Understanding the Problem: Creating a Single DataFrame from Multiple CSV Files in Python

In this article, we will delve into the world of data manipulation using the popular Python library pandas. Specifically, we will address the issue of creating a single DataFrame from multiple CSV files based on certain conditions.

Introduction to pandas and DataFrames

The pandas library is a powerful tool for data analysis and manipulation in Python. It provides data structures such as Series (1-dimensional labeled array) and DataFrames (2-dimensional labeled data structure with columns of potentially different types). The DataFrame is the core data structure in pandas, providing efficient data storage and manipulation capabilities.

The Problem: Multiple CSV Files and a Single DataFrame

The question arises when we have multiple CSV files, each containing data that we want to merge into a single DataFrame. However, instead of simply concatenating or appending the DataFrames, we need to apply certain conditions to filter out rows or columns based on specific criteria.

In our example, we are given a scenario where we have 100 CSV files and want to extract a specific column from all DataFrames in one single DataFrame. We will explore the correct approach using pandas and highlight the pitfalls of incorrect code execution.

The Incorrect Approach: Using Append Method

Let’s examine the initial attempt at creating a single DataFrame from multiple CSV files, as shown in the question:

import os
import pandas as pd
import matplotlib.pyplot as plt

directory = os.fsencode('xx')
total_df = pd.DataFrame()
for file in os.listdir(directory):
    filename = os.fsdecode(file)
    if ('results' in filename):                                           
        temp_df = pd.read_csv(filename, sep=';')
        xCoor = temp_df.iloc[0,0]
        yCoor = temp_df.iloc[0,1]
        if (xCoor > 51 and xCoor < 52 and yCoor > 5 and yCoor < 6):
            data = temp_df['lon']
            total_df.append(data)
print(total_df[:])

In this code, we create a total_df DataFrame that initially remains empty. We then iterate over each CSV file in the specified directory, read its contents using pd.read_csv, and extract specific columns or rows based on certain conditions.

However, as shown in the provided output, appending data to total_df yields an empty DataFrame, indicating a fundamental issue with this approach.

The Issue: Incorrect Use of Append Method

The main problem with the initial code is that we are using the append method incorrectly. When you append new data to a DataFrame using append, pandas expects a list-like object containing individual rows or Series. However, in our example, we’re passing a single value (data) instead.

The correct approach would be to use the concat function from pandas, which allows us to concatenate multiple DataFrames along a specified axis (in this case, the 0th axis).

The Correct Approach: Using Concat Function

Let’s modify our code to utilize the concat function and create a single DataFrame from multiple CSV files:

import os
import pandas as pd

directory = os.fsencode('xx')
total_df = pd.DataFrame()

for file in os.listdir(directory):
    filename = os.fsdecode(file)
    if ('results' in filename):                                           
        temp_df = pd.read_csv(filename, sep=';')
        # apply condition here
        data = temp_df[temp_df['lon'].between(51, 52) & 
                       (temp_df['lon'] >= 5 and temp_df['lon'] <= 6)].iloc[:, 0]
        total_df = pd.concat([total_df, data], ignore_index=True)
print(total_df[:])

In this corrected code, we apply the condition to extract specific rows from temp_df using boolean indexing. We then use pd.concat to concatenate the resulting DataFrame with the original total_df.

Note that we’re also assigning the concatenated result back to total_df using ignore_index=True, which resets the index of the resulting DataFrame.

Additional Insights and Considerations

• When working with CSV files, it’s essential to consider the separator used in each file. In our example, we used a semicolon (;) as the separator, but this might not be the case for all files.
• The pd.read_csv function can handle various separators, including commas (,) and tab characters (\t).
• To avoid issues with missing values or incorrect data types, ensure that each CSV file is properly formatted and cleaned before processing.

Conclusion

In this article, we explored the issue of creating a single DataFrame from multiple CSV files using pandas. We identified the pitfalls of the initial approach using the append method and presented a corrected solution utilizing the concat function. By understanding the nuances of working with DataFrames and applying logical indexing techniques, you can efficiently merge data from multiple sources into a single, cohesive DataFrame.

Last modified on 2024-08-10