pythonpandasdataframe

What are the reasons for the differences in output results when using the groupby function in the Python pandas package?

Hi, I have recently been practicing data processing with Python pandas, and I have encountered an issue related to the groupby function,here is my file and code:

#my file
data = {
    'species': ['a', 'b', 'c', 'd', 'e', 'rt', 'gh', 'ed', 'e', 'd', 'd', 'q', 'ws', 'f', 'fg', 'a', 'a', 'a', 'a', 'a'],
    's1': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20],
    's2': [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],
    's3': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21],
    's4': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]
}

df = pd.DataFrame(data)

#my code:
grouped_df1 = df.groupby(df.columns[0], as_index=False).sum()

grouped_df2 = df.groupby(df.iloc[:, 0], as_index=False).sum()

I want to understand that both grouped_df1 and grouped_df2 group by the data in the 0th column, but when outputting, grouped_df1 successfully merges rows with the same values in the 0th column into one, which is the result I want. However, grouped_df2 instead combines the identical strings in the 0th column into one long string during the merge, rather than merging them into a single row.Here are the outputs:

print(grouped_df1)
   species  s1  s2  s3  s4
0        a  91  54  97  60
1        b   2   9   3  10
2        c   3   9   4  10
3        d  25  27  28  30
4        e  14  18  16  20
5       ed   8   9   9  10
6        f  14   9  15  10
7       fg  15   9  16  10
8       gh   7   9   8  10
9        q  12   9  13  10
10      rt   6   9   7  10
11      ws  13   9  14  10

print(grouped_df2)
   species  s1  s2  s3  s4
0   aaaaaa  91  54  97  60
1        b   2   9   3  10
2        c   3   9   4  10
3      ddd  25  27  28  30
4       ee  14  18  16  20
5       ed   8   9   9  10
6        f  14   9  15  10
7       fg  15   9  16  10
8       gh   7   9   8  10
9        q  12   9  13  10
10      rt   6   9   7  10
11      ws  13   9  14  10

So far, I still don't know the reason. I would greatly appreciate it if you could help answer this question.

Solution

  • In groupby - column name is treated as an intrinsic grouping key, while a Series is treated as an external key.

    Reference - https://pandas.pydata.org/docs/reference/groupby.html

    When using df.iloc[:, 0]:

    Pandas considers the string values in the species column as a separate grouping key independent of the DataFrame structure.

    When using df.columns[0]:

    Pandas directly uses the column 'species' within the DataFrame as the grouping key. This allows Pandas to manage the grouping and summation correctly.

    Code COrrection

    You should always reference the column name explicitly

    grouped_df1 = df.groupby('species', as_index=False).sum()

    Or this also works

    grouped_df1 = df.groupby(df[df.columns[0]], as_index=False).sum()