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How can we store a DataFrame object into a
.csv file in Pandas?
We can achieve this with the
to_csv() function, which provides plenty of parameters to suit our use cases.
Suppose we have a DataFrame object
df that looks like this.
Dog Age 0 corgi 7 1 shih tzu 5
to_csv() function will produce a CSV file with the following:
,Dog,Age 0,corgi,7 1,shih tzu,5
Write without index
By default, the first column of our CSV will contain the index of each row in
If we don’t want to include the index in our CSV, we can set the
index parameter to be
Our CSV file would then look something like this.
Dog,Age corgi,7 shih tzu,5
All subsequent examples will assume
Use a different separator
The default separator is a comma
We can change this using the
sep parameter. For instance, we can delimit by tab or semicolon.
df.to_csv("filename.csv", sep='\t') # Tab df.to_csv("filename.csv", sep=';') # Semicolon
Here’s what the semicolon delimiter would look like.
Dog;Age corgi;7 shih tzu;5
Write without header
If we don’t want to include the header in our CSV file, we can set
Let’s take out that header.
corgi,7 shih tzu,5
Write specific columns
If we want to only write a subset of columns to our CSV, we can specify the columns as a list of strings in the
Only dogs, no age.
Dog corgi shih tzu
Change file format encoding
To be safe, we can set the
encoding parameter so other applications know how to read our CSV file.
Compress a CSV
If we’re writing hundreds of thousands of rows into a CSV file, it might be best to compress the CSV.
gzip file will be smaller, but the write and read times will involve compressing and decompressing, making the process take a bit longer.
NaN with string
We can replace all instances of
df with a string like
"N/A" using the
We can specify a format for all
datetime columns using the
Now you learned, How you can use & How to Export a DataFrame to CSV with Pandas in Python.
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