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================================================================================ pyexcel - Let you focus on data, instead of file formats

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Support the project

If your company has embedded pyexcel and its components into a revenue generating product, please support me on github, patreon <https://www.patreon.com/bePatron?u=5537627>_ or bounty source <https://salt.bountysource.com/teams/chfw-pyexcel>_ to maintain the project and develop it further.

If you are an individual, you are welcome to support me too and for however long you feel like. As my backer, you will receive early access to pyexcel related contents <https://www.patreon.com/pyexcel/posts>_.

And your issues will get prioritized if you would like to become my patreon as pyexcel pro user.

With your financial support, I will be able to invest a little bit more time in coding, documentation and writing interesting posts.

Known constraints

Fonts, colors and charts are not supported.

Nor to read password protected xls, xlsx and ods files.

Introduction

Feature Highlights

.. table:: A list of supported file formats

============ =======================================================
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 [#f3]_
xlsx         MS-Excel Extensions to the Office Open XML
             SpreadsheetML File Format. [#f4]_
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
============ =======================================================

.. [f3] quoted from whatis.com <http://whatis.techtarget.com/fileformat/XLS-Worksheet-file-Microsoft-Excel>. Technical details can be found at MSDN XLS <https://msdn.microsoft.com/en-us/library/office/gg615597(v=office.14).aspx> .. [f4] xlsx is used by MS-Excel 2007, more information can be found at MSDN XLSX <https://msdn.microsoft.com/en-us/library/dd922181(v=office.12).aspx>_

.. image:: https://github.com/pyexcel/pyexcel/raw/dev/docs/source/_static/images/architecture.svg

  1. One application programming interface(API) to handle multiple data sources:

    • physical file
    • memory file
    • SQLAlchemy table
    • Django Model
    • Python data structures: dictionary, records and array
  2. One API to read and write data in various excel file formats.

  3. For large data sets, data streaming are supported. A genenerator can be returned to you. Checkout iget_records, iget_array, isave_as and isave_book_as.

Installation

You can install pyexcel via pip:

.. code-block:: bash

$ pip install pyexcel

or clone it and install it:

.. code-block:: bash

$ git clone https://github.com/pyexcel/pyexcel.git
$ cd pyexcel
$ python setup.py install

One liners

This section shows you how to get data from your excel files and how to export data to excel files in one line

Read from the excel files

Get a list of dictionaries


Suppose you want to process the following coffee data (data source coffee chart <https://cspinet.org/eating-healthy/ingredients-of-concern/caffeine-chart>_ on the center for science in the public interest):

Top 5 coffeine drinks:

===================================== =============== ============= Coffees Serving Size Caffeine (mg) Starbucks Coffee Blonde Roast venti(20 oz) 475 Dunkin' Donuts Coffee with Turbo Shot large(20 oz.) 398 Starbucks Coffee Pike Place Roast grande(16 oz.) 310 Panera Coffee Light Roast regular(16 oz.) 300 ===================================== =============== =============

Let's get a list of dictionary out from the xls file:

.. code-block:: python

records = p.get_records(file_name="your_file.xls")

And let's check what do we have:

.. code-block:: python

for r in records: ... print(f"{r['Serving Size']} of {r['Coffees']} has {r['Caffeine (mg)']} mg") venti(20 oz) of Starbucks Coffee Blonde Roast has 475 mg large(20 oz.) of Dunkin' Donuts Coffee with Turbo Shot has 398 mg grande(16 oz.) of Starbucks Coffee Pike Place Roast has 310 mg regular(16 oz.) of Panera Coffee Light Roast has 300 mg

Get two dimensional array


Instead, what if you have to use pyexcel.get_array to do the same:

.. code-block:: python

for row in p.get_array(file_name="your_file.xls", start_row=1): ... print(f"{row[1]} of {row[0]} has {row[2]} mg") venti(20 oz) of Starbucks Coffee Blonde Roast has 475 mg large(20 oz.) of Dunkin' Donuts Coffee with Turbo Shot has 398 mg grande(16 oz.) of Starbucks Coffee Pike Place Roast has 310 mg regular(16 oz.) of Panera Coffee Light Roast has 300 mg

where start_row skips the header row.

Get a dictionary


You can get a dictionary too:

Now let's get a dictionary out from the spreadsheet:

.. code-block:: python

my_dict = p.get_dict(file_name="your_file.xls", name_columns_by_row=0)

And check what do we have:

.. code-block:: python

from pyexcel._compact import OrderedDict isinstance(my_dict, OrderedDict) True for key, values in my_dict.items(): ... print(key + " : " + ','.join([str(item) for item in values])) Coffees : Starbucks Coffee Blonde Roast,Dunkin' Donuts Coffee with Turbo Shot,Starbucks Coffee Pike Place Roast,Panera Coffee Light Roast Serving Size : venti(20 oz),large(20 oz.),grande(16 oz.),regular(16 oz.) Caffeine (mg) : 475,398,310,300

Please note that my_dict is an OrderedDict.

Get a dictionary of two dimensional array


Suppose you have a multiple sheet book as the following:

pyexcel:Sheet 1:

===================== = = 1 2 3 4 5 6 7 8 9 ===================== = =

pyexcel:Sheet 2:

===================== = = X Y Z 1 2 3 4 5 6 ===================== = =

pyexcel:Sheet 3:

===================== = = O P Q 3 2 1 4 3 2 ===================== = =

Here is the code to obtain those sheets as a single dictionary:

.. code-block:: python

book_dict = p.get_book_dict(file_name="book.xls")

And check:

.. code-block:: python

isinstance(book_dict, OrderedDict) True import json for key, item in book_dict.items(): ... print(json.dumps({key: item})) {"Sheet 1": [[1, 2, 3], [4, 5, 6], [7, 8, 9]]} {"Sheet 2": [["X", "Y", "Z"], [1, 2, 3], [4, 5, 6]]} {"Sheet 3": [["O", "P", "Q"], [3, 2, 1], [4, 3, 2]]}

Write data

Export an array


Suppose you have the following array:

.. code-block:: python

data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

And here is the code to save it as an excel file :

.. code-block:: python

p.save_as(array=data, dest_file_name="example.xls")

Let's verify it:

.. code-block:: python

>>> p.get_sheet(file_name="example.xls")
pyexcel_sheet1:
+---+---+---+
| 1 | 2 | 3 |
+---+---+---+
| 4 | 5 | 6 |
+---+---+---+
| 7 | 8 | 9 |
+---+---+---+

And here is the code to save it as a csv file :

.. code-block:: python

p.save_as(array=data, ... dest_file_name="example.csv", ... dest_delimiter=':')

Let's verify it:

.. code-block:: python

with open("example.csv") as f: ... for line in f.readlines(): ... print(line.rstrip()) ... 1:2:3 4:5:6 7:8:9

Export a list of dictionaries


.. code-block:: python

>>> records = [
...     {"year": 1903, "country": "Germany", "speed": "206.7km/h"},
...     {"year": 1964, "country": "Japan", "speed": "210km/h"},
...     {"year": 2008, "country": "China", "speed": "350km/h"}
... ]
>>> p.save_as(records=records, dest_file_name='high_speed_rail.xls')

Export a dictionary of single key value pair


.. code-block:: python

>>> henley_on_thames_facts = {
...     "area": "5.58 square meters",
...     "population": "11,619",
...     "civial parish": "Henley-on-Thames",
...     "latitude": "51.536",
...     "longitude": "-0.898"
... }
>>> p.save_as(adict=henley_on_thames_facts, dest_file_name='henley.xlsx')

Export a dictionary of single dimensonal array


.. code-block:: python

>>> ccs_insights = {
...     "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]
... }
>>> p.save_as(adict=ccs_insights, dest_file_name='ccs.csv')

Export a dictionary of two dimensional array as a book


Suppose you want to save the below dictionary to an excel file :

.. code-block:: python

a_dictionary_of_two_dimensional_arrays = { ... 'Sheet 1': ... [ ... [1.0, 2.0, 3.0], ... [4.0, 5.0, 6.0], ... [7.0, 8.0, 9.0] ... ], ... 'Sheet 2': ... [ ... ['X', 'Y', 'Z'], ... [1.0, 2.0, 3.0], ... [4.0, 5.0, 6.0] ... ], ... 'Sheet 3': ... [ ... ['O', 'P', 'Q'], ... [3.0, 2.0, 1.0], ... [4.0, 3.0, 2.0] ... ] ... }

Here is the code:

.. code-block:: python

p.save_book_as( ... bookdict=a_dictionary_of_two_dimensional_arrays, ... dest_file_name="book.xls" ... )

If you want to preserve the order of sheets in your dictionary, you have to pass on an ordered dictionary to the function itself. For example:

.. code-block:: python

data = OrderedDict() data.update({"Sheet 2": a_dictionary_of_two_dimensional_arrays['Sheet 2']}) data.update({"Sheet 1": a_dictionary_of_two_dimensional_arrays['Sheet 1']}) data.update({"Sheet 3": a_dictionary_of_two_dimensional_arrays['Sheet 3']}) p.save_book_as(bookdict=data, dest_file_name="book.xls")

Let's verify its order:

.. code-block:: python

book_dict = p.get_book_dict(file_name="book.xls") for key, item in book_dict.items(): ... print(json.dumps({key: item})) {"Sheet 2": [["X", "Y", "Z"], [1, 2, 3], [4, 5, 6]]} {"Sheet 1": [[1, 2, 3], [4, 5, 6], [7, 8, 9]]} {"Sheet 3": [["O", "P", "Q"], [3, 2, 1], [4, 3, 2]]}

Please notice that "Sheet 2" is the first item in the book_dict, meaning the order of sheets are preserved.

Transcoding

.. note::

Please note that pyexcel-cli can perform file transcoding at command line. No need to open your editor, save the problem, then python run.

The following code does a simple file format transcoding from xls to csv:

.. code-block:: python

p.save_as(file_name="birth.xls", dest_file_name="birth.csv")

Again it is really simple. Let's verify what we have gotten:

.. code-block:: python

sheet = p.get_sheet(file_name="birth.csv") sheet birth.csv: +-------+--------+----------+ | name | weight | birth | +-------+--------+----------+ | Adam | 3.4 | 03/02/15 | +-------+--------+----------+ | Smith | 4.2 | 12/11/14 | +-------+--------+----------+

.. NOTE::

Please note that csv(comma separate value) file is pure text file. Formula, charts, images and formatting in xls file will disappear no matter which transcoding tool you use. Hence, pyexcel is a quick alternative for this transcoding job.

Let use previous example and save it as xlsx instead

.. code-block:: python

p.save_as(file_name="birth.xls", ... dest_file_name="birth.xlsx") # change the file extension

Again let's verify what we have gotten:

.. code-block:: python

sheet = p.get_sheet(file_name="birth.xlsx") sheet pyexcel_sheet1: +-------+--------+----------+ | name | weight | birth | +-------+--------+----------+ | Adam | 3.4 | 03/02/15 | +-------+--------+----------+ | Smith | 4.2 | 12/11/14 | +-------+--------+----------+

Excel book merge and split operation in one line

Merge all excel files in directory into a book where each file become a sheet


The following code will merge every excel files into one file, say "output.xls":

.. code-block:: python

from pyexcel.cookbook import merge_all_to_a_book
import glob


merge_all_to_a_book(glob.glob("your_csv_directory\*.csv"), "output.xls")

You can mix and match with other excel formats: xls, xlsm and ods. For example, if you are sure you have only xls, xlsm, xlsx, ods and csv files in your_excel_file_directory, you can do the following:

.. code-block:: python

from pyexcel.cookbook import merge_all_to_a_book
import glob


merge_all_to_a_book(glob.glob("your_excel_file_directory\*.*"), "output.xls")

Split a book into single sheet files


Suppose you have many sheets in a work book and you would like to separate each into a single sheet excel file. You can easily do this:

.. code-block:: python

from pyexcel.cookbook import split_a_book split_a_book("megabook.xls", "output.xls") import glob outputfiles = glob.glob("*_output.xls") for file in sorted(outputfiles): ... print(file) ... Sheet 1_output.xls Sheet 2_output.xls Sheet 3_output.xls

for the output file, you can specify any of the supported formats

Extract just one sheet from a book


Suppose you just want to extract one sheet from many sheets that exists in a work book and you would like to separate it into a single sheet excel file. You can easily do this:

.. code-block:: python

>>> from pyexcel.cookbook import extract_a_sheet_from_a_book
>>> extract_a_sheet_from_a_book("megabook.xls", "Sheet 1", "output.xls")
>>> if os.path.exists("Sheet 1_output.xls"):
...     print("Sheet 1_output.xls exists")
...
Sheet 1_output.xls exists

for the output file, you can specify any of the supported formats

Hidden feature: partial read

Most pyexcel users do not know, but other library users were requesting the similar features <https://github.com/jazzband/tablib/issues/467>_

When you are dealing with huge amount of data, e.g. 64GB, obviously you would not like to fill up your memory with those data. What you may want to do is, record data from Nth line, take M records and stop. And you only want to use your memory for the M records, not for beginning part nor for the tail part.

Hence partial read feature is developed to read partial data into memory for processing.

You can paginate by row, by column and by both, hence you dictate what portion of the data to read back. But remember only row limit features help you save memory. Let's you use this feature to record data from Nth column, take M number of columns and skip the rest. You are not going to reduce your memory footprint.

Why did not I see above benefit?

This feature depends heavily on the implementation details.

pyexcel-xls_ (xlrd), pyexcel-xlsx_ (openpyxl), pyexcel-ods_ (odfpy) and pyexcel-ods3_ (pyexcel-ezodf) will read all data into memory. Because xls, xlsx and ods file are effective a zipped folder, all four will unzip the folder and read the content in xml format in full, so as to make sense of all details.

Hence, during the partial data is been returned, the memory consumption won't differ from reading the whole data back. Only after the partial data is returned, the memory comsumption curve shall jump the cliff. So pagination code here only limits the data returned to your program.

With that said, pyexcel-xlsxr, pyexcel-odsr and pyexcel-htmlr_ DOES read partial data into memory. Those three are implemented in such a way that they consume the xml(html) when needed. When they have read designated portion of the data, they stop, even if they are half way through.

In addition, pyexcel's csv readers can read partial data into memory too.

Let's assume the following file is a huge csv file:

.. code-block:: python

import datetime import pyexcel as pe data = [ ... [1, 21, 31], ... [2, 22, 32], ... [3, 23, 33], ... [4, 24, 34], ... [5, 25, 35], ... [6, 26, 36] ... ] pe.save_as(array=data, dest_file_name="your_file.csv")

And let's pretend to read partial data:

.. code-block:: python

pe.get_sheet(file_name="your_file.csv", start_row=2, row_limit=3) your_file.csv: +---+----+----+ | 3 | 23 | 33 | +---+----+----+ | 4 | 24 | 34 | +---+----+----+ | 5 | 25 | 35 | +---+----+----+

And you could as well do the same for columns:

.. code-block:: python

pe.get_sheet(file_name="your_file.csv", start_column=1, column_limit=2) your_file.csv: +----+----+ | 21 | 31 | +----+----+ | 22 | 32 | +----+----+ | 23 | 33 | +----+----+ | 24 | 34 | +----+----+ | 25 | 35 | +----+----+ | 26 | 36 | +----+----+

Obvious, you could do both at the same time:

.. code-block:: python

pe.get_sheet(file_name="your_file.csv", ... start_row=2, row_limit=3, ... start_column=1, column_limit=2) your_file.csv: +----+----+ | 23 | 33 | +----+----+ | 24 | 34 | +----+----+ | 25 | 35 | +----+----+

The pagination support is available across all pyexcel plugins.

.. note::

No column pagination support for query sets as data source.

Formatting while transcoding a big data file

If you are transcoding a big data set, conventional formatting method would not help unless a on-demand free RAM is available. However, there is a way to minimize the memory footprint of pyexcel while the formatting is performed.

Let's continue from previous example. Suppose we want to transcode "your_file.csv" to "your_file.xls" but increase each element by 1.

What we can do is to define a row renderer function as the following:

.. code-block:: python

def increment_by_one(row): ... for element in row: ... yield element + 1

Then pass it onto save_as function using row_renderer:

.. code-block:: python

pe.isave_as(file_name="your_file.csv", ... row_renderer=increment_by_one, ... dest_file_name="your_file.xlsx")

.. note::

If the data content is from a generator, isave_as has to be used.

We can verify if it was done correctly:

.. code-block:: python

pe.get_sheet(file_name="your_file.xlsx") your_file.csv: +---+----+----+ | 2 | 22 | 32 | +---+----+----+ | 3 | 23 | 33 | +---+----+----+ | 4 | 24 | 34 | +---+----+----+ | 5 | 25 | 35 | +---+----+----+ | 6 | 26 | 36 | +---+----+----+ | 7 | 27 | 37 | +---+----+----+

Stream APIs for big file : A set of two liners

When you are dealing with BIG excel files, you will want pyexcel to use constant memory.

This section shows you how to get data from your BIG excel files and how to export data to excel files in two lines at most, without eating all your computer memory.

Two liners for get data from big excel files

Get a list of dictionaries


Suppose you want to process the following coffee data again:

Top 5 coffeine drinks:

===================================== =============== ============= Coffees Serving Size Caffeine (mg) Starbucks Coffee Blonde Roast venti(20 oz) 475 Dunkin' Donuts Coffee with Turbo Shot large(20 oz.) 398 Starbucks Coffee Pike Place Roast grande(16 oz.) 310 Panera Coffee Light Roast regular(16 oz.) 300 ===================================== =============== =============

Let's get a list of dictionary out from the xls file:

.. code-block:: python

records = p.iget_records(file_name="your_file.xls")

And let's check what do we have:

.. code-block:: python

for r in records: ... print(f"{r['Serving Size']} of {r['Coffees']} has {r['Caffeine (mg)']} mg") venti(20 oz) of Starbucks Coffee Blonde Roast has 475 mg large(20 oz.) of Dunkin' Donuts Coffee with Turbo Shot has 398 mg grande(16 oz.) of Starbucks Coffee Pike Place Roast has 310 mg regular(16 oz.) of Panera Coffee Light Roast has 300 mg

Please do not forgot the second line to close the opened file handle:

.. code-block:: python

p.free_resources()

Get two dimensional array


Instead, what if you have to use pyexcel.get_array to do the same:

.. code-block:: python

for row in p.iget_array(file_name="your_file.xls", start_row=1): ... print(f"{row[1]} of {row[0]} has {row[2]} mg") venti(20 oz) of Starbucks Coffee Blonde Roast has 475 mg large(20 oz.) of Dunkin' Donuts Coffee with Turbo Shot has 398 mg grande(16 oz.) of Starbucks Coffee Pike Place Roast has 310 mg regular(16 oz.) of Panera Coffee Light Roast has 300 mg

Again, do not forgot the second line:

.. code-block:: python

p.free_resources()

where start_row skips the header row.

Data export in one liners

Export an array


Suppose you have the following array:

.. code-block:: python

data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

And here is the code to save it as an excel file :

.. code-block:: python

p.isave_as(array=data, dest_file_name="example.xls")

But the following line is not required because the data source are not file sources:

.. code-block:: python

p.free_resources()

Let's verify it:

.. code-block:: python

>>> p.get_sheet(file_name="example.xls")
pyexcel_sheet1:
+---+---+---+
| 1 | 2 | 3 |
+---+---+---+
| 4 | 5 | 6 |
+---+---+---+
| 7 | 8 | 9 |
+---+---+---+

And here is the code to save it as a csv file :

.. code-block:: python

p.isave_as(array=data, ... dest_file_name="example.csv", ... dest_delimiter=':')

Let's verify it:

.. code-block:: python

with open("example.csv") as f: ... for line in f.readlines(): ... print(line.rstrip()) ... 1:2:3 4:5:6 7:8:9

Export a list of dictionaries


.. code-block:: python

>>> records = [
...     {"year": 1903, "country": "Germany", "speed": "206.7km/h"},
...     {"year": 1964, "country": "Japan", "speed": "210km/h"},
...     {"year": 2008, "country": "China", "speed": "350km/h"}
... ]
>>> p.isave_as(records=records, dest_file_name='high_speed_rail.xls')

Export a dictionary of single key value pair


.. code-block:: python

>>> henley_on_thames_facts = {
...     "area": "5.58 square meters",
...     "population": "11,619",
...     "civial parish": "Henley-on-Thames",
...     "latitude": "51.536",
...     "longitude": "-0.898"
... }
>>> p.isave_as(adict=henley_on_thames_facts, dest_file_name='henley.xlsx')

Export a dictionary of single dimensonal array


.. code-block:: python

>>> ccs_insights = {
...     "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]
... }
>>> p.isave_as(adict=ccs_insights, dest_file_name='ccs.csv')
>>> p.free_resources()

Export a dictionary of two dimensional array as a book


Suppose you want to save the below dictionary to an excel file :

.. code-block:: python

a_dictionary_of_two_dimensional_arrays = { ... 'Sheet 1': ... [ ... [1.0, 2.0, 3.0], ... [4.0, 5.0, 6.0], ... [7.0, 8.0, 9.0] ... ], ... 'Sheet 2': ... [ ... ['X', 'Y', 'Z'], ... [1.0, 2.0, 3.0], ... [4.0, 5.0, 6.0] ... ], ... 'Sheet 3': ... [ ... ['O', 'P', 'Q'], ... [3.0, 2.0, 1.0], ... [4.0, 3.0, 2.0] ... ] ... }

Here is the code:

.. code-block:: python

p.isave_book_as( ... bookdict=a_dictionary_of_two_dimensional_arrays, ... dest_file_name="book.xls" ... )

If you want to preserve the order of sheets in your dictionary, you have to pass on an ordered dictionary to the function itself. For example:

.. code-block:: python

from pyexcel._compact import OrderedDict data = OrderedDict() data.update({"Sheet 2": a_dictionary_of_two_dimensional_arrays['Sheet 2']}) data.update({"Sheet 1": a_dictionary_of_two_dimensional_arrays['Sheet 1']}) data.update({"Sheet 3": a_dictionary_of_two_dimensional_arrays['Sheet 3']}) p.isave_book_as(bookdict=data, dest_file_name="book.xls") p.free_resources()

Let's verify its order:

.. code-block:: python

import json book_dict = p.get_book_dict(file_name="book.xls") for key, item in book_dict.items(): ... print(json.dumps({key: item})) {"Sheet 2": [["X", "Y", "Z"], [1, 2, 3], [4, 5, 6]]} {"Sheet 1": [[1, 2, 3], [4, 5, 6], [7, 8, 9]]} {"Sheet 3": [["O", "P", "Q"], [3, 2, 1], [4, 3, 2]]}

Please notice that "Sheet 2" is the first item in the book_dict, meaning the order of sheets are preserved.

File format transcoding on one line

.. note::

Please note that the following file transcoding could be with zero line. Please install pyexcel-cli and you will do the transcode in one command. No need to open your editor, save the problem, then python run.

The following code does a simple file format transcoding from xls to csv:

.. code-block:: python

import pyexcel p.save_as(file_name="birth.xls", dest_file_name="birth.csv")

Again it is really simple. Let's verify what we have gotten:

.. code-block:: python

sheet = p.get_sheet(file_name="birth.csv") sheet birth.csv: +-------+--------+----------+ | name | weight | birth | +-------+--------+----------+ | Adam | 3.4 | 03/02/15 | +-------+--------+----------+ | Smith | 4.2 | 12/11/14 | +-------+--------+----------+

.. note::

Please note that csv(comma separate value) file is pure text file. Formula, charts, images and formatting in xls file will disappear no matter which transcoding tool you use. Hence, pyexcel is a quick alternative for this transcoding job.

Let use previous example and save it as xlsx instead

.. code-block:: python

import pyexcel p.isave_as(file_name="birth.xls", ... dest_file_name="birth.xlsx") # change the file extension

Again let's verify what we have gotten:

.. code-block:: python

sheet = p.get_sheet(file_name="birth.xlsx") sheet pyexcel_sheet1: +-------+--------+----------+ | name | weight | birth | +-------+--------+----------+ | Adam | 3.4 | 03/02/15 | +-------+--------+----------+ | Smith | 4.2 | 12/11/14 | +-------+--------+----------+

Available Plugins

.. _file-format-list: .. _a-map-of-plugins-and-file-formats:

.. table:: A list of file formats supported by external plugins

======================== ======================= ================= Package name Supported file formats Dependencies ======================== ======================= ================= pyexcel-io_ csv, csvz [#f1], tsv, tsvz [#f2] pyexcel-xls_ xls, xlsx(read only), xlrd, xlsm(read only) xlwt pyexcel-xlsx_ xlsx openpyxl_ pyexcel-ods3_ ods pyexcel-ezodf, lxml pyexcel-ods ods odfpy_ ======================== ======================= =================

.. table:: Dedicated file reader and writers

======================== ======================= ================= Package name Supported file formats Dependencies ======================== ======================= ================= pyexcel-xlsxw_ xlsx(write only) XlsxWriter_ pyexcel-libxlsxw_ xlsx(write only) libxlsxwriter_ pyexcel-xlsxr_ xlsx(read only) lxml pyexcel-xlsbr_ xlsb(read only) pyxlsb pyexcel-odsr_ read only for ods, fods lxml pyexcel-odsw_ write only for ods loxun pyexcel-htmlr_ html(read only) lxml,html5lib pyexcel-pdfr_ pdf(read only) camelot ======================== ======================= =================

Plugin shopping guide

Since 2020, all pyexcel-io plugins have dropped the support for python version lower than 3.6. If you want to use any python verions, please use pyexcel-io and its plugins version 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').

.. _pyexcel-io: https://github.com/pyexcel/pyexcel-io .. _pyexcel-xls: https://github.com/pyexcel/pyexcel-xls .. _pyexcel-xlsx: https://github.com/pyexcel/pyexcel-xlsx .. _pyexcel-ods: https://github.com/pyexcel/pyexcel-ods .. _pyexcel-ods3: https://github.com/pyexcel/pyexcel-ods3 .. _pyexcel-odsr: https://github.com/pyexcel/pyexcel-odsr .. _pyexcel-odsw: https://github.com/pyexcel/pyexcel-odsw .. _pyexcel-pdfr: https://github.com/pyexcel/pyexcel-pdfr

.. _pyexcel-xlsxw: https://github.com/pyexcel/pyexcel-xlsxw .. _pyexcel-libxlsxw: https://github.com/pyexcel/pyexcel-libxlsxw .. _pyexcel-xlsxr: https://github.com/pyexcel/pyexcel-xlsxr .. _pyexcel-xlsbr: https://github.com/pyexcel/pyexcel-xlsbr .. _pyexcel-htmlr: https://github.com/pyexcel/pyexcel-htmlr

.. _xlrd: https://github.com/python-excel/xlrd .. _xlwt: https://github.com/python-excel/xlwt .. _openpyxl: https://bitbucket.org/openpyxl/openpyxl .. _XlsxWriter: https://github.com/jmcnamara/XlsxWriter .. _pyexcel-ezodf: https://github.com/pyexcel/pyexcel-ezodf .. _odfpy: https://github.com/eea/odfpy .. _libxlsxwriter: http://libxlsxwriter.github.io/getting_started.html

.. table:: Other data renderers

======================== ======================= ================= ================== Package name Supported file formats Dependencies Python versions ======================== ======================= ================= ================== pyexcel-text_ write only:rst, tabulate_ 2.6, 2.7, 3.3, 3.4 mediawiki, html, 3.5, 3.6, pypy latex, grid, pipe, orgtbl, plain simple read only: ndjson r/w: json pyexcel-handsontable_ handsontable in html handsontable_ same as above pyexcel-pygal_ svg chart pygal_ 2.7, 3.3, 3.4, 3.5 3.6, pypy pyexcel-sortable_ sortable table in html csvtotable_ same as above pyexcel-gantt_ gantt chart in html frappe-gantt_ except pypy, same as above ======================== ======================= ================= ==================

.. _pyexcel-text: https://github.com/pyexcel/pyexcel-text .. _tabulate: https://bitbucket.org/astanin/python-tabulate .. _pyexcel-handsontable: https://github.com/pyexcel/pyexcel-handsontable .. _handsontable: https://cdnjs.com/libraries/handsontable .. _pyexcel-pygal: https://github.com/pyexcel/pyexcel-chart .. _pygal: https://github.com/Kozea/pygal .. _pyexcel-matplotlib: https://github.com/pyexcel/pyexcel-matplotlib .. _matplotlib: https://matplotlib.org .. _pyexcel-sortable: https://github.com/pyexcel/pyexcel-sortable .. _csvtotable: https://github.com/vividvilla/csvtotable .. _pyexcel-gantt: https://github.com/pyexcel/pyexcel-gantt .. _frappe-gantt: https://github.com/frappe/gantt

.. rubric:: Footnotes

.. [#f1] zipped csv file .. [#f2] zipped tsv file

Acknowledgement

All great work have been done by odf, ezodf, xlrd, xlwt, tabulate and other individual developers. This library unites only the data access code.

License

New BSD License


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