Simple ways to rename column names in pandas DataFrame
pandas DataFrame provides various functions such as rename(), set_axis(), add_prefix(), and add_suffix() to
rename the columns of the DataFrame. You can rename either all columns or specific columns using these functions.
Rename all columns
You can rename all columns of a DataFrame using the pandas DataFrame columns attribute. This method is also used for
setting column names if DatFrame does not have column names.
# create a DataFrame
import pandas as pd
df = pd.DataFrame({'name':['A', 'B'], 'age':[25, 30]})
# view DataFrame
df
# output
name age
0 A 25
1 B 30
# rename all columns
df.columns = ['first_name', 'age_years']
# view updated DataFrame
df
# output
first_name age_years
0 A 25
1 B 30
Similarly, you can also use the set_axis() function of pandas DataFrame for renaming the column. Set the axis parameter to
columns or 1 to rename the columns.
df.set_axis(['first_name', 'age_years'], axis='columns', inplace=True)
Rename specific column names
Sometimes, we want to rename specific columns of a pandas DataFrame. To rename specific columns of DataFrame, you can use
the pandas DataFrame rename() function. The rename function takes a dictionary with old column names as keys and new
column names as values.
# create a DataFrame
import pandas as pd
df = pd.DataFrame({'name':['A', 'B'], 'age':[25, 30], 'height': [5, 5.5]})
# rename name and height column
# use inplace=True to modify and update the current DataFrame
df.rename(columns={'name': 'first_name', 'height': 'height_feet'}, inplace=True)
# view updated DataFrame
df
# output
first_name age height_feet
0 A 25 5.0
1 B 30 5.5
Similarly, you can also dfply package to rename the specific columns. dfply Python package is similar to R’s
dplyr and supports the data manipulation with pipes on pandas DataFrame.
from dfply import *
# rename columns
# argument should be new column and parameter is old column name
df >> rename(first_name='name', height_feet='height')
Rename column names using add_prefix() and add_suffix() functions
Sometimes, you need to rename columns by adding some additional characters at the start or end of column names.
In this case, you can use pandas DataFrame add_prefix() and add_suffix() functions.
# create a DataFrame
import pandas as pd
df = pd.DataFrame({'name':['A', 'B'], 'age':[25, 30], 'height': [5, 5.5]})
# add prefix f_
df = df.add_prefix('f_')
df
# output
f_name f_age f_height
0 A 25 5.0
1 B 30 5.5
# add suffix f_
df = df.add_suffix('_f')
df.head(2)
# output
col1_S col2_S col3_S
0 A 1 0.1
1 B 2 0.2
Replace specific texts of column names
Sometimes, you would like to replace some specific characters in the column names. For example, if column names contain some special characters.
# create a DataFrame
import pandas as pd
df = pd.DataFrame({'name_':['A', 'B'], 'age_':[25, 30], 'height_': [5, 5.5]})
df
# output
name_ age_ height_
0 A 25 5.0
1 B 30 5.5
# replace _ with empty character
df.columns = df.columns.str.replace('_', '')
df
# output
name age height
0 A 25 5.0
1 B 30 5.5
Enhance your skills with courses on Python and pandas
- Python for Data Analysis: Pandas & NumPy
- Mastering Data Analysis with Pandas: Learning Path Part 1
- Data Analysis Using Python
Learn more about Python
If you have any questions, comments or recommendations, please email me at reneshbe@gmail.com
This work is licensed under a Creative Commons Attribution 4.0 International License
