Apply A Function To A Single Column In A Pandas  Dataframe in Python

Pandas is a powerful library for data manipulation in Python, and it offers many built-in functions and tools to help you analyze and manipulate data. The Pandas library allows you to create data frames and perform various operations on them. One of the most useful functions provided by Pandas is the apply method. You can use the apply method to apply a function to each element of a DataFrame, including a single column.

There are several ways to apply a function to a single column in a Pandas DataFrame in Python. In this tutorial, we will explore a few of them.

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Method 1: using apply() operation on selected dataframe

One way to apply a function to a single column in a Pandas DataFrame is to use the apply() operation on the selected DataFrame. Here’s an example:

import pandas as pd

# Create a DataFrame with a single column named 'star'
df = pd.DataFrame({'star': ['*', '**', '***', '****']})

# Define a function that multiplies its argument by 2
def times2(x):
    return x * 2

# Apply the function to the 'star' column using apply() method
df['star'].apply(times2)

In the above code, we first create a DataFrame with a single column named ‘star’. Then, we define a function called times2 that multiplies its argument by 2. Finally, we apply the times2 function to the ‘star’ column of the DataFrame using the apply() method. The output of the code will be a new Series containing the result of the multiplication for each element in the ‘star’ column.

0          **
1        ****
2      ******
3    ********
Name: flower, dtype: object

You can apply any function you like to a single column in a similar way. Just select the column you want to apply the function to and use the apply() method.

Method 2: Adding a single column in a Pandas DataFrame

You can also add a single column to a Pandas DataFrame by applying a function to an existing column. Here’s an example:

import pandas as pd

# Create a DataFrame with two columns
df = pd.DataFrame({'num': [0, 2, 3, 4], 'num1': [0, 1, 2, 4]})

# Define a function that multiplies its argument by 2 and returns the result
def multiply(x):
    return x * 2

# Apply the function to the 'num1' column using apply() method and store the result in a new column 'num2'
df['num2'] = df['num1'].apply(multiply)

# Print the DataFrame
print(df)

In the above code, we create a DataFrame with two columns named ‘num’ and ‘num1’. Then, we define a function called multiply that multiplies its argument by 2. Next, we apply the multiply function to the ‘num1’ column of the DataFrame using the apply() method and store the result in a new column called ‘num2’. Finally, we print the resulting DataFrame. The output of the code will be a new DataFrame with three columns where the ‘num2’ column contains the result of the multiplication.

0    0
1    2
2    4
3    8
Name: num1, dtype: int64

Method 3: Passing boolean using the if…else condition in the define function to a single column

Another approach to applying a function to a single column in a Pandas DataFrame is to use an if…else condition within the function. This can be useful if you want the output of the column to be in boolean format based on certain conditions.

import pandas as pd

df = pd.DataFrame(data={'flower':['Red Ginger','Tree Poppy','passion flower','water lily'],'test':['similarities','accuracy','correctness','classification']})

def fnc(x):
    if len(x) > (12):
        return True
    else:
        return False
    return x

df['flower'].apply(fnc)

In this example, we create a dataframe with two columns ‘flower’ and ‘test’. We define a function fnc that takes an iterator ‘x’ and returns True if the length of the element in the ‘flower’ column is greater than 12, otherwise False. We then use the apply() method on the ‘flower’ column and pass the fnc function as an argument.

0    False
1    False
2     True
3    False
Name: flower, dtype: bool

Conclusion 

In this article, there is a detailed discussion about how to apply a function to a single column in a pandas dataframe in Python. To get the desired results, the apply() method is used by defining a function. This would be helpful if you want to change or append the elements of the columns or rows in Pandas dataframes.