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rename columns pandas
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
pandas rename column
df.rename(columns={"old_col1": "new_col1", "old_col2": "new_col2"}, inplace=True)
rename column name pandas dataframe
df.rename(columns={"old_col1": "new_col1", "old_col2": "new_col2"})
rename column in dataframe
df.rename({"current": "updated"}, axis=1, inplace=True)
print(df.dtypes)
pandas dataframe column rename
>>> 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
rename one dataframe column python
#suppy as dict the column name to replace
df1 = df.rename(columns={'Name': 'EmpName'})
print(df1)
pandas dataframe rename column
df = df.rename(columns={'oldName1': 'newName1', 'oldName2': 'newName2'})
# Or rename the existing DataFrame (rather than creating a copy)
df.rename(columns={'oldName1': 'newName1', 'oldName2': 'newName2'}, inplace=True)
pandas rename column
df.rename({'current':'updated'},axis = 1, inplace = True)
rename columns in dataframe
df.rename(columns = {'old_col1':'new_col1', 'old_col2':'new_col2'}, inplace = True)
dataframe rename column
# Define df
df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
# Option 1
df = df.rename({"A": "a", "B": "c"}, axis=1)
# or
df.rename({"A": "a", "B": "c"}, axis=1, inplace=True)
# Option 2
df = df.rename(columns={"A": "a", "B": "c"})
# or
df.rename(columns={"A": "a", "B": "c"}, inplace=True)
# Result
>>> df
a c
0 1 4
1 2 5
2 3 6
rename column pandas
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
renaming column in dataframe pandas
df.rename({'a': 'X', 'b': 'Y'}, axis=1, inplace=True)
df
X Y c d e
0 x x x x x
1 x x x x x
2 x x x x x
df rename columns
df.rename(columns = {'col1':'new_name', 'col2':'newcol2',
'col3':'newcol3'}, inplace = True)
rename column pandas
# 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 |
|_____________|
rename column pandas
>>> df.rename({1: 2, 2: 4}, axis='index')
A B
0 1 4
2 2 5
4 3 6
pd df rename
df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
df.rename(columns={"A": "a", "B": "c"})
how to rename columns in pandas dataframe
energy = energy.rename(columns={2: 'Country', 3: 'Energy Supply' , 4:'Energy Supply per Capita',5:'% Renewable'})
rename column pandas
>>> df.rename(index={0: "x", 1: "y", 2: "z"})
A B
x 1 4
y 2 5
z 3 6
rename column pandas
>>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
Traceback (most recent call last):
KeyError: ['C'] not found in axis
How to rename columns in Pandas DataFrame
# import pandas library
import pandas as pd
# create pandas DataFrame
df = pd.DataFrame({'team': ['India', 'South Africa', 'New Zealand', 'England'],
'points': ['10', '8', '3', '5'],
'runrate': ['0.5', '1.4', '2', '-0.6'],
'wins': ['5', '4', '2', '2']})
# print the column names of DataFrame
print(list(df))
# rename the column names of DataFrame
df.rename(columns={'points': 'total_points',
'runrate': 'run_rate'}, inplace=True)
# print the new column names of DataFrame
print(list(df))
pd df rename
df.rename(index={0: "x", 1: "y", 2: "z"})
rename colums dataframe pandas
df.columns = ['new_col1', 'new_col2', 'new_col3', 'new_col4']
rename column pandas
>>> df.rename(str.lower, axis='columns')
a b
0 1 4
1 2 5
2 3 6
rename column in dataframe
DataFrame.rename() method
how to rename columns using panda object
df2.columns = stocks['Ticker'][:3]
[:3] is just use first 3
[5::] skip first 5
price price price
2021-01-11 131.90 15.00 179.07
2021-01-12 128.09 15.74 182.65
to
Ticker A AAL AAP
2021-01-11 131.90 15.00 179.07
2021-01-12 128.09 15.74 182.65
rename data columns pandas
data.columns = ['New_column_name', 'New_column_name2']
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