Adventures in Machine Learning

Retrieving Specific Rows: A Guide to Using groupby() Function in Pandas

Retrieving Specific Rows After Using groupby() Function in PandasPandas is a popular data manipulation library in Python that provides powerful tools for handling and analyzing tabular data. One of the commonly used functions in pandas is groupby().

It is used to group data based on a categorical variable and apply a function to each group. However, once the data is grouped, it may be necessary to retrieve specific rows from the grouped data.

This article will explore two methods for retrieving specific rows after using groupby() function in pandas, while providing an example dataframe for better understanding. Method 1: Get Group After Using groupby()

After grouping data using groupby() function, the get_group() method can be used to retrieve specific groups from the grouped data.

The get_group() method takes a single argument which is the name of the group to be retrieved. The argument can be any valid group name which is any unique value in the column used for grouping.

Example

Consider the following dataframe:

“`

import pandas as pd

df = pd.DataFrame({

‘Fruit’: [‘Apple’, ‘Orange’, ‘Banana’, ‘Apple’, ‘Banana’, ‘Orange’],

‘Color’: [‘Red’, ‘Orange’, ‘Yellow’, ‘Red’, ‘Yellow’, ‘Orange’],

‘Price’: [20, 15, 10, 17, 12, 16]

})

grouped_df = df.groupby(‘Fruit’)

“`

The above code groups the dataframe by Fruits. Now, to retrieve the group of “Apple”, we can use get_group() method.

“`

apple_df = grouped_df.get_group(‘Apple’)

print(apple_df)

“`

Output:

“`

Fruit Color Price

0 Apple Red 20

3 Apple Red 17

“`

From the above output, we can see that the get_group() method retrieves only the rows belonging to the group of “Apple”. Method 2: Get Specific Columns of Group After Using groupby()

Sometimes, it may be necessary to retrieve specific columns of a specific group after using groupby() function.

To achieve this, we can use the same get_group() method from method 1 and then select the columns we want to retrieve.

Example

Consider the following dataframe:

“`

import pandas as pd

df = pd.DataFrame({

‘Fruit’: [‘Apple’, ‘Orange’, ‘Banana’, ‘Apple’, ‘Banana’, ‘Orange’],

‘Color’: [‘Red’, ‘Orange’, ‘Yellow’, ‘Red’, ‘Yellow’, ‘Orange’],

‘Price’: [20, 15, 10, 17, 12, 16]

})

grouped_df = df.groupby(‘Fruit’)

“`

To retrieve only the “Color” and “Price” columns of the group of “Apple”, we can execute the following code. “`

apple_df = grouped_df.get_group(‘Apple’)[[‘Color’, ‘Price’]]

print(apple_df)

“`

Output:

“`

Color Price

0 Red 20

3 Red 17

“`

From the above output, we can see that only the “Color” and “Price” columns of the group of “Apple” are retrieved.

Example DataFrame for Retrieving Specific Rows

As mentioned earlier, this article uses an example dataframe for a better understanding of retrieving specific rows after using groupby() function in pandas. Consider the following dataframe:

“`

import pandas as pd

df = pd.DataFrame({

‘Gender’: [‘Male’, ‘Female’, ‘Male’, ‘Female’, ‘Male’, ‘Female’, ‘Male’, ‘Female’],

‘Age’: [25, 22, 28, 31, 30, 24, 27, 26],

‘Salary’: [50000, 60000, 55000, 65000, 70000, 75000, 80000, 85000]

})

grouped_df = df.groupby(‘Gender’)

“`

The above code groups the dataframe by Gender. Now, let’s demonstrate how to retrieve specific rows with the methods discussed above.

To retrieve only the rows of females, we can execute the following code. “`

female_df = grouped_df.get_group(‘Female’)

print(female_df)

“`

Output:

“`

Gender Age Salary

1 Female 22 60000

3 Female 31 65000

5 Female 24 75000

7 Female 26 85000

“`

From the above output, we can see that only the rows of females are retrieved. Now, to retrieve only the age of males, we can execute the following code.

“`

male_df = grouped_df.get_group(‘Male’)[[‘Age’]]

print(male_df)

“`

Output:

“`

Age

0 25

2 28

4 30

6 27

“`

From the above output, we can see that only the age column of the male group is retrieved.

Conclusion

Retrieving specific rows is an essential task after using groupby() function in pandas. In this article, we explored two methods to retrieve specific rows: Get Group After Using groupby() Function, and Get Specific Columns of Group After Using groupby() Function.

By using these methods, we can easily retrieve the rows of interest from our grouped data.

Example 1: Get Group After Using groupby()

Sometimes, we may want to retrieve only a specific group from a DataFrame after using groupby() function in pandas. For example, consider a DataFrame of employees and their salaries, where we want to retrieve only the salaries of employees who belong to a specific department.

To achieve this, we can use the get_group() method of the grouped DataFrame. “`

import pandas as pd

data = {

‘Emp_id’: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],

‘Name’: [‘John’, ‘Kate’, ‘Tom’, ‘Harry’, ‘Ron’, ‘Hermione’, ‘Ginny’, ‘Percy’, ‘Charlie’, ‘Bill’],

‘Department’: [‘HR’, ‘Finance’, ‘Sales’, ‘HR’, ‘Sales’, ‘Finance’, ‘Sales’, ‘HR’, ‘Sales’, ‘Finance’],

‘Salary’: [50000, 60000, 55000, 65000, 70000, 75000, 80000, 85000, 90000, 95000]

}

df = pd.DataFrame(data)

grouped_df = df.groupby(‘Department’)

“`

The above code groups the DataFrame by the ‘Department’ column. Now, to retrieve the salaries of employees who belong to the ‘Finance’ department, we can use the get_group() method as follows:

“`

finance_employees = grouped_df.get_group(‘Finance’)

“`

The above code retrieves only the rows of employees who belong to the ‘Finance’ department.

It returns a new DataFrame that contains only the data of employees in the ‘Finance’ department. “`

Emp_id Name Department Salary

1 2 Kate Finance 60000

5 6 Hermione Finance 75000

9 10 Bill Finance 95000

“`

From the above output, we can see that the get_group() method retrieves only the rows belonging to the ‘Finance’ department.

Example 2: Get Specific Columns of Group After Using groupby()

Another scenario is when we want to retrieve only specific columns of a specific group after using groupby() function in pandas. For example, consider the same DataFrame of employees and their salaries.

Suppose we are interested in only the name and salary of employees who belong to the ‘Sales’ department. We can achieve this by using the get_group() method along with selecting the specific columns of interest.

“`

import pandas as pd

data = {

‘Emp_id’: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],

‘Name’: [‘John’, ‘Kate’, ‘Tom’, ‘Harry’, ‘Ron’, ‘Hermione’, ‘Ginny’, ‘Percy’, ‘Charlie’, ‘Bill’],

‘Department’: [‘HR’, ‘Finance’, ‘Sales’, ‘HR’, ‘Sales’, ‘Finance’, ‘Sales’, ‘HR’, ‘Sales’, ‘Finance’],

‘Salary’: [50000, 60000, 55000, 65000, 70000, 75000, 80000, 85000, 90000, 95000]

}

df = pd.DataFrame(data)

grouped_df = df.groupby(‘Department’)

“`

The above code groups the DataFrame by the ‘Department’ column. Now, to retrieve only the name and salary columns of employees who belong to the ‘Sales’ department, we can use the get_group() method as follows:

“`

sales_employees = grouped_df.get_group(‘Sales’)[[‘Name’, ‘Salary’]]

“`

The above code retrieves only the name and salary columns of employees who belong to the ‘Sales’ department.

It returns a new DataFrame that contains only the data of employees in the ‘Sales’ department with the desired columns. “`

Name Salary

2 Tom 55000

4 Ron 70000

6 Ginny 80000

8 Charlie 90000

“`

From the above output, we can see that only the name and salary columns of the ‘Sales’ department are retrieved.

Conclusion

In conclusion, this article has explained two methods for retrieving specific rows and columns of a DataFrame after using groupby() function in pandas. The get_group() method of the grouped DataFrame is used to retrieve only the rows belonging to a specific group, while selecting specific columns of interest can be achieved by using the same method along with column selection.

These methods can be used to retrieve the data of interest from a large dataframe and simplify complex data analysis tasks. Pandas is a powerful data manipulation library in Python that provides various operations to manipulate and analyze tabular data.

Some of the common operations in pandas include filtering, sorting, grouping, merging, and joining dataframes, among others. As we have seen in earlier sections of this article, the groupby() function is one of the most commonly used functions in pandas.

However, there are many other operations that can be performed using pandas. In this section, we will discuss some additional resources for performing common operations in pandas.

Tutorials

One of the best resources for learning pandas is the official pandas documentation. The documentation provides an in-depth guide to the pandas library along with numerous examples and use cases.

It covers all the basic and advanced operations in pandas, making it an excellent resource for beginners and advanced users alike. In addition to the official documentation, there are many online tutorials and courses available that offer step-by-step guides to pandas.

Some of the popular sources include DataCamp, Codecademy, and Kaggle. These resources offer interactive learning experiences, making it easy to follow along with examples and test your knowledge as you learn.

Common Operations

Here are some of the most common operations that can be performed in pandas:

1. Filtering data: Filtering data involves selecting a subset of rows or columns that meet specific criteria.

Pandas provides several methods such as loc[], iloc[] and boolean indexing, which can be used to filter dataframes based on certain conditions. 2.

Sorting data: Sorting data involves arranging the rows or columns of a dataframe in a specific order based on certain criteria. Pandas provides the sort_values() method to sort dataframes based on one or more columns.

3. Grouping data: As we have seen earlier, grouping data involves splitting the data into groups based on certain criteria and performing calculations on each group.

Pandas provides the groupby() method to group dataframes and perform calculations on each group. 4.

Merging data: Merging data involves combining two or more dataframes into a single dataframe based on a shared column. Pandas provides the merge() method to merge dataframes.

5. Joining data: Joining data is similar to merging data, but instead of combining dataframes based on a column, it combines dataframes based on the index.

Pandas provides the join() method to join dataframes. 6.

Reshaping data: Reshaping data involves transforming dataframes from one format to another. Pandas provides several methods such as pivot(), melt() and stack() to reshape dataframes.

In addition to the above operations, pandas provides many other functions and methods that can be used to manipulate, clean, and analyze data.

Conclusion

Pandas is a versatile library that provides a wide range of operations for manipulating and analyzing tabular data. The groupby() function is one of the most commonly used functions in pandas, but there are many other operations that can be performed using pandas.

The official pandas documentation and online tutorials such as DataCamp, Codecademy, and Kaggle provide excellent resources for learning pandas and performing common operations. By mastering pandas, data analysts and data scientists can easily manipulate and analyze large datasets and extract insights from data.

In conclusion, this article has provided an in-depth exploration of two methods for retrieving specific rows and columns of a DataFrame after using groupby() function in pandas, along with examples and an example DataFrame. The get_group() method of the grouped DataFrame is used to retrieve only the rows belonging to a specific group, while selecting specific columns of interest can be achieved by using the same method along with column selection.

Pandas provides various operations to manipulate and analyze tabular data, such as filtering, sorting, grouping, merging, and joining dataframes. The pandas documentation and online tutorials offer excellent resources for mastering these operations.

By understanding these operations and mastering pandas, data analysts and data scientists can easily manipulate and analyze large datasets, extract insights from data, and make informed data-driven decisions.

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