How to Filter Pandas DataFrames by Date Range for the Next Two Months?

Filtering Pandas DataFrames by Date Range

When dealing with time-series data in Pandas, it's often necessary to filter rows based on specific date ranges. This article addresses how to efficiently filter a Pandas DataFrame to retain only rows within the next two months.

Date Column as Index

If the 'date' column is set as the index of the DataFrame, you can use label-based indexing or positional indexing to extract the desired rows. For instance, to select rows with dates within the next two months:

df.loc['2023-03-01':'2023-04-30']  # Label-based indexing
df.iloc[pd.date_range('2023-03-01', '2023-04-30', freq='D').index]  # Positional indexing

Date Column Not as Index

If the 'date' column is not the index, you have two options:

1. Convert the Date Column to Index: You can convert the 'date' column to the index temporarily or permanently, making data access easier and more efficient.
2. Use Boolean Indexing: You can use logical operations and comparison operators to filter rows based on the 'date' column's values:

df[(df['date'] >= '2023-03-01') & (df['date'] <= '2023-04-30')]

Note that the .ix accessor is deprecated, and it's recommended to use .loc or .iloc instead.

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