You’ll usefulness one in every of please see 3 modes to rename columns in a pandas DataFrame:
Form 1: Rename Explicit Columns
df.rename(columns = {'old_col1':'new_col1', 'old_col2':'new_col2'}, inplace = True)
Form 2: Rename All Columns
df.columns = ['new_col1', 'new_col2', 'new_col3', 'new_col4']
Form 3: Exchange Explicit Characters in Columns
df.columns = df.columns.str.substitute('old_char', 'new_char')
Please see examples display the way to usefulness each and every of those modes in observe.
Alike: The way to Get Column Names in Pandas (3 Forms)
Form 1: Rename Explicit Columns
Please see code presentations the way to rename explicit columns in a pandas DataFrame:
import pandas as pd #outline DataFrame df = pd.DataFrame({'workforce':['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'], 'issues': [25, 12, 15, 14, 19, 23, 25, 29], 'assists': [5, 7, 7, 9, 12, 9, 9, 4], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]}) #record column names record(df) ['team', 'points', 'assists', 'rebounds'] #rename explicit column names df.rename(columns = {'workforce':'team_name', 'issues':'points_scored'}, inplace = True) #view up to date record of column names record(df) ['team_name', 'points_scored', 'assists', 'rebounds']
Understand that the ‘team’ and ‘points’ columns have been renamed presen all alternative column names remained the similar.
Form 2: Rename All Columns
Please see code presentations the way to rename all columns in a pandas DataFrame:
import pandas as pd #outline DataFrame df = pd.DataFrame({'workforce':['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'], 'issues': [25, 12, 15, 14, 19, 23, 25, 29], 'assists': [5, 7, 7, 9, 12, 9, 9, 4], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]}) #record column names record(df) ['team', 'points', 'assists', 'rebounds'] #rename all column names df.columns = ['_team', '_points', '_assists', '_rebounds'] #view up to date record of column names record(df) ['_team', '_points', '_assists', '_rebounds']
Be aware that it’s sooner to usefulness this form when you wish to have to rename maximum or the entire column names within the DataFrame.
Form 3: Exchange Explicit Characters in Columns
Please see code presentations the way to substitute a particular persona in each and every column title:
import pandas as pd #outline DataFrame df = pd.DataFrame({'$workforce':['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'], '$issues': [25, 12, 15, 14, 19, 23, 25, 29], '$assists': [5, 7, 7, 9, 12, 9, 9, 4], '$rebounds': [11, 8, 10, 6, 6, 5, 9, 12]}) #record column names record(df) ['team', 'points', 'assists', 'rebounds'] #rename $ with emptied in each column title df.columns = df.columns.str.substitute('$', '') #view up to date record of column names record(df) ['team', 'points', 'assists', 'rebounds']
Understand that this form allowed us to briefly take away the ‘$’ from each and every column title.
Extra Assets
Please see tutorials give an explanation for the way to carry out alternative ordinary operations in pandas:
The way to Record All Column Names in Pandas
The way to Type Columns by means of Identify in Pandas
The way to Leave Replica Columns in Pandas
