The Pandas `apply( )`

function is used to apply the functions on the Pandas objects. We have so many built-in aggregation functions in pandas on Series and DataFrame objects. But, to apply some application-specific functions, we can leverage the `apply( )`

function. Pandas `apply( )`

is both the Series method and DataFrame method.

# Pandas apply function to one column – apply( ) as Series method

Let’s construct a DataFrame in which we have the information of 4 persons.

>>> import pandas as pd >>> df = pd.DataFrame( ... { ... 'Name': ['Edward', 'Natalie', 'Chris M', 'Priyatham'], ... 'Sex' : ['M', 'F', 'M', 'M'], ... 'Age': [45, 35, 29, 26], ... 'weight(kgs)': [68.4, 58.2, 64.3, 53.1] ... } ... ) >>> print(df) Name Sex Age weight(kgs) 0 Edward M 45 68.4 1 Natalie F 35 58.2 2 Chris M M 29 64.3 3 Priyatham M 26 53.1

`pandas.Series.apply`

takes any of the below two different kinds of functions as an argument. They are:

- Python functions
- Numpy’s universal functions (ufuncs)

## 1. Python functions

In Python, there are 3 different kinds of functions in general;

- Built-in functions
- User-defined functions
- Lambda functions

## a) Applying Python built-in functions on Series

If we would like to know the length of the names of each person, we can do so using the `len( )`

function in python.

For example, if we want to know the length of the “Python” string, we can get by the following code;

>>> len("Python") 6

A single column in the DataFrame is a Series object. Now, we would like to apply the same `len( )`

function on the whole “Name” column of the DataFrame. This can be achieved using the `apply( )`

function in the below code;

>>> df['Name'].apply(len) 0 6 1 7 2 7 3 9 Name: Name, dtype: int64

If you observe the above code snippet, the `len`

inside the `apply( )`

function is not taking any argument. In general, any function takes some data to operate on them. In the `len(“Python”)`

code snippet, it’s taking the `“Python”`

string as input data to calculate its length. Here, the input data is directly taken from the Series object that called the function using `apply( )`

.

When applying the Python functions, each value in the Series is applied one by one and returns the Series object.

The above process can be visualised as:

In the above visualisation, you can observe that each element of Series is applied to the function one by one.

## b) Applying user-defined functions on Series

Let’s assume that the data we have is a year old. So, we would like to update the age of each person by adding 1. We can do so by applying a user-defined function on the Series object using the `apply( )`

method.

The code for it is,

>>> def add_age(age): ... return age + 1 >>> df['Age'].apply(add_age) 0 46 1 36 2 30 3 27 Name: Age, dtype: int64 >>> df['Age'] = df['Age'].apply(add_age) >>> df Name Sex Age weight(kgs) 0 Edward M 46 68.4 1 Natalie F 36 58.2 2 Chris M M 30 64.3 3 Priyatham M 27 53.1

From the above result, the major point to be noted is,

- The index of the resultant Series is equal to the index of the caller Series object. This makes the process of adding the resultant Series as a column to the DataFrame easier.

It operates in the same way as applying built-in functions. Each element in the Series is passed one by one to the function.

**User-defined functions are used majorly when we would like to apply some application-specific complex functions.**

## c) Applying Lambda functions on Series

Lambda functions are used a lot along with the `apply( ) `

method. We used a user-defined function for an easy addition operation in the above section. Let’s achieve the same result using a Lambda function.

The code for it is,

>>> df['Age'].apply(lambda x: x+1) 0 46 1 36 2 30 3 27 Name: Age, dtype: int64 >>> # Comparing the results of applying both the user-defined function and Lambda function >>> df['Age'].apply(lambda x: x+1) == df['Age'].apply(add_age) 0 True 1 True 2 True 3 True Name: Age, dtype: bool

From the above result, you can observe the results of applying the user-defined function and Lambda function are the same.

**Lambda functions are used majorly when we would like to apply some application-specific small functions.**

## 2. Numpy’s universal functions (ufuncs)

Numpy has so many built-in universal functions (ufuncs). We can provide any of the ufuncs as an argument to the `apply( )`

method on Series. A series object can be thought of as a NumPy array.

The difference between applying Python functions and ufuncs is;

- When applying the Python Functions, each element in the Series is operated one by one.
- When applying the ufuncs, the entire Series is operated at once.

Let’s choose to use a ufunc to floor the floating-point values of the weight column. We have `numpy.floor( )`

ufunc to achieve this.

The code for it is,

>>> import numpy as np >>> df['weight(kgs)'] 0 68.4 1 58.2 2 64.3 3 53.1 Name: weight(kgs), dtype: float64 >>> df['weight(kgs)'].apply(np.floor) 0 68.0 1 58.0 2 64.0 3 53.0 Name: weight(kgs), dtype: float64

In the above result, you can observe the floored to the nearest lower decimal point value and maintain its float64 data type.

We can visualise the above process as:

In the above visualisation, you can observe that all elements of Series are applied to the function at once.

**Whenever we have a**`ufunc`

to achieve our functionality, we can use it instead of defining a Python function.

# Pandas apply( ) as a DataFrame method

We will take a look at the official documentation of the `apply( )`

method on DataFrame:

`pandas.DataFrame.apply`

has two important arguments;

`func`

– Function to be applied along the mentioned axis`axis`

– Axis along which function is applied

Again the axis also has 2 possible values;

`axis=0`

– Apply function to multiple columns`axis=1`

– Apply function to every row

## 1. Pandas apply function to multiple columns

Let’s say the people in our dataset provided their height (in cms) information. It can be added using the following code,

>>> df['height(cms)'] = [178, 160, 173, 168] >>> df Name Sex Age weight(kgs) height(cms) 0 Edward M 45 68.4 178 1 Natalie F 35 58.2 160 2 Chris M M 29 64.3 173 3 Priyatham M 26 53.1 168

We’ll make the “Name” column the index of the DataFrame. Also, we’ll get the subset of the DataFrame with “Age”, “weight(kgs)”, and “height(cms)” columns.

>>> data = df.set_index('Name') >>> data Sex Age weight(kgs) height(cms) Name Edward M 45 68.4 178 Natalie F 35 58.2 160 Chris M M 29 64.3 173 Priyatham M 26 53.1 168 >>> data_subset = data[['Age', 'weight(kgs)', 'height(cms)']] >>> data_subset Age weight(kgs) height(cms) Name Edward 45 68.4 178 Natalie 35 58.2 160 Chris M 29 64.3 173 Priyatham 26 53.1 168

If we would like to get the average age, weight, and height of all the people, we can use the numpy `ufunc`

`numpy.mean( )`

.

The code for it is,

>>> import numpy as np >>> data_subset.apply(np.mean, axis=0) Age 33.75 weight(kgs) 61.00 height(cms) 169.75 dtype: float64

We directly have a Pandas DataFrame aggregation function called `mean( )`

which does the same as above;

>>> data_subset.mean() Age 33.75 weight(kgs) 61.00 height(cms) 169.75 dtype: float64

If you observe the results above, the results of Pandas DataFrame aggregation function and applying `ufunc`

are equal. So, we don’t use the `apply( )`

method in such simple scenarios where we have aggregation functions available.

**Whenever you have to apply some complex functions on DataFrames, then use the**`apply( )`

method.

## 2. Pandas apply function to every row

Based upon the height and weight, we can know whether they’re fit or thin, or obese. The fitness criteria are different for men and women as setup by international standards. Let’s grab the fitness criteria data for the heights and weights of the people in our data.

This can be represented using a dictionary;

>>> male_fitness = { ... #height : (weight_lower_cap, weight_upper_cap) ... 178 : ( 67.5 , 83 ), ... 173 : ( 63 , 70.6 ), ... 168 : ( 58 , 70.7 ) ... } >>> female_fitness = { ... #height : (weight_lower_cap, weight_upper_cap) ... 160 : ( 47.2 , 57.6 ) ... }

In the above dictionary, the keys are the heights and the values are tuples of the lower and upper limit of ideal weight respectively.

If someone is below the ideal weight for their respective height, they are “Thin”. If someone is above the ideal weight for their respective height, they are “Obese”. If someone is in the range of ideal weight for their respective height, they are “Fit”.

Let’s build a function that can be used in the `apply( )`

method that takes all the rows one by one.

>>> def fitness_check(seq): ... if seq.loc['Sex'] == 'M': ... if (seq.loc['weight(kgs)'] > male_fitness[seq.loc['height(cms)']][0]) & (seq.loc['weight(kgs)'] < male_fitness[seq.loc['height(cms)']][1]): ... return "Fit" ... elif (seq.loc['weight(kgs)'] < male_fitness[seq.loc['height(cms)']][0]): ... return "Thin" ... else: ... return "Obese" ... else: ... if (seq.loc['weight(kgs)'] > female_fitness[seq.loc['height(cms)']][0]) & (seq.loc['weight(kgs)'] < female_fitness[seq.loc['height(cms)']][1]): ... return "Fit" ... elif (seq.loc['weight(kgs)'] < female_fitness[seq.loc['height(cms)']][0]): ... return "Thin" ... else: ... return "Obese"

The function returns whether a given person is “Fit” or “Thin” or “Obese”. It uses the different fitness criteria dictionaries for male and female created above.

Finally, let’s apply the above function to every row using the `apply( )`

method;

>>> data.apply(fitness_check, axis=1) Name Edward Fit Natalie Obese Chris M Fit Priyatham Thin dtype: object

From the above result, we got to know who is Fit or Thin or Obese.

# Conclusion and Next Steps

Using the `apply( )`

method when you want to achieve some complex functionality is preferred and recommended. Mostly built-in aggregation functions in Pandas come in handy. If you liked this tutorial on the `apply( )`

function and like quiz-based learning, please consider giving it a try to read our Coffee Break Pandas book.