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Unveiling the Power of Data Manipulation with Pandas
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Unveiling the Power of Data Manipulation with Pandas

Unveiling the Power of Data Manipulation with Pandas

IntroductionDataFramesImport and Export FilesFiltering the DataFrameGrouping in PandasData CleaningMerging in Pandas

bookFiltering the DataFrame

Filtering a pandas DataFrame refers to selecting rows based on a specific condition. You can filter a DataFrame using the [] operator with conditions inside, such as df[df['column'] > value], or the .query() method, like df.query('column > value').

For example, suppose you have a DataFrame df with columns "Name", "Age", and "Gender", and you want to select all rows where the values of the "Age" column are greater than 30. You can use the following code to filter the DataFrame:

You can also use the "&" (logical and) and "|" (logical or) operators to combine multiple conditions.

Tarea

  1. Filter the data DataFrame using multiple conditions (use the logical and operator to combine them):
    • The value of the 'MANAGER_ID' column is greater than 100.
    • The value of the 'LOCATION_ID' column is equal to 1700.

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Filtering a pandas DataFrame refers to selecting rows based on a specific condition. You can filter a DataFrame using the [] operator with conditions inside, such as df[df['column'] > value], or the .query() method, like df.query('column > value').

For example, suppose you have a DataFrame df with columns "Name", "Age", and "Gender", and you want to select all rows where the values of the "Age" column are greater than 30. You can use the following code to filter the DataFrame:

You can also use the "&" (logical and) and "|" (logical or) operators to combine multiple conditions.

Tarea

  1. Filter the data DataFrame using multiple conditions (use the logical and operator to combine them):
    • The value of the 'MANAGER_ID' column is greater than 100.
    • The value of the 'LOCATION_ID' column is equal to 1700.

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Sección 1. Capítulo 4
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