How to unstack the table based on specific columns in python

Question

I have a table like this name df1

ID       M1  M2   NO        DTime         Result(P2-P3) (P1-P2) (P1-P7) (P3-P7) 
2801596 288 371  536529 08-02-2023 11:07    1    NaN    0.085    NaN      NaN
2801596 289 371  536529 08-02-2023 11:07    1     1     0.032   1.081     NaN
2801584 290 372  541278 08-02-2023 11:10    1    NaN    0.081    NaN      NaN
2801584 291 372  541278 08-02-2023 11:10    0     1     0.037   1.065     NaN
2801598 288 371  541279 08-02-2023 11:12    1    NaN    0.076    NaN      NaN
2801599 288 371  555623 08-02-2023 11:14    1     1      NaN     NaN     3.871
2801599 289 371  555623 08-02-2023 11:14    1     1      NaN     NaN     2.389
2801600 291 372  555624 08-02-2023 11:18    1    NaN    0.0835   NaN      NaN

I have tried using Pivot table but it is giving a table full of Nan.

df2 = pd.pivot_table(df1, values=['Result','(P2-P3)','(P1-P2)','(P1-P7)','(P3-P7)'], index=['ID','No','DTime'],columns=['M2','M1'], aggfunc='first')

I want this table as output

jezrael · Accepted Answer · 2023-06-12 09:35:54Z

2

Add DataFrame.reorder_levels with DataFrame.sort_index:

df2 = df1.pivot_table(values=['Result','(P2-P3)','(P1-P2)','(P1-P7)','(P3-P7)'],
                     index=['ID','NO','DTime'],
                     columns=['M2','M1'],
                     aggfunc='first').reorder_levels([1,2,0], axis=1).sort_index(axis=1)

If need processing all columns from input DataFrame not set in index and columns parameter is possible omit values:

df2 = df1.pivot_table(index=['ID','NO','DTime'],
                     columns=['M2','M1'],
                     aggfunc='first').reorder_levels([1,2,0], axis=1).sort_index(axis=1)

Input data:

df1 = pd.DataFrame({'ID': {0: 2801596, 1: 2801596, 2: 2801584, 3: 2801584, 4: 2801598, 5: 2801599, 6: 2801599, 7: 2801600}, 
                    'M1': {0: 288, 1: 289, 2: 290, 3: 291, 4: 288, 5: 288, 6: 289, 7: 291},
                    'M2': {0: 371, 1: 371, 2: 372, 3: 372, 4: 371, 5: 371, 6: 371, 7: 372}, 
                    'NO': {0: 536529, 1: 536529, 2: 541278, 3: 541278, 4: 541279, 5: 555623, 6: 555623, 7: 555624}, 
                    'DTime': {0: '08-02-2023 11:07', 1: '08-02-2023 11:07', 2: '08-02-2023 11:10', 3: '08-02-2023 11:10', 
                              4: '08-02-2023 11:12', 5: '08-02-2023 11:14', 6: '08-02-2023 11:14', 7: '08-02-2023 11:18'}, 
                    'Result': {0: 1, 1: 1, 2: 1, 3: 0, 4: 1, 5: 1, 6: 1, 7: 1}, 
                    '(P2-P3)': {0: np.nan , 1: 1.0, 2: np.nan , 3: 1.0, 4: np.nan , 5: 1.0, 6: 1.0, 7: np.nan }, 
                    '(P1-P2)': {0: 0.085, 1: 0.032, 2: 0.081, 3: 0.037, 4: 0.076, 5: np.nan , 6: np.nan , 7: 0.0835},
                    '(P1-P7)': {0: np.nan , 1: 1.081, 2: np.nan , 3: 1.065, 4: np.nan , 5: np.nan , 6: np.nan , 7: np.nan },
                    '(P3-P7)': {0: np.nan , 1: np.nan , 2: np.nan , 3: np.nan , 4: np.nan , 5: 3.871, 6: 2.389, 7: np.nan }})

edited Jun 12, 2023 at 9:35

answered Jun 12, 2023 at 9:15

jezrael

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2 Comments

Nandan Over a year ago

I have tried this, but not getting any values in the resultant table. all the table entires are filled with Nan values.

jezrael Over a year ago

@Nandan - Problem is with your data - only seems NaNs in all rows, but there is at least one no NaN value per row. Reason is because many combinations ['ID','NO','DTime'] and ['M2','M1'] in real data. Pandas get intersection of value and all another values per row not existed, so added missing values.

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