How to retrieve the first row of each group in a Pandas DataFrame based on multiple columns?

Retrieve the First Row of Each Group in a Pandas DataFrame

Question:

How can you efficiently extract the first row of each group from a Pandas DataFrame, where the grouping is defined by multiple columns?

Answer:

To retrieve the first row of each group in a Pandas DataFrame based on multiple columns:

1. Group the Data: Group the DataFrame by the desired columns using the groupby() method:

   df_grouped = df.groupby(['id', 'value'])

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2. Apply an Aggregation Function: Apply the first() function to each group to obtain the first non-null element:

   df_first_rows = df_grouped.first()

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3. Reset the Index (Optional): If you need the 'id' and 'value' columns as separate columns, use the reset_index() method:

   df_first_rows = df_first_rows.reset_index()

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Example:

Consider the following DataFrame:

df = pd.DataFrame({'id': [1, 1, 1, 2, 2, 3, 3, 3, 3, 4, 4, 5, 6, 6, 6, 7, 7],
                   'value': ["first", "second", "second", "first",
                             "second", "first", "third", "fourth",
                             "fifth", "second", "fifth", "first",
                             "first", "second", "third", "fourth", "fifth"]})

Applying the上記の steps:

df_grouped = df.groupby(['id', 'value'])
df_first_rows = df_grouped.first()
df_first_rows = df_first_rows.reset_index()

print(df_first_rows)

Output:

   id   value
0   1   first
1   2   first
2   3   first
3   4   second
4   5   first
5   6   first
6   7   fourth

This code successfully retrieves the first row of each group defined by the 'id' and 'value' columns.

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