Using User Input in Pandas DataFrame Operations

As data scientists and analysts, we often find ourselves working with datasets that are constantly changing. One common challenge is handling user input, especially when it comes to selecting specific columns for analysis or filtering. In this article, we’ll explore a way to use user input as a subset in pandas functions.

Introduction to User Input in Pandas

When working with large datasets, it’s essential to ensure that the user input is accurate and reliable. Using user input can be tricky, especially when dealing with complex operations like data selection or filtering. In this article, we’ll explore how to use user input as a subset in pandas functions.

Problem Statement

The question at hand is: “Is there a way to input column names without quotes and use that as a subset in another function in pandas?” Let’s take a closer look at the provided Stack Overflow post and analyze the issue.

The original script uses input() to get user input, which returns a string. When working with pandas dataframes, we often need to select specific columns using the subset parameter of functions like duplicated(). However, if the user enters column names with quotes or spaces, it can lead to unexpected behavior.

Proposed Solution

To address this issue, we’ll explore two approaches:

1. Inputting column names without quotes: We’ll show how to use user input as a subset without quotes.
2. Getting user input for a list of columns: We’ll demonstrate how to get a list of columns from the user and use it in pandas functions.

Approach 1: Inputting Column Names Without Quotes

In this approach, we’ll create a way for the user to enter column names without quotes. We can achieve this by using the int() function to convert the input into an integer index, which is what pandas uses to access columns.

Here’s an example code snippet:

# number of elements as input 
n = int(input("Enter number of elements : ")) 

# iterating till the range 
for i in range(0, n): 
    ele = int(input()) # adding the element 

This approach is more straightforward but may not be suitable for all use cases. We’ll explore a better solution next.

Approach 2: Getting User Input for a List of Columns

A more user-friendly approach would be to ask the user to enter column names one-by-one, which we can then store in a list and use as a subset.

Here’s an example code snippet:

column_subset = []

# get each column name from the user 
for i in range(10): # assuming max 10 columns 
    ele = input(f"Enter column {i+1} (or 'done' to finish) ")
    
    if ele.lower() == 'done':
        break

    column_subset.append(ele)

This approach is more intuitive for the user but may be less efficient for larger datasets.

Using User Input with Pandas Duplicated()

Now that we have two approaches to getting user input, let’s see how we can use it in pandas functions like duplicated().

Here’s an example code snippet using Approach 1:

import pandas as pd

# get column subset from the user 
column_subset = []

n = int(input("Enter number of elements : ")) 

for i in range(0, n): 
    ele = int(input()) 
    column_subset.append(ele) # adding the element 

df = pd.DataFrame({
    'A': [1, 2, 3, 4, 5],
    'B': [6, 7, 8, 9, 10]
})

# use user input as a subset in duplicated()
duplicated_df = df.duplicated(subset=column_subset, keep=False)

print(duplicated_df)

And here’s an example code snippet using Approach 2:

import pandas as pd

# get column subset from the user 
column_subset = []

for i in range(2): # assuming max 2 columns 
    ele = input(f"Enter column {i+1} (or 'done' to finish) ")
    
    if ele.lower() == 'done':
        break
    
    column_subset.append(ele)

df = pd.DataFrame({
    'A': [1, 2, 3, 4, 5],
    'B': [6, 7, 8, 9, 10]
})

# use user input as a subset in duplicated()
duplicated_df = df.duplicated(subset=column_subset, keep=False)

print(duplicated_df)

Both approaches work, but Approach 2 is more intuitive for users.

Conclusion

In this article, we explored two ways to use user input as a subset in pandas functions. Approach 1 uses integer indices, while Approach 2 gets column names one-by-one and stores them in a list. Both approaches have their pros and cons, and the choice ultimately depends on the specific use case.

By following these steps and using our proposed solutions, you can create user-friendly scripts that handle complex data operations with ease.

References

• Stack Overflow Post
• Pandas Documentation

Last modified on 2025-01-30