Pandas Dataframe 按列索引范围对列进行分组并获取组总和

Question

I have a dataframe, formed of survey responses the column headers are scores and the number row beneath is counts of responses for those values

index          |  1  |  2 |  3 |  4 |  5  |  6 |  7 |  8 |  9  | 10 | 11 |
--------------------------------------------------------------------------
Business unit  | 100 | 50 | 25 | 50 | 100 | 60 | 80 | 75 | 100 | 50 | 50 |

How do I group these columns and retain the counts per the below

index          | <=6 | 7=> |
--------------------------------
Business unit  | 385 | 355 |

Answer 1

If you dataframe looks like this:

    1   2   3   4    5   6   7   8    9   10  11
0  100  50  25  50  100  60  80  75  100  50  50

You can use either pd.cut or np.digitize to bin your columns and operate on them with groupby(..., axis=1) to group horizontally instead of vertically.

Using pd.cut

bins = pd.cut(df.columns, [0, 6, 11], labels=["<=6", ">=7"])
summary_df = df.groupby(bins, axis=1).sum()

print(summary_df)
   <=6  >=7
0  385  355

Using np.digitize

bins = np.digitize(df.columns, [6], right=True)  # np.digitize is not aware of labels
summary_df = (
    df.groupby(bins, axis=1)
    .sum()
    .rename(columns={0: "<=6", 1: ">=7"})  # add our labels
)

print(summary_df)
   <=6  >=7
0  385  355

Answer 2

You can use .groupby() on boolean index (created by comparing column label values with certain value eg 7) and use GroupBy.sum() to sum up the columns content values. Finally, we rename the resulting dataframe column labels, as follows:

df.groupby(df.columns >= 7, axis=1).sum().rename({True: '>=7', False: '<=6'}, axis=1)

Output:

   <=6  >=7
0  385  355

Test Data Preparation:

data = {1: [100], 2: [50], 3: [25], 4: [50], 5: [100], 6: [60], 7: [80], 8: [75], 9: [100], 10: [50], 11: [50]}
df = pd.DataFrame(data)