In pandas we have a .to_csv() method which gives a way to export a dataframe to a csv file.
Syntax :
df.to_csv(path_or_buf, sep=',', na_rep='', float_format=None,
columns=None, index=True, index_label=None, mode='w',
encoding=None, compression=None, quoting=None, quotechar='"',
line_terminator='\n', chunksize=None, date_format=None,
doublequote=True, escapechar=None,
decimal=',', thousands=None, dtype=None)
We have a long list of parameters in the to_csv() function but we will go through only few of the important parameters.
- Path_or_buf: This specifies the file path or dataframe object.
- sep: Used to separate column values in the output file. The default value is a comma (,).
- na_rep: A string used to represent missing values. By default it is an empty string.
- Float_format : Format floating point for example, ‘%.2f’ would format decimals to two decimal places
- Columns :Mention column list to export
- Index: A boolean value to indicate if we want to write row index or row labels. The default is True, which means the row index will be written in the file. If we don’t want it we need just the value from True to False.
- Encoding :We need to mention character encoding to use in the output file default value is ‘utf-8’
- Compression :If we want to compress a file we can mention ‘zip’, ‘gzip’, ‘infer’ etc.
- Date_format : A format string for datetime objects. For example : “%Y-%m-%d”.
How to Save a DataFrame to CSV without Index
Using index=False in to_csv()
To remove the index while exporting a CSV file we just need to set the index parameter to False. We can follow the below code to do the same
Python code
Python code
df.to_csv('output.csv', index=False)
In default state the to_csv() method includes the dataframe’s index as a column in the exported CSV file as the index parameter’s default value is True. Code for exporting pandas dataframe to csv without index
The following Python code shows how to get a csv file without the row index of the dataframe in Pandas.
Python code
Python code
import pandas as pd
data = {'column1': [‘one’, ‘two’, ‘three’],
'column2': ['a', 'b', 'c']}>
df = pd.DataFrame(data)
df.to_csv('output.csv', index=False)
output
Column1 Column2 -------------------------------- One a Two b three c
Code Comparison: With and Without Index in CSV Output
Coding showing row index inclusion while exporting a csv
import pandas as pd
data = {'column1': [‘one’, ‘two’, ‘three’],
'column2': ['a', 'b', 'c']}
df = pd.DataFrame(data)
df.to_csv('output.csv')
OR
import pandas as pd
data = {'column1': [‘one’, ‘two’, ‘three’],
'column2': ['a', 'b', 'c']}
df = pd.DataFrame(data)
df.to_csv('output.csv', index=True)
output
Column1 Column2 -------------------------------- 1 One a 2 Two b 3 three c
Code showing how the row index is excluded of dataframe when exporting a file as CSV.
import pandas as pd
data = {'column1': [‘one’, ‘two’, ‘three’],
'column2': ['a', 'b', 'c']}
df = pd.DataFrame(data)
df.to_csv('output.csv', index=False)
output
Column1 Column2 ------------------------- One a Two b three c
Why do we want to exclude the index
- Excluding the index makes our file more reableable as if we have our own index in dataframe and adding one more index while exporting file doesn’t make sense.
- Adding one more column to file raises data integrity issues.
Common Mistakes When Exporting CSVs
Forgetting to Set index=False
We tend to overlook sometimes and also forget to set index=False while exporting CSV file.
Impact on CSV Structure when Index is Retained
When we retain index it introduces a extra column in the file. This changes the structure of file.
Troubleshooting Issues with Missing Data or Wrong Format
- Always check for data types of the columns in your dataframe.
- Use the necessary delimiter which are compatible with the tools we would be used for analyzing the CSV file.
- Verify that the CSV file is saved to correct location and there is no permission issues
Practical Use Cases
- When we are doing data analysis, we only want the data on which we want to get insights. Excluding the index makes our data more accurate.
- When we are preparing training data for machine learning model we don’t need index value as it doesn’t add any value.
- For creating reports or visualizations, excluding the index can helps in getting clarity and readability of the data.
Additional Parameters in to_csv() for Enhanced Control while exporting dataframe to CSV file without index
Customizing Delimiters (e.g., Comma, Tab, etc.)
We can customize the delimiter using the sep parameter. For example, In the below code we are using tab as a separator in the below code.
Python code
Python code
df.to_csv('output.tsv', sep='\t', index=False) Handling Header and Column Names
We can change the column name in the CSV file which is going to be created using the header parameter.
Python code
Python code
df.to_csv('output.csv', header=['First Name', 'Age'], index=False) Exporting Subset of Columns
We can also export only the columns which we require using the columns parameter in to_csv() function.
Python code
Python code
df.to_csv('output.csv', columns=['Name'], index=False) Conclusion
In the blog we learned how to convert pandas dataframe to csv without index in Python. Clean CSV file is needed for data analysis, machine learning and reporting.
Knowing the index parameter in the to_csv() method helps us to get a CSV file without a row index.
Learn about one hot encoding in Python.
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