Export Pandas dataframe to a CSV file
Suppose you are working on a Data Science project and you tackle one of the most important tasks, i.e, Data Cleaning. After data cleaning, you don’t want to lose your cleaned data frame, so you want to save your cleaned data frame as a CSV. Let us see how to export a Pandas DataFrame to a CSV file.
Pandas enable us to do so with its inbuilt to_csv() function.
First, let’s create a sample data frame
Python3
import pandas as pd
scores = { 'Name' : [ 'a' , 'b' , 'c' , 'd' ],
'Score' : [ 90 , 80 , 95 , 20 ]}
df = pd.DataFrame(scores)
print (df)
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Output :
Now let us export this DataFrame as a CSV file named your_name.csv :
Python3
df.to_csv( "your_name.csv" )
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Output
File Successfully saved
In case you get a UnicodeEncodeError, just pass the encoding parameter with ‘utf-8’ value.
Python3
df.to_csv( "your_name.csv" , encoding = 'utf-8' )
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Possible Customizations
1. Include index number
You can choose if you want to add automatic index. The default value is True. To set it to False.
Python3
df.to_csv( 'your_name.csv' , index = False )
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Output :
2. Export only selected columns
If you want to export only a few selected columns, you may pass it in to_csv() as ‘columns = [“col1”, “col2”]
Python3
df.to_csv( "your_name.csv" , columns = [ 'Name' ])
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Output :
3. Export header
You can choose if you want your column names to be exported or not by setting the header parameter to True or False. The default value is True.
Python3
df.to_csv( 'your_name.csv' , header = False )
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Output :
4. Handle NaN
In case your data frame has NaN values, you can choose it to replace by some other string. The default value is ”.
Python3
df.to_csv( "your_name.csv" , na_rep = 'nothing' )
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5. Separate with something else
If instead of separating the values with a ‘comma’, we can separate it using custom values.
Python3
df.to_csv( "your_name.csv" , sep = '\t' )
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Output :
Last Updated :
01 Sep, 2021
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