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Add suffix/prefix to column names of Pandas DataFrame

If you have a pandas DataFrame and want to add a suffix or prefix to the column name, there are a couple of ways to do it. In this post, we will describe them one by one.

First, let's create a DataFrame

import pandas as pd

# create a dataframe
df = pd.DataFrame({
    'name': ['Rick', 'Carol', 'Daryl', 'Negan'],
    'place': ['Alexendria', 'Kingdom', 'Hilltop', 'Saviours'],
    'episode': [50, 30, 35, 29]
})

print(df)
╒════╤════════╤════════════╤═══════════╕
│    │ name   │ place      │   episode │
╞════╪════════╪════════════╪═══════════╡
│  0 │ Rick   │ Alexendria │        50 │
├────┼────────┼────────────┼───────────┤
│  1 │ Carol  │ Kingdom    │        30 │
├────┼────────┼────────────┼───────────┤
│  2 │ Daryl  │ Hilltop    │        35 │
├────┼────────┼────────────┼───────────┤
│  3 │ Negan  │ Saviours   │        29 │
╘════╧════════╧════════════╧═══════════╛

In the above DataFrame, we want to add a suffix or prefix to the column names. We can use the below methods in order to do that.

Solution 1: Use DataFrame.add_suffix() function to add a suffix to all column names

A DataFrame is a two-dimensional data structure that contains columns and rows. The add_suffix() function is used to add a suffix to all column names in a DataFrame. This function is useful when we want to add a specific character to all column names in a DataFrame, such as an underscore "_".

Code example

import pandas as pd

# create a dataframe
df = pd.DataFrame({
    'name': ['Rick', 'Carol', 'Daryl', 'Negan'],
    'place': ['Alexendria', 'Kingdom', 'Hilltop', 'Saviours'],
    'episode': [50, 30, 35, 29]
})

# add suffix to each column
df = df.add_suffix('_watch')

print(df)

Output

╒════╤══════════════╤═══════════════╤═════════════════╕
│    │ name_watch   │ place_watch   │   episode_watch │
╞════╪══════════════╪═══════════════╪═════════════════╡
│  0 │ Rick         │ Alexendria    │              50 │
├────┼──────────────┼───────────────┼─────────────────┤
│  1 │ Carol        │ Kingdom       │              30 │
├────┼──────────────┼───────────────┼─────────────────┤
│  2 │ Daryl        │ Hilltop       │              35 │
├────┼──────────────┼───────────────┼─────────────────┤
│  3 │ Negan        │ Saviours      │              29 │
╘════╧══════════════╧═══════════════╧═════════════════╛

The code example above creates a DataFrame with four columns and four rows. The columns are 'name', 'place', and 'episode'. The rows are labeled 'Rick', 'Carol', 'Daryl', and 'Negan'. The code then adds a suffix to each column header. The suffix is '_watch'.

Solution 2: Add prefix to all column names using add_prefix() function

The Pandas add_prefix() function is used to prefix labels of columns in a DataFrame with a string. The function takes a string as an argument and returns a new DataFrame with the columns labeled with the given string as a prefix.

Syntax

DataFrame.add_prefix('prefix_name')

Code example

import pandas as pd

# create a dataframe
df = pd.DataFrame({
    'name': ['Rick', 'Carol', 'Daryl', 'Negan'],
    'place': ['Alexendria', 'Kingdom', 'Hilltop', 'Saviours'],
    'episode': [50, 30, 35, 29]
})

# add prefix to each column
df = df.add_prefix('pre_')

print(df)

Output

╒════╤════════════╤═════════════╤═══════════════╕
│    │ pre_name   │ pre_place   │   pre_episode │
╞════╪════════════╪═════════════╪═══════════════╡
│  0 │ Rick       │ Alexendria  │            50 │
├────┼────────────┼─────────────┼───────────────┤
│  1 │ Carol      │ Kingdom     │            30 │
├────┼────────────┼─────────────┼───────────────┤
│  2 │ Daryl      │ Hilltop     │            35 │
├────┼────────────┼─────────────┼───────────────┤
│  3 │ Negan      │ Saviours    │            29 │
╘════╧════════════╧═════════════╧═══════════════╛

The above code example creates a DataFrame with four columns ('name', 'place', 'episode') and four rows of data. It then adds a prefix to each column in the DataFrame. The resulting DataFrame is printed.

If you want to change the names of a few columns then you can read the below articles.

Change the name of columns in a pandas dataframe

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