Pandas dataframes allow you the flexibility of applying a function along a particular axis of a dataframe. In this tutorial, we’ll look at how to apply a function to a pandas dataframe through some examples.

## The pandas DataFrame `apply()`

function

The pandas dataframe `apply()`

function is used to apply a function along a particular axis of a dataframe. The following is the syntax:

`result = df.apply(func, axis=0)`

We pass the function to be applied and the axis along which to apply it as arguments. To apply the function to each column, pass `0`

or `'index'`

to the `axis`

parameter which is `0`

by default. And to apply the function to each row, pass `1`

or `'columns'`

to the `axis`

parameter. The examples below illustrate the difference.

## Examples

Let’s look at some of the use-cases of the `apply()`

function through examples.

### 1. Apply a function to each column of the dataframe

Let’s say you want to apply a function to each column of a dataframe, that is, along the index axis. For instance, you’re working with a dataframe having all numerical columns and you want to find the mean for each of those columns.

```
import pandas as pd
# create a sample dataframe
df = pd.DataFrame({
'History': [76, 84, 68, 94],
'Math': [81, 67, 91, 86],
'English': [72, 93, 84, 76]
})
# print the dataframe
print("The original dataframe:\n")
print(df)
# function to be applied
def get_mean(scores):
return sum(scores)/len(scores)
# get the mean score for each subject
result = df.apply(get_mean)
print("\nThe result of applying the function on the dataframe:\n")
print(result)
```

Output:

```
The original dataframe:
History Math English
0 76 81 72
1 84 67 93
2 68 91 84
3 94 86 76
The result of applying the function on the dataframe:
History 80.50
Math 81.25
English 81.25
dtype: float64
```

In the above example, the dataframe `df`

contains scores of students in three subjects. Rows represent the students whereas columns represent the subjects. Here, the `apply()`

function is used to get the average score for each of the subjects across all students. Note that, since the function `get_mean()`

is applied to each column, we didn’t need to explicitly pass `0`

to the `axis`

parameter since it is its default value.

### 2. Apply a function to each row of the dataframe

Now, let’s say you want to apply a function to each row of a dataframe, that is, along the columns axis. For instance, you’re working with a dataframe having all numerical rows and you want to find the mean for each of those rows.

```
import pandas as pd
# create a sample dataframe
df = pd.DataFrame({
'History': [76, 84, 68, 94],
'Math': [81, 67, 91, 86],
'English': [72, 93, 84, 76]
}, index=['Sam', 'Greta', 'Mike', 'Emma'])
# print the dataframe
print("The original dataframe:\n")
print(df)
# function to be applied
def get_mean(scores):
return sum(scores)/len(scores)
# get the mean score for each student
result = df.apply(get_mean, axis=1)
print("\nThe result of applying the function on the dataframe:\n")
print(result)
```

Output:

```
The original dataframe:
History Math English
Sam 76 81 72
Greta 84 67 93
Mike 68 91 84
Emma 94 86 76
The result of applying the function on the dataframe:
Sam 76.333333
Greta 81.333333
Mike 81.000000
Emma 85.333333
dtype: float64
```

In the above example, the dataframe `df`

contains scores of students in three subjects. Rows represent the students whereas columns represent the subjects. For more clarity, we give each row the name of its corresponding student. Here, the `apply()`

function is used to get the average score for each student across all the three subjects. Note that, we had to pass `axis=1`

since we wanted the `get_mean()`

function to be applied to each row.

For more on the `apply()`

function, refer to its official documentation.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5

More on Pandas DataFrames –

- Pandas – Sort a DataFrame
- Change Order of Columns of a Pandas DataFrame
- Pandas DataFrame to a List in Python
- Pandas – Count of Unique Values in Each Column
- Pandas – Replace Values in a DataFrame
- Pandas – Filter DataFrame for multiple conditions
- Pandas – Random Sample of Rows
- Pandas – Random Sample of Columns
- Save Pandas DataFrame to a CSV file
- Pandas – Save DataFrame to an Excel file
- Create a Pandas DataFrame from Dictionary
- Convert Pandas DataFrame to a Dictionary
- Drop Duplicates from a Pandas DataFrame
- Concat DataFrames in Pandas
- Append Rows to a Pandas DataFrame
- Compare Two DataFrames for Equality in Pandas
- Get Column Names as List in Pandas DataFrame
- Select One or More Columns in Pandas
- Pandas – Rename Column Names
- Pandas – Drop one or more Columns from a Dataframe
- Pandas – Iterate over Rows of a Dataframe
- How to Reset Index of a Pandas DataFrame?
- Read CSV files using Pandas – With Examples
- Apply a Function to a Pandas DataFrame

**Subscribe to our newsletter for more informative guides and tutorials. ****We do not spam and you can opt out any time.**