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.
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()
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df.groupby(df.columns[0]... correctly groups on the first column although it would be usual just to use df.groupby('species').... When you use df.groupby(df.iloc[:, 0]... then this applies sum to the content of the first column (ie. concatenates the String values) as well applying sum to the other numerical columns .
If you try print(df.columns[0]) and also print(df.iloc[:, 0]) then you will see that the first is the selected column name and the second is a Pandas Series with the values in the column.
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