df.rename(columns={'oldName1': 'newName1',
'oldName2': 'newName2'},
inplace=True, errors='raise')
# Make sure you set inplace to True if you want the change
# to be applied to the dataframe>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(columns={"A": "a", "B": "c"})
a c
0 1 4
1 2 5
2 3 6
df_new = df.rename(columns={'A': 'a'}, index={'ONE': 'one'})
print(df_new)
# a B C
# one 11 12 13
# TWO 21 22 23
# THREE 31 32 33
print(df)
# A B C
# ONE 11 12 13
# TWO 21 22 23
# THREE 31 32 33
>>> df.rename({1: 2, 2: 4}, axis='index')
A B
0 1 4
2 2 5
4 3 6
# Simple use case for pd.rename()
'''
old parameter = 'Data.Population'
new parameter = 'Population'
df.rename(columns={'old parameter': 'new parameter'}, inplace = True)
inplace = True : means to change object in real time
'''
# view below for visual aids
df.rename(columns={'Data.Population': 'Population'}, inplace = True)
# old columns
|'Data.Population'|
|_________________|
| 0 |
|_________________|
# new output:
# new rename column
|'Population' |
|_____________|
| 0 |
|_____________|
>>> df.index
RangeIndex(start=0, stop=3, step=1)
>>> df.rename(index=str).index
Index(['0', '1', '2'], dtype='object')