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You are here: Home / Pandas 101 / How to Save Pandas Dataframe as gzip/zip File?

How to Save Pandas Dataframe as gzip/zip File?

May 7, 2020 by cmdlinetips

In this post, we will learn one of the most useful things you might do in doing data analysis. Here we will learn how to save the dataframe as a compressed file like gzip or zip file. Saving a file in compressed form can be of help when space is an issue.

Let us load Pandas.

# load pandas
import pandas as pd

First, we will create a toy dataframe from scratch. We create two lists.

education = ["Bachelor's", "Less than Bachelor's","Master's","PhD","Professional"]
salary = [110000,105000,126000,144200,96000]

And use the two lists as input to Pandas’ DataFrame function to create a new dataframe.

# Create dataframe in one step
df = pd.DataFrame({"Education":education,
                  "Salary":salary})
df
	Education	Salary
0	Bachelor's	110000
1	Less than Bachelor's	105000
2	Master's	126000
3	PhD	144200
4	Professional	95967

Now that we have a Pandas dataframe, we are ready to learn to save the dataframe as CSV/TSV file.

Pandas to_csv() function is extremely versatile and can handle variety of situation in writing a dataframe to a file including saving as compressed file.

To save a Pandas dataframe as gzip file, we use ‘compression=”gzip”‘ in addition to the filename as argument to to_csv() function.

In this example below, we save our dataframe as csv file without row index in compressed, i.e. gzip file, form.

# write a pandas dataframe to gzipped CSV file
df.to_csv("education_salary.csv.gz", 
           index=False, 
           compression="gzip")

In addition to gzip file, we can also compress the file in other formats. For example, to save the dataframe as zip file, we would use ‘compression=”zip”‘ as one of the arguments to to_csv() function.

# write a pandas dataframe to zipped CSV file
df.to_csv("education_salary.csv.zip", 
           index=False, 
           compression="zip")

This post is part of the series on Byte Size Pandas: Pandas 101, a tutorial covering tips and tricks on using Pandas for data munging and analysis.

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