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

Pandas has many built-in functions and tools that make it easy to analyze and manipulate data. The panda’s library allows you to create dataframes and perform operations. You can use the apply method of a pandas dataframe to apply a function to each element in the dataframe. If you want to apply a function to a single column in your dataframe, Let’s see how this works with an example.

Here is a quick guide to applying a function to a single column in a pandas dataframe in Python.

*Working on Jupyter Notebook Anaconda Environment. 

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using apply() operation on selected dataframe

It’s possible to apply a function to a single column in Pandas Dataframe in Python using apply() operation on the selected dataframe. 

When defining a function, you usually need to include the def keyword, its name, parameters, and the body of the code. The function times2 takes a parameter n. In this function, there is one-liner code, but the execution is multiplying a parameter by 2. By returning the result to the calling code instead of storing it, this function avoids the need to allocate memory. 

Here’s how you do it:

1. Select the column you want to apply the function to.

2. Add an apply operation between the column.

3. Define a function using the “def” keyword. times2 will multiply the parameter x of a selected column by 2. The function returns the result after applying the multiplication on each element of the selected column. 

4. Run the apply operation on your dataframe.

import pandas as pd
df = pd.DataFrame({'star':['*','**','***','****']})
def times2(x):
    return x*(2)
df['star'].apply(times2)
0          **
1        ****
2      ******
3    ********
Name: flower, dtype: object

The same is the case for adding a single column in a pandas dataframe

import pandas as pd
df = pd.DataFrame(data={'num':[0,2,3,4],'num1':[0,1,2,4]})
def multiply(x):
    y=(x)*2
    return (y)
df['num1'].apply(add2)
0    0
1    2
2    4
3    8
Name: num1, dtype: int64

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

However, there is another approach to applying a function to a single column in the Pandas dataframe in Python. The execution process is the same a described earlier, however, if you want your output of a single column in a dataframe in boolean then it’s possible to use the if…else condition within a function.

Here is how it executes:

1. Create a dataframe and define a function passing an iterator ‘x’ as an argument. 

2. Now to transform the selected column elements to boolean, pass the if..else condition.  

In the if statement do the operation what you want for dataframe output looks like. 

3. Finally on your selected dataframe execute the apply() operation. 

4. In the following example, in ‘flower’, if the element is greater than 12, the output will return True, otherwise it will return False.

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)
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.  

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