How To Change The Columns Name From A Tuple To String?

I have used pd.pivot_table in pandas dataframe, and the columns names becomes tuples like ('A1', 'B1'), ('A1', 'B2')... and I want them to be like 'A1_B1', 'A1_B2'... I tried to us

Solution 1:

setup

df = pd.DataFrame(
    np.arange(8).reshape(2, 4),
    columns=[('A1', 'B1'), ('A2', 'B1'), ('A1', 'B2'), ('A2', 'B2')])

print(df)

   (A1, B1)  (A2, B1)  (A1, B2)  (A2, B2)
0         0         1         2         3
1         4         5         6         7

rename

df.rename(columns='_'.join, inplace=True)
print(df)

   A1_B1  A2_B1  A1_B2  A2_B2
0      0      1      2      3
1      4      5      6      7

map

df.columns = df.columns.map('_'.join)
print(df)

   A1_B1  A2_B1  A1_B2  A2_B2
0      0      1      2      3
1      4      5      6      7

Solution 2:

Use list comprehension:

df.columns = ['{}_{}'.format(x[0], x[1]) for x in df.columns]
print(df)
   A1_B1  A2_B1  A1_B2  A2_B2
0      0      1      2      3
1      4      5      6      7

Or:

df.columns = ['_'.join(x) for x in df.columns]
print(df)
   A1_B1  A2_B1  A1_B2  A2_B2
0      0      1      2      3
1      4      5      6      7

Solution 3:

You can use df.DataFrame.Index.map for this:

df1.columns.map(lambda t: t[0] + "_" + t[1])

Solution 4:

You might need to iterate.

final=[]
forxin df.columns.values:
    final.append(x[0]+'_'+x[1])
df.columns.values = final

Solution 5:

I used this approach:

mydic = dict() 
for i,var in enumerate(df.columns):
    ifisinstance(var, tuple): 
        mydic[var] = '{}_{}'.format(var[0], var[1])
df.rename(columns = mydic) 

This allows me to also handle the fact that the second input in my tuple was an integer which had become a float (and been appended an annoying ".0" decimal), by instead rounding off and specifying an integer

mydic[var] = '{}_{:d}'.format(var[0], round(var[1]))