Rename column pandas

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

178

Source: grepper.com

Rename column name pandas dataframe

df.rename(columns={"old_col1": "new_col1", "old_col2": "new_col2"})

Source: grepper.com

Pandas rename column

df.rename(columns={"old_col1": "new_col1", "old_col2": "new_col2"}, inplace=True)

Source: grepper.com

Rename df column

import pandas as pd
data = pd.read_csv(file)
data.rename(columns={'original':'new_name'}, inplace=True)

Source: grepper.com

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

Source: pandas.pydata.org

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

Source: note.nkmk.me

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)

Source: stackoverflow.com

Rename column pandas

>>> df.rename({1: 2, 2: 4}, axis='index')
   A  B
0  1  4
2  2  5
4  3  6

Source: pandas.pydata.org

Rename columns in dataframe

df.rename(columns = {'old_col1':'new_col1', 'old_col2':'new_col2'}, inplace = True)

Source: statology.org

Rename column in dataframe

df.rename({"current": "updated"}, axis=1, inplace=True)
print(df.dtypes)

Source: app.dataquest.io

Rename column pandas

>>> df.rename(index={0: "x", 1: "y", 2: "z"})
   A  B
x  1  4
y  2  5
z  3  6

Source: pandas.pydata.org

Pandas rename column

df.rename({'current':'updated'},axis = 1, inplace = True)

Source: app.dataquest.io

Rename column dataframe python

# Give new names to all columns
df.columns = ['A','B','C']
# Change specific column names
df.rename(columns={'old_col': 'A', 'abc': 'B'}, inplace=True)

Source: grepper.com

How to rename columns in pandas dataframe

energy = energy.rename(columns={2: 'Country', 3: 'Energy Supply' , 4:'Energy Supply per Capita',5:'% Renewable'})

Source: grepper.com

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

Source: pandas.pydata.org

Rename column pandas

>>> df.rename(str.lower, axis='columns')
   a  b
0  1  4
1  2  5
2  3  6

Source: pandas.pydata.org

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

Source: pandas.pydata.org

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

Source: stackoverflow.com

Df rename columns

df.rename(columns = {'col1':'new_name', 'col2':'newcol2',
                              'col3':'newcol3'}, inplace = True)

Source: geeksforgeeks.org

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         |
|_____________|

Source: grepper.com

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))

Source: itsmycode.com

Rename colums dataframe pandas

df.columns = ['new_col1', 'new_col2', 'new_col3', 'new_col4']

Source: statology.org

Renamecolumns pandas

df.columns = ['V', 'W', 'X', 'Y', 'Z']
df

   V  W  X  Y  Z
0  x  x  x  x  x
1  x  x  x  x  x
2  x  x  x  x  x

Source: stackoverflow.com

Rename column in dataframe

DataFrame.rename() method

Source: app.dataquest.io

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

Source: stackoverflow.com

Rename data columns pandas

data.columns = ['New_column_name', 'New_column_name2']

-2

Source: grepper.com

Renamecolumns pandas

>>> df.columns = ['a', 'b']
>>> df
   a   b
0  1  10
1  2  20

-2

Source: stackoverflow.com

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