pd.merge two dataframes on index that is a date in python

To merge two dataframes using the index as a date in Python, you can use the pd.merge() function from the pandas library. Here's an example:

main.py
import pandas as pd

# Create two dataframes with date as the index
df1 = pd.DataFrame({'A': [1, 2, 3]}, index=pd.to_datetime(['2020-01-01', '2020-01-02', '2020-01-03']))
df2 = pd.DataFrame({'B': [4, 5, 6]}, index=pd.to_datetime(['2020-01-01', '2020-01-03', '2020-01-04']))

# Merge the dataframes on the index (date)
merged_df = pd.merge(df1, df2, left_index=True, right_index=True, how='inner')

print(merged_df)
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In this example, we have two dataframes, df1 and df2, with the same index (date). We use the pd.merge() function and pass left_index=True and right_index=True to indicate that we want to merge on the index. The how='inner' parameter specifies that we want to perform an inner join, only keeping the rows that have matching index values in both dataframes.

The resulting merged dataframe, merged_df, will contain only the rows where the index (date) is present in both df1 and df2.

Output:

main.py
            A  B
2020-01-01  1  4
2020-01-03  3  5
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Note that if your dataframes have different column names, you can also specify the left_on and right_on parameters instead of left_index and right_index to merge on specific columns instead of the index.