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Python - Change column names and row indexes in Pandas DataFrame
Pandas is a python library offering many features for data analysis which is not available in python standard library. One such feature is the use of Data Frames. They are rectangular grids representing columns and rows. While creating a Data frame, we decide on the names of the columns and refer them in subsequent data manipulation. But there may be a situation when we need to change the name of the columns after the data frame has been created. In this article, we will see how to achieve that.
Using rename()
This is the most preferred method as we can change both the column and row index using this method. We just pass in the old and new values as a dictionary of key-value pairs to this method and save the data frame with a new name.
Example
import pandas as pd
df = pd.DataFrame({
'ColumnA': [23, 92, 32],
'ColumnB': [54, 76, 43],
'ColumnC': [16, 45, 10]
},
index=['10-20', '20-30', '30-40'])
df_renamed = df.rename(columns={'ColumnA': 'Col1', 'ColumnB': 'Col2', 'ColumnC': 'Col3'},
index={'10-20': '1', '20-30': '2', '30-40': '3'})
print(df)
print("\n",df_renamed)Output
Running the above code gives us the following result:
ColumnA ColumnB ColumnC 10-20 23 54 16 20-30 92 76 45 30-40 32 43 10 Col1 Col2 Col3 1 23 54 16 2 92 76 45 3 32 43 10
Using df.columns
The df.columns can be assigned new column names directly. When the Data frame is used again, the new column names are referred.
Example
import pandas as pd
df = pd.DataFrame({
'ColumnA': [23, 92, 32],
'ColumnB': [54, 76, 43],
'ColumnC': [16, 45, 10]
},
index=['10-20', '20-30', '30-40'])
df.columns=["Length","Breadth","Depth"]
print(df)Output
Running the above code gives us the following result:
Length Breadth Depth 10-20 23 54 16 20-30 92 76 45 30-40 32 43 10
By Adding Prefix
Pandas dataframe provides methods for adding prefix and suffix to the column names. We simply use this method to add the desired prefix which gets appended to each column names.
Example
import pandas as pd
df = pd.DataFrame({
'ColA': [23, 92, 32],
'ColB': [54, 76, 43],
'ColC': [16, 45, 10]
},
index=['10-20', '20-30', '30-40'])
print(df.add_prefix('Jan-'))Output
Running the above code gives us the following result:
Jan-ColA Jan-ColB Jan-ColC 10-20 23 54 16 20-30 92 76 45 30-40 32 43 10
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