Pandas.DataFrame.to_csv: Saving a DataFrame as a CSV

Pandas DataFrame.to_csv() method writes (exports) a DataFrame object to a comma-separated values (csv) file or a string.

import pandas as pd

dict = {
    'Transport_Mode': ["Metro", "Bus", "Train", "Metro", "Ferry", "Train", "Bus"],
    'City': ["NY", "Chicago", "LA", "NY", "SF", "Boston", "Chicago"],
    'Daily_Ridership': [5000000, 800000, 600000, 5000000, 200000, 500000, 800000]
}

df = pd.DataFrame(dict)

df.to_csv('transport.csv')

print("Exported the data to transport.csv")

# Output: Exported the data to transport.csv

From the above output figure, you can see that it transformed the input DataFrame df into a transport.csv file with a proper header and index.

Syntax

DataFrame.to_csv(
    path_or_buf=None,
    sep=',',
    na_rep='',
    float_format=None,
    columns=None,
    header=True,
    index=True,
    index_label=None,
    mode='w',
    encoding=None,
    compression='infer',
    quoting=None,
    quotechar='"',
    line_terminator=None,
    chunksize=None,
    date_format=None,
    doublequote=True,
    escapechar=None,
    decimal='.',
    errors='strict',
    storage_options=None,
)

Parameters

Name	Description
filepath_or_buffer	It represents the path to the output file or file-like object. If it is None, the output will be a CSV data string (not a file).
sep or delimiter	It represents a character to break strings into columns. It is a field delimiter.
na_rep	It represents missing (NaN) values.
float_format	It formats for floating-point numbers (e.g., ‘%.2f’).
columns	It is a subset of columns to write.
header	It is a True or False flag. If it is True, it writes column names in the output CSV file. If False, it won’t write column names.
index	If it is True, it writes the index in the output CSV file.
index_label	It represents a label(s) for an index column(s).
mode	It defines file mode: ‘w’ truncates, ‘x’ creates exclusively (fails if exists), ‘a’ appends.
encoding	It is an encoding for the output file.
compression	It defines the compression formats and supports ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, ‘zstd’, ‘tar’ formats.
quoting	It is a quotation strategy.
quotechar	It is a character for quoting fields.
lineterminator	It is a line ending (e.g., ‘\n’ on Linux, ‘\r\n’ on Windows).
chunksize	It represents the number of rows to write per chunk.
date_format	It is a format for datetime objects.
doublequote	If it is True, it escapes quotechar by doubling it inside fields.
escapechar	It is a character to escape special characters.
decimal	It is a decimal separator.
errors	It is a handle for encoding/decoding errors.
storage_options	It is an extra option for storage backends.

Exporting without an index and a header

Let’s pass header=False and index=False to the to_csv() method to exclude index and header from the output CSV file.

import pandas as pd

dict = {
    'Transport_Mode': ["Metro", "Bus", "Train", "Metro", "Ferry", "Train", "Bus"],
    'City': ["NY", "Chicago", "LA", "NY", "SF", "Boston", "Chicago"],
    'Daily_Ridership': [5000000, 800000, 600000, 5000000, 200000, 500000, 800000]
}

df = pd.DataFrame(dict)

df.to_csv('transport.csv', index=False, header=False)

print("Exported the data without header and index to transport.csv file")

# Output: Exported the data without header and index to transport.csv file

Selecting columns

If you want specific columns that need to be exported, you can use the columns argument and pass the array of only the columns that need to be there in the output csv file.

import pandas as pd

dict = {
    'Transport_Mode': ["Metro", "Bus", "Train", "Metro", "Ferry", "Train", "Bus"],
    'City': ["NY", "Chicago", "LA", "NY", "SF", "Boston", "Chicago"],
    'Daily_Ridership': [5000000, 800000, 600000, 5000000, 200000, 500000, 800000]
}

df = pd.DataFrame(dict)

df.to_csv('transport.csv', columns=['City', 'Daily_Ridership'])

print("Exported the data with only two columns")

# Output: Exported the data with only two columns

That’s all!

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Krunal Lathiya

With a career spanning over eight years in the field of Computer Science, Krunal’s expertise is rooted in a solid foundation of hands-on experience, complemented by a continuous pursuit of knowledge.