Understanding pandas.read_csv’s Behavior with Leading Zeros and Floating Point Numbers

When working with CSV files in Python, it’s common to encounter issues with leading zeros and floating point numbers. In this article, we’ll explore why pandas.read_csv might write out original data back to the file, including how to fix these issues.

Introduction to pandas.read_csv

pandas.read_csv is a function used to read CSV files into a DataFrame. It’s a powerful tool for data analysis and manipulation in Python. However, like any function, it has its quirks and limitations.

The Issue with Leading Zeros

Let’s take a closer look at the issue with leading zeros. When using pandas.read_csv without specifying a dtype, anything that looks like a number is read in as a float. This can cause problems when working with CSV files that contain data with leading zeros.

For example, consider the following line of code:

grid = pandas.read_csv("thirdparty.csv", dtype={'ZIP': int, 'REFERENCE': int})

In this case, the ZIP code 01234 is read in as a float instead of an integer. When printing out the DataFrame, this leads to unexpected results.

The Issue with Floating Point Numbers

Another issue arises when working with floating point numbers. By default, pandas.read_csv reads all numbers as floats, even if they’re actually integers. This can cause problems when writing the CSV file back out, leading to incorrect values.

For example, consider the following line of code:

grid = pandas.read_csv("thirdparty.csv", dtype={'REFERENCE': int})

In this case, the order number 22276 is read in as a float instead of an integer. When writing the CSV file back out, this leads to unexpected results.

How to Fix the Issue

So, how can we fix these issues? The solution lies in specifying the correct dtype for each column when reading in the CSV file.

Reading All Columns as Strings

One simple solution is to read in all columns as strings using the dtype=str argument. This preserves the values exactly as they were in the original CSV file.

data = pd.read_csv("thirdparty.csv", dtype=str)

While this works, it’s not necessarily the best solution. We can specify the desired dtype for each column to avoid unnecessary conversions.

Specifying Desired Dtype for Each Column

A better solution is to specify the desired dtype for each column when reading in the CSV file.

data = pd.read_csv("thirdparty.csv", dtype={'ZIP': str, 'REFERENCE': int})

In this case, the ZIP code is read in as a string and the order number is read in as an integer.

Writing the CSV File Back Out with float_format

When writing the CSV file back out, we can use the float_format argument to ensure any floats are written as desired.

data.to_csv("output.csv", float_format="%d")

In this case, the order number is written as an integer instead of a float.

Example Use Case

Let’s take a closer look at an example use case:

import pandas as pd

# Read in CSV file with dtype specified for each column
data = pd.read_csv("thirdparty.csv", dtype={'ZIP': str, 'REFERENCE': int})

# Print out the DataFrame
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
    print(data)

# Write the DataFrame back out to a new CSV file with float_format specified
data.to_csv("output.csv", float_format="%d")

In this example, we read in the CSV file with dtype specified for each column. We then print out the DataFrame and write it back out to a new CSV file with float_format specified.

Conclusion

pandas.read_csv’s behavior can be surprising at first, but understanding the underlying causes is key to working effectively with this powerful tool. By specifying the correct dtype for each column when reading in the CSV file, we can avoid unnecessary conversions and ensure accurate data manipulation. Additionally, using float_format when writing the CSV file back out can help ensure consistent results.

Last modified on 2023-08-24