How to Rename pandas columns | pandas.DataFrame.rename function
If you want rename for columns in pandas, you have to use the inbuild pandas function pandas.DataFrame.rename function().
Syntax: DataFrame.rename(mapper=None, index=None, columns=None, axis=None, copy=True, inplace=False, level=None)
Alter axes labels.
Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don’t throw an error.
Parameters:
mapper, index, columns : dict-like or function, optional
dict-like or functions transformations to apply to that axis’ values. Use either mapper and axis to specify the axis to target with mapper, or index and columns.
axis : int or str, optional
Axis to target with mapper. Can be either the axis name (‘index’, ‘columns’) or number (0, 1). The default is ‘index’.
copy : boolean, default True
Also copy underlying data
inplace : boolean, default False
Whether to return a new DataFrame. If True then value of copy is ignored.
level : int or level name, default None
In case of a MultiIndex, only rename labels in the specified level.
Returns:
renamed : DataFrame
# example program on how to rename pandas columns
import pandas as pd
df = pd.DataFrame({"X": [1, 2, 3], "Y": [20000, 25000, 30000]})
print(df)
df =df.rename(index=str, columns={"X": "exp", "Y": "salary"})
print(df)
Output:
X Y 0 1 20000 1 2 25000 2 3 30000 exp salary 0 1 20000 1 2 25000 2 3 30000