Learning_Pandas.ipynb

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      "source": [
        "importar   pandas   como   pd  \n",
        "importar   numpy   como   np"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creando series "
      ],
      "metadata": {
        "id": "t7eI8pO2aNhu"
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    {
      "cell_type": "code",
      "source": [
        "#crear diferentes tipos de datos\n",
        "labels = ['a','b','c'] #lista de etiquetas\n",
        "my_list = [10,20,30] # lista con valores\n",
        "arr = np.array([10,20,30]) #convertir lista de valores en arreglo numpy\n",
        "d = {'a':10,'b':20,'c':30} #creación de un diccionario"
      ],
      "metadata": {
        "id": "sUz1Cs63ZXST"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Desde Listas"
      ],
      "metadata": {
        "id": "1t3Lv5ZFaQSO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Convertir una lista en series utilizando el método pd.Series\n",
        "#Observe que se crean los nombres con las posiciones de cada elemento\n",
        "pd.Series(data=my_list)"
      ],
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    10\n",
              "1    20\n",
              "2    30\n",
              "dtype: int64"
            ]
          },
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          "execution_count": 11
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      ]
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    {
      "cell_type": "code",
      "source": [
        "#También se puede escribir la lista directamente dentro de pd.Series()\n",
        "pd.Series([10,20,20])"
      ],
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              "0    10\n",
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              "2    20\n",
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    {
      "cell_type": "code",
      "source": [
        "#Convertir una lista en series utilizando pd.Series()\n",
        "#Se puede ingresar el nombre de las posiciones\n",
        "pd.Series(data=my_list, index=labels)"
      ],
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              "a    10\n",
              "b    20\n",
              "c    30\n",
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          "execution_count": 13
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      "cell_type": "code",
      "source": [
        "#No es obligatorio escribir la palabra 'data'= dentro del argumento, tampoco el 'index'=\n",
        "pd.Series(my_list, labels)"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "Desde arreglos numpy"
      ],
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    {
      "cell_type": "code",
      "source": [
        "#Un arreglo numpy se puede comvertir en una serie utilizando el método pd.Series \n",
        "pd.Series(arr)"
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              "1    20\n",
              "2    30\n",
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          "metadata": {},
          "execution_count": 15
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    {
      "cell_type": "code",
      "source": [
        "#Convertir un arreglo en series indicando también los valores del index\n",
        "pd.Series(arr,labels)"
      ],
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              "a    10\n",
              "b    20\n",
              "c    30\n",
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          },
          "metadata": {},
          "execution_count": 16
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      "cell_type": "markdown",
      "source": [
        "Desde un diccionario"
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    {
      "cell_type": "code",
      "source": [
        "#Convertir el diccionario en series utilizando el método pd.Series\n",
        "#Como el diccionario ya tiene clave entonces se le asigna como valor de la posición\n",
        "pd.Series(d)"
      ],
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              "a    10\n",
              "b    20\n",
              "c    30\n",
              "dtype: int64"
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          "metadata": {},
          "execution_count": 17
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    {
      "cell_type": "markdown",
      "source": [
        "Datos de una serie                                                                      \n",
        "Una serie de pandas puede contener una variedad de tipos de objetos"
      ],
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    {
      "cell_type": "code",
      "source": [
        "#Creando una serie basado solo en una lista de letras\n",
        "#fíjese que la avriable es de tipo objeto\n",
        "pd.Series(data=labels)"
      ],
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              "0    a\n",
              "1    b\n",
              "2    c\n",
              "dtype: object"
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          },
          "metadata": {},
          "execution_count": 18
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    {
      "cell_type": "markdown",
      "source": [
        "Indexación"
      ],
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      "cell_type": "code",
      "source": [
        "#Creación de una serie con sus labels o índices\n",
        "ser1 =pd.Series([1,2,3,4], index =['USA','GERMANY','USSR','JAPAN'])\n",
        "ser1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "yxsaUR6nd11c",
        "outputId": "614ac7be-5e17-4227-8f30-42b489ae1e40"
      },
      "execution_count": null,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "USA        1\n",
              "GERMANY    2\n",
              "USSR       3\n",
              "JAPAN      4\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
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    {
      "cell_type": "code",
      "source": [
        "#Creación de una serie con sus labels o índices\n",
        "ser2 =pd.Series([1,2,5,4], index =['USA','GERMANY','ITALY','JAPAN'])\n",
        "ser2"
      ],
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "USA        1\n",
              "GERMANY    2\n",
              "ITALY      5\n",
              "JAPAN      4\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#La búsqueda de una serie es igual como un dicionario\n",
        "#Busquemos la posición de 'USA'\n",
        "ser1['USA']"
      ],
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        "colab": {
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        "id": "1uqKl871fJJC",
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            "text/plain": [
              "1"
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          "metadata": {},
          "execution_count": 21
        }
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    {
      "cell_type": "code",
      "source": [
        "#La búsqueda de una serie es igual como un dicionario\n",
        "#Busquemos la posición de 'GERMANY'\n",
        "ser2['GERMANY']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i5gW47g2fQ9_",
        "outputId": "c678c8be-68d4-4de1-d695-19f686a54189"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Las operaciones en las series de Pandas se realizan según el índice\n"
      ],
      "metadata": {
        "id": "fSzpuwecfjYS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Observe los resultados de los países que sólo están en una serie y no en las dos \n",
        "ser1 + ser2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ItN6xlNhfpyc",
        "outputId": "a9adbbc5-c8e0-4f7a-9ecb-805eff09550a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GERMANY    4.0\n",
              "ITALY      NaN\n",
              "JAPAN      8.0\n",
              "USA        2.0\n",
              "USSR       NaN\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Frames (Marcos de datos)**"
      ],
      "metadata": {
        "id": "mg7fIB-QhXZ_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Un Dataframe es en términos sencillos un conjunto de series reunidas que comparten el mismo índice. En un dataframe podemos específicar el índice (nombre de filas) como las columnas (nombre de las columnas)\n"
      ],
      "metadata": {
        "id": "6F9auAjVhNbO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Importar la función de NumPy para crear arreglos de números enteros\n",
        "from numpy.random import randn\n",
        "#Iniciemos el generador aleatorio\n",
        "np.random.seed(101) \n",
        "#Una forma rápida de crear una lista de python desde strings\n",
        "'A B C D F E'.split()"
      ],
      "metadata": {
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        "id": "8r_zK3YLh2Zb",
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          "data": {
            "text/plain": [
              "['A', 'B', 'C', 'D', 'F', 'E']"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Ahora crearemos un dataframe con números aleatorios de 4 columnas y 5 filas\n",
        "#Crearemos listas rápidamente usando la función split 'A B C D E'.split() lo que evita estar escribiendo repetidamente las comas\n",
        "df = pd.DataFrame(randn(5,4),index='A B C D E'.split(),columns='W X Y Z'.split())\n",
        "df\n"
      ],
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              "          W         X         Y         Z\n",
              "A  2.706850  0.628133  0.907969  0.503826\n",
              "B  0.651118 -0.319318 -0.848077  0.605965\n",
              "C -2.018168  0.740122  0.528813 -0.589001\n",
              "D  0.188695 -0.758872 -0.933237  0.955057\n",
              "E  0.190794  1.978757  2.605967  0.683509"
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Descripción general del data frame**"
      ],
      "metadata": {
        "id": "CsVEInABjrzv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Número de filas y columnas"
      ],
      "metadata": {
        "id": "7Lj9NZ0jjxiQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#retorna una tupla así: (filas,columnas)\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ctlV6NJ-j0DP",
        "outputId": "b2267384-d4f7-43f7-e56a-50ed87573016"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Información general de los datos"
      ],
      "metadata": {
        "id": "xmpFRIu9kD2h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Información general de los datos de cada columna\n",
        "#Indica el número de filas del dataset\n",
        "# Muestra el número de datos No Nulos por columna (valores válidos)\n",
        "#Tipo de datos de cada columna\n",
        "#Tamaño del dataset\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vwovrmU5kHGf",
        "outputId": "b40a6a18-6f5e-4b44-a22e-f7cf9f3f4d3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 5 entries, A to E\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   W       5 non-null      float64\n",
            " 1   X       5 non-null      float64\n",
            " 2   Y       5 non-null      float64\n",
            " 3   Z       5 non-null      float64\n",
            "dtypes: float64(4)\n",
            "memory usage: 200.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Tipos de datos que existe\n",
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9uBQgcCfmLWB",
        "outputId": "b9c8bed8-da84-4933-f012-9380bfd73196"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "W    float64\n",
              "X    float64\n",
              "Y    float64\n",
              "Z    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Resumen de estadítica descriptiva general"
      ],
      "metadata": {
        "id": "Dk_7RfvRm3fy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#el método .describe() da un resumen de conteo, promedio, mínimo, máximo, cuartiles.\n",
        "#el método .describe() no muestra información de columnas (con variables) de tipo categóricas\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "tATNMn4-m7dp",
        "outputId": "7d21a6d6-dd93-451f-b7a0-bd17b74c544e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              W         X         Y         Z\n",
              "count  5.000000  5.000000  5.000000  5.000000\n",
              "mean   0.343858  0.453764  0.452287  0.431871\n",
              "std    1.681131  1.061385  1.454516  0.594708\n",
              "min   -2.018168 -0.758872 -0.933237 -0.589001\n",
              "25%    0.188695 -0.319318 -0.848077  0.503826\n",
              "50%    0.190794  0.628133  0.528813  0.605965\n",
              "75%    0.651118  0.740122  0.907969  0.683509\n",
              "max    2.706850  1.978757  2.605967  0.955057"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ed111d2d-26ed-44eb-8f05-c9d93c40b102\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>W</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>Z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.343858</td>\n",
              "      <td>0.453764</td>\n",
              "      <td>0.452287</td>\n",
              "      <td>0.431871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.681131</td>\n",
              "      <td>1.061385</td>\n",
              "      <td>1.454516</td>\n",
              "      <td>0.594708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ed111d2d-26ed-44eb-8f05-c9d93c40b102')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ver primeros elementos del data frame"
      ],
      "metadata": {
        "id": "9a1-dyPsnyoG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
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          "height": 206
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        "id": "YjjKsxmnn2K0",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          W         X         Y         Z\n",
              "A  2.706850  0.628133  0.907969  0.503826\n",
              "B  0.651118 -0.319318 -0.848077  0.605965\n",
              "C -2.018168  0.740122  0.528813 -0.589001\n",
              "D  0.188695 -0.758872 -0.933237  0.955057\n",
              "E  0.190794  1.978757  2.605967  0.683509"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>W</th>\n",
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              "      <th>Z</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ff0a182-625c-4aa2-985a-afcb8991c5fc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\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",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1ff0a182-625c-4aa2-985a-afcb8991c5fc 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-1ff0a182-625c-4aa2-985a-afcb8991c5fc');\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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              "  "
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          "metadata": {},
          "execution_count": 30
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    {
      "cell_type": "markdown",
      "source": [
        "Ver últimos elementos del data frame"
      ],
      "metadata": {
        "id": "VCg8dDoCn4ek"
      }
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    {
      "cell_type": "code",
      "source": [
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        "id": "NovX9iuCn7Uc",
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      "execution_count": null,
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        {
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              "          W         X         Y         Z\n",
              "A  2.706850  0.628133  0.907969  0.503826\n",
              "B  0.651118 -0.319318 -0.848077  0.605965\n",
              "C -2.018168  0.740122  0.528813 -0.589001\n",
              "D  0.188695 -0.758872 -0.933237  0.955057\n",
              "E  0.190794  1.978757  2.605967  0.683509"
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>W</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>Z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
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              "  </tbody>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Selección e Indexación**"
      ],
      "metadata": {
        "id": "0FmkoEw3oeHW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Existen diversos métodos para extraer datos de un data frame"
      ],
      "metadata": {
        "id": "zS7vPCiCos9t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Regresar todos los datos de la columna W\n",
        "df['W']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_ygW6yco0Tt",
        "outputId": "8afe6d52-ea38-4792-b02a-41c975b52880"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A    2.706850\n",
              "B    0.651118\n",
              "C   -2.018168\n",
              "D    0.188695\n",
              "E    0.190794\n",
              "Name: W, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Seleccionar dos o más columnas\n",
        "#Pasar una lista con los nombres de las columnas"
      ],
      "metadata": {
        "id": "_3NXHT3io_Bp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[['W','Z']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "bXRjQpDorS4s",
        "outputId": "2a052776-2654-479c-db50-1f3f150d789a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          W         Z\n",
              "A  2.706850  0.503826\n",
              "B  0.651118  0.605965\n",
              "C -2.018168 -0.589001\n",
              "D  0.188695  0.955057\n",
              "E  0.190794  0.683509"
            ],
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-be750d21-c2bb-457c-96e6-99e48774aa79');\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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              "  "
            ]
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        }
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    },
    {
      "cell_type": "code",
      "source": [
        "#Seleccionar dos o más columnas\n",
        "#Pasar una lista con los nombres de las columnas\n",
        "#Puedes indicar el orden de las columnas\n",
        "df[['X','W','Z']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zfDzvorUrmjz",
        "outputId": "2a743889-5746-4df0-a874-7d01195dcd43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          X         W         Z\n",
              "A  0.628133  2.706850  0.503826\n",
              "B -0.319318  0.651118  0.605965\n",
              "C  0.740122 -2.018168 -0.589001\n",
              "D -0.758872  0.188695  0.955057\n",
              "E  1.978757  0.190794  0.683509"
            ],
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              "      <th>A</th>\n",
              "      <td>0.628133</td>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
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              "      <th>B</th>\n",
              "      <td>-0.319318</td>\n",
              "      <td>0.651118</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
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              "      <th>C</th>\n",
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              "      <td>-0.589001</td>\n",
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              "      <td>-0.758872</td>\n",
              "      <td>0.188695</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>1.978757</td>\n",
              "      <td>0.190794</td>\n",
              "      <td>0.683509</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",
              "          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": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Las columnas de un dataframe son sólo series"
      ],
      "metadata": {
        "id": "hTLLurseHifF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#tipo de datos\n",
        "type(df['W'])"
      ],
      "metadata": {
        "id": "aJrIK2lyHmV6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "827dc438-ceaf-452b-c82a-53cfb80a537e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Creando una nueva columna**"
      ],
      "metadata": {
        "id": "3kwqVxBWIz0R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Nueva columna igual a la suma de otras dos\n",
        "# Operación vectorizada\n",
        "df['new'] = df['W'] + df['Y']\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "S_5Zlui8I3PA",
        "outputId": "8464414b-7a8e-4abb-eb0f-6821d6c9b8ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          W         X         Y         Z       new\n",
              "A  2.706850  0.628133  0.907969  0.503826  3.614819\n",
              "B  0.651118 -0.319318 -0.848077  0.605965 -0.196959\n",
              "C -2.018168  0.740122  0.528813 -0.589001 -1.489355\n",
              "D  0.188695 -0.758872 -0.933237  0.955057 -0.744542\n",
              "E  0.190794  1.978757  2.605967  0.683509  2.796762"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-973abf66-d372-4f6a-9710-fd9cc17503a0\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>W</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>Z</th>\n",
              "      <th>new</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "      <td>3.614819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "      <td>-0.196959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "      <td>-1.489355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "      <td>-0.744542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "      <td>2.796762</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-973abf66-d372-4f6a-9710-fd9cc17503a0')\"\n",
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              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "  </style>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-973abf66-d372-4f6a-9710-fd9cc17503a0');\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": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Eliminando columnas**"
      ],
      "metadata": {
        "id": "N5PFMtBbJgey"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# axis=0 elimina filas axis=1 elimina columnas\n",
        "df.drop('new',axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xEl4zIQiJjUJ",
        "outputId": "55e72c6b-4d89-45c9-8e63-8a5dcaf42596"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          W         X         Y         Z\n",
              "A  2.706850  0.628133  0.907969  0.503826\n",
              "B  0.651118 -0.319318 -0.848077  0.605965\n",
              "C -2.018168  0.740122  0.528813 -0.589001\n",
              "D  0.188695 -0.758872 -0.933237  0.955057\n",
              "E  0.190794  1.978757  2.605967  0.683509"
            ],
            "text/html": [
              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>W</th>\n",
              "      <th>X</th>\n",
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              "      <th>Z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7edd2cd2-b3eb-45b4-bf3b-7df670746302')\"\n",
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              "              style=\"display:none;\">\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7edd2cd2-b3eb-45b4-bf3b-7df670746302 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-7edd2cd2-b3eb-45b4-bf3b-7df670746302');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Aún no está grabado el dataframe, para esto se pueden hacer dos cosas\n",
        "# Forma 1\n",
        "#df = df.drop('new',axis=1)\n",
        "# Forma 2\n",
        "df.drop('new',axis=1,inplace=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "eD_744kVKv2R",
        "outputId": "1d1dbf06-4a3b-4455-8ebb-204943ba77a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          W         X         Y         Z\n",
              "A  2.706850  0.628133  0.907969  0.503826\n",
              "B  0.651118 -0.319318 -0.848077  0.605965\n",
              "C -2.018168  0.740122  0.528813 -0.589001\n",
              "D  0.188695 -0.758872 -0.933237  0.955057\n",
              "E  0.190794  1.978757  2.605967  0.683509"
            ],
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              "\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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th>W</th>\n",
              "      <th>X</th>\n",
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              "      <th>Z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0334b576-eb8e-4494-b03a-01e622660665')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Eliminando filas**"
      ],
      "metadata": {
        "id": "PxKf7IBPL7Se"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop('E',axis=0)"
      ],
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          "height": 175
        },
        "id": "9t5-3moFMB75",
        "outputId": "4c901b0b-c5b9-42a9-e6fa-dc0bd984b552"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          W         X         Y         Z\n",
              "A  2.706850  0.628133  0.907969  0.503826\n",
              "B  0.651118 -0.319318 -0.848077  0.605965\n",
              "C -2.018168  0.740122  0.528813 -0.589001\n",
              "D  0.188695 -0.758872 -0.933237  0.955057"
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              "      <th>A</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
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              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
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              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</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": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Otra manera de borrar las columnas\n",
        "del df['X'] \n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "qtE61rDUMf2P",
        "outputId": "85ee0889-aff0-479e-ac8b-2ac339c2afa3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          W         Y         Z\n",
              "A  2.706850  0.907969  0.503826\n",
              "B  0.651118 -0.848077  0.605965\n",
              "C -2.018168  0.528813 -0.589001\n",
              "D  0.188695 -0.933237  0.955057\n",
              "E  0.190794  2.605967  0.683509"
            ],
            "text/html": [
              "\n",
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              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e2df8dd3-3247-42e5-9a78-1dc988679bc0 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e2df8dd3-3247-42e5-9a78-1dc988679bc0');\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": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Obtener los nombres de las columnas y los índices(index)**"
      ],
      "metadata": {
        "id": "pxmKX7jjNO5n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns #nombres de las columnas "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kZreCDX3NQEk",
        "outputId": "1f4ec9c6-e20c-4b0d-9159-3a97ac39f2dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['W', 'Y', 'Z'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.index #Nombre de los índices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0G4gP24YNkAL",
        "outputId": "26663597-ea23-4c9e-ab72-f0b974d785a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['A', 'B', 'C', 'D', 'E'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Seleccionando Filas y Columnas**"
      ],
      "metadata": {
        "id": "DnfXkijBQ2H6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Las dos formas de selección principal son:\n",
        "DataFrame.loc[etiqueta_fila,etiqueta_columna] <- por etiquetas\n",
        "DataFrame.iloc[indice_fila,indice_columna]<- por índices"
      ],
      "metadata": {
        "id": "2BA4oFQrQ8Ir"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#La función loc busca por medio de los nombres de los indices y columnas\n",
        "df.loc['A'] #se selecciona todos los valores de la fila 'A'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xQ4GyEUuRRr6",
        "outputId": "57f76548-49e1-4511-91cd-ff564e45b498"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "W    2.706850\n",
              "Y    0.907969\n",
              "Z    0.503826\n",
              "Name: A, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "O basado en la posición (index) en vez de usar la etiqueta"
      ],
      "metadata": {
        "id": "mAPlZpxTSFoC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[2] #Se seleccionan los valores de la fila con indice #Recordar que los index empiezan en cero\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iP3BXmGCSNwv",
        "outputId": "0bb800d4-0b00-470f-de33-4bf6daf1c4cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "W   -2.018168\n",
              "Y    0.528813\n",
              "Z   -0.589001\n",
              "Name: C, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Seleccione un subconjunto de Filas y Columnas**"
      ],
      "metadata": {
        "id": "XRbDZwzrOAXN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Mediante etiquetas \n",
        "#Se selecciona el elemento que está en la fila=B Col=Y\n",
        "df.loc['B','Y'] "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8vrd15mUWNk",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.8480769834036315"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Mediante etiquetas se selecciona un subconjunto de datos que están entre\n",
        "#filas = A,B Cols= W,Y\n",
        "df.loc[['A','B'],['W','Y']]"
      ],
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          W         Y\n",
              "A  2.706850  0.907969\n",
              "B  0.651118 -0.848077"
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        "df.loc[['B','A'],['Y','W']]"
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          "height": 112
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        {
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              "          Y         W\n",
              "B -0.848077  0.651118\n",
              "A  0.907969  2.706850"
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_excel('/content/dataprueba.xlsx') \n",
        "df.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "ospFGJQrluhB",
        "outputId": "71fe8dba-dc26-4ee8-ded2-776775995421"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Customer Branch  Sell Out Value Sales Day (Real)\n",
              "0                              Chiper          1.313133e+07 2022-04-01\n",
              "1                              Chiper          8.972393e+06 2022-04-02\n",
              "2                              Chiper          2.204033e+06 2022-04-03\n",
              "3                              Chiper          2.515698e+05 2022-04-04\n",
              "4   Colombiana De Comercio - Apartadó          1.522300e+07 2022-04-01\n",
              "5   Colombiana De Comercio - Apartadó          2.956585e+06 2022-04-02\n",
              "6   Colombiana De Comercio - Apartadó          8.542644e+06 2022-04-04\n",
              "7     Colombiana De Comercio - Bogotá          1.226255e+08 2022-04-01\n",
              "8     Colombiana De Comercio - Bogotá          7.977356e+07 2022-04-02\n",
              "9     Colombiana De Comercio - Bogotá          9.021100e+07 2022-04-04\n",
              "10      Colombiana De Comercio - Cali          7.085489e+07 2022-04-01\n",
              "11      Colombiana De Comercio - Cali         -2.060601e+06 2022-04-02\n",
              "12      Colombiana De Comercio - Cali          6.788505e+06 2022-04-04\n",
              "13   Colombiana De Comercio - Caquetá          1.320381e+07 2022-04-01\n",
              "14   Colombiana De Comercio - Caquetá          2.367988e+05 2022-04-02\n",
              "15  Colombiana De Comercio - Medellin          4.537394e+07 2022-04-01\n",
              "16  Colombiana De Comercio - Medellin          4.002170e+07 2022-04-02\n",
              "17  Colombiana De Comercio - Medellin          3.628416e+07 2022-04-04\n",
              "18     Colombiana De Comercio - Pasto          6.749266e+07 2022-04-01\n",
              "19     Colombiana De Comercio - Pasto          4.047051e+07 2022-04-02"
            ],
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              "      <th>1</th>\n",
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              "      <th>2</th>\n",
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              "      <td>2.204033e+06</td>\n",
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              "      <th>3</th>\n",
              "      <td>Chiper</td>\n",
              "      <td>2.515698e+05</td>\n",
              "      <td>2022-04-04</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>Colombiana De Comercio - Apartadó</td>\n",
              "      <td>1.522300e+07</td>\n",
              "      <td>2022-04-01</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Colombiana De Comercio - Apartadó</td>\n",
              "      <td>2.956585e+06</td>\n",
              "      <td>2022-04-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Colombiana De Comercio - Apartadó</td>\n",
              "      <td>8.542644e+06</td>\n",
              "      <td>2022-04-04</td>\n",
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              "      <th>7</th>\n",
              "      <td>Colombiana De Comercio - Bogotá</td>\n",
              "      <td>1.226255e+08</td>\n",
              "      <td>2022-04-01</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Colombiana De Comercio - Bogotá</td>\n",
              "      <td>7.977356e+07</td>\n",
              "      <td>2022-04-02</td>\n",
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              "      <th>9</th>\n",
              "      <td>Colombiana De Comercio - Bogotá</td>\n",
              "      <td>9.021100e+07</td>\n",
              "      <td>2022-04-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Colombiana De Comercio - Cali</td>\n",
              "      <td>7.085489e+07</td>\n",
              "      <td>2022-04-01</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Colombiana De Comercio - Cali</td>\n",
              "      <td>-2.060601e+06</td>\n",
              "      <td>2022-04-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Colombiana De Comercio - Cali</td>\n",
              "      <td>6.788505e+06</td>\n",
              "      <td>2022-04-04</td>\n",
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              "      <th>13</th>\n",
              "      <td>Colombiana De Comercio - Caquetá</td>\n",
              "      <td>1.320381e+07</td>\n",
              "      <td>2022-04-01</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Colombiana De Comercio - Caquetá</td>\n",
              "      <td>2.367988e+05</td>\n",
              "      <td>2022-04-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Colombiana De Comercio - Medellin</td>\n",
              "      <td>4.537394e+07</td>\n",
              "      <td>2022-04-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Colombiana De Comercio - Medellin</td>\n",
              "      <td>4.002170e+07</td>\n",
              "      <td>2022-04-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Colombiana De Comercio - Medellin</td>\n",
              "      <td>3.628416e+07</td>\n",
              "      <td>2022-04-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Colombiana De Comercio - Pasto</td>\n",
              "      <td>6.749266e+07</td>\n",
              "      <td>2022-04-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Colombiana De Comercio - Pasto</td>\n",
              "      <td>4.047051e+07</td>\n",
              "      <td>2022-04-02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7591b32d-5512-4c7c-a6b2-69068cc2ffcf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \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",
              "      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-7591b32d-5512-4c7c-a6b2-69068cc2ffcf 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-7591b32d-5512-4c7c-a6b2-69068cc2ffcf');\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": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "Pb6vWZyBv7Yp",
        "outputId": "c154caa0-a226-498a-c1fb-b1c30120c39b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-69-43569c297307>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m df.transpose('[Day (Real)]',\n\u001b[0;32m----> 2\u001b[0;31m                     copy= False)\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mtranspose\u001b[0;34m(self, copy, *args)\u001b[0m\n\u001b[1;32m   3323\u001b[0m         \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3324\u001b[0m         \"\"\"\n\u001b[0;32m-> 3325\u001b[0;31m         \u001b[0mnv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_transpose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3326\u001b[0m         \u001b[0;31m# construct the args\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/compat/numpy/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, args, kwargs, fname, max_fname_arg_count, method)\u001b[0m\n\u001b[1;32m     70\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"both\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m                 validate_args_and_kwargs(\n\u001b[0;32m---> 72\u001b[0;31m                     \u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_fname_arg_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m                 )\n\u001b[1;32m     74\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/util/_validators.py\u001b[0m in \u001b[0;36mvalidate_args_and_kwargs\u001b[0;34m(fname, args, kwargs, max_fname_arg_count, compat_args)\u001b[0m\n\u001b[1;32m    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 210\u001b[0;31m     \u001b[0mvalidate_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompat_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/util/_validators.py\u001b[0m in \u001b[0;36mvalidate_kwargs\u001b[0;34m(fname, kwargs, compat_args)\u001b[0m\n\u001b[1;32m    152\u001b[0m     \u001b[0mkwds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[0m_check_for_invalid_keys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompat_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m     \u001b[0m_check_for_default_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompat_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/util/_validators.py\u001b[0m in \u001b[0;36m_check_for_default_values\u001b[0;34m(fname, arg_val_dict, compat_args)\u001b[0m\n\u001b[1;32m     69\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmatch\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m             raise ValueError(\n\u001b[0;32m---> 71\u001b[0;31m                 \u001b[0;34mf\"the '{key}' parameter is not supported in \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     72\u001b[0m                 \u001b[0;34mf\"the pandas implementation of {fname}()\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m             )\n",
            "\u001b[0;31mValueError\u001b[0m: the 'axes' parameter is not supported in the pandas implementation of transpose()"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Creating, Reading and Writing Data**"
      ],
      "metadata": {
        "id": "_djjviLDk5G8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A DataFrame is a table, it contains an array of individual entries, each of which has a certain value. Each entry correspond to a row or record and a column."
      ],
      "metadata": {
        "id": "jRWXk2gjlElf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "C6DNZvl-m-2P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " # DataFrame with integers\n",
        " pd.DataFrame({'Yes':[50,21],'No':[131,2]})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "SEnn0_a9mNzk",
        "outputId": "35ebb9e1-54c1-4f79-ae2d-7935e958fc2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Yes   No\n",
              "0   50  131\n",
              "1   21    2"
            ],
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "    }\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Yes</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>50</td>\n",
              "      <td>131</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>21</td>\n",
              "      <td>2</td>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .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",
              "      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-f63ccedd-6fe3-4a76-942f-f526b35f6c4e 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-f63ccedd-6fe3-4a76-942f-f526b35f6c4e');\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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#DataFrame with strings\n",
        "pd.DataFrame({'Bob': ['I liked it.', 'It was awful.'], \n",
        "              'Sue': ['Pretty good.', 'Bland.']},\n",
        "             index=['Product A', 'Product B'])"
      ],
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        "id": "pns7-fnmk4Wg",
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        {
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          "data": {
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              "                     Bob           Sue\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Series**"
      ],
      "metadata": {
        "id": "1HOGIVwMosGA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A series is a sequence of data values. If a DataFrame is a table, a series is a list."
      ],
      "metadata": {
        "id": "drYzSimyoxqT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.Series([1,2,3,4,])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPOXIitEovDC",
        "outputId": "f72e864d-064d-4e3d-8278-1a889b825d3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    2\n",
              "2    3\n",
              "3    4\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A series is a single column of a DataFrame, you can assign row labels to the Series the same way as before, using and index parameter. Note that a series does not have a column name, it only have an overall name:"
      ],
      "metadata": {
        "id": "zZC4I0wdpSoR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.Series([30,35,40], index=['2015 Sales','2016 Sales','2017 Sales'], name= 'Product A')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E_AfTyajp2hv",
        "outputId": "2271372b-2863-4c10-d20a-5ca5befca544"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2015 Sales    30\n",
              "2016 Sales    35\n",
              "2017 Sales    40\n",
              "Name: Product A, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can think about a DataFrame as actually being a bunch of Series glued together."
      ],
      "metadata": {
        "id": "wf-ot91sqPw1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reading Files"
      ],
      "metadata": {
        "id": "YOhurqcOqZ4f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/dataTEST1.csv')\n",
        "data"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        },
        "id": "WYNPhQUKqcru",
        "outputId": "8d608bce-7535-4c30-c202-87510d1700fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Local Customer Name                    Customer Branch  \\\n",
              "0     Colombiana de Comercio  Colombiana De Comercio - Apartadó   \n",
              "1     Colombiana de Comercio  Colombiana De Comercio - Apartadó   \n",
              "2     Colombiana de Comercio  Colombiana De Comercio - Apartadó   \n",
              "3     Colombiana de Comercio  Colombiana De Comercio - Apartadó   \n",
              "4     Colombiana de Comercio  Colombiana De Comercio - Apartadó   \n",
              "...                      ...                                ...   \n",
              "5879  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "5880  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "5881  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "5882  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "5883  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "\n",
              "                  Sub Sector      Item GTIN Month (Real)  Shipment GIV Value  \n",
              "0                Fabric Care  7500435011884      2022M02            973066.0  \n",
              "1                Fabric Care  7500435011884      2022M03            486533.0  \n",
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              "3                Fabric Care  7500435011884      2022M06            583840.0  \n",
              "4                Fabric Care  7500435011884      2022M07            583840.0  \n",
              "...                      ...            ...          ...                 ...  \n",
              "5879  Skin and Personal Care  7702018913640      2022M06           1810920.0  \n",
              "5880  Skin and Personal Care  7702018913640      2022M07           3621840.0  \n",
              "5881  Skin and Personal Care  7702018913664      2022M03            672011.0  \n",
              "5882  Skin and Personal Care  7702018913664      2022M05            672011.0  \n",
              "5883  Skin and Personal Care  7702018913664      2022M06            940815.0  \n",
              "\n",
              "[5884 rows x 6 columns]"
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              "      <td>Skin and Personal Care</td>\n",
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              "<p>5884 rows × 6 columns</p>\n",
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              "\n",
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              "          const element = document.querySelector('#df-42b49f65-89b3-4b60-94ad-cfbd65206c05');\n",
              "          const dataTable =\n",
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              "                                                     [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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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Indexing, Selecting & Assigning**"
      ],
      "metadata": {
        "id": "AAYHRSi2Pdal"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "data = pd.read_csv('/content/dataTEST1.csv')\n",
        "data.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "id": "Q2VMOT2EPaJN",
        "outputId": "5e79893e-e876-4221-b517-be9306b3871e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Local Customer Name                    Customer Branch   Sub Sector  \\\n",
              "0  Colombiana de Comercio  Colombiana De Comercio - Apartadó  Fabric Care   \n",
              "\n",
              "       Item GTIN Month (Real)  Shipment GIV Value  \n",
              "0  7500435011884      2022M02            973066.0  "
            ],
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              "      <td>Colombiana de Comercio</td>\n",
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              "      <td>Fabric Care</td>\n",
              "      <td>7500435011884</td>\n",
              "      <td>2022M02</td>\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Customer Branch']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FeJ8j3v0bwg1",
        "outputId": "195899ff-14a9-492b-c463-4a1a4618738b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       Colombiana De Comercio - Apartadó\n",
              "1       Colombiana De Comercio - Apartadó\n",
              "2       Colombiana De Comercio - Apartadó\n",
              "3       Colombiana De Comercio - Apartadó\n",
              "4       Colombiana De Comercio - Apartadó\n",
              "                      ...                \n",
              "5879    Colombiana De Comercio - Medellin\n",
              "5880    Colombiana De Comercio - Medellin\n",
              "5881    Colombiana De Comercio - Medellin\n",
              "5882    Colombiana De Comercio - Medellin\n",
              "5883    Colombiana De Comercio - Medellin\n",
              "Name: Customer Branch, Length: 5884, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JisKH4BcpQYW",
        "outputId": "60a9c65b-d477-4b37-d277-00af4cd4589c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5884, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Sub Sector'][47]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "7oNVEkKRncuG",
        "outputId": "394934d8-63e4-47e9-9ed4-5ae7d4fb6257"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Fabric Care'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# iloc to get a row\n",
        "data.iloc[4]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l4E205-voG-P",
        "outputId": "ba6a0ec8-7f12-42b5-ba05-1e620d7196a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Local Customer Name               Colombiana de Comercio\n",
              "Customer Branch        Colombiana De Comercio - Apartadó\n",
              "Sub Sector                                   Fabric Care\n",
              "Item GTIN                                  7500435011884\n",
              "Month (Real)                                     2022M07\n",
              "Shipment GIV Value                              583840.0\n",
              "Name: 4, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# return a specific register from a specific column\n",
        "data['Customer Branch'][20]"
      ],
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "d1fa4caf-3b50-4156-9b69-48fa4dd3f08e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Colombiana De Comercio - Apartadó'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#indexing\n",
        "# iloc to get a column\n",
        "data.iloc[:,4] # iloc[ row index position , column index position]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gTnpKG3opxcl",
        "outputId": "9ab61229-5c9b-4c99-feeb-5fbd95b94be3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       2022M02\n",
              "1       2022M03\n",
              "2       2022M04\n",
              "3       2022M06\n",
              "4       2022M07\n",
              "         ...   \n",
              "5879    2022M06\n",
              "5880    2022M07\n",
              "5881    2022M03\n",
              "5882    2022M05\n",
              "5883    2022M06\n",
              "Name: Month (Real), Length: 5884, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# iloc to get de second and third entries\n",
        "data.iloc[3:,2] #df.iloc[ row index , column index]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4fJzcNZlqQoP",
        "outputId": "f2c2af8e-8a8e-4b86-9ee5-1d7f762ddd34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3                  Fabric Care\n",
              "4                  Fabric Care\n",
              "5                  Fabric Care\n",
              "6                  Fabric Care\n",
              "7                  Fabric Care\n",
              "                 ...          \n",
              "5879    Skin and Personal Care\n",
              "5880    Skin and Personal Care\n",
              "5881    Skin and Personal Care\n",
              "5882    Skin and Personal Care\n",
              "5883    Skin and Personal Care\n",
              "Name: Sub Sector, Length: 5881, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
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      "source": [
        "# iloc to pass a list\n",
        "data.iloc[[0,1,2],2:5] # df.iloc[[1,2,3],2:5]"
      ],
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        "outputId": "6d3264ee-1660-4e8e-e655-c623b5158c0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Sub Sector      Item GTIN Month (Real)\n",
              "0  Fabric Care  7500435011884      2022M02\n",
              "1  Fabric Care  7500435011884      2022M03\n",
              "2  Fabric Care  7500435011884      2022M04"
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        "# Last five elements (rows) of the df with rows 2 to 4\n",
        "data.iloc[-5:, 1:5] #iloc[-5:,1:2]"
      ],
      "metadata": {
        "colab": {
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "                        Customer Branch              Sub Sector  \\\n",
              "5879  Colombiana De Comercio - Medellin  Skin and Personal Care   \n",
              "5880  Colombiana De Comercio - Medellin  Skin and Personal Care   \n",
              "5881  Colombiana De Comercio - Medellin  Skin and Personal Care   \n",
              "5882  Colombiana De Comercio - Medellin  Skin and Personal Care   \n",
              "5883  Colombiana De Comercio - Medellin  Skin and Personal Care   \n",
              "\n",
              "          Item GTIN Month (Real)  \n",
              "5879  7702018913640      2022M06  \n",
              "5880  7702018913640      2022M07  \n",
              "5881  7702018913664      2022M03  \n",
              "5882  7702018913664      2022M05  \n",
              "5883  7702018913664      2022M06  "
            ],
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              "      <td>Skin and Personal Care</td>\n",
              "      <td>7702018913640</td>\n",
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              "    <tr>\n",
              "      <th>5880</th>\n",
              "      <td>Colombiana De Comercio - Medellin</td>\n",
              "      <td>Skin and Personal Care</td>\n",
              "      <td>7702018913640</td>\n",
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              "      <td>7702018913664</td>\n",
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              "    <tr>\n",
              "      <th>5882</th>\n",
              "      <td>Colombiana De Comercio - Medellin</td>\n",
              "      <td>Skin and Personal Care</td>\n",
              "      <td>7702018913664</td>\n",
              "      <td>2022M05</td>\n",
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              "    <tr>\n",
              "      <th>5883</th>\n",
              "      <td>Colombiana De Comercio - Medellin</td>\n",
              "      <td>Skin and Personal Care</td>\n",
              "      <td>7702018913664</td>\n",
              "      <td>2022M06</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Label-based selection (based on index name)"
      ],
      "metadata": {
        "id": "KUOa6r8IyylU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# get de first entry in dataframe\n",
        "data.loc[0,'Customer Branch'] #df.loc[row position, label index name]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "Xb7BHxs-ykgz",
        "outputId": "92d4125f-eec5-46b2-9da8-dbc8d08f9d0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Colombiana De Comercio - Apartadó'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.loc[:, [\"Local Customer Name\", \"Customer Branch\"]] #df.loc[row position, [[column name 1, column name 2,]]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "pZYNfWYJzUbt",
        "outputId": "65bc54dd-f26e-4744-e44e-dc912aeb911e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Local Customer Name                    Customer Branch\n",
              "0     Colombiana de Comercio  Colombiana De Comercio - Apartadó\n",
              "1     Colombiana de Comercio  Colombiana De Comercio - Apartadó\n",
              "2     Colombiana de Comercio  Colombiana De Comercio - Apartadó\n",
              "3     Colombiana de Comercio  Colombiana De Comercio - Apartadó\n",
              "4     Colombiana de Comercio  Colombiana De Comercio - Apartadó\n",
              "...                      ...                                ...\n",
              "5879  Colombiana de Comercio  Colombiana De Comercio - Medellin\n",
              "5880  Colombiana de Comercio  Colombiana De Comercio - Medellin\n",
              "5881  Colombiana de Comercio  Colombiana De Comercio - Medellin\n",
              "5882  Colombiana de Comercio  Colombiana De Comercio - Medellin\n",
              "5883  Colombiana de Comercio  Colombiana De Comercio - Medellin\n",
              "\n",
              "[5884 rows x 2 columns]"
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              "      <td>Colombiana De Comercio - Apartadó</td>\n",
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              "      <td>Colombiana De Comercio - Apartadó</td>\n",
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              "    <tr>\n",
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              "<p>5884 rows × 2 columns</p>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To get 1000 elements using loc, you will need to go one lower and ask for *df.loc[0:999]*"
      ],
      "metadata": {
        "id": "ELFwMWF225J8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Manipulating the index"
      ],
      "metadata": {
        "id": "BdAonCUc3SbD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.set_index(\"Local Customer Name\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "id": "Re4IjBwq0SwA",
        "outputId": "97a01213-48e5-47fa-dd46-395c32c53086"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "                                          Customer Branch  \\\n",
              "Local Customer Name                                         \n",
              "Colombiana de Comercio  Colombiana De Comercio - Apartadó   \n",
              "Colombiana de Comercio  Colombiana De Comercio - Apartadó   \n",
              "Colombiana de Comercio  Colombiana De Comercio - Apartadó   \n",
              "Colombiana de Comercio  Colombiana De Comercio - Apartadó   \n",
              "Colombiana de Comercio  Colombiana De Comercio - Apartadó   \n",
              "...                                                   ...   \n",
              "Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "\n",
              "                                    Sub Sector      Item GTIN Month (Real)  \\\n",
              "Local Customer Name                                                          \n",
              "Colombiana de Comercio             Fabric Care  7500435011884      2022M02   \n",
              "Colombiana de Comercio             Fabric Care  7500435011884      2022M03   \n",
              "Colombiana de Comercio             Fabric Care  7500435011884      2022M04   \n",
              "Colombiana de Comercio             Fabric Care  7500435011884      2022M06   \n",
              "Colombiana de Comercio             Fabric Care  7500435011884      2022M07   \n",
              "...                                        ...            ...          ...   \n",
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              "\n",
              "                        Shipment GIV Value  \n",
              "Local Customer Name                         \n",
              "Colombiana de Comercio            973066.0  \n",
              "Colombiana de Comercio            486533.0  \n",
              "Colombiana de Comercio            973066.0  \n",
              "Colombiana de Comercio            583840.0  \n",
              "Colombiana de Comercio            583840.0  \n",
              "...                                    ...  \n",
              "Colombiana de Comercio           1810920.0  \n",
              "Colombiana de Comercio           3621840.0  \n",
              "Colombiana de Comercio            672011.0  \n",
              "Colombiana de Comercio            672011.0  \n",
              "Colombiana de Comercio            940815.0  \n",
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              "      <td>2022M06</td>\n",
              "      <td>583840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Colombiana de Comercio</th>\n",
              "      <td>Colombiana De Comercio - Apartadó</td>\n",
              "      <td>Fabric Care</td>\n",
              "      <td>7500435011884</td>\n",
              "      <td>2022M07</td>\n",
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              "      <th>Colombiana de Comercio</th>\n",
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              "      <td>1810920.0</td>\n",
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              "    <tr>\n",
              "      <th>Colombiana de Comercio</th>\n",
              "      <td>Colombiana De Comercio - Medellin</td>\n",
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              "      <td>2022M07</td>\n",
              "      <td>3621840.0</td>\n",
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              "    <tr>\n",
              "      <th>Colombiana de Comercio</th>\n",
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              "      <td>7702018913664</td>\n",
              "      <td>2022M03</td>\n",
              "      <td>672011.0</td>\n",
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              "    <tr>\n",
              "      <th>Colombiana de Comercio</th>\n",
              "      <td>Colombiana De Comercio - Medellin</td>\n",
              "      <td>Skin and Personal Care</td>\n",
              "      <td>7702018913664</td>\n",
              "      <td>2022M05</td>\n",
              "      <td>672011.0</td>\n",
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              "      <th>Colombiana de Comercio</th>\n",
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              "      <td>7702018913664</td>\n",
              "      <td>2022M06</td>\n",
              "      <td>940815.0</td>\n",
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              "<p>5884 rows × 5 columns</p>\n",
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              "                                                     [key], {});\n",
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              "  "
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          "metadata": {},
          "execution_count": 29
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataloc = data.loc[data[\"Customer Branch\"] ==\"Colombiana De Comercio - Medellin\"]\n",
        "dataloc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "Mjv-7zou9a6D",
        "outputId": "4a97153d-41f4-4eed-d56e-9d7ded87e4e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Local Customer Name                    Customer Branch  \\\n",
              "4376  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "4377  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "4378  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "4379  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "4380  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "...                      ...                                ...   \n",
              "5879  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "5880  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "5881  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "5882  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "5883  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "\n",
              "                  Sub Sector      Item GTIN Month (Real)  Shipment GIV Value  \n",
              "4376             Fabric Care  7500435011884      2022M02          19461330.0  \n",
              "4377             Fabric Care  7500435011884      2022M03          26272794.0  \n",
              "4378             Fabric Care  7500435011884      2022M04           9730665.0  \n",
              "4379             Fabric Care  7500435011884      2022M05           4865332.0  \n",
              "4380             Fabric Care  7500435011884      2022M06          14109464.0  \n",
              "...                      ...            ...          ...                 ...  \n",
              "5879  Skin and Personal Care  7702018913640      2022M06           1810920.0  \n",
              "5880  Skin and Personal Care  7702018913640      2022M07           3621840.0  \n",
              "5881  Skin and Personal Care  7702018913664      2022M03            672011.0  \n",
              "5882  Skin and Personal Care  7702018913664      2022M05            672011.0  \n",
              "5883  Skin and Personal Care  7702018913664      2022M06            940815.0  \n",
              "\n",
              "[1508 rows x 6 columns]"
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              "      <th>4380</th>\n",
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              "      <td>Fabric Care</td>\n",
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              "      <td>Colombiana de Comercio</td>\n",
              "      <td>Colombiana De Comercio - Medellin</td>\n",
              "      <td>Skin and Personal Care</td>\n",
              "      <td>7702018913640</td>\n",
              "      <td>2022M07</td>\n",
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              "      <th>5881</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>Colombiana De Comercio - Medellin</td>\n",
              "      <td>Skin and Personal Care</td>\n",
              "      <td>7702018913664</td>\n",
              "      <td>2022M03</td>\n",
              "      <td>672011.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5882</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>Colombiana De Comercio - Medellin</td>\n",
              "      <td>Skin and Personal Care</td>\n",
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              "      <th>5883</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>Colombiana De Comercio - Medellin</td>\n",
              "      <td>Skin and Personal Care</td>\n",
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              "      <td>940815.0</td>\n",
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              "<p>1508 rows × 6 columns</p>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-ba7841b4-f691-4d3f-a1a8-e25c04aa5b47');\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",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.loc[(data[\"Customer Branch\"]==\"Colombiana De Comercio - Medellin\" ) & (data[\"Shipment GIV Value\"]>=60000) & (data[\"Shipment GIV Value\"]<=100000)]\n",
        "# filtering with loc, that means using index labels, it is very useful with multiple conditions\n",
        "# df.loc[df[\"column label\"]==\"some string\"] \n",
        "# df.loc[df[\"column labe\"]]==some value] \n",
        "#With multiple conditions, we can include parenthesis at the start and at the end of each condition"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "ySuSXSyZBAZp",
        "outputId": "55f45776-aeb3-47d0-a44e-0f91c12eac05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Local Customer Name                    Customer Branch Sub Sector  \\\n",
              "4729  Colombiana de Comercio  Colombiana De Comercio - Medellin  Hair Care   \n",
              "4731  Colombiana de Comercio  Colombiana De Comercio - Medellin  Hair Care   \n",
              "4894  Colombiana de Comercio  Colombiana De Comercio - Medellin  Hair Care   \n",
              "4938  Colombiana de Comercio  Colombiana De Comercio - Medellin  Hair Care   \n",
              "5492  Colombiana de Comercio  Colombiana De Comercio - Medellin        PHC   \n",
              "\n",
              "          Item GTIN Month (Real)  Shipment GIV Value  \n",
              "4729   190679000200      2022M03            86373.00  \n",
              "4731   190679000200      2022M05            86373.00  \n",
              "4894  7500435139588      2022M05            86373.00  \n",
              "4938  7500435145695      2022M04            86373.00  \n",
              "5492  7500435170857      2022M05            73519.27  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Local Customer Name</th>\n",
              "      <th>Customer Branch</th>\n",
              "      <th>Sub Sector</th>\n",
              "      <th>Item GTIN</th>\n",
              "      <th>Month (Real)</th>\n",
              "      <th>Shipment GIV Value</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4729</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>Colombiana De Comercio - Medellin</td>\n",
              "      <td>Hair Care</td>\n",
              "      <td>190679000200</td>\n",
              "      <td>2022M03</td>\n",
              "      <td>86373.00</td>\n",
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              "    <tr>\n",
              "      <th>4731</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>Colombiana De Comercio - Medellin</td>\n",
              "      <td>Hair Care</td>\n",
              "      <td>190679000200</td>\n",
              "      <td>2022M05</td>\n",
              "      <td>86373.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4894</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>Colombiana De Comercio - Medellin</td>\n",
              "      <td>Hair Care</td>\n",
              "      <td>7500435139588</td>\n",
              "      <td>2022M05</td>\n",
              "      <td>86373.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4938</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>Colombiana De Comercio - Medellin</td>\n",
              "      <td>Hair Care</td>\n",
              "      <td>7500435145695</td>\n",
              "      <td>2022M04</td>\n",
              "      <td>86373.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5492</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>Colombiana De Comercio - Medellin</td>\n",
              "      <td>PHC</td>\n",
              "      <td>7500435170857</td>\n",
              "      <td>2022M05</td>\n",
              "      <td>73519.27</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7146990e-7194-4aa8-a745-c70d8dae0741')\"\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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              "  </div>\n",
              "  "
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          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Also working with multiple loc conditions using OR clause\n",
        "data2 = data.loc[(data[\"Customer Branch\"]==\"Colombiana De Comercio - Medellin\") | (data[\"Shipment GIV Value\"]>=60000)]\n",
        "data2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "5pcMDj4dECti",
        "outputId": "13cdc09a-de37-403d-bba6-06226d95bf6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Local Customer Name                    Customer Branch  \\\n",
              "0     Colombiana de Comercio  Colombiana De Comercio - Apartadó   \n",
              "1     Colombiana de Comercio  Colombiana De Comercio - Apartadó   \n",
              "2     Colombiana de Comercio  Colombiana De Comercio - Apartadó   \n",
              "3     Colombiana de Comercio  Colombiana De Comercio - Apartadó   \n",
              "4     Colombiana de Comercio  Colombiana De Comercio - Apartadó   \n",
              "...                      ...                                ...   \n",
              "5879  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "5880  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "5881  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "5882  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "5883  Colombiana de Comercio  Colombiana De Comercio - Medellin   \n",
              "\n",
              "                  Sub Sector      Item GTIN Month (Real)  Shipment GIV Value  \n",
              "0                Fabric Care  7500435011884      2022M02            973066.0  \n",
              "1                Fabric Care  7500435011884      2022M03            486533.0  \n",
              "2                Fabric Care  7500435011884      2022M04            973066.0  \n",
              "3                Fabric Care  7500435011884      2022M06            583840.0  \n",
              "4                Fabric Care  7500435011884      2022M07            583840.0  \n",
              "...                      ...            ...          ...                 ...  \n",
              "5879  Skin and Personal Care  7702018913640      2022M06           1810920.0  \n",
              "5880  Skin and Personal Care  7702018913640      2022M07           3621840.0  \n",
              "5881  Skin and Personal Care  7702018913664      2022M03            672011.0  \n",
              "5882  Skin and Personal Care  7702018913664      2022M05            672011.0  \n",
              "5883  Skin and Personal Care  7702018913664      2022M06            940815.0  \n",
              "\n",
              "[5846 rows x 6 columns]"
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              "      <th>1</th>\n",
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              "      <th>5881</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
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              "      <td>Skin and Personal Care</td>\n",
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              "      <th>5882</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>Colombiana De Comercio - Medellin</td>\n",
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              "          const element = document.querySelector('#df-31223e17-0f49-46bb-8524-4f1cac32b366');\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": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = data.loc[data['Customer Branch'].notnull()]\n",
        "new_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
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        "id": "GykMkUDJIejt",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Local Customer Name                    Customer Branch   Sub Sector  \\\n",
              "0  Colombiana de Comercio  Colombiana De Comercio - Apartadó  Fabric Care   \n",
              "1  Colombiana de Comercio  Colombiana De Comercio - Apartadó  Fabric Care   \n",
              "2  Colombiana de Comercio  Colombiana De Comercio - Apartadó  Fabric Care   \n",
              "3  Colombiana de Comercio  Colombiana De Comercio - Apartadó  Fabric Care   \n",
              "4  Colombiana de Comercio  Colombiana De Comercio - Apartadó  Fabric Care   \n",
              "\n",
              "       Item GTIN Month (Real)  Shipment GIV Value  \n",
              "0  7500435011884      2022M02            973066.0  \n",
              "1  7500435011884      2022M03            486533.0  \n",
              "2  7500435011884      2022M04            973066.0  \n",
              "3  7500435011884      2022M06            583840.0  \n",
              "4  7500435011884      2022M07            583840.0  "
            ],
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              "      <td>Colombiana de Comercio</td>\n",
              "      <td>Colombiana De Comercio - Apartadó</td>\n",
              "      <td>Fabric Care</td>\n",
              "      <td>7500435011884</td>\n",
              "      <td>2022M02</td>\n",
              "      <td>973066.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>Colombiana De Comercio - Apartadó</td>\n",
              "      <td>Fabric Care</td>\n",
              "      <td>7500435011884</td>\n",
              "      <td>2022M03</td>\n",
              "      <td>486533.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>Colombiana De Comercio - Apartadó</td>\n",
              "      <td>Fabric Care</td>\n",
              "      <td>7500435011884</td>\n",
              "      <td>2022M04</td>\n",
              "      <td>973066.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>Colombiana De Comercio - Apartadó</td>\n",
              "      <td>Fabric Care</td>\n",
              "      <td>7500435011884</td>\n",
              "      <td>2022M06</td>\n",
              "      <td>583840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>Colombiana De Comercio - Apartadó</td>\n",
              "      <td>Fabric Care</td>\n",
              "      <td>7500435011884</td>\n",
              "      <td>2022M07</td>\n",
              "      <td>583840.0</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
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        "new_df = data.loc[data['Customer Branch'].isnull()]\n",
        "new_df.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 93
        },
        "id": "mAcmHrpDIeqp",
        "outputId": "7deb684a-8b5f-43dc-dc93-294b6e214ff7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Local Customer Name, Customer Branch, Sub Sector, Item GTIN, Month (Real), Shipment GIV Value]\n",
              "Index: []"
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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": "markdown",
      "source": [
        "Assigning Data"
      ],
      "metadata": {
        "id": "iwIC3mhxJTo7"
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    },
    {
      "cell_type": "code",
      "source": [
        "data['Customer Branch'] = data['Customer Branch'] = 'Dilson'\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "pIRCEsnGJSNB",
        "outputId": "09f4d9e0-b0e5-43be-d567-7fe00683ec34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Local Customer Name Customer Branch              Sub Sector  \\\n",
              "0     Colombiana de Comercio          Dilson             Fabric Care   \n",
              "1     Colombiana de Comercio          Dilson             Fabric Care   \n",
              "2     Colombiana de Comercio          Dilson             Fabric Care   \n",
              "3     Colombiana de Comercio          Dilson             Fabric Care   \n",
              "4     Colombiana de Comercio          Dilson             Fabric Care   \n",
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              "5883  Colombiana de Comercio          Dilson  Skin and Personal Care   \n",
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              "          Item GTIN Month (Real)  Shipment GIV Value  \n",
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              "      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-03c6d3f4-9d6d-483e-adfb-869606165dd3 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-03c6d3f4-9d6d-483e-adfb-869606165dd3');\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": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Customer Branch'] = data['Customer Branch'] = range(5884,0,-1) #creating a descending id column values\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "w8OhQwtEKIL1",
        "outputId": "9a56bf89-91fe-4153-b71a-4a611f32d748"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Local Customer Name  Customer Branch              Sub Sector  \\\n",
              "0     Colombiana de Comercio             5884             Fabric Care   \n",
              "1     Colombiana de Comercio             5883             Fabric Care   \n",
              "2     Colombiana de Comercio             5882             Fabric Care   \n",
              "3     Colombiana de Comercio             5881             Fabric Care   \n",
              "4     Colombiana de Comercio             5880             Fabric Care   \n",
              "...                      ...              ...                     ...   \n",
              "5879  Colombiana de Comercio                5  Skin and Personal Care   \n",
              "5880  Colombiana de Comercio                4  Skin and Personal Care   \n",
              "5881  Colombiana de Comercio                3  Skin and Personal Care   \n",
              "5882  Colombiana de Comercio                2  Skin and Personal Care   \n",
              "5883  Colombiana de Comercio                1  Skin and Personal Care   \n",
              "\n",
              "          Item GTIN Month (Real)  Shipment GIV Value  \n",
              "0     7500435011884      2022M02            973066.0  \n",
              "1     7500435011884      2022M03            486533.0  \n",
              "2     7500435011884      2022M04            973066.0  \n",
              "3     7500435011884      2022M06            583840.0  \n",
              "4     7500435011884      2022M07            583840.0  \n",
              "...             ...          ...                 ...  \n",
              "5879  7702018913640      2022M06           1810920.0  \n",
              "5880  7702018913640      2022M07           3621840.0  \n",
              "5881  7702018913664      2022M03            672011.0  \n",
              "5882  7702018913664      2022M05            672011.0  \n",
              "5883  7702018913664      2022M06            940815.0  \n",
              "\n",
              "[5884 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Local Customer Name</th>\n",
              "      <th>Customer Branch</th>\n",
              "      <th>Sub Sector</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>5884</td>\n",
              "      <td>Fabric Care</td>\n",
              "      <td>7500435011884</td>\n",
              "      <td>2022M02</td>\n",
              "      <td>973066.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>5883</td>\n",
              "      <td>Fabric Care</td>\n",
              "      <td>7500435011884</td>\n",
              "      <td>2022M03</td>\n",
              "      <td>486533.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>5882</td>\n",
              "      <td>Fabric Care</td>\n",
              "      <td>7500435011884</td>\n",
              "      <td>2022M04</td>\n",
              "      <td>973066.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>5881</td>\n",
              "      <td>Fabric Care</td>\n",
              "      <td>7500435011884</td>\n",
              "      <td>2022M06</td>\n",
              "      <td>583840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>5880</td>\n",
              "      <td>Fabric Care</td>\n",
              "      <td>7500435011884</td>\n",
              "      <td>2022M07</td>\n",
              "      <td>583840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>5879</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>5</td>\n",
              "      <td>Skin and Personal Care</td>\n",
              "      <td>7702018913640</td>\n",
              "      <td>2022M06</td>\n",
              "      <td>1810920.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5880</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>4</td>\n",
              "      <td>Skin and Personal Care</td>\n",
              "      <td>7702018913640</td>\n",
              "      <td>2022M07</td>\n",
              "      <td>3621840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5881</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>3</td>\n",
              "      <td>Skin and Personal Care</td>\n",
              "      <td>7702018913664</td>\n",
              "      <td>2022M03</td>\n",
              "      <td>672011.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5882</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>2</td>\n",
              "      <td>Skin and Personal Care</td>\n",
              "      <td>7702018913664</td>\n",
              "      <td>2022M05</td>\n",
              "      <td>672011.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5883</th>\n",
              "      <td>Colombiana de Comercio</td>\n",
              "      <td>1</td>\n",
              "      <td>Skin and Personal Care</td>\n",
              "      <td>7702018913664</td>\n",
              "      <td>2022M06</td>\n",
              "      <td>940815.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5884 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1105bc00-01a2-4fbd-af47-99b705adc1b6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \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",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "      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-1105bc00-01a2-4fbd-af47-99b705adc1b6 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-1105bc00-01a2-4fbd-af47-99b705adc1b6');\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": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "k6knIKu0Klqd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}