At the heart of data science is the ability to manipulate and transform data. The Pandas library, a popular data manipulation library in Python, provides us with powerful tools for transforming data. Two of these tools are map and apply. In this article, we will explore how to use map and apply to transform Pandas columns.
What is the "map" function in Pandas?
Before we dive into how to add or insert a row into a Pandas DataFrame, let’s first create a sample DataFrame to work with. For the purposes of this tutorial, let’s create a simple DataFrame that contains information about employees in a company. We’ll create columns for their names, job titles, and salaries.
What is the "apply" function in Pandas?
The “apply” function in Pandas is used to apply a function to each element in a DataFrame or Series. It takes an argument that can be a function, which is applied to each element in the DataFrame or Series. The function can be a lambda function or a user-defined function.
Creating a Sample Pandas DataFrame
Before we dive into let’s first create a sample DataFrame to work with. For this tutorial, let’s create a simple DataFrame with three columns (‘Name’, ‘Age’, and ‘City’) and four rows of data. You can modify the data dictionary to create your DataFrame or load data from a CSV or other file format.
import pandas as pd
# create sample data
data = {'Name': ['John', 'Alice', 'Bob', 'Sarah'],
'Age': [25, 30, 22, 28],
'City': ['New York', 'Los Angeles', 'Chicago', 'San Francisco']}
# create DataFrame from data
df = pd.DataFrame(data)
# print DataFrame
print(df)
Output:
# Name Job Title Salary
#0 John Manager 100000
#1 Mary Engineer 80000
#2 Peter Analyst 75000
#3 Samantha Designer 90000
Understanding Vectorized Functions in Pandas
Vectorization is a technique that enables the application of operations to an entire array or data frame without having to use loops, thus resulting in faster and more efficient computation. In Pandas, vectorization is accomplished through the use of universal functions (ufuncs). Ufuncs are functions that operate element-wise on arrays, and they are optimized for fast execution.
In Pandas, ufuncs are used extensively to apply mathematical and statistical operations to data frames and series. The ability to apply these operations element-wise across large data sets means that vectorized functions in Pandas can process data much faster than standard Python functions or loops. This is particularly important when working with large data sets, as the performance gains can be significant.
How do Vectorized Functions Work in Pandas?
Vectorized functions in Pandas operate element-wise on arrays or data frames. This means that the function is applied to each element of the array or data frame, resulting in an array or data frame with the same shape as the original. For example, if we have a data frame with two columns and ten rows, applying a vectorized function will result in a data frame with the same shape.
The key advantage of vectorized functions in Pandas is that they are optimized for fast execution. Because the operations are applied element-wise, they can be parallelized across multiple CPU cores, resulting in even faster computation. This is particularly important when working with large data sets, as the speed gains can be significant.
Here’s an example:
import random
import numpy as np
ages = [random.randint(18, 80) for _ in range(1000)]
length = 0
age_sum = 0
for age in ages:
length += 1
age_sum += age
average_age_scalar = age_sum / length
print("Average age (scalar):", average_age_scalar)
ages_array = np.array(ages)
average_age_vectorized = np.mean(ages_array)
print("Average age (vectorized):", average_age_vectorized)
Output:
Average age (scalar): 50.104
Average age (vectorized): 50.104
Here we create a NumPy array from the list of ages, and then use the np.mean() function to calculate the average age. This operation is applied to the entire array at once, without the need for a loop.
As you can see, the vectorized implementation is simpler and more concise than the scalar operation. It also tends to be faster, especially for large datasets.
Using the Pandas map Method
The Pandas .map() function can be used with a Pandas Series, which implies that it can be used with a Pandas DataFrame column. There are three different shapes that the map function can assume, which makes it interesting. Depending on the parameters you give to the method, this changes. Here are some examples of the objects that can be handed in:
- Dictionaries: Pandas will use the
.map()method to map items pair-wise, based on akey:valepair - Functions: Pandas will apply the function row-wise, evaluating against the row’s value
- Series: Pandas will replace the Series to which the method is applied with the Series that’s passed in
You can learn more about how the .map() method can be used to transform and map a Pandas column by reading the parts that follow.
Using the Pandas map Method to Map a Dictionary
The map() method in Pandas can be used to map a dictionary to replace values in a DataFrame or Series with new values.
Here’s an example of using the map() method with a dictionary:
import pandas as pd
# create a sample DataFrame
data = {'Name': ['John', 'Alice', 'Bob', 'Sarah'],
'Age': [25, 30, 22, 28],
'City': ['New York', 'Los Angeles', 'Chicago', 'San Francisco']}
df = pd.DataFrame(data)
# create a dictionary to map cities to regions
region_dict = {'New York': 'Northeast',
'Los Angeles': 'West',
'Chicago': 'Midwest',
'San Francisco': 'West'}
# use the map method to replace the City column with regions
df['Region'] = df['City'].map(region_dict)
# create a function to map ages to age groups
def map_age(age):
if age < 18:
return 'Under 18'
elif age < 25:
return '18-24'
elif age < 35:
return '25-34'
elif age < 45:
return '35-44'
elif age < 55:
return '45-54'
else:
return '55+'
# use the map method to replace the Age column with age groups
df['Age Group'] = df['Age'].map(map_age)
# print DataFrame
print(df)
Output:
# Name Age City Region Age Group
#0 John 25 New York Northeast 25-34
#1 Alice 30 Los Angeles West 25-34
#2 Bob 22 Chicago Midwest 18-24
#3 Sarah 28 San Francisco West 25-34
We create a dictionary region_dict that maps each city to its corresponding region. We then use the map() method to create a new column ‘Region’ in the DataFrame by replacing the values in the ‘City’ column with their corresponding region values from the region_dict.
The map() method applies the dictionary key-value pairs to the column values in the DataFrame, and returns a new Series with the mapped values. If a value in the column does not have a corresponding key in the dictionary, it will be replaced with NaN (Not a Number). We can also use the map() method with a Series or function. In the case of a function, the function should take a single argument and return a value.
Function map_age() that maps each age to its corresponding age group. We then use the map() method to create a new column ‘Age Group’ in the DataFrame by replacing the values in the ‘Age’ column with their corresponding age group values from the map_age() function.
Using the map() method with a dictionary, Series, or function can be a quick and easy way to replace values in a DataFrame or Series.
Using the Pandas map Method to Map a Function
The map() method in Pandas can be used to apply a function to a DataFrame or Series to transform the values.
Here’s an example of using the map() method with a function:
import pandas as pd
# create a sample DataFrame
data = {'Name': ['John', 'Alice', 'Bob', 'Sarah'],
'Age': [25, 30, 22, 28],
'City': ['New York', 'Los Angeles', 'Chicago', 'San Francisco']}
df = pd.DataFrame(data)
# define a function to capitalize city names
def capitalize_city(city):
return city.upper()
# use the map method to capitalize the City column
df['City'] = df['City'].map(capitalize_city)
# print DataFrame
print(df)
Output:
# Name Age City
#0 John 25 NEW YORK
#1 Alice 30 LOS ANGELES
#2 Bob 22 CHICAGO
#3 Sarah 28 SAN FRANCISCO
In this example, we define a function capitalize_city() that capitalizes the input string. We then use the map() method to apply this function to the ‘City’ column of the DataFrame, which replaces each city name with its capitalized version.
The map() method applies the function to each value in the column and returns a new Series with the transformed values.
We can also use the map() method with a dictionary or Series. In the case of a dictionary, the keys represent the original values and the values represent the new values. In the case of a Series, the index represents the original values and the values represent the new values.
Using the map() method with a function, dictionary, or Series can be a powerful tool to transform values in a DataFrame or Series.
Using the Pandas map Method to Map an Anonymous Lambda Function
The map() method in Pandas can also be used to apply an anonymous lambda function to a DataFrame or Series to transform the values.
Here’s an example of using the map() method with a lambda function:
import pandas as pd
# create a sample DataFrame
data = {'Name': ['John', 'Alice', 'Bob', 'Sarah'],
'Age': [25, 30, 22, 28],
'City': ['New York', 'Los Angeles', 'Chicago', 'San Francisco']}
df = pd.DataFrame(data)
# use the map method with a lambda function to capitalize the City column
df['City'] = df['City'].map(lambda x: x.upper())
# print DataFrame
print(df)
Output:
# Name Age City
#0 John 25 NEW YORK
#1 Alice 30 LOS ANGELES
#2 Bob 22 CHICAGO
#3 Sarah 28 SAN FRANCISCO
In this example, we use an anonymous lambda function to capitalize the input string. We then use the map() method to apply this lambda function to the ‘City’ column of the DataFrame, which replaces each city name with its capitalized version.
The map() method applies the lambda function to each value in the column and returns a new Series with the transformed values.
We can also use the map() method with a dictionary, Series, or function. In the case of a dictionary, the keys represent the original values and the values represent the new values. In the case of a Series, the index represents the original values and the values represent the new values. In the case of a function, it should take a single argument and return a value.
Using the map() method with an anonymous lambda function can be useful for quick and simple transformations of values in a DataFrame or Series.
Using the Pandas map Method to Map an Indexed Series
The map() method in Pandas can be used to map a DataFrame or Series to another Series or DataFrame using a lookup table. One way to create a lookup table is to use a Pandas Series with the same index as the original DataFrame or Series.
Here’s an example of using the map() method with an indexed Series:
import pandas as pd
# create a sample DataFrame
data = {'Name': ['John', 'Alice', 'Bob', 'Sarah'],
'Age': [25, 30, 22, 28],
'City': ['New York', 'Los Angeles', 'Chicago', 'San Francisco']}
df = pd.DataFrame(data)
# create an indexed Series for city abbreviations
city_abbr = pd.Series(['NY', 'LA', 'CHI', 'SF'], index=['New York', 'Los Angeles', 'Chicago', 'San Francisco'])
# use the map method with the city_abbr Series to add a new CityAbbr column to the DataFrame
df['CityAbbr'] = df['City'].map(city_abbr)
# print DataFrame
print(df)
Output:
# Name Age City CityAbbr
#0 John 25 New York NY
#1 Alice 30 Los Angeles LA
#2 Bob 22 Chicago CHI
#3 Sarah 28 San Francisco SF
In this example, we create an indexed Series city_abbr that maps each city name to its abbreviation. We then use the map() method to apply this indexed Series to the ‘City’ column of the DataFrame, which creates a new column ‘CityAbbr’ with the corresponding abbreviations.
The map() method applies the lookup table to each value in the ‘City’ column and returns a new Series with the corresponding values from the ‘CityAbbr’ column of the indexed Series.
Using the map() method with an indexed Series can be useful when we want to create a new column in a DataFrame that depends on the values in an existing column and a lookup table.
Using the Pandas apply Method to Apply a Function
The apply() method in Pandas can be used to apply a custom function to a DataFrame or Series. This function can be either user-defined or built-in.
Here’s an example of using the apply() method with a user-defined function:
import pandas as pd
# create a sample DataFrame
data = {'Name': ['John', 'Alice', 'Bob', 'Sarah'],
'Age': [25, 30, 22, 28],
'City': ['New York', 'Los Angeles', 'Chicago', 'San Francisco']}
df = pd.DataFrame(data)
# define a function to calculate the square of a number
def square(x):
return x**2
# use the apply method to apply the square function to the Age column
df['AgeSquared'] = df['Age'].apply(square)
# print DataFrame
print(df)
Output:
# Name Age City AgeSquared
#0 John 25 New York 625
#1 Alice 30 Los Angeles 900
#2 Bob 22 Chicago 484
#3 Sarah 28 San Francisco 784
In this example, we define a function square() that calculates the square of a number. We then use the apply() method to apply this function to the ‘Age’ column of the DataFrame, which creates a new column ‘AgeSquared’ with the square of each age.
The apply() method applies the function to each value or column along the specified axis and returns a new DataFrame or Series with the transformed values.
Passing in Arguments with Pandas apply
The apply() method in Pandas allows us to pass in additional arguments to the function that we are applying. These arguments can be used to customize the behavior of the function.
Here’s an example of using the apply() method with additional arguments:
import pandas as pd
# create a sample DataFrame
data = {'Name': ['John', 'Alice', 'Bob', 'Sarah'],
'Age': [25, 30, 22, 28],
'City': ['New York', 'Los Angeles', 'Chicago', 'San Francisco']}
df = pd.DataFrame(data)
# define a function to calculate the discounted price of an item
def calculate_discounted_price(price, discount_rate):
return price * (1 - discount_rate)
# use the apply method to apply the calculate_discounted_price function to the Age column
df['DiscountedPrice'] = df['Age'].apply(calculate_discounted_price, args=(0.1,))
# print DataFrame
print(df)
Output:
# Name Age City DiscountedPrice
#0 John 25 New York 22.5
#1 Alice 30 Los Angeles 27.0
#2 Bob 22 Chicago 19.8
#3 Sarah 28 San Francisco 25.2
In this example, we define a function calculate_discounted_price() that takes in two arguments: price and discount_rate. We then use the apply() method to apply this function to the ‘Age’ column of the DataFrame, with an additional argument of 0.1 passed in as the discount rate. This creates a new column ‘DiscountedPrice’ with the discounted price of each age.
The args parameter is used to pass in additional arguments to the function. In this case, we pass in a tuple with a single element of 0.1 as the discount rate.
Performance Implications of Pandas map and apply
While the map() and apply() methods in Pandas are powerful tools for manipulating data, they can have performance implications when dealing with large datasets.
The map() method is generally faster than the apply() method because it operates on a Series object element-wise, without the overhead of a Python function call. However, it can only be used with Series objects and not DataFrames.
On the other hand, the apply() method applies a function to each row or column of a DataFrame, which can be more computationally intensive. Additionally, when using the apply() method with a Python function, there is an additional overhead associated with calling the function for each row or column.
To mitigate the performance implications of using apply(), we can use the apply() method with the axis parameter set to either 0 or 1 to apply the function row-wise or column-wise, respectively. This can be more efficient than applying the function to each element.
Another way to improve performance is to use vectorized functions provided by NumPy or Pandas whenever possible. Vectorized functions are designed to operate on entire arrays or Series objects at once, and can be much faster than functions applied element-wise.
In summary, while map() and apply() are useful tools for data manipulation, they can have performance implications when dealing with large datasets. It’s important to consider the size of the dataset and the computational complexity of the function being applied when choosing between these methods and to use vectorized functions whenever possible to improve performance.
Wrap up
Pandas provide powerful tools for manipulating and transforming data, including the map() and apply() methods. These methods allow us to apply functions to DataFrames and Series objects and can be used with both user-defined functions and built-in Python functions.
However, it’s important to consider the performance implications of using these methods, especially when dealing with large datasets. Using vectorized functions and applying functions row-wise or column-wise can help to improve performance.
Overall, understanding the capabilities and limitations of these tools can help us to effectively and efficiently manipulate data in Pandas.
Here you find Pandas’ Official Documentation
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.pop.html
Thanks for reading. Happy coding!
