How do I read and write CSV files?

Here are some minimal complete examples how to read CSV files and how to write CSV files with Python.

Pure Python:

import csv

# Define data
data = [
    (1, "A towel,", 1.0),
    (42, " it says, ", 2.0),
    (1337, "is about the most ", -1),
    (0, "massively useful thing ", 123),
    (-2, "an interstellar hitchhiker can have.", 3),

# Write CSV file
with open("test.csv", "wt") as fp:
    writer = csv.writer(fp, delimiter=",")
    # writer.writerow(["your", "header", "foo"])  # write header

# Read CSV file
with open("test.csv") as fp:
    reader = csv.reader(fp, delimiter=",", quotechar=""")
    # next(reader, None)  # skip the headers
    data_read = [row for row in reader]


After that, the contents of data_read are

[['1', 'A towel,', '1.0'],
 ['42', ' it says, ', '2.0'],
 ['1337', 'is about the most ', '-1'],
 ['0', 'massively useful thing ', '123'],
 ['-2', 'an interstellar hitchhiker can have.', '3']]

Please note that CSV reads only strings. You need to convert to the column types manually.

A Python 2+3 version was here before (link), but Python 2 support is dropped. Removing the Python 2 stuff massively simplified this answer.


  • How do I write data into csv format as string (not file)?
  • How can I use io.StringIO() with the csv module?: This is interesting if you want to serve a CSV on-the-fly with Flask, without actually storing the CSV on the server.


Have a look at my utility package mpu for a super simple and easy to remember one:

data ='example.csv', delimiter=",", quotechar=""", skiprows=None)'example.csv', data)


import pandas as pd

# Read the CSV into a pandas data frame (df)
#   With a df you can do many things
#   most important: visualize data with Seaborn
df = pd.read_csv('myfile.csv', sep=',')

# Or export it in many ways, e.g. a list of tuples
tuples = [tuple(x) for x in df.values]

# or export it as a list of dicts
dicts = df.to_dict().values()

See read_csv docs for more information. Please note that pandas automatically infers if there is a header line, but you can set it manually, too.

If you haven’t heard of Seaborn, I recommend having a look at it.


Reading CSV files is supported by a bunch of other libraries, for example:

  • dask.dataframe.read_csv

Created CSV file

1,"A towel,",1.0
42," it says, ",2.0
1337,is about the most ,-1
0,massively useful thing ,123
-2,an interstellar hitchhiker can have.,3

Common file endings


Working with the data

After reading the CSV file to a list of tuples / dicts or a Pandas dataframe, it is simply working with this kind of data. Nothing CSV specific.


  • JSON: Nice for writing human-readable data; VERY commonly used (read & write)
  • CSV: Super simple format (read & write)
  • YAML: Nice to read, similar to JSON (read & write)
  • pickle: A Python serialization format (read & write)
  • MessagePack (Python package): More compact representation (read & write)
  • HDF5 (Python package): Nice for matrices (read & write)
  • XML: exists too *sigh* (read & write)

For your application, the following might be important:

  • Support by other programming languages
  • Reading / writing performance
  • Compactness (file size)

See also: Comparison of data serialization formats

In case you are rather looking for a way to make configuration files, you might want to read my short article Configuration files in Python

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