Adventures in Machine Learning

Simplifying Data Filtering in Pandas: Using isin() Function with Query() Method

Data analysis is central to solving complex problems, and Python is a popular language for data analysis. When working with data in Python, the pandas library provides an extensive array of functionalities to simplify data manipulation and analysis.

In this article, we will examine how to use the isin() function in the pandas query() method to filter rows in pandas DataFrames. We will also explore how to store a list of values and reference it in the query() method.

1. Using isin() Function in pandas Query() Method

The isin() function in pandas is used to determine the values that are present in a set of values or an array.

This function returns a Boolean array that can be used to index a pandas DataFrame. The query() method in pandas allows us to provide a Boolean expression and returns the rows of a DataFrame that satisfy the expression.

By combining the isin() function with the query() method, we can filter rows in a DataFrame based on a list of values.

Syntax for Using isin() Function in pandas Query() Method

To use the isin() function in the pandas query() method, we first need to use the in operator to compare a column in a DataFrame to a list of values. Here’s an example:

df.query(‘team column in [A, B, D]’)

In this syntax, ‘team column’ refers to the name of the column we want to filter the values on, while A, B, and D represent the list of values we want to filter the DataFrame on.

Example: Using query() Method to Filter for Values in List

Suppose we have a pandas DataFrame with the records of basketball players from different teams. The DataFrame contains three columns: team, points, assists, and rebounds.

We want to filter the DataFrame for players on teams A, B, or D. To achieve this, we can use the isin() function in the query() method, as shown below:

import pandas as pd

sports_data = {‘team’: [‘A’, ‘B’, ‘C’, ‘D’, ‘E’], ‘points’: [180, 230, 156, 213, 255], ‘assists’: [25, 34, 17, 28, 33],’rebounds’: [3, 6, 2, 7, 8]}

sports_df = pd.DataFrame(sports_data)

sports_df.query(‘team in [“A”, “B”, “D”]’)

In this example, we use the isin() function to filter the DataFrame for players on teams A, B, or D. The query() method is used to return the rows that satisfy this condition.

2. Variable Assignment and Querying for Values in List

In some cases, we might need to filter a DataFrame based on more than three or four values.

Instead of typing all the values in the query() method, we can store the list of values in a variable and reference it in the query() function. Here’s how to do this:

Storing a List of Values in a Variable

We can store a list of values in a variable using the Python list data structure. Here is an example:

team_names = [“A”, “B”, “D” ,”E”]

In this example, we define a list called team_names that contains the team names we want to filter on.

Referencing Variable in Query() Function Using the @ Operator

To use the variable in the query() function, we precede the name of the variable with the @ operator. This tells pandas that we are referencing a variable and not a column in the DataFrame.

Here’s how to do this:

sports_df.query(‘team in @team_names’)

In this example, we use the @ operator to reference the team_names variable in the query() function, which filters the DataFrame for players on teams A, B, D, or E.

Conclusion

In conclusion, the isin() function in pandas is a power tool for filtering data in DataFrames using a set of values. We can combine the isin() function with the query() method to return the rows of a DataFrame that meet a certain condition based on a list of values.

We can store the list of values in a variable and reference it in the query() method using the @ operator.

Using these techniques can significantly simplify the process of filtering data in pandas DataFrames, and they should be in every data analyst’s toolkit.

In this article, we explored how to use the isin() function in the pandas query() method to filter rows in pandas DataFrames. We also learned the syntax for using the isin() function in the query() method and saw an example of filtering basketball players based on a list of team names.

We discussed how to store a list of values in a variable and reference it in the query() method using the @ operator. Filtering data is an essential part of data analysis, and mastering the isin() function combined with the query() method can significantly simplify the process.

The takeaways from this article are that using the isin() function combined with the query() method and storing values in variables are powerful tools for filtering DataFrames.

Popular Posts