Understanding Pandas Data Cleaning: Checking for Numerical Values with Scientific Notation

In this article, we’ll delve into the world of data cleaning using Python’s popular Pandas library. We’ll explore how to check if a column contains numerical values, including scientific notation, and how to handle non-numerical characters in that column.

Introduction to Pandas Data Structures

Before diving into the solution, let’s first understand the basics of Pandas data structures. In Pandas, a DataFrame is similar to an Excel spreadsheet or a table in a relational database. It consists of rows (or records) and columns (or fields), where each cell contains a value.

In our case, we have a DataFrame df2 with three columns: ‘Nodes’, ‘disp1’, ‘disp2’, and ‘disp3’. The ‘Nodes’ column is the one that needs to be cleaned of non-numerical characters.

Understanding Scientific Notation

Scientific notation is a way of expressing numbers in the form a × 10^b, where a is a number between 1 and 10, and b is an integer. For example, the number 1234.56 can be expressed as 1.23456 × 10^3.

In Pandas, when you read a file with scientific notation numbers, they are stored as strings, not floats or integers. This is because Python’s built-in float data type cannot represent scientific notation numbers exactly.

The Problem: Handling Non-Numerical Characters

Our initial approach was to use the str.split method to split the ‘Nodes’ column into separate columns. However, this resulted in a string value for each cell, which is not what we want.

We also tried using pd.to_numeric with the errors='coerce' argument to convert non-numerical characters to NaN (Not a Number) values. While this worked to some extent, it didn’t handle scientific notation numbers correctly.

The Solution: Applying pd.to_numeric

The solution lies in applying the pd.to_numeric function to the ‘Nodes’ column. This function converts the string values to numeric values, including scientific notation numbers.

Here’s the code snippet that applies pd.to_numeric:

df2 = df2.apply(pd.to_numeric, errors='coerce')

This line of code applies the pd.to_numeric function to each column in the DataFrame. The errors='coerce' argument tells Pandas to convert non-numerical characters to NaN values.

Handling NaN Values

After applying pd.to_numeric, we get NaN values in our DataFrame, which represent blank cells. To replace these NaN values with an empty string, we can use the following code snippet:

df2 = df2.fillna('')

This line of code replaces all NaN values with an empty string.

Putting it All Together

Here’s the complete code snippet that solves our problem:

import pandas as pd

# Load the data
Location = r'file.rpt'
df = pd.read_fwf(Location, delim_whitespace=True)
df = df.iloc[12:]  # skip the first 11 rows

# Apply pd.to_numeric to convert non-numerical characters to NaN values
df2 = df.apply(pd.to_numeric, errors='coerce')

# Replace NaN values with an empty string
df2 = df2.fillna('')

# Split the 'Nodes' column into separate columns
df2[['Nodes', 'disp1', 'disp2', 'disp3']] = df2['Nodes'].str.split(n=3, expand=True)

Conclusion

In this article, we’ve seen how to check if a column contains numerical values with scientific notation and how to handle non-numerical characters in that column. We’ve used Pandas’ built-in functions, such as apply and pd.to_numeric, to solve our problem.

By applying these techniques, you can clean your data effectively and efficiently using Python’s Pandas library.

Additional Tips and Variations

• If you want to handle non-numerical characters more aggressively, you can use regular expressions to replace them with NaN values.
• To handle missing values in a different way, you can use the fillna method with a custom value or a function that calculates the replacement value.
• If you’re working with large datasets, it’s essential to optimize your code for performance. You can do this by using vectorized operations and avoiding loops whenever possible.

I hope this article has helped you understand how to clean your data effectively using Python’s Pandas library. Happy coding!

Last modified on 2024-05-05