Pandas：如何将 pivot 行转换为列

Question

I am looking for a way to pivot around 600 columns into rows. Here's a sample with only 4 of those columns (good, bad, ok, Horrible):

df:

RecordID   good   bad   ok  Horrible
  A          0      0    1     0 
  B          1      0    0     1

desired output:

 RecordID     Column    Value
   A           Good       0
   A            Bad       0
   A            Ok        1
   A          Horrible    0
   B           Good       1
   B            Bad       0
   B            Ok        0
   B          Horrible    1

Answer 1

You can use .stack() as follows. Using .stack() is preferred as it naturally resulted in rows already sorted in the order of RecordID so that you don't need to waste processing time sorting on it again, especially important when you have a large number of columns .

df = df.set_index('RecordID').stack().reset_index().rename(columns={'level_1': 'Column', 0: 'Value'})

Output:

  RecordID    Column  Value
0        A      good      0
1        A       bad      0
2        A        ok      1
3        A  Horrible      0
4        B      good      1
5        B       bad      0
6        B        ok      0
7        B  Horrible      1

Answer 2

You can use melt function:

(df.melt(id_vars='RecordID', var_name='Column', value_name='Value')
   .sort_values('RecordID')
   .reset_index(drop=True)
)

Output:

    RecordID      Column    Value
0          A        good        0
1          A         bad        0
2          A          ok        1
3          A    Horrible        0
4          B        good        1
5          B         bad        0
6          B          ok        0
7          B    Horrible        1

Answer 3

Adding dataframe:

import pandas as pd
import numpy as np

data2 = {'RecordID': ['a', 'b', 'c'],
        'good': [0, 1, 1],
        'bad': [0, 0, 1],
        'horrible': [0, 1, 1],
        'ok': [1, 0, 0]}
  
# Convert the dictionary into DataFrame
df = pd.DataFrame(data2)

Melt data: https://pandas.pydata.org/docs/reference/api/pandas.melt.html

melted = df.melt(id_vars='RecordID', var_name='Column', value_name='Value')
melted

Optionally: Group By - for summ or mean values:

f2 = melted.groupby(['Column']).sum()
df2

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