How to Filter Pandas DataFrames on Dates to Keep Only Rows Within the Next Two Months?

Filtering Pandas DataFrames on Dates

In this scenario, you encounter a Pandas DataFrame containing a 'date' column. Your objective is to filter out rows having dates that fall outside the next two months, retaining only those within this time frame.

Methodological Approach

To achieve this goal effectively, consider the following methodologies:

1. Label-Based Indexing: If the 'date' column is set as the index, you can use .loc for label-based indexing. For instance:

df.loc['2014-01-01':'2014-02-01']

2. Positional Indexing: Alternatively, .iloc can be employed for positional indexing.

3. Column Conversion: If the 'date' column is not the index, you can either:

   a. Make it the index (temporarily or permanently for time-series data).

   b. Utilize the following filter:

df[(df['date'] > '2013-01-01') & (df['date'] < '2013-02-01')]

Additional Considerations

Note that .ix is now deprecated. For further insights into indexing in Pandas DataFrames, refer to the documentation available at http://pandas.pydata.org/pandas-docs/stable/dsintro.html#indexing-selection.

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