              "      <th>grades.1</th>\n",
              "      <th>grades.2</th>\n",
              "      <th>average grades</th>\n",
              "      <th>mean grades</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Mike</th>\n",
              "      <td>Law</td>\n",
              "      <td>88</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>44.333333</td>\n",
              "      <td>44.333333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     School  grades  grades.1  grades.2  average grades  mean grades\n",
              "Name                                                                \n",
              "Mike    Law      88         1        44       44.333333    44.333333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "metadata": {
        "id": "yiXZssE-V5fy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Ordenar por columna"
      ]
    },
    {
      "metadata": {
        "id": "jjk1gtdBWjHQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "451a2a68-c929-456a-cb3e-f17f6fbb3ab9"
      },
      "cell_type": "code",
      "source": [
        "student_df.sort_values(by=\"grades\", ascending=False)"
      ],
      "execution_count": 26,
      "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>School</th>\n",
              "      <th>grades</th>\n",
              "      <th>grades.1</th>\n",
              "      <th>grades.2</th>\n",
              "      <th>average grades</th>\n",
              "      <th>mean grades</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Mike</th>\n",
              "      <td>Law</td>\n",
              "      <td>88</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>44.333333</td>\n",
              "      <td>44.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>James</th>\n",
              "      <td>Business</td>\n",
              "      <td>67</td>\n",
              "      <td>51</td>\n",
              "      <td>90</td>\n",
              "      <td>69.333333</td>\n",
              "      <td>69.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sally</th>\n",
              "      <td>Engineering</td>\n",
              "      <td>49</td>\n",
              "      <td>57</td>\n",
              "      <td>17</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>41.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            School  grades  grades.1  grades.2  average grades  mean grades\n",
              "Name                                                                       \n",
              "Mike           Law      88         1        44       44.333333    44.333333\n",
              "James     Business      67        51        90       69.333333    69.333333\n",
              "Sally  Engineering      49        57        17       41.000000    41.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "iHEjTIaFXf4M",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Cambiar de índice"
      ]
    },
    {
      "metadata": {
        "id": "8FP8qpuGWr5w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "f92bec78-7040-4325-f740-20cdee798a75"
      },
      "cell_type": "code",
      "source": [
        "student_df.set_index(\"grades\")"
      ],
      "execution_count": 29,
      "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>School</th>\n",
              "      <th>grades.1</th>\n",
              "      <th>grades.2</th>\n",
              "      <th>average grades</th>\n",
              "      <th>mean grades</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>grades</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Business</td>\n",
              "      <td>51</td>\n",
              "      <td>90</td>\n",
              "      <td>69.333333</td>\n",
              "      <td>69.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>Law</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>44.333333</td>\n",
              "      <td>44.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Engineering</td>\n",
              "      <td>57</td>\n",
              "      <td>17</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>41.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             School  grades.1  grades.2  average grades  mean grades\n",
              "grades                                                              \n",
              "67         Business        51        90       69.333333    69.333333\n",
              "88              Law         1        44       44.333333    44.333333\n",
              "49      Engineering        57        17       41.000000    41.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "metadata": {
        "id": "9N6VGJiGXFZg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}