pyexcel - Let you focus on data, instead of file formats
- Author:
C.W.
- Source code:
- Issues:
- License:
New BSD License
- Released:
0.7.2
- Generated:
Mar 23, 2025
Introduction
pyexcel provides one unified API for reading, manipulating, and writing data in various Excel formats. It simplifies the process of handling Excel files, making it an enjoyable task. Data in Excel files can be easily converted into arrays or dictionaries with minimal code, and vice versa. This library focuses purely on data processing and does not address features like fonts, colors, or charts.
The idea behind pyexcel originated from a common usability problem: when Excel-driven web applications are delivered to non-developer users (e.g., project assistants, human resources administrators), they often are not aware of the differences between file formats such as CSV, XLS, and XLSX. Rather than training users on these formats, pyexcel provides web developers with a unified interface to handle most Excel file types.
To add support for a specific Excel format in your application, simply install an additional pyexcel plugin—no code changes required. This eliminates issues with different file formats. In the broader community, pyexcel and its associated libraries aim to be a simple, easy-to-install alternative to Pandas, where minimal data manipulation is needed.
Support the project
If your company uses pyexcel and its components in a revenue-generating product, please consider supporting the project on GitHub or Patreon. Your financial support will enable me to dedicate more time to coding, improving documentation, and creating engaging content.
Installation
You can install pyexcel via pip:
$ pip install pyexcel
or clone it and install it:
$ git clone https://github.com/pyexcel/pyexcel.git
$ cd pyexcel
$ python setup.py install
Suppose you have the following data in a dictionary:
Name |
Age |
---|---|
Adam |
28 |
Beatrice |
29 |
Ceri |
30 |
Dean |
26 |
you can easily save it into an excel file using the following code:
>>> import pyexcel
>>> # make sure you had pyexcel-xls installed
>>> a_list_of_dictionaries = [
... {
... "Name": 'Adam',
... "Age": 28
... },
... {
... "Name": 'Beatrice',
... "Age": 29
... },
... {
... "Name": 'Ceri',
... "Age": 30
... },
... {
... "Name": 'Dean',
... "Age": 26
... }
... ]
>>> pyexcel.save_as(records=a_list_of_dictionaries, dest_file_name="your_file.xls")
And here’s how to obtain the records:
>>> import pyexcel as p
>>> records = p.iget_records(file_name="your_file.xls")
>>> for record in records:
... print("%s is aged at %d" % (record['Name'], record['Age']))
Adam is aged at 28
Beatrice is aged at 29
Ceri is aged at 30
Dean is aged at 26
>>> p.free_resources()
Custom data rendering:
>>> # pip install pyexcel-text==0.2.7.1
>>> import pyexcel as p
>>> ccs_insight2 = p.Sheet()
>>> ccs_insight2.name = "Worldwide Mobile Phone Shipments (Billions), 2017-2021"
>>> ccs_insight2.ndjson = """
... {"year": ["2017", "2018", "2019", "2020", "2021"]}
... {"smart phones": [1.53, 1.64, 1.74, 1.82, 1.90]}
... {"feature phones": [0.46, 0.38, 0.30, 0.23, 0.17]}
... """.strip()
>>> ccs_insight2
pyexcel sheet:
+----------------+------+------+------+------+------+
| year | 2017 | 2018 | 2019 | 2020 | 2021 |
+----------------+------+------+------+------+------+
| smart phones | 1.53 | 1.64 | 1.74 | 1.82 | 1.9 |
+----------------+------+------+------+------+------+
| feature phones | 0.46 | 0.38 | 0.3 | 0.23 | 0.17 |
+----------------+------+------+------+------+------+
Advanced usage :fire:
If you are dealing with big data, please consider these usages:
>>> def increase_everyones_age(generator):
... for row in generator:
... row['Age'] += 1
... yield row
>>> def duplicate_each_record(generator):
... for row in generator:
... yield row
... yield row
>>> records = p.iget_records(file_name="your_file.xls")
>>> io=p.isave_as(records=duplicate_each_record(increase_everyones_age(records)),
... dest_file_type='csv', dest_lineterminator='\n')
>>> print(io.getvalue())
Age,Name
29,Adam
29,Adam
30,Beatrice
30,Beatrice
31,Ceri
31,Ceri
27,Dean
27,Dean
Two advantages of above method:
Add as many wrapping functions as you want.
Constant memory consumption
For individual excel file formats, please install them as you wish:
Package name |
Supported file formats |
Dependencies |
---|---|---|
csvz,tsvz readers depends on chardet |
||
xls, xlsx(read only), xlsm(read only) |
||
xlsx |
||
ods |
pyexcel-ezodf, lxml |
|
ods |
Package name |
Supported file formats |
Dependencies |
---|---|---|
xlsx(write only) |
||
xlsx(write only) |
||
xlsx(read only) |
lxml |
|
xlsb(read only) |
pyxlsb |
|
read only for ods, fods |
lxml |
|
write only for ods |
loxun |
|
html(read only) |
lxml,html5lib |
|
pdf(read only) |
camelot |
Plugin shopping guide
Since 2020, all pyexcel-io plugins have dropped the support for python versions which are lower than 3.6. If you want to use any of those Python versions, please use pyexcel-io and its plugins versions that are lower than 0.6.0.
Except csv files, xls, xlsx and ods files are a zip of a folder containing a lot of xml files
The dedicated readers for excel files can stream read
In order to manage the list of plugins installed, you need to use pip to add or remove a plugin. When you use virtualenv, you can have different plugins per virtual environment. In the situation where you have multiple plugins that does the same thing in your environment, you need to tell pyexcel which plugin to use per function call. For example, pyexcel-ods and pyexcel-odsr, and you want to get_array to use pyexcel-odsr. You need to append get_array(…, library=’pyexcel-odsr’).
Package name |
Supported file formats |
Dependencies |
Python versions |
---|---|---|---|
write only:rst, mediawiki, html, latex, grid, pipe, orgtbl, plain simple read only: ndjson r/w: json |
2.6, 2.7, 3.3, 3.4 3.5, 3.6, pypy |
||
handsontable in html |
same as above |
||
svg chart |
2.7, 3.3, 3.4, 3.5 3.6, pypy |
||
sortable table in html |
same as above |
||
gantt chart in html |
except pypy, same as above |
Footnotes
For compatibility tables of pyexcel-io plugins, please click here
pyexcel |
pyexcel-io |
pyexcel-text |
pyexcel-handsontable |
pyexcel-pygal |
pyexcel-gantt |
---|---|---|---|---|---|
0.6.5+ |
0.6.2+ |
0.2.6+ |
0.0.1+ |
0.0.1 |
0.0.1 |
0.5.15+ |
0.5.19+ |
0.2.6+ |
0.0.1+ |
0.0.1 |
0.0.1 |
0.5.14 |
0.5.18 |
0.2.6+ |
0.0.1+ |
0.0.1 |
0.0.1 |
0.5.10+ |
0.5.11+ |
0.2.6+ |
0.0.1+ |
0.0.1 |
0.0.1 |
0.5.9.1+ |
0.5.9.1+ |
0.2.6+ |
0.0.1 |
0.0.1 |
0.0.1 |
0.5.4+ |
0.5.1+ |
0.2.6+ |
0.0.1 |
0.0.1 |
0.0.1 |
0.5.0+ |
0.4.0+ |
0.2.6+ |
0.0.1 |
0.0.1 |
0.0.1 |
0.4.0+ |
0.3.0+ |
0.2.5 |
file format |
definition |
---|---|
csv |
comma separated values |
tsv |
tab separated values |
csvz |
a zip file that contains one or many csv files |
tsvz |
a zip file that contains one or many tsv files |
xls |
a spreadsheet file format created by MS-Excel 97-2003 |
xlsx |
MS-Excel Extensions to the Office Open XML SpreadsheetML File Format. |
xlsm |
an MS-Excel Macro-Enabled Workbook file |
ods |
open document spreadsheet |
fods |
flat open document spreadsheet |
json |
java script object notation |
html |
html table of the data structure |
simple |
simple presentation |
rst |
rStructured Text presentation of the data |
mediawiki |
media wiki table |
Usage
Suppose you want to process the following excel data :
Here are the example usages:
>>> import pyexcel as pe
>>> records = pe.iget_records(file_name="your_file.xls")
>>> for record in records:
... print("%s is aged at %d" % (record['Name'], record['Age']))
Adam is aged at 28
Beatrice is aged at 29
Ceri is aged at 30
Dean is aged at 26
>>> pe.free_resources()
Design
New tutorial
- One liners
- Stream APIs for big file : A set of two liners
- For web developer
- Pyexcel data renderers
- Sheet
- Book
- Working with databases
Old tutorial
- Work with excel files
- Work with excel files in memory
- Sheet: Data conversion
- How to obtain records from an excel sheet
- How to save an python array as an excel file
- How to save an python array as a csv file with special delimiter
- How to get a dictionary from an excel sheet
- How to obtain a dictionary from a multiple sheet book
- How to save a dictionary of two dimensional array as an excel file
- How to import an excel sheet to a database using SQLAlchemy
- How to open an xls file and save it as csv
- How to open an xls file and save it as xlsx
- How to open a xls multiple sheet excel book and save it as csv
- Dot notation for data source
- Read partial data
- Sheet: Data Access
- Sheet: Data manipulation
- Sheet: Data filtering
- Sheet: Formatting
- Book: Sheet operations
Cook book
- Recipes
- Update one column of a data file
- Update one row of a data file
- Merge two files into one
- Select candidate columns of two files and form a new one
- Merge two files into a book where each file become a sheet
- Merge all excel files in directory into a book where each file become a sheet
- Split a book into single sheet files
- Extract just one sheet from a book
- Loading from other sources
Real world cases
API documentation
Developer’s guide
Change log
- What’s breaking in 0.7.0
- What’s breaking in 0.6.0
- What’s breaking in 0.5.9
- Migrate away from 0.4.3
- Migrate from 0.2.x to 0.3.0+
- Migrate from 0.2.1 to 0.2.2+
- Migrate from 0.1.x to 0.2.x
- Change log
- 0.7.2 - 23.03.2025
- 0.7.1 - 11.09.2024
- 0.7.0 - 12.2.2022
- 0.6.7 - 12.09.2021
- 0.6.6 - 14.11.2020
- 0.6.5 - 8.10.2020
- 0.6.4 - 18.08.2020
- 0.6.3 - 01.08.2020
- 0.6.2 - 8.06.2020
- 0.6.1 - 02.05.2020
- 0.6.0 - 21.04.2020
- 0.5.15 - 07.07.2019
- 0.5.14 - 12.06.2019
- 0.5.13 - 12.03.2019
- 0.5.12 - 25.02.2019
- 0.5.11 - 22.02.2019
- 0.5.10 - 3.12.2018
- 0.5.9.1 - 30.08.2018
- 0.5.9 - 30.08.2018
- 0.5.8 - 26.03.2018
- 0.5.7 - 11.01.2018
- 0.5.6 - 23.10.2017
- 0.5.5 - 20.10.2017
- 0.5.4 - 27.09.2017
- 0.5.3 - 01-08-2017
- 0.5.2 - 26-07-2017
- 0.5.1 - 12.06.2017
- 0.5.0 - 19.06.2017
- 0.4.5 - 17.03.2017
- 0.4.4 - 06.02.2017
- 0.4.3 - 26.01.2017
- 0.4.2 - 17.01.2017
- 0.4.1 - 23.12.2016
- 0.4.0 - 22.12.2016
- 0.3.3 - 07.11.2016
- 0.3.2 - 02.11.2016
- 0.3.0 - 28.10.2016
- 0.2.5 - 31.08.2016
- 0.2.4 - 14.07.2016
- 0.2.3 - 11.07.2016
- 0.2.2 - 01.06.2016
- 0.2.1 - 23.04.2016
- 0.2.0 - 17.01.2016
- 0.1.7 - 03.07.2015
- 0.1.6 - 13.06.2015
- 0.0.13 - 07.02.2015
- 0.0.12 - 25.01.2015
- 0.0.10 - 15.12.2015
- 0.0.4 - 12.10.2014
- 0.0.1 - 14.09.2014