limpieza.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# Limpieza de datos"
      ],
      "metadata": {
        "id": "fOOlgJNH2cpv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "La limpieza de datos implica observar más de cerca los problemas en los datos que ha seleccionado incluir en el análisis.\n",
        "\n",
        "### Problema de datos "
      ],
      "metadata": {
        "id": "7N8p5jpd5Inf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "TovbM7qJXFGF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos crear un base de datos (DataFrame):"
      ],
      "metadata": {
        "id": "sBPpvD3A-kY8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = {'Company': ['Ford', 'Ford', 'VW', 'BMW', 'Cooper', 'Cooper'], \n",
        "     'Stars' : [1, 2, np.nan, 2, 1, 1], \n",
        "     'Weight' : [2, 4, 2, 2, 3, None], \n",
        "     'Origin' : ['China', 'Mexico', 'Mexico', None, 'China', np.nan], \n",
        "     'Length': [40, 50, 30, np.nan, 45, pd.NaT]\n",
        "}"
      ],
      "metadata": {
        "id": "2GC7dWBwTh1W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df  = pd.DataFrame(data = x)\n",
        "df"
      ],
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        "id": "OFQ-JyePXD_g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
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        "outputId": "2de25f0a-0125-4831-d1f4-9b807e44b5fd"
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "  Company  Stars  Weight  Origin Length\n",
              "0    Ford    1.0     2.0   China     40\n",
              "1    Ford    2.0     4.0  Mexico     50\n",
              "2      VW    NaN     2.0  Mexico     30\n",
              "3     BMW    2.0     2.0    None    NaN\n",
              "4  Cooper    1.0     3.0   China     45\n",
              "5  Cooper    1.0     NaN     NaN    NaT"
            ],
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              "      <th>Company</th>\n",
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              "      <td>Mexico</td>\n",
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              "      <th>3</th>\n",
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              "      <th>4</th>\n",
              "      <td>Cooper</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>China</td>\n",
              "      <td>45</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Cooper</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Guardamos en un archivo CSV (coma separated value)"
      ],
      "metadata": {
        "id": "e9cdTkWX-xj5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('data.csv')"
      ],
      "metadata": {
        "id": "HznSD5BI-vcN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Alternativamente, podemos leer un conjunto de datos ya disponible:"
      ],
      "metadata": {
        "id": "ZKh13NpO-564"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/data.csv', index_col=0)\n",
        "df"
      ],
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Company  Stars  Weight  Origin  Length\n",
              "0    Ford    1.0     2.0   China    40.0\n",
              "1    Ford    2.0     4.0  Mexico    50.0\n",
              "2      VW    NaN     2.0  Mexico    30.0\n",
              "3     BMW    2.0     2.0     NaN     NaN\n",
              "4  Cooper    1.0     3.0   China    45.0\n",
              "5  Cooper    1.0     NaN     NaN     NaN"
            ],
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              "      <th>Stars</th>\n",
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              "      <th>Length</th>\n",
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              "      <td>Ford</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>50.0</td>\n",
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              "      <td>VW</td>\n",
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              "      <td>2.0</td>\n",
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              "      <td>30.0</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>1.0</td>\n",
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              "                                                     [key], {});\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para verificar, ¿falta algún dato?:"
      ],
      "metadata": {
        "id": "dKpA0GXt_OBn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().values.any()"
      ],
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        },
        "outputId": "55c3d399-e0af-42e0-960f-6dd2b711b2b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().any()"
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        "outputId": "31e10cba-3f98-458c-c9ad-675527191388"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Company    False\n",
              "Stars       True\n",
              "Weight      True\n",
              "Origin      True\n",
              "Length      True\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
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    },
    {
      "cell_type": "markdown",
      "source": [
        "alternativamente:"
      ],
      "metadata": {
        "id": "NNGpDgWP_QPj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().values.any()"
      ],
      "metadata": {
        "id": "Rhws9Sa-ajZB",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b63a47ca-2829-488e-c393-4796404f88ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().any()"
      ],
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        "id": "qAQve11na8aR",
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        },
        "outputId": "3399670f-32cd-44db-e5b2-baf7fb75bf2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Company    False\n",
              "Stars       True\n",
              "Weight      True\n",
              "Origin      True\n",
              "Length      True\n",
              "dtype: bool"
            ]
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          "metadata": {},
          "execution_count": 9
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      "source": [
        "df"
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              "  Company  Stars  Weight  Origin  Length\n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### Solucion 1: \n",
        "\n",
        "Descartar las observaciones con valores faltantes"
      ],
      "metadata": {
        "id": "jDBUTePP_i09"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(inplace = True)"
      ],
      "metadata": {
        "id": "emd31KUTbQkR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().values.any()"
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        "id": "bhVOp7OGbVCt",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd5826f4-f45c-4833-b498-eb280580c7d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
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        "df"
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          "height": 203
        },
        "outputId": "2d5bbda4-c61e-461b-a8c4-490e8804daaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Company  Stars  Weight  Origin  Length\n",
              "0    Ford    1.0     2.0   China    40.0\n",
              "1    Ford    2.0     4.0  Mexico    50.0\n",
              "4  Cooper    1.0     3.0   China    45.0"
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8b2d8fef-075b-4cd2-8eb5-308433eee7cf button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8b2d8fef-075b-4cd2-8eb5-308433eee7cf');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "El problema con esta estrategia es que, \n",
        ">> si falta algún dato en todo el conjunto de datos, la fila correspondiente se elimina."
      ],
      "metadata": {
        "id": "Lr1VwxjX_1XP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/data.csv', index_col=0)\n",
        "df.isna().any()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MCOKbvrdAy5f",
        "outputId": "07b191d1-48a1-4cff-b5f6-4f61b842d7bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Company    False\n",
              "Stars       True\n",
              "Weight      True\n",
              "Origin      True\n",
              "Length      True\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "ndf = df.copy()\n",
        "ndf"
      ],
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          "height": 295
        },
        "id": "CDBCh_e-A-9j",
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      "execution_count": null,
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        {
          "output_type": "execute_result",
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              "  Company  Stars  Weight  Origin  Length\n",
              "0    Ford    1.0     2.0   China    40.0\n",
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              "3     BMW    2.0     2.0     NaN     NaN\n",
              "4  Cooper    1.0     3.0   China    45.0\n",
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          },
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Suelte las columnas donde falta al menos un elemento."
      ],
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        "id": "yjYu-zzdCoDx"
      }
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    {
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        "ndf"
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        {
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              "  Company\n",
              "0    Ford\n",
              "1    Ford\n",
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        "ndf = df.copy()\n",
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              "  Company  Stars  Weight  Origin  Length\n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Drop the rows where all elements are missing."
      ],
      "metadata": {
        "id": "__egBqraC41S"
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    },
    {
      "cell_type": "code",
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        "ndf"
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      "execution_count": null,
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              "  Company  Stars  Weight  Origin  Length\n",
              "0    Ford    1.0     2.0   China    40.0\n",
              "1    Ford    2.0     4.0  Mexico    50.0\n",
              "2      VW    NaN     2.0  Mexico    30.0\n",
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              "      <td>Mexico</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BMW</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cooper</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>China</td>\n",
              "      <td>45.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Cooper</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Alternativamente: usamos Threshold. \n",
        "\n",
        "Mantenga solo las filas con al menos 2 valores que  **NO SEAN** `nan`\n"
      ],
      "metadata": {
        "id": "fO5YjZyVBESQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ndf = df.copy()\n",
        "ndf"
      ],
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        "id": "BkU6Ek10EHGJ",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Company  Stars  Weight  Origin  Length\n",
              "0    Ford    1.0     2.0   China    40.0\n",
              "1    Ford    2.0     4.0  Mexico    50.0\n",
              "2      VW    NaN     2.0  Mexico    30.0\n",
              "3     BMW    2.0     2.0     NaN     NaN\n",
              "4  Cooper    1.0     3.0   China    45.0\n",
              "5  Cooper    1.0     NaN     NaN     NaN"
            ],
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              "          const element = document.querySelector('#df-3b0f0a8c-205b-4216-bd57-83695fcabc83');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ndf.dropna(thresh=4, inplace = True) # In a row, it needs at least 4 nan values is needed, to maintain in df\n",
        "ndf # in case of column  add   axis=1 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "yEgUhF6WBC7g",
        "outputId": "5f4cba23-c77f-458a-e6c4-117c0dd9e688"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Company  Stars  Weight  Origin  Length\n",
              "0    Ford    1.0     2.0   China    40.0\n",
              "1    Ford    2.0     4.0  Mexico    50.0\n",
              "2      VW    NaN     2.0  Mexico    30.0\n",
              "4  Cooper    1.0     3.0   China    45.0"
            ],
            "text/html": [
              "\n",
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              "      <td>NaN</td>\n",
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              "      <td>30.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cooper</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>China</td>\n",
              "      <td>45.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7446b880-b279-464c-943d-9966e8236156')\"\n",
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              "      </button>\n",
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              "\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7446b880-b279-464c-943d-9966e8236156 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7446b880-b279-464c-943d-9966e8236156');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Defina en qué columnas buscar valores faltantes."
      ],
      "metadata": {
        "id": "A5Wn2-FWG7I-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ndf = df.copy()\n",
        "ndf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "D1FRfrxeG6Zn",
        "outputId": "0efde259-4faa-43a5-aee6-f7e390149a15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "  Company  Stars  Weight  Origin  Length\n",
              "0    Ford    1.0     2.0   China    40.0\n",
              "1    Ford    2.0     4.0  Mexico    50.0\n",
              "2      VW    NaN     2.0  Mexico    30.0\n",
              "3     BMW    2.0     2.0     NaN     NaN\n",
              "4  Cooper    1.0     3.0   China    45.0\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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    {
      "cell_type": "code",
      "source": [
        "ndf.dropna(thresh = 5,  #if there is not 5 nan values, the column will be eliminated \n",
        "           axis = 1, \n",
        "           inplace = True\n",
        "           ) \n",
        "ndf "
      ],
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        "id": "Z2CplmlWHHyf",
        "outputId": "bf87c3be-c3dc-49c1-971d-57c3b215d2e2"
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "  Company  Stars  Weight\n",
              "0    Ford    1.0     2.0\n",
              "1    Ford    2.0     4.0\n",
              "2      VW    NaN     2.0\n",
              "3     BMW    2.0     2.0\n",
              "4  Cooper    1.0     3.0\n",
              "5  Cooper    1.0     NaN"
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              "\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-742260fb-c51c-4636-ba01-e6bf7228ad18 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-742260fb-c51c-4636-ba01-e6bf7228ad18');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para saber mas: \n",
        "\n",
        "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html"
      ],
      "metadata": {
        "id": "l0zfi1RjAmfE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Solucion 2: \n",
        "\n"
      ],
      "metadata": {
        "id": "4yVrVyYdAeca"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ndf = df.copy()"
      ],
      "metadata": {
        "id": "DSygVPcaXUH7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wm = ndf.Weight.mean()\n",
        "wm"
      ],
      "metadata": {
        "id": "a1AtpG_-b4Pm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d39a007d-eb1a-42ab-ced6-d6faf00d8a90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.6"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ndf['Weight'].fillna(value = wm, \n",
        "                    inplace = True)\n",
        "ndf"
      ],
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        "id": "2-Tayd3McAuW",
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          "height": 295
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        "outputId": "4a43059a-8e4e-4824-9781-8d9a54fe40e9"
      },
      "execution_count": null,
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              "  Company  Stars  Weight  Origin  Length\n",
              "0    Ford    1.0     2.0   China    40.0\n",
              "1    Ford    2.0     4.0  Mexico    50.0\n",
              "2      VW    NaN     2.0  Mexico    30.0\n",
              "3     BMW    2.0     2.0     NaN     NaN\n",
              "4  Cooper    1.0     3.0   China    45.0\n",
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              "\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "\n",
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              "          const element = document.querySelector('#df-199174eb-d899-474c-8228-144a6fd379bf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "[Datos faltantes: dos grandes problemas con la imputación media](https://www.theanalysisfactor.com/mean-imputation/)"
      ],
      "metadata": {
        "id": "wDuqIKO7Q9OZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ndf['Length'].fillna(value = ndf.Length.median(), \n",
        "                    inplace = True)\n",
        "ndf"
      ],
      "metadata": {
        "id": "lValNi9Ycd-6",
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          "height": 295
        },
        "outputId": "bfb4d797-8263-45de-ca45-a0ad8725d34a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Company  Stars  Weight  Origin  Length\n",
              "0    Ford    1.0     2.0   China    40.0\n",
              "1    Ford    2.0     4.0  Mexico    50.0\n",
              "2      VW    NaN     2.0  Mexico    30.0\n",
              "3     BMW    2.0     2.0     NaN    42.5\n",
              "4  Cooper    1.0     3.0   China    45.0\n",
              "5  Cooper    1.0     2.6     NaN    42.5"
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mm  = ndf.Origin.mode()\n",
        "mm"
      ],
      "metadata": {
        "id": "EK3Mk2SMenth",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b3b3b47-84af-4879-e698-8f9eac57bfa8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     China\n",
              "1    Mexico\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mm[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        },
        "id": "FgssXUnwOf8s",
        "outputId": "5ae301e4-9979-4fe0-c40c-519d9d0d8410"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Mexico'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ndf['Origin'].fillna(value = mm[1], #'NoPais', \n",
        "                    inplace = True)\n",
        "ndf"
      ],
      "metadata": {
        "id": "wb6bCCyNeaBB",
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          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "3ee637e5-d042-4f9f-f1ca-6463869c1c22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Company  Stars  Weight  Origin  Length\n",
              "0    Ford    1.0     2.0   China    40.0\n",
              "1    Ford    2.0     4.0  Mexico    50.0\n",
              "2      VW    NaN     2.0  Mexico    30.0\n",
              "3     BMW    2.0     2.0  Mexico    42.5\n",
              "4  Cooper    1.0     3.0   China    45.0\n",
              "5  Cooper    1.0     2.6  Mexico    42.5"
            ],
            "text/html": [
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              "  </svg>\n",
              "      </button>\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-b60490de-ae02-4b6a-9a32-76924fed88c7');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ndf.isnull().values.any()"
      ],
      "metadata": {
        "id": "kGs1fmilewCT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45603930-0ca7-46e8-88e9-06ce63b93716"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ndf = df.copy()\n",
        "ndf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "sJl1S1AHXscC",
        "outputId": "ea65591f-57b3-482d-f1b6-87ed18f7cceb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  Company  Stars  Weight  Origin  Length\n",
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              "  Company  Stars  Weight  Origin  Length\n",
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              "4  Cooper    1.0     3.0   China    45.0\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fill values in each column with favorite strategy: "
      ],
      "metadata": {
        "id": "Kzg6taAKYjiH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ndf.Origin.mode()[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 169
        },
        "id": "Ufp1EYbeY-tc",
        "outputId": "804c761c-fc90-4661-ae22-8a51e965d558"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'China'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "favs = {'Origin': ndf.Origin.mode()[0], 'Length': ndf['Length'].mean()}"
      ],
      "metadata": {
        "id": "o45pXlnJYojU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ndf.Origin.fillna(ndf.Origin.mode()[0], inplace=True)\n",
        "ndf.Length.fillna(ndf.Length.mean(), inplace=True)\n",
        "ndf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "e6xF8jGdYIa7",
        "outputId": "ef9021f1-307d-4cb6-f37d-56d9537435d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Company  Stars  Weight  Origin  Length\n",
              "0    Ford    1.0     2.0   China   40.00\n",
              "1    Ford    2.0     4.0  Mexico   50.00\n",
              "2      VW    NaN     2.0  Mexico   30.00\n",
              "3     BMW    2.0     2.0   China   41.25\n",
              "4  Cooper    1.0     3.0   China   45.00\n",
              "5  Cooper    1.0     NaN   China   41.25"
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              "      <td>China</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>China</td>\n",
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              "      <td>Cooper</td>\n",
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              "      <td>NaN</td>\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "more on `fillna` method : https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.fillna.html\n",
        "\n",
        "More on Imputing strategies: https://www.theanalysisfactor.com/seven-ways-to-make-up-data-common-methods-to-imputing-missing-data/"
      ],
      "metadata": {
        "id": "Tzh2xKksPzQd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ¿Cuándo es una mediana mejor en comparación con la media?"
      ],
      "metadata": {
        "id": "SQ06PX_mUa4B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = {'Salary':  [28, 30, 30, 35, 37, 40, 400]\n",
        "}\n",
        "adf = pd.DataFrame(data)\n",
        "adf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "7dK-WUW1UlFv",
        "outputId": "74ed9d6c-6c2a-4ab3-f30e-22df5819d5ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Salary\n",
              "0      28\n",
              "1      30\n",
              "2      30\n",
              "3      35\n",
              "4      37\n",
              "5      40\n",
              "6     400"
            ],
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              "      <td>37</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>40</td>\n",
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              "      <th>6</th>\n",
              "      <td>400</td>\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "adf.describe()"
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      "metadata": {
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          "height": 291
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        "id": "IDw-HDT3U9fT",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Salary\n",
              "count    7.000000\n",
              "mean    85.714286\n",
              "std    138.653903\n",
              "min     28.000000\n",
              "25%     30.000000\n",
              "50%     35.000000\n",
              "75%     38.500000\n",
              "max    400.000000"
            ],
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              "      <td>7.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>85.714286</td>\n",
              "    </tr>\n",
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              "      <td>138.653903</td>\n",
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              "      <td>28.000000</td>\n",
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              "      <th>50%</th>\n",
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              "      <th>75%</th>\n",
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              "      <td>400.000000</td>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b4daaff-a29d-4ee3-9061-743dd26e912b')\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8b4daaff-a29d-4ee3-9061-743dd26e912b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8b4daaff-a29d-4ee3-9061-743dd26e912b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Para seleccionar las columnas de la base de datos, puede usar la siguiente codigos: \n",
        "\n"
      ],
      "metadata": {
        "id": "f_h9Q13FbAKV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/data.csv', index_col=0)\n",
        "ndf = df.copy()\n",
        "ndf"
      ],
      "metadata": {
        "id": "2hG-7VtCZH1r",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "334987ca-b2ce-4bb5-ed56-113fd2edf296"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Company  Stars  Weight  Origin  Length\n",
              "0    Ford    1.0     2.0   China    40.0\n",
              "1    Ford    2.0     4.0  Mexico    50.0\n",
              "2      VW    NaN     2.0  Mexico    30.0\n",
              "3     BMW    2.0     2.0     NaN     NaN\n",
              "4  Cooper    1.0     3.0   China    45.0\n",
              "5  Cooper    1.0     NaN     NaN     NaN"
            ],
            "text/html": [
              "\n",
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              "      <div>\n",
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              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Ford</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>Mexico</td>\n",
              "      <td>50.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>VW</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Mexico</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BMW</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cooper</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>China</td>\n",
              "      <td>45.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Cooper</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c4fc7adc-98ea-411e-a882-e5b1e86663ab')\"\n",
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              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c4fc7adc-98ea-411e-a882-e5b1e86663ab button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c4fc7adc-98ea-411e-a882-e5b1e86663ab');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "** Syntaxis ** de loc & iloc\n",
        "\n",
        "* loc : If you use, `loc`, use the `names`\n",
        "\n",
        "> df.`loc` [ row-start:row-end,  column-start:column_end]  \n",
        "\n",
        "\n",
        "\n",
        "* iloc : If you use, `loc`, use the `indices`\n",
        "\n",
        "> df.`iloc` [row-start:row-end, column-start:columnEnd]  "
      ],
      "metadata": {
        "id": "kBIJJ9HpZ1H0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ndf.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qj4lJSbub57y",
        "outputId": "b19ef46e-3c2f-4187-db0a-57b951e9db06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Company', 'Stars', 'Weight', 'Origin', 'Length'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ndf.columns.sort_values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ava0RdT1b9_m",
        "outputId": "f401e226-0a6d-4c5a-c999-71ac9a4ef68f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Company', 'Length', 'Origin', 'Stars', 'Weight'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ndf.loc[2:5 , 'Company':'Origin']  # rows 2 to 5, columns  'Company' to 'Origin'"
      ],
      "metadata": {
        "id": "L9nhEa2CZJkQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "outputId": "49f14b0a-d030-48a6-c362-559905e62cbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Company  Stars  Weight  Origin\n",
              "2      VW    NaN     2.0  Mexico\n",
              "3     BMW    2.0     2.0     NaN\n",
              "4  Cooper    1.0     3.0   China\n",
              "5  Cooper    1.0     NaN     NaN"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
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              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cf3f79ea-18da-4cf3-a558-5c13a969c194 button.colab-df-convert');\n",
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              "\n",
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              "          const element = document.querySelector('#df-cf3f79ea-18da-4cf3-a558-5c13a969c194');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "favs = ['Stars', 'Weight', 'Origin']"
      ],
      "metadata": {
        "id": "tJZjKFClbeGp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ndf.loc[2:5 , favs]"
      ],
      "metadata": {
        "id": "qMEO9uiKayHi",
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          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "outputId": "98aae247-0283-4c35-f0bf-637cf621b774"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "   Stars  Weight  Origin\n",
              "2    NaN     2.0  Mexico\n",
              "3    2.0     2.0     NaN\n",
              "4    1.0     3.0   China\n",
              "5    1.0     NaN     NaN"
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              "  "
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          },
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          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ndf.iloc[2:5, [1,2, 3]] # iloc  - so, indices"
      ],
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        "id": "CyW66Ig0a6xx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "b2c85bfc-2748-44bd-ace5-b95352012f01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Stars  Weight  Origin\n",
              "2    NaN     2.0  Mexico\n",
              "3    2.0     2.0     NaN\n",
              "4    1.0     3.0   China"
            ],
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              "  </style>\n",
              "\n",
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              "          const element = document.querySelector('#df-e9be1046-6bcc-4159-ab44-0f270d895ab8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ndf.columns"
      ],
      "metadata": {
        "id": "1SzWmEoFcM6t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8e1b5b9-a179-4c4c-89d5-0fecf1afa0f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Company', 'Stars', 'Weight', 'Origin', 'Length'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in ndf.columns:\n",
        "  print(i)"
      ],
      "metadata": {
        "id": "oub49H-2cw6r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f02935c4-046e-4d6e-db2d-7340eb667982"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Company\n",
            "Stars\n",
            "Weight\n",
            "Origin\n",
            "Length\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ndf.head(4)"
      ],
      "metadata": {
        "id": "u77v9QQBdU7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "outputId": "24f26007-7b50-4c1c-9094-99bead28811e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Company  Stars  Weight  Origin  Length\n",
              "0    Ford    1.0     2.0   China    40.0\n",
              "1    Ford    2.0     4.0  Mexico    50.0\n",
              "2      VW    NaN     2.0  Mexico    30.0\n",
              "3     BMW    2.0     2.0     NaN     NaN"
            ],
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              "\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ndf.Company.unique()"
      ],
      "metadata": {
        "id": "K2lytCc9dhb6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b96aa3a5-e935-48c3-992c-0bade1c1ca03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Ford', 'VW', 'BMW', 'Cooper'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(['Company', 'Origin']).size()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K0e8CVMBeTnF",
        "outputId": "0af01ba9-8846-4efd-eb97-6127ab62c565"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Company  Origin\n",
              "Cooper   China     1\n",
              "Ford     China     1\n",
              "         Mexico    1\n",
              "VW       Mexico    1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['Company', 'Origin']].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5UFyQ-AVe9Hl",
        "outputId": "83cd0b65-15a5-4d0b-9d3b-a26fe1ee84eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Company  Origin\n",
              "Cooper   China     1\n",
              "Ford     China     1\n",
              "         Mexico    1\n",
              "VW       Mexico    1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Eliminar columns / Cambiar nombre de las columns"
      ],
      "metadata": {
        "id": "8KAvVO6Edf7b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ndf"
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        "id": "4Nms3kwpdeYr",
        "outputId": "36c81a5f-b8a7-4097-b794-020cc0f3ca01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Company  Stars  Weight  Origin  Length\n",
              "0    Ford    1.0     2.0   China    40.0\n",
              "1    Ford    2.0     4.0  Mexico    50.0\n",
              "2      VW    NaN     2.0  Mexico    30.0\n",
              "3     BMW    2.0     2.0     NaN     NaN\n",
              "4  Cooper    1.0     3.0   China    45.0\n",
              "5  Cooper    1.0     NaN     NaN     NaN"
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              "  "
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    {
      "cell_type": "code",
      "source": [
        "ndf2 = ndf.drop(['Stars', 'Origin',], axis = 1)\n",
        "ndf2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
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        "id": "M4XTkEGodmUk",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Company  Weight  Length\n",
              "0    Ford     2.0    40.0\n",
              "1    Ford     4.0    50.0\n",
              "2      VW     2.0    30.0\n",
              "3     BMW     2.0     NaN\n",
              "4  Cooper     3.0    45.0\n",
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              "                                                     [key], {});\n",
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              "\n",
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    {
      "cell_type": "code",
      "source": [
        "ndf2.rename(columns = {'Company' : 'Empresa', 'Weight': 'Peso'}, inplace = True)\n",
        "ndf2"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "Y6z80VZidw13",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Empresa  Peso  Length\n",
              "0    Ford   2.0    40.0\n",
              "1    Ford   4.0    50.0\n",
              "2      VW   2.0    30.0\n",
              "3     BMW   2.0     NaN\n",
              "4  Cooper   3.0    45.0\n",
              "5  Cooper   NaN     NaN"
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-11761bd1-d8a9-47b9-9119-5596efb79784 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-11761bd1-d8a9-47b9-9119-5596efb79784');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Datos perdidos - Tener en cuenta"
      ],
      "metadata": {
        "id": "r_KFTeDh5eHg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Excluya las filas o características. \n",
        "* Cumpliméntelas con un valor estimado."
      ],
      "metadata": {
        "id": "_8cWsu5Q5xbX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Errores de datos\tUtilice recursos lógicos para descubrir errores manuales y corríjalos. O, excluya las características.\n"
      ],
      "metadata": {
        "id": "ph2iFrhW5VRh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Incoherencias de codificación\tDecida un esquema de codificación simple y convierta y sustituya los valores.\n"
      ],
      "metadata": {
        "id": "25z2JgNF5Wx6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Metadatos perdidos o erróneos\tExamine manualmente los campos sospechosos y compruebe el significado correcto."
      ],
      "metadata": {
        "id": "qvHNH_E25Yf1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Crear un informe de limpieza de datos\n",
        "\n",
        "\n",
        "Registrar sus actividades de limpieza de datos es esencial para registrar las modificaciones de los datos. \n",
        "\n",
        "Los futuros proyectos de minería de datos se beneficiarán de los detalles del trabajo disponible.\n",
        "\n",
        "\n",
        "Es una excelente idea considerar las siguientes cuestiones cuando genere el informe:\n",
        "\n",
        "\n",
        "* ¿Qué tipos de ruido se han producido en los datos?\n",
        "* ¿Qué métodos utiliza para eliminar el ruido? \n",
        "    \n",
        "    > ¿Qué técnicas han demostrado ser eficaces?\n",
        "\n",
        "* ¿Existen casos o atributos que no se pueden recuperar? \n",
        "> Asegúrese de registrar los datos que se han excluido por causas del ruido."
      ],
      "metadata": {
        "id": "Pz0nRTw62Y-B"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kGd2pbKI2XMK"
      },
      "outputs": [],
      "source": []
    }
  ]
}