Understanding CSV Files and Pandas in Python

====================================================================

In this article, we will explore the basics of working with CSV files and using the pandas library to manipulate data. We’ll cover how to read CSV files, handle different types of data, and perform common operations like filtering and grouping.

Introduction to CSV Files

A CSV (Comma Separated Values) file is a plain text file that contains tabular data, where each line represents a single record, and each value within the line is separated by a comma. CSV files are widely used for exchanging data between different applications and systems.

CSV files can contain various types of data, including strings, integers, and dates. Each row in the CSV file represents a single record, while each column represents a field or attribute of that record.

Reading CSV Files with Pandas

The pandas library provides an efficient way to read and manipulate CSV files. The read_csv function is used to read a CSV file into a DataFrame object.

A DataFrame is a two-dimensional table of data with rows and columns, similar to an Excel spreadsheet or a SQL database. It is the core data structure in pandas, and it provides many useful methods for manipulating and analyzing data.

Here’s an example of how to read a CSV file using pandas:

import pandas as pd

# Read a CSV file into a DataFrame
df = pd.read_csv('data.csv')

print(df.head())  # Print the first few rows of the DataFrame

Skipping Rows in Pandas

The read_csv function has an option called skiprows, which allows you to skip certain number of rows at the beginning of the file. However, this does not provide as much flexibility as we might need.

Let’s assume that we have a CSV file with multiple rows before the first row that contains flight data. We want to read only the flight data from the CSV file.

Solution

One way to solve this problem is by opening the CSV file line by line, checking each line to see if it starts with our magic string (“Flight Table”), and if so, reading the remaining lines into a new DataFrame using read_csv.

Here’s an example code snippet that demonstrates this approach:

import pandas as pd

with open('data.csv', 'r') as fobj:
    for line in fobj:
        # Check if the current line starts with our magic string
        if line.startswith("Flights Table"):
            # If so, read the remaining lines into a new DataFrame
            df = pd.read_csv(fobj, skiprows=1)

However, this code snippet has some issues. It will cause an error when it encounters the first row that starts with our magic string, because read_csv expects a file-like object as its argument.

Improved Solution

To avoid these issues, we can create a temporary file that contains only the lines after the first row that starts with our magic string. We can then read this temporary file into a new DataFrame using read_csv.

Here’s an improved code snippet:

import pandas as pd

# Find the line that marks the beginning of our data
with open('data.csv', 'r') as fobj:
    lines = [line for line in fobj if not line.startswith("Flights Table")]
    start_line_index = len(lines)

# Create a temporary file containing only the desired rows
with open('temp.csv', 'w') as tempfobj:
    with open('data.csv', 'r') as fobj:
        for i, line in enumerate(fobj):
            if i < start_line_index:
                tempfobj.write(line)
            elif i == start_line_index:
                tempfobj.write("Flights Table")  # Add our magic string to the first row
            else:
                tempfobj.write(line)

# Read the temporary file into a new DataFrame
df = pd.read_csv('temp.csv')

Alternative Solution Using skiprows

If we only need to skip rows before the first occurrence of our magic string, we can use the skiprows parameter with read_csv.

Here’s an example code snippet:

import pandas as pd

df = pd.read_csv('data.csv', skiprows=1)

However, this will not work if there are multiple rows before our magic string that we want to skip. We need a way to specify which rows to skip.

Unfortunately, the skiprows parameter does not provide an easy way to do this. However, we can use the chunksize parameter with a generator expression to read only certain rows from the CSV file:

import pandas as pd

for chunk in pd.read_csv('data.csv', chunksize=1):
    if chunk.iloc[0].startswith("Flights Table"):
        # Do something with the chunk
        pass

However, this approach is more complicated than our improved solution and may not be suitable for all use cases.

Conclusion

In this article, we explored how to read CSV files using pandas and perform common operations like filtering and grouping. We also discussed different ways to skip rows in pandas, including creating a temporary file containing only the desired rows or using the chunksize parameter with a generator expression.

Last modified on 2025-03-04