Reading CSV Files with Variable Header Positions Using Pandas: A Solution for Unconventional Data Structures

Reading CSV Files with Variable Header Positions using Pandas

Understanding the Problem

When working with CSV files, it’s common to encounter files with variable header positions. This means that the headers are not always at the top of the file, but rather can be located anywhere in the file. In such cases, using the standard read_csv function from pandas does not work as expected.

A Typical CSV File Structure

A typical CSV file structure would look something like this:

Days    Page Impressions    Visits  Bounces
2012-12-15  692041  87973   31500
2012-12-16  602356  78663   29298
2012-12-17  730902  99356   37436
2012-12-18  730071  97844   37199
2012-12-19  774964  110446  43858
2012-12-20  419256  44592   13961
2012-12-21  320966  33692   10076
2012-12-22  200992  18840   5170

A CSV File with Variable Header Positions

However, sometimes the CSV files come in a different format. The headers might be located somewhere in the middle of the file like this:

SomeName ABCD           
Account: AccountHolder Name         
Report Author: Analysis         
Description: Some variable length description       

Pivot           

Pivot           
Days    Page Impressions    Visits  Bounces
2012-12-15  367143  69147   30222
2012-12-16  334675  63702   28040
2012-12-17  409260  77171   33642
2012-12-18  427765  78221   33575
2012-12-19  434781  79850   34300
2012-12-20  463448  81361   34501
2012-12-21  447964  81897   35242
2012-12-22  368477  70352   31014
2012-12-23  321891  61973   27521

Time of Calculation: 2013-03-15 02:14:58

Understanding the Issue

In such cases, we need a way to read the file and get only the data that is associated with the columns of Days, Page Impressions, Visits, and Bounces. We cannot use the standard read_csv function because it assumes that the headers are always at the top of the file.

Solution Overview

To solve this problem, we need to read the file in two passes. The first pass is used to enumerate the lines in the file until we find the row where the headers are located. Then, we use that row number as an argument to the csv parser’s skiprows parameter to skip over all the rows before the header row.

Step 1: Enumerate Lines Until We Find the Header Row

We start by opening the file in binary mode ('rb') and enumerating each line using a for loop. When we find a line that starts with four spaces followed by Days, we break out of the loop because we have found the header row.

with open('file.csv', 'rb') as infile:
    for lineno, line in enumerate(infile):
        if line[:4] == 'Days':
            break

Step 2: Read the Rest of the File Using `skiprows`

Once we have found the header row, we can read the rest of the file using the csv parser’s skiprows parameter. We pass in the row number that we broke out of the loop at (lineno) to skip over all the rows before the header.

df = pd.read_csv('file.csv', skiprows=lineno)

Putting It All Together

Here is the complete code:

with open('file.csv', 'rb') as infile:
    for lineno, line in enumerate(infile):
        if line[:4] == 'Days':
            break

df = pd.read_csv('file.csv', skiprows=lineno)

Conclusion

Reading CSV files with variable header positions is not a trivial task. However, by using the csv parser’s skiprows parameter and enumerating lines until we find the header row, we can solve this problem in Python using pandas.

In conclusion, understanding how to read CSV files with variable header positions requires knowledge of file I/O operations, string manipulation, and the csv module. Additionally, it involves a good grasp of how the read_csv function works under the hood.

Example Use Cases

This code can be used in any situation where you need to read a CSV file that does not have a fixed header row. Some examples include:

Reading a log file with variable headers.
Parsing data from an Excel file with multiple worksheets, each with different header rows.
Importing data from a database that uses variable-width fields.

Future Work

There are many ways to improve this code. For example, we could add some error checking to handle cases where the skiprows parameter is not used correctly. We could also optimize the code for performance by using more advanced techniques such as buffer management or multi-threading.

However, these improvements would require a significant amount of additional work and are outside the scope of this example.

Last modified on 2025-04-25