How to select all rows with the same name but different values in Python

Say I have the pandas DataFarme looks like this:

    Name  VALUE
0    A      1
1    A      2
2    A      3
3    B      4
4    B      5
5    C      6
6    C      7
7    C      8
8    C      9
9    D     10

I would like to select all the rows with the same name but different values below:

    Name  Value
0    A      1
1    A      2
2    A      3


    Name  Value
0    C      6
1    C      7
2    C      8
3    C      9

I tried to use .T to transform the rows and columns, and then use .groupby to select all columns with the same name. But, there are two questions: firstly, .groupby can not choose the same columns; secondly, .groupby actually adds all values in one row with the same name, which is not the result I want.

Then, I tried to use .duplicated(keep=False), but this code only tells me if the data is the same or not.

What code should I use? Actually. I have a thousand data and more than 20 of the same name rows as “A”, “B”, “C”, “D”, up to “Z”. Each name has a thousand different values.

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Answers:

Method 1

Answers:

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Method 1

If you want to get different dataframes then here’s how you can do it:

df_splits = [v for k, v in df.groupby('Name')]
for df_split in df_splits:
    print(df_split, sep = 'n')

Output:

  Name  VALUE
0    A      1
1    A      2
2    A      3
  Name  VALUE
3    B      4
4    B      5
  Name  VALUE
5    C      6
6    C      7
7    C      8
8    C      9
  Name  VALUE
9    D     10

To access dataframes individually use:

df_splits[0], df_splits[1]….

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0

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