How to Skip Rows in a CSV File Using Python

In this tutorial, we will walk through the process of how to skip rows while reading a CSV file using Python’s pandas library. This technique is often used to avoid processing unnecessary data or headers in a CSV file, greatly improving the efficiency and speed of your code.

File & Data

The CSV file we will be using for this example contains the following rows:

Name,Location,Age
John,USA,28
Jane,Canada,36
Doe,UK,30
Tim,Australia,45

Step 1: Import Necessary Libraries

The first step is to import the necessary library which is pandas. If you haven’t installed it already, you can use pip install pandas on the terminal to install.

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import pandas as pd

Step 2: Load Your CSV file

Now we will load our CSV file using the pandas read_csv() function.

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data = pd.read_csv('file.csv')

Step 3: Skipping Rows

The read_csv() function has a parameter called skiprows that allows us to specify which rows to skip. Let’s see how we can use it to skip the first row (header) from the CSV.

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

Here, argument 1 tells pandas to skip the first row.

Output:

After skipping the first row, this is what our data looks like:

   John        USA  28
0  Jane     Canada  36
1   Doe         UK  30
2   Tim  Australia  45

Full Code

The complete code for skipping a row using pandas in Python:

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import pandas as pd
 
# Load the csv file and skip the first row
data = pd.read_csv('file.csv', skiprows=1)
print(data)

Conclusion

In conclusion, the skiprows parameter provided by pandas.read_csv() allows us to easily skip rows while reading a CSV file. It is notably useful when dealing with large datasets where you only need specific parts of the information, allowing for greater efficiency and speed in your Python programming work.

Feel free to play around with the ‘skiprows’ parameter to become comfortable with it, and remember that you can always refer back to the official pandas documentation if you encounter any issues!