In this tutorial, we’ll be learning how to delete rows in a CSV file using Python. Python offers a few libraries to deal with CSV files. Here we will use pandas, a powerful Python library for data structuring and analysis, to perform the task.
Step 1: Installation
Firstly, ensure that the Pandas library is installed in your Python environment. You can install it using pip with the following command:
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pip install pandas |
Step 2: Load the CSV file
Load the CSV file using pandas. We use the function pd.read_csv() for this.
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import pandas as pd df = pd.read_csv('file.csv') |
Name, Age, Occupation John, 28, Engineer Mike, 30, Doctor Sarah, 25, Designer Jane, 22, Student
This code reads the ‘file.csv’ and stores it in a pandas DataFrame. DataFrame is a two-dimensional data structure with columns of potentially different types, akin to a spreadsheet or SQL table.
Step 3: Check the data
It is advised to have a glance over the data, to understand what and where to delete. We can do that using the function print(df).
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print(df) |
This code will return the data from the ‘file.csv’ in a pretty format.
Step 4: Delete the desired rows
Let’s say we wish to delete the row at index 3. We use the df.drop() function from pandas to do so.
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df = df.drop(2) |
This code removes the row at index 3 from the DataFrame and returns a new DataFrame.
Step 5: Save changes to the CSV file
After deleting the rows, you want to save the changes back to the CSV file. This is done by the df.to_csv() function.
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df.to_csv('file.csv', index=False) |
The ‘index=False’ parameter prevents pandas from saving the index as a separate column.
Entire Python Code:
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import pandas as pd df = pd.read_csv('file.csv') df = df.drop(2) df.to_csv('file.csv', index=False) |
Name, Age, Occupation John,28, Engineer Mike,30, Doctor Jane,22, Student
Conclusion
In this tutorial, we’ve learned how to delete specific rows in a CSV file using Python’s pandas library. This task has wide applications in data cleaning and preprocessing stages of various projects involving large data sets.
Always remember to save the DataFrame after making modifications to persist the changes to the CSV file.