PYTHON

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

rename df column

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

python: change column name

df = df.rename(columns = {'myvar':'myvar_new'})

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"})

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

change name of column pandas

#df.rename() will only return a new df with the new headers
#df = df.rename() will change the heders of the current dataframe 
df = df.rename(columns={"old_col1": "new_col1", "old_col2": "new_col2"})

df change column names

df.rename(columns={"A": "a", "B": "b", "C": "c"},
errors="raise", inplace=True)

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)

Renaming Column Name Dataframe

df = df.rename(columns={'oldName1': 'newName1', 'oldName2': 'newName2'})

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

change column name pandas

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

how to change column name in pandas

print(df.rename(columns={'A': 'a', 'C': 'c'}))
#         a   B   c
# ONE    11  12  13
# TWO    21  22  23
# THREE  31  32  33

change column names with number pd dataframe

df.columns = ['Bill', 'name', 'type']
df

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 pandas columns with list of new names

df.columns = list_of_names

rename column pandas

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

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

change column names pandas

df.columns = ['A','B']

changing names of column pandas

import pandas as pd

# You don't need these two lines
# as you already have your DataFrame in memory
df = pd.read_csv("nor.txt", sep="|")
df.drop(df.columns[-1], axis=1)

# Get column names
cols = df.columns

# Create a new DataFrame with just the markdown
# strings
df2 = pd.DataFrame([['---',]*len(cols)], columns=cols)

#Create a new concatenated DataFrame
df3 = pd.concat([df2, df])

#Save as markdown
df3.to_csv("nor.md", sep="|", index=False)

rename column pandas

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