Working with User Input in Pandas Queries

Introduction

When working with data frames, it’s often necessary to filter the data based on user input. This can be a powerful way to interact with users and provide them with personalized results. However, when dealing with complex queries, it can be challenging to handle multiple values or specific conditions.

In this article, we’ll explore how to pass a list of user input values to a pandas query using the query() method. We’ll cover the different ways to handle user input, including appending values to an array and using the == notation for single values.

Understanding Pandas Queries

Before we dive into handling user input, let’s take a look at how pandas queries work. The query() method allows you to filter a data frame based on conditions specified in a string. These conditions can include equality checks (==), inequality checks (!=), membership checks (in), and more.

The general syntax for using the query() method is as follows:

df.query('condition')

Where condition is a string that specifies the filtering criteria.

For example, if we have a data frame with columns for Drug Name, U&E, and C, we can use the following query to filter the results based on the Drug Name column:

df.query('`Drug Name` in @search')

This will return only the rows where the value in the Drug Name column is present in the @search array.

Handling Multiple Values

When dealing with multiple values, we need to handle them differently. One way to do this is by appending each value to an array using the append() method.

query = []
in_ = input("Drug name: ")
query.append(in_)
print(df.query('`Drug Name` in @search'))

This will add each new input value to the query array, which can be used in the query string.

However, this approach has some limitations. For example, it doesn’t allow us to specify multiple conditions or use logical operators like AND or OR. To overcome these limitations, we can use the np.in1d() function from NumPy.

import numpy as np

query = []
in_ = input("Drug name: ")
query.append(in_)
print(df.query('`Drug Name` in @np.in1d(@search, df["Drug Name"])'))

This will return only the rows where the value in the Drug Name column is present in both the current input value and the entire @search array.

Handling Single Values

When dealing with single values, we can use the == notation to specify exact matches. For example:

user_input = input("Drug name: ")
subframe = df.query('`Drug Name` == @user_input')

This will return only the rows where the value in the Drug Name column is exactly equal to the user’s input.

Putting it All Together

Now that we’ve covered how to handle multiple and single values, let’s put it all together. We can use a loop to repeatedly prompt the user for input until they enter “exit”.

while True:
    search = input("Enter drug name (or 'exit' to quit): ")
    if search == "exit":
        break
    print(df.query('`Drug Name` in @search'))

This will allow the user to enter multiple values and filter the results accordingly.

Conclusion

Passing a list of user input values to a pandas query can be done using various approaches, including appending values to an array or using the == notation for single values. By understanding how the query() method works and leveraging NumPy functions like np.in1d(), we can create powerful filtering tools that interact with users in a meaningful way.

In this article, we explored the different ways to handle user input in pandas queries, including multiple values and single values. We covered the basics of using the query() method, handling arrays and single values, and put it all together with a simple example loop. Whether you’re working with complex data sets or need to create interactive tools for your users, this article should provide a solid foundation for getting started.

Last modified on 2025-02-04