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

Author:
  1. Wang
Source code:

http://github.com/pyexcel/pyexcel.git

Issues:

http://github.com/pyexcel/pyexcel/issues

License:

New BSD License

Development:

0.6.0

Released:

0.5.7

Generated:

Aug 08, 2018

Note

The documentation of pyexcel v0.6.0 is under review and rewrite. If you have missed some information, please read v0.5.3

Introduction

pyexcel provides single application programming interface(API) to read, write and manipulate data in different excel file formats, in different storage media(disk, memory, database) and in different python data structures. Its loosely coupled architecture makes it extremely extensible.

The idea originated from the common usability problem: when an excel file driven web application is delivered for non-developer users (ie: team assistant, human resource administrator etc). The fact is that not everyone knows (or cares) about the differences between various excel formats: csv, xls, xlsx are all the same to them. Instead of training those users about file formats, this library helps web developers to handle most of the excel file formats by providing a common programming interface. To add a specific excel file format type to you application, all you need is to install an extra pyexcel plugin. Hence no code changes to your application and no issues with excel file formats any more. Looking at the community, this library and its associated ones try to become a small and easy to install alternative to Pandas.

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

For individual excel file formats, please install them as you wish:

A list of file formats supported by external plugins
Package name Supported file formats Dependencies Python versions
pyexcel-io csv, csvz [#f1]_, tsv, tsvz [#f2]_   2.6, 2.7, 3.3, 3.4, 3.5, 3.6 pypy
pyexcel-xls xls, xlsx(read only), xlsm(read only) xlrd, xlwt same as above
pyexcel-xlsx xlsx openpyxl same as above
pyexcel-ods3 ods pyexcel-ezodf, lxml 2.6, 2.7, 3.3, 3.4 3.5, 3.6
pyexcel-ods ods odfpy same as above
Dedicated file reader and writers
Package name Supported file formats Dependencies Python versions
pyexcel-xlsxw xlsx(write only) XlsxWriter Python 2 and 3
pyexcel-xlsxr xlsx(read only) lxml same as above
pyexcel-odsr read only for ods, fods lxml same as above
pyexcel-htmlr html(read only) lxml,html5lib same as above
Other data renderers
Package name Supported file formats Dependencies Python versions
pyexcel-text write only:rst, mediawiki, html, latex, grid, pipe, orgtbl, plain simple read only: ndjson r/w: json tabulate 2.6, 2.7, 3.3, 3.4 3.5, 3.6, pypy
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

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’).

Footnotes

[1]zipped csv file
[2]zipped tsv file

For compatibility tables of pyexcel-io plugins, please click here

Plugin compatibility table
pyexcel pyexcel-io pyexcel-text pyexcel-handsontable pyexcel-pygal pyexcel-gantt
0.6.0+ 0.4.0+ 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      
a list of support 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 [#f1]_
xlsx MS-Excel Extensions to the Office Open XML SpreadsheetML File Format. [#f2]_
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
[f1]quoted from whatis.com. Technical details can be found at MSDN XLS
[f2]xlsx is used by MS-Excel 2007, more information can be found at MSDN XLSX

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()

Tutorial

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

One liner to get data from the excel files

Get a list of dictionaries

Suppose you want to process the following coffee data (data source coffee chart on the center for science in the public interest):

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

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

And let’s check what do we have:

>>> for record in records:
...     print("%s of %s has %s mg" % (
...         record['Serving Size'],
...         record['Coffees'],
...         record['Caffeine (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:

>>> for row in p.get_array(file_name="your_file.xls", start_row=1):
...     print("%s of %s has %s mg" % (
...         row[1],
...         row[0],
...         row[2]))
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:

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

And check what do we have:

>>> 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:

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

>>> book_dict = p.get_book_dict(file_name="book.xls")
And check::
>>> 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]]}

Data export in one line

Export an array

Suppose you have the following array:

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

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

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

Let’s verify it:

>>> 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

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

Let’s verify it:

>>> 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
>>> 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
>>> 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
>>> 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

>>> 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:

>>> 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:

>>> 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:

>>> 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:

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

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

>>> 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

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

Again let’s verify what we have gotten:

>>> 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”:

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:

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:

>>> 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:

>>> 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

Stream APIs for big file : A set of two liners

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.

Two liners for get data from big excel files

Get a list of dictionaries

Suppose you want to process the following coffee data:

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

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

And let’s check what do we have:

>>> for record in records:
...     print("%s of %s has %s mg" % (
...         record['Serving Size'],
...         record['Coffees'],
...         record['Caffeine (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:

>>> p.free_resources()
Get two dimensional array

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

>>> for row in p.iget_array(file_name="your_file.xls", start_row=1):
...     print("%s of %s has %s mg" % (
...         row[1],
...         row[0],
...         row[2]))
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:

>>> p.free_resources()

where start_row skips the header row.

Data export in one liners

Export an array

Suppose you have the following array:

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

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

>>> 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:

>>> # p.free_resources()

Let’s verify it:

>>> 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

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

Let’s verify it:

>>> 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
>>> 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
>>> 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
>>> 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

>>> 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:

>>> 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:

>>> 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:

>>> 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:

>>> 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:

>>> 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

>>> 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:

>>> 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 |
+-------+--------+----------+

Pyexcel-io Plugin guide

There has been a lot of plugins for reading and writing a file types. Here is a guide for you to choose them.

A list of file formats supported by external plugins
Package name Supported file formats Dependencies Python versions
pyexcel-io csv, csvz [1], tsv, tsvz [2]   2.6, 2.7, 3.3, 3.4, 3.5, 3.6 pypy
pyexcel-xls xls, xlsx(read only), xlsm(read only) xlrd, xlwt same as above
pyexcel-xlsx xlsx openpyxl same as above
pyexcel-ods3 ods pyexcel-ezodf, lxml 2.6, 2.7, 3.3, 3.4 3.5, 3.6
pyexcel-ods ods odfpy same as above
Dedicated file reader and writers
Package name Supported file formats Dependencies Python versions
pyexcel-xlsxw xlsx(write only) XlsxWriter Python 2 and 3
pyexcel-xlsxr xlsx(read only) lxml same as above
pyexcel-odsr read only for ods, fods lxml same as above
pyexcel-htmlr html(read only) lxml,html5lib same as above

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’).

Footnotes

[1]zipped csv file
[2]zipped tsv file

Read and write with performance

Partial reading

csv, tsv by pyexcel-io, ods by pyexcel-odsr, html by pyexcel-htmlr are implemented in partial read mode. If you only need first half of the file, the second half of the data will not be read into the memory if and only if you use igetters(iget_records, iget_array) and isaveer(isave_as and isave_book_as).

Read on demand

xls by pyexcel-xls promised to read sheet on demand. It means if you need only one sheet from a multi-sheet book, the rest of the sheets in the book will not be read.

Streaming write

csv, tsv by `pyexce-io`_ can do streaming write.

Write with constant memory

xlsx by pyexcel-xlsxw can write big data with constant memory consumption.

For web developer

The following libraries are written to facilitate the daily import and export of excel data.

framework plugin/middleware/extension
Flask Flask-Excel
Django django-excel
Pyramid pyramid-excel

And you may make your own by using pyexcel-webio

Read any supported excel and respond its content in json

You can find a real world example in examples/memoryfile/ directory: pyexcel_server.py. Here is the example snippet

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def upload():
    if request.method == 'POST' and 'excel' in request.files:
        # handle file upload
        filename = request.files['excel'].filename
        extension = filename.split(".")[-1]
        # Obtain the file extension and content
        # pass a tuple instead of a file name
        content = request.files['excel'].read()
        if sys.version_info[0] > 2:
            # in order to support python 3
            # have to decode bytes to str
            content = content.decode('utf-8')
        sheet = pe.get_sheet(file_type=extension, file_content=content)
        # then use it as usual
        sheet.name_columns_by_row(0)
        # respond with a json
        return jsonify({"result": sheet.dict})
    return render_template('upload.html')

request.files[‘excel’] in line 4 holds the file object. line 5 finds out the file extension. line 13 obtains a sheet instance. line 15 uses the first row as data header. line 17 sends the json representation of the excel file back to client browser.

Write to memory and respond to download

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
data = [
    [...],
    ...
]

@app.route('/download')
def download():
    sheet = pe.Sheet(data)
    output = make_response(sheet.csv)
    output.headers["Content-Disposition"] = "attachment; filename=export.csv"
    output.headers["Content-type"] = "text/csv"
    return output

make_response is a Flask utility to make a memory content as http response.

Note

You can find the corresponding source code at examples/memoryfile

Pyexcel data renderers

There exist a few data renderers for pyexcel data. This chapter will walk you through them.

View pyexcel data in ndjson and other formats

With pyexcel-text, you can get pyexcel data in newline delimited json, normal json and other formats.

View the pyexcel data in a browser

You can use pyexcel-handsontable to render your data.

Include excel data in your python documentation

sphinxcontrib-excel help you present your excel data in various formats inside your sphinx documentation.

Draw charts from your excel data

pyexcel-pygal helps you with all charting options and give you charts in svg format.

pyexcel-echarts draws 2D, 3D, geo charts from pyexcel data and has awesome animations too, but it is under development.

pyexcel-matplotlib helps you with scentific charts and is under developmement.

Gantt chart visualization for your excel data

`pyexcel-gantt`_ is a specialist renderer for gantt chart.

Sheet

Random access

To randomly access a cell of Sheet instance, two syntax are available:

sheet[row, column]

or:

sheet['A1']

The former syntax is handy when you know the row and column numbers. The latter syntax is introduced to help you convert the excel column header such as “AX” to integer numbers.

Suppose you have the following data, you can get value 5 by reader[2, 2].

Here is the example code showing how you can randomly access a cell:

>>> sheet = pyexcel.get_sheet(file_name="example.xls")
>>> sheet.content
+---------+---+---+---+
| Example | X | Y | Z |
+---------+---+---+---+
| a       | 1 | 2 | 3 |
+---------+---+---+---+
| b       | 4 | 5 | 6 |
+---------+---+---+---+
| c       | 7 | 8 | 9 |
+---------+---+---+---+
>>> print(sheet[2, 2])
5
>>> print(sheet["C3"])
5
>>> sheet[3, 3] = 10
>>> print(sheet[3, 3])
10

Note

In order to set a value to a cell, please use sheet[row_index, column_index] = new_value

Random access to rows and columns

Continue with previous excel file, you can access row and column separately:

>>> sheet.row[1]
['a', 1, 2, 3]
>>> sheet.column[2]
['Y', 2, 5, 8]

Use custom names instead of index Alternatively, it is possible to use the first row to refer to each columns:

>>> sheet.name_columns_by_row(0)
>>> print(sheet[1, "Y"])
5
>>> sheet[1, "Y"] = 100
>>> print(sheet[1, "Y"])
100

You have noticed the row index has been changed. It is because first row is taken as the column names, hence all rows after the first row are shifted. Now accessing the columns are changed too:

>>> sheet.column['Y']
[2, 100, 8]

Hence access the same cell, this statement also works:

>>> sheet.column['Y'][1]
100

Further more, it is possible to use first column to refer to each rows:

>>> sheet.name_rows_by_column(0)

To access the same cell, we can use this line:

>>> sheet.row["b"][1]
100

For the same reason, the row index has been reduced by 1. Since we have named columns and rows, it is possible to access the same cell like this:

>>> print(sheet["b", "Y"])
100
>>> sheet["b", "Y"] = 200
>>> print(sheet["b", "Y"])
200

Play with data

Suppose you have the following data in any of the supported excel formats again:

>>> sheet = pyexcel.get_sheet(file_name="example_series.xls",
...      name_columns_by_row=0)

You can get headers:

>>> print(list(sheet.colnames))
['Column 1', 'Column 2', 'Column 3']

You can use a utility function to get all in a dictionary:

>>> sheet.to_dict()
OrderedDict([('Column 1', [1, 4, 7]), ('Column 2', [2, 5, 8]), ('Column 3', [3, 6, 9])])

Maybe you want to get only the data without the column headers. You can call rows() instead:

>>> list(sheet.rows())
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]
You can get data from the bottom to the top one by

calling rrows():

>>> list(sheet.rrows())
[[7, 8, 9], [4, 5, 6], [1, 2, 3]]

You might want the data arranged vertically. You can call columns():

>>> list(sheet.columns())
[[1, 4, 7], [2, 5, 8], [3, 6, 9]]

You can get columns in reverse sequence as well by calling rcolumns():

>>> list(sheet.rcolumns())
[[3, 6, 9], [2, 5, 8], [1, 4, 7]]

Do you want to flatten the data? You can get the content in one dimensional array. If you are interested in playing with one dimensional enumeration, you can check out these functions enumerate(), reverse(), vertical(), and rvertical():

>>> list(sheet.enumerate())
[1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> list(sheet.reverse())
[9, 8, 7, 6, 5, 4, 3, 2, 1]
>>> list(sheet.vertical())
[1, 4, 7, 2, 5, 8, 3, 6, 9]
>>> list(sheet.rvertical())
[9, 6, 3, 8, 5, 2, 7, 4, 1]

attributes

Attributes:

>>> import pyexcel
>>> content = "1,2,3\n3,4,5"
>>> sheet = pyexcel.get_sheet(file_type="csv", file_content=content)
>>> sheet.tsv
'1\t2\t3\r\n3\t4\t5\r\n'
>>> print(sheet.simple)
csv:
-  -  -
1  2  3
3  4  5
-  -  -
What’s more, you could as well set value to an attribute, for example::
>>> import pyexcel
>>> content = "1,2,3\n3,4,5"
>>> sheet = pyexcel.Sheet()
>>> sheet.csv = content
>>> sheet.array
[[1, 2, 3], [3, 4, 5]]

You can get the direct access to underneath stream object. In some situation, it is desired:

>>> stream = sheet.stream.tsv

The returned stream object has tsv formatted content for reading.

What you could further do is to set a memory stream of any supported file format to a sheet. For example:

>>> another_sheet = pyexcel.Sheet()
>>> another_sheet.xls = sheet.xls
>>> another_sheet.content
+---+---+---+
| 1 | 2 | 3 |
+---+---+---+
| 3 | 4 | 5 |
+---+---+---+

Yet, it is possible assign a absolute url to an online excel file to an instance of pyexcel.Sheet.

custom attributes

You can pass on source specific parameters to getter and setter functions.

>>> content = "1-2-3\n3-4-5"
>>> sheet = pyexcel.Sheet()
>>> sheet.set_csv(content, delimiter="-")
>>> sheet.csv
'1,2,3\r\n3,4,5\r\n'
>>> sheet.get_csv(delimiter="|")
'1|2|3\r\n3|4|5\r\n'

Data manipulation

The data in a sheet is represented by Sheet which maintains the data as a list of lists. You can regard Sheet as a two dimensional array with additional iterators. Random access to individual column and row is exposed by Column and Row

Column manipulation

Suppose have one data file as the following:

>>> sheet = pyexcel.get_sheet(file_name="example.xls", name_columns_by_row=0)
>>> sheet
pyexcel sheet:
+----------+----------+----------+
| Column 1 | Column 2 | Column 3 |
+==========+==========+==========+
| 1        | 4        | 7        |
+----------+----------+----------+
| 2        | 5        | 8        |
+----------+----------+----------+
| 3        | 6        | 9        |
+----------+----------+----------+

And you want to update Column 2 with these data: [11, 12, 13]

>>> sheet.column["Column 2"] = [11, 12, 13]
>>> sheet.column[1]
[11, 12, 13]
>>> sheet
pyexcel sheet:
+----------+----------+----------+
| Column 1 | Column 2 | Column 3 |
+==========+==========+==========+
| 1        | 11       | 7        |
+----------+----------+----------+
| 2        | 12       | 8        |
+----------+----------+----------+
| 3        | 13       | 9        |
+----------+----------+----------+
Remove one column of a data file

If you want to remove Column 2, you can just call:

>>> del sheet.column["Column 2"]
>>> sheet.column["Column 3"]
[7, 8, 9]

The sheet content will become:

>>> sheet
pyexcel sheet:
+----------+----------+
| Column 1 | Column 3 |
+==========+==========+
| 1        | 7        |
+----------+----------+
| 2        | 8        |
+----------+----------+
| 3        | 9        |
+----------+----------+
Append more columns to a data file

Continue from previous example. Suppose you want add two more columns to the data file

Column 4 Column 5
10 13
11 14
12 15

Here is the example code to append two extra columns:

>>> extra_data = [
...    ["Column 4", "Column 5"],
...    [10, 13],
...    [11, 14],
...    [12, 15]
... ]
>>> sheet2 = pyexcel.Sheet(extra_data)
>>> sheet.column += sheet2
>>> sheet.column["Column 4"]
[10, 11, 12]
>>> sheet.column["Column 5"]
[13, 14, 15]

Here is what you will get:

>>> sheet
pyexcel sheet:
+----------+----------+----------+----------+
| Column 1 | Column 3 | Column 4 | Column 5 |
+==========+==========+==========+==========+
| 1        | 7        | 10       | 13       |
+----------+----------+----------+----------+
| 2        | 8        | 11       | 14       |
+----------+----------+----------+----------+
| 3        | 9        | 12       | 15       |
+----------+----------+----------+----------+
Cherry pick some columns to be removed

Suppose you have the following data:

>>> data = [
...     ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'],
...     [1,2,3,4,5,6,7,9],
... ]
>>> sheet = pyexcel.Sheet(data, name_columns_by_row=0)
>>> sheet
pyexcel sheet:
+---+---+---+---+---+---+---+---+
| a | b | c | d | e | f | g | h |
+===+===+===+===+===+===+===+===+
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 9 |
+---+---+---+---+---+---+---+---+

And you want to remove columns named as: ‘a’, ‘c, ‘e’, ‘h’. This is how you do it:

>>> del sheet.column['a', 'c', 'e', 'h']
>>> sheet
pyexcel sheet:
+---+---+---+---+
| b | d | f | g |
+===+===+===+===+
| 2 | 4 | 6 | 7 |
+---+---+---+---+
What if the headers are in a different row

Suppose you have the following data:

>>> sheet
pyexcel sheet:
+----------+----------+----------+
| 1        | 2        | 3        |
+----------+----------+----------+
| Column 1 | Column 2 | Column 3 |
+----------+----------+----------+
| 4        | 5        | 6        |
+----------+----------+----------+

The way to name your columns is to use index 1:

>>> sheet.name_columns_by_row(1)

Here is what you get:

>>> sheet
pyexcel sheet:
+----------+----------+----------+
| Column 1 | Column 2 | Column 3 |
+==========+==========+==========+
| 1        | 2        | 3        |
+----------+----------+----------+
| 4        | 5        | 6        |
+----------+----------+----------+
Row manipulation

Suppose you have the following data:

>>> sheet
pyexcel sheet:
+---+---+---+-------+
| a | b | c | Row 1 |
+---+---+---+-------+
| e | f | g | Row 2 |
+---+---+---+-------+
| 1 | 2 | 3 | Row 3 |
+---+---+---+-------+

You can name your rows by column index at 3:

>>> sheet.name_rows_by_column(3)
>>> sheet
pyexcel sheet:
+-------+---+---+---+
| Row 1 | a | b | c |
+-------+---+---+---+
| Row 2 | e | f | g |
+-------+---+---+---+
| Row 3 | 1 | 2 | 3 |
+-------+---+---+---+

Then you can access rows by its name:

>>> sheet.row["Row 1"]
['a', 'b', 'c']

Formatting

Previous section has assumed the data is in the format that you want. In reality, you have to manipulate the data types a bit to suit your needs. Hence, formatters comes into the scene. use format() to apply formatter immediately.

Note

int, float and datetime values are automatically detected in csv files since pyexcel version 0.2.2

Convert a column of numbers to strings

Suppose you have the following data:

>>> import pyexcel
>>> data = [
...     ["userid","name"],
...     [10120,"Adam"],
...     [10121,"Bella"],
...     [10122,"Cedar"]
... ]
>>> sheet = pyexcel.Sheet(data)
>>> sheet.name_columns_by_row(0)
>>> sheet.column["userid"]
[10120, 10121, 10122]

As you can see, userid column is of int type. Next, let’s convert the column to string format:

>>> sheet.column.format("userid", str)
>>> sheet.column["userid"]
['10120', '10121', '10122']
Cleanse the cells in a spread sheet

Sometimes, the data in a spreadsheet may have unwanted strings in all or some cells. Let’s take an example. Suppose we have a spread sheet that contains all strings but it as random spaces before and after the text values. Some field had weird characters, such as “  ”:

>>> data = [
...     ["        Version", "        Comments", "       Author  "],
...     ["  v0.0.1       ", " Release versions","            Eda"],
...     ["  v0.0.2  ", "Useful updates    ", "   Freud"]
... ]
>>> sheet = pyexcel.Sheet(data)
>>> sheet.content
+-----------------+------------------------------+----------------------+
|         Version |         Comments             |        Author   |
+-----------------+------------------------------+----------------------+
|   v0.0.1        |  Release versions            |             Eda |
+-----------------+------------------------------+----------------------+
|   v0.0.2   | Useful updates     |    Freud        |
+-----------------+------------------------------+----------------------+

Now try to create a custom cleanse function:

.. code-block:: python
>>> def cleanse_func(v):
...     v = v.replace(" ", "")
...     v = v.rstrip().strip()
...     return v
...

Then let’s create a SheetFormatter and apply it:

.. code-block:: python
>>> sheet.map(cleanse_func)

So in the end, you get this:

>>> sheet.content
+---------+------------------+--------+
| Version | Comments         | Author |
+---------+------------------+--------+
| v0.0.1  | Release versions | Eda    |
+---------+------------------+--------+
| v0.0.2  | Useful updates   | Freud  |
+---------+------------------+--------+

Data filtering

use filter() function to apply a filter immediately. The content is modified.

Suppose you have the following data in any of the supported excel formats:

Column 1 Column 2 Column 3
1 4 7
2 5 8
3 6 9
>>> import pyexcel
>>> sheet = pyexcel.get_sheet(file_name="example_series.xls", name_columns_by_row=0)
>>> sheet.content
+----------+----------+----------+
| Column 1 | Column 2 | Column 3 |
+==========+==========+==========+
| 1        | 2        | 3        |
+----------+----------+----------+
| 4        | 5        | 6        |
+----------+----------+----------+
| 7        | 8        | 9        |
+----------+----------+----------+
Filter out some data

You may want to filter odd rows and print them in an array of dictionaries:

>>> sheet.filter(row_indices=[0, 2])
>>> sheet.content
+----------+----------+----------+
| Column 1 | Column 2 | Column 3 |
+==========+==========+==========+
| 4        | 5        | 6        |
+----------+----------+----------+

Let’s try to further filter out even columns:

>>> sheet.filter(column_indices=[1])
>>> sheet.content
+----------+----------+
| Column 1 | Column 3 |
+==========+==========+
| 4        | 6        |
+----------+----------+
Save the data

Let’s save the previous filtered data:

>>> sheet.save_as("example_series_filter.xls")

When you open example_series_filter.xls, you will find these data

Column 1 Column 3
2 8
How to filter out empty rows in my sheet?

Suppose you have the following data in a sheet and you want to remove those rows with blanks:

>>> import pyexcel as pe
>>> sheet = pe.Sheet([[1,2,3],['','',''],['','',''],[1,2,3]])

You can use pyexcel.filters.RowValueFilter, which examines each row, return True if the row should be filtered out. So, let’s define a filter function:

>>> def filter_row(row_index, row):
...     result = [element for element in row if element != '']
...     return len(result)==0

And then apply the filter on the sheet:

>>> del sheet.row[filter_row]
>>> sheet
pyexcel sheet:
+---+---+---+
| 1 | 2 | 3 |
+---+---+---+
| 1 | 2 | 3 |
+---+---+---+

Book

You access each cell via this syntax:

book[sheet_index][row, column]

or:

book["sheet_name"][row, column]

Suppose you have the following sheets:

And you can randomly access a cell in a sheet:

>>> book = pyexcel.get_book(file_name="example.xls")
>>> print(book["Sheet 1"][0,0])
1
>>> print(book[0][0,0]) # the same cell
1

Tip

With pyexcel, you can regard single sheet reader as an two dimensional array and multi-sheet excel book reader as a ordered dictionary of two dimensional arrays.

Write multiple sheet excel file

Suppose you have previous data as a dictionary and you want to save it as multiple sheet excel file:

>>> content = {
...     '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]
...         ]
... }
>>> book = pyexcel.get_book(bookdict=content)
>>> book.save_as("output.xls")

You shall get a xls file

Read multiple sheet excel file

Let’s read the previous file back:

>>> book = pyexcel.get_book(file_name="output.xls")
>>> sheets = book.to_dict()
>>> for name in sheets.keys():
...     print(name)
Sheet 1
Sheet 2
Sheet 3

Get content

>>> book_dict = {
...      '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]
...          ],
...      'Sheet 1':
...          [
...              [1.0, 2.0, 3.0],
...              [4.0, 5.0, 6.0],
...              [7.0, 8.0, 9.0]
...          ]
...  }
>>> book = pyexcel.get_book(bookdict=book_dict)
>>> book
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 |
+-----+-----+-----+
>>> print(book.rst)
Sheet 1:
=  =  =
1  2  3
4  5  6
7  8  9
=  =  =
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
===  ===  ===

You can get the direct access to underneath stream object. In some situation, it is desired.

>>> stream = book.stream.plain

The returned stream object has the content formatted in plain format for further reading.

Set content

Surely, you could set content to an instance of pyexcel.Book.

>>> other_book = pyexcel.Book()
>>> other_book.bookdict = book_dict
>>> print(other_book.plain)
Sheet 1:
1  2  3
4  5  6
7  8  9
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

You can set via ‘xls’ attribute too.

>>> another_book = pyexcel.Book()
>>> another_book.xls = other_book.xls
>>> print(another_book.mediawiki)
Sheet 1:
{| class="wikitable" style="text-align: left;"
|+ <!-- caption -->
|-
| align="right"| 1 || align="right"| 2 || align="right"| 3
|-
| align="right"| 4 || align="right"| 5 || align="right"| 6
|-
| align="right"| 7 || align="right"| 8 || align="right"| 9
|}
Sheet 2:
{| class="wikitable" style="text-align: left;"
|+ <!-- caption -->
|-
| X || Y || Z
|-
| 1 || 2 || 3
|-
| 4 || 5 || 6
|}
Sheet 3:
{| class="wikitable" style="text-align: left;"
|+ <!-- caption -->
|-
| O || P || Q
|-
| 3 || 2 || 1
|-
| 4 || 3 || 2
|}
Access to individual sheets

You can access individual sheet of a book via attribute:

>>> book = pyexcel.get_book(file_name="book.xls")
>>> book.sheet3
sheet3:
+---+---+---+
| 1 | 2 | 3 |
+---+---+---+
| 4 | 5 | 6 |
+---+---+---+
| 7 | 8 | 9 |
+---+---+---+

or via array notations:

>>> book["sheet 1"] # there is a space in the sheet name
sheet 1:
+---+---+---+
| 1 | 2 | 3 |
+---+---+---+
| 1 | 2 | 3 |
+---+---+---+
| 4 | 5 | 6 |
+---+---+---+
Merge excel books

Suppose you have two excel books and each had three sheets. You can merge them and get a new book:

You also can merge individual sheets:

>>> book1 = pyexcel.get_book(file_name="book1.xls")
>>> book2 = pyexcel.get_book(file_name="book2.xlsx")
>>> merged_book = book1 + book2
>>> merged_book = book1["Sheet 1"] + book2["Sheet 2"]
>>> merged_book = book1["Sheet 1"] + book2
>>> merged_book = book1 + book2["Sheet 2"]
Manipulate individual sheets

merge sheets into a single sheet

Suppose you want to merge many csv files row by row into a new sheet.

>>> import glob
>>> merged = pyexcel.Sheet()
>>> for file in glob.glob("*.csv"):
...     merged.row += pyexcel.get_sheet(file_name=file)
>>> merged.save_as("merged.csv")
How do I read a book, process it and save to a new book

Yes, you can do that. The code looks like this:

import pyexcel

book = pyexcel.get_book(file_name="yourfile.xls")
for sheet in book:
    # do you processing with sheet
    # do filtering?
    pass
book.save_as("output.xls")
What would happen if I save a multi sheet book into “csv” file

Well, you will get one csv file per each sheet. Suppose you have these code:

>>> content = {
...     '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]
...         ]
... }
>>> book = pyexcel.Book(content)
>>> book.save_as("myfile.csv")

You will end up with three csv files:

>>> import glob
>>> outputfiles = glob.glob("myfile_*.csv")
>>> for file in sorted(outputfiles):
...     print(file)
...
myfile__Sheet 1__0.csv
myfile__Sheet 2__1.csv
myfile__Sheet 3__2.csv

and their content is the value of the dictionary at the corresponding key

Alternatively, you could use save_book_as() function

>>> pyexcel.save_book_as(bookdict=content, dest_file_name="myfile.csv")
After I have saved my multiple sheet book in csv format, how do I get them back

First of all, you can read them back individual as csv file using meth:~pyexcel.get_sheet method. Secondly, the pyexcel can do the magic to load all of them back into a book. You will just need to provide the common name before the separator “__”:

>>> book2 = pyexcel.get_book(file_name="myfile.csv")
>>> book2
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 |
+-----+-----+-----+

Working with databases

How to import an excel sheet to a database using SQLAlchemy

Note

You can find the complete code of this example in examples folder on github

Before going ahead, let’s import the needed components and initialize sql engine and table base:

>>> import os
>>> import pyexcel as p
>>> from sqlalchemy import create_engine
>>> from sqlalchemy.ext.declarative import declarative_base
>>> from sqlalchemy import Column , Integer, String, Float, Date
>>> from sqlalchemy.orm import sessionmaker
>>> engine = create_engine("sqlite:///birth.db")
>>> Base = declarative_base()
>>> Session = sessionmaker(bind=engine)

Let’s suppose we have the following database model:

>>> class BirthRegister(Base):
...     __tablename__='birth'
...     id=Column(Integer, primary_key=True)
...     name=Column(String)
...     weight=Column(Float)
...     birth=Column(Date)

Let’s create the table:

>>> Base.metadata.create_all(engine)

Now here is a sample excel file to be saved to the table:

Here is the code to import it:

>>> session = Session() # obtain a sql session
>>> p.save_as(file_name="birth.xls", name_columns_by_row=0, dest_session=session, dest_table=BirthRegister)

Done it. It is that simple. Let’s verify what has been imported to make sure.

>>> sheet = p.get_sheet(session=session, table=BirthRegister)
>>> sheet
birth:
+------------+----+-------+--------+
| birth      | id | name  | weight |
+------------+----+-------+--------+
| 2015-02-03 | 1  | Adam  | 3.4    |
+------------+----+-------+--------+
| 2014-11-12 | 2  | Smith | 4.2    |
+------------+----+-------+--------+

Cook book

Recipes

Warning

The pyexcel DOES NOT consider Fonts, Styles and Charts at all. In the resulting excel files, fonts, styles and charts will not be transferred.

These recipes give a one-stop utility functions for known use cases. Similar functionality can be achieved using other application interfaces.

Update one column of a data file

Suppose you have one data file as the following:

example.xls

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

And you want to update Column 2 with these data: [11, 12, 13]

Here is the code:

>>> from pyexcel.cookbook import update_columns
>>> custom_column = {"Column 2":[11, 12, 13]}
>>> update_columns("example.xls", custom_column, "output.xls")

Your output.xls will have these data:

Column 1 Column 2 Column 3
1 11 7
2 12 8
3 13 9

Update one row of a data file

Suppose you have the same data file:

example.xls

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

And you want to update the second row with these data: [7, 4, 1]

Here is the code:

>>> from pyexcel.cookbook import update_rows
>>> custom_row = {"Row 1":[11, 12, 13]}
>>> update_rows("example.xls", custom_row, "output.xls")

Your output.xls will have these data:

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

Merge two files into one

Suppose you want to merge the following two data files:

example.csv

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

example.xls

Column 4 Column 5
10 12
11 13

The following code will merge the tow into one file, say “output.xls”:

>>> from pyexcel.cookbook import merge_two_files
>>> merge_two_files("example.csv", "example.xls", "output.xls")

The output.xls would have the following data:

Column 1 Column 2 Column 3 Column 4 Column 5
1 4 7 10 12
2 5 8 11 13
3 6 9    

Select candidate columns of two files and form a new one

Suppose you have these two files:

example.ods

Column 1 Column 2 Column 3 Column 4 Column 5
1 4 7 10 13
2 5 8 11 14
3 6 9 12 15

example.xls

Column 6 Column 7 Column 8 Column 9 Column 10
16 17 18 19 20
>>> data = [
...      ["Column 1", "Column 2", "Column 3", "Column 4", "Column 5"],
...      [1, 4, 7, 10, 13],
...      [2, 5, 8, 11, 14],
...      [3, 6, 9, 12, 15]
...  ]
>>> s = pyexcel.Sheet(data)
>>> s.save_as("example.csv")
>>> data = [
...      ["Column 6", "Column 7", "Column 8", "Column 9", "Column 10"],
...      [16, 17, 18, 19, 20]
...  ]
>>> s = pyexcel.Sheet(data)
>>> s.save_as("example.xls")

And you want to filter out column 2 and 4 from example.ods, filter out column 6 and 7 and merge them:

Column 1 Column 3 Column 5 Column 8 Column 9 Column 10
1 7 13 18 19 20
2 8 14      
3 9 15      

The following code will do the job:

>>> from pyexcel.cookbook import merge_two_readers
>>> sheet1 = pyexcel.get_sheet(file_name="example.csv", name_columns_by_row=0)
>>> sheet2 = pyexcel.get_sheet(file_name="example.xls", name_columns_by_row=0)
>>> del sheet1.column[1, 3, 5]
>>> del sheet2.column[0, 1]
>>> merge_two_readers(sheet1, sheet2, "output.xls")

Merge two files into a book where each file become a sheet

Suppose you want to merge the following two data files:

example.csv

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

example.xls

Column 4 Column 5
10 12
11 13
>>> data = [
...      ["Column 1", "Column 2", "Column 3"],
...      [1, 2, 3],
...      [4, 5, 6],
...      [7, 8, 9]
...  ]
>>> s = pyexcel.Sheet(data)
>>> s.save_as("example.csv")
>>> data = [
...      ["Column 4", "Column 5"],
...      [10, 12],
...      [11, 13]
...  ]
>>> s = pyexcel.Sheet(data)
>>> s.save_as("example.xls")

The following code will merge the tow into one file, say “output.xls”:

>>> from pyexcel.cookbook import merge_all_to_a_book
>>> merge_all_to_a_book(["example.csv", "example.xls"], "output.xls")

The output.xls would have the following data:

example.csv as sheet name and inside the sheet, you have:

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

example.ods as sheet name and inside the sheet, you have:

Column 4 Column 5
10 12
11 13

Loading from other sources

Get back into pyexcel

list
>>> import pyexcel as p
>>> two_dimensional_list = [
...    [1, 2, 3, 4],
...    [5, 6, 7, 8],
...    [9, 10, 11, 12],
... ]
>>> sheet = p.get_sheet(array=two_dimensional_list)
>>> sheet
pyexcel_sheet1:
+---+----+----+----+
| 1 | 2  | 3  | 4  |
+---+----+----+----+
| 5 | 6  | 7  | 8  |
+---+----+----+----+
| 9 | 10 | 11 | 12 |
+---+----+----+----+
dict
>>> a_dictionary_of_key_value_pair = {
...    "IE": 0.2,
...    "Firefox": 0.3
... }
>>> sheet = p.get_sheet(adict=a_dictionary_of_key_value_pair)
>>> sheet
pyexcel_sheet1:
+---------+-----+
| Firefox | IE  |
+---------+-----+
| 0.3     | 0.2 |
+---------+-----+
>>> a_dictionary_of_one_dimensional_arrays = {
...     "Column 1": [1, 2, 3, 4],
...     "Column 2": [5, 6, 7, 8],
...     "Column 3": [9, 10, 11, 12],
... }
>>> sheet = p.get_sheet(adict=a_dictionary_of_one_dimensional_arrays)
>>> sheet
pyexcel_sheet1:
+----------+----------+----------+
| Column 1 | Column 2 | Column 3 |
+----------+----------+----------+
| 1        | 5        | 9        |
+----------+----------+----------+
| 2        | 6        | 10       |
+----------+----------+----------+
| 3        | 7        | 11       |
+----------+----------+----------+
| 4        | 8        | 12       |
+----------+----------+----------+
records
>>> a_list_of_dictionaries = [
...     {
...         "Name": 'Adam',
...         "Age": 28
...     },
...     {
...         "Name": 'Beatrice',
...         "Age": 29
...     },
...     {
...         "Name": 'Ceri',
...         "Age": 30
...     },
...     {
...         "Name": 'Dean',
...         "Age": 26
...     }
... ]
>>> sheet = p.get_sheet(records=a_list_of_dictionaries)
>>> sheet
pyexcel_sheet1:
+-----+----------+
| Age | Name     |
+-----+----------+
| 28  | Adam     |
+-----+----------+
| 29  | Beatrice |
+-----+----------+
| 30  | Ceri     |
+-----+----------+
| 26  | Dean     |
+-----+----------+
book dict
>>> 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]
...          ]
...  }
>>> book = p.get_book(bookdict=a_dictionary_of_two_dimensional_arrays)
>>> book
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 |
+-----+-----+-----+
How to load a sheet from a url

Suppose you have excel file somewhere hosted:

>>> sheet = pe.get_sheet(url='http://yourdomain.com/test.csv')
>>> sheet
csv:
+---+---+---+
| 1 | 2 | 3 |
+---+---+---+
For sheet
Get content
>>> another_sheet = p.Sheet()
>>> another_sheet.url = "https://github.com/pyexcel/pyexcel/raw/master/examples/basics/multiple-sheets-example.xls"
>>> another_sheet.content
+---+---+---+
| 1 | 2 | 3 |
+---+---+---+
| 4 | 5 | 6 |
+---+---+---+
| 7 | 8 | 9 |
+---+---+---+
For book

How about setting content via a url?

>>> another_book = p.Book()
>>> another_book.url = "https://github.com/pyexcel/pyexcel/raw/master/examples/basics/multiple-sheets-example.xls"
>>> another_book
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 |
+---+---+---+

Real world cases

How to inject csv data to database

Here is real case in the stack-overflow. Due to the author’s ignorance, the user would like to have the code in matlab than Python. Hence, I am sharing my pyexcel solution here.

Problem definition

Here is my CSV file:

PDB_Id      123442  234335  234336  3549867
a001        6       0       0       8
b001        4       2       0       0
c003        0       0       0       5

I want to put this data in a MYSQL table in the form:

PROTEIN_ID  PROTEIN_KEY     VALUE_OF_KEY
a001            123442          6
a001            234335          0
a001            234336          0
a001            3549867         8
b001            123442          4
b001            234335          2
b001            234336          0
b001            234336          0
c003            123442          0
c003            234335          0
c003            234336          0
c003            3549867         5

I have created table with the following code:

sql = """CREATE TABLE ALLPROTEINS (
         Protein_ID CHAR(20),
         PROTEIN_KEY INT ,
         VALUE_OF_KEY INT
         )"""

I need the code for insert.

Pyexcel solution

If you could insert an id field to act as the primary key, it can be mapped using sqlalchemy’s ORM:

$ sqlite3 /tmp/stack2.db
sqlite> CREATE TABLE ALLPROTEINS (
   ...>          ID INT,
   ...>          Protein_ID CHAR(20),
   ...>          PROTEIN_KEY INT,
   ...>          VALUE_OF_KEY INT
   ...>          );

Here is the data mapping script vis sqlalchemy:

>>> # mapping your database via sqlalchemy
>>> from sqlalchemy import create_engine
>>> from sqlalchemy.ext.declarative import declarative_base
>>> from sqlalchemy import Column, Integer, String
>>> from sqlalchemy.orm import sessionmaker
>>> # checkout http://docs.sqlalchemy.org/en/latest/dialects/index.html
>>> # for a different database server
>>> engine = create_engine("sqlite:////tmp/stack2.db")
>>> Base = declarative_base()
>>> class Proteins(Base):
...     __tablename__ = 'ALLPROTEINS'
...     id = Column(Integer, primary_key=True, autoincrement=True) # <-- appended field
...     protein_id = Column(String(20))
...     protein_key = Column(Integer)
...     value_of_key = Column(Integer)
>>> Session = sessionmaker(bind=engine)
>>>

Here is the short script to get data inserted into the database:

>>> import pyexcel as p
>>> from itertools import product
>>> # data insertion code starts here
>>> sheet = p.get_sheet(file_name="csv-to-mysql-in-matlab-code.csv", delimiter='\t')
>>> sheet.name_columns_by_row(0)
>>> sheet.name_rows_by_column(0)
>>> print(sheet)
csv-to-mysql-in-matlab-code.csv:
+------+--------+--------+--------+---------+
|      | 123442 | 234335 | 234336 | 3549867 |
+======+========+========+========+=========+
| a001 | 6      | 0      | 0      | 8       |
+------+--------+--------+--------+---------+
| b001 | 4      | 2      | 0      | 0       |
+------+--------+--------+--------+---------+
| c003 | 0      | 0      | 0      | 5       |
+------+--------+--------+--------+---------+
>>> results = []
>>> for protein_id, protein_key in product(sheet.rownames, sheet.colnames):
...     results.append([protein_id, protein_key, sheet[str(protein_id), protein_key]])
>>>
>>> sheet2 = p.get_sheet(array=results)
>>> sheet2.colnames = ['protein_id', 'protein_key', 'value_of_key']
>>> print(sheet2)
pyexcel_sheet1:
+------------+-------------+--------------+
| protein_id | protein_key | value_of_key |
+============+=============+==============+
| a001       | 123442      | 6            |
+------------+-------------+--------------+
| a001       | 234335      | 0            |
+------------+-------------+--------------+
| a001       | 234336      | 0            |
+------------+-------------+--------------+
| a001       | 3549867     | 8            |
+------------+-------------+--------------+
| b001       | 123442      | 4            |
+------------+-------------+--------------+
| b001       | 234335      | 2            |
+------------+-------------+--------------+
| b001       | 234336      | 0            |
+------------+-------------+--------------+
| b001       | 3549867     | 0            |
+------------+-------------+--------------+
| c003       | 123442      | 0            |
+------------+-------------+--------------+
| c003       | 234335      | 0            |
+------------+-------------+--------------+
| c003       | 234336      | 0            |
+------------+-------------+--------------+
| c003       | 3549867     | 5            |
+------------+-------------+--------------+
>>> sheet2.save_to_database(session=Session(), table=Proteins)

Here is the data inserted:

$ sqlite3 /tmp/stack2.db
sqlite> select * from allproteins
   ...> ;
|a001|123442|6
|a001|234335|0
|a001|234336|0
|a001|3549867|8
|b001|123442|4
|b001|234335|2
|b001|234336|0
|b001|234336|0
|c003|123442|0
|c003|234335|0
|c003|234336|0
|c003|3549867|5

API documentation

API Reference

This is intended for users of pyexcel.

Signature functions

Obtaining data from excel file

It is believed that once a Python developer could easily operate on list, dictionary and various mixture of both. This library provides four module level functions to help you obtain excel data in those formats. Please refer to “A list of module level functions”, the first three functions operates on any one sheet from an excel book and the fourth one returns all data in all sheets in an excel book.

get_array(**keywords) Obtain an array from an excel source
get_dict([name_columns_by_row]) Obtain a dictionary from an excel source
get_records([name_columns_by_row]) Obtain a list of records from an excel source
get_book_dict(**keywords) Obtain a dictionary of two dimensional arrays

In cases where the excel data needs custom manipulations, a pyexcel user got a few choices: one is to use Sheet and Book, the other is to look for more sophisticated ones:

  • Pandas, for numerical analysis
  • Do-it-yourself
get_book(**keywords) Get an instance of Book from an excel source
get_sheet(**keywords) Get an instance of Sheet from an excel source

The following two variants of the data access function use generator and should work well with big data files. However, you will need to call free_resources() to make sure file handles are closed.

iget_array(**keywords) Obtain a generator of an two dimensional array from an excel source
iget_records([custom_headers]) Obtain a generator of a list of records from an excel source
free_resources() Close file handles opened by signature functions that starts with ‘i’
Saving data to excel file
save_as(**keywords) Save a sheet from a data source to another one
save_book_as(**keywords) Save a book from a data source to another one

The following functions would work with big data and will work every well with iget_array() and iget_records().

isave_as(**keywords) Save a sheet from a data source to another one with less memory
isave_book_as(**keywords) Save a book from a data source to another one

If you would only use these two functions to do format transcoding, you may enjoy a speed boost using isave_as() and isave_book_as(), because they use yield keyword and minimize memory footprint. However, you will need to call free_resources() to make sure file handles are closed. And save_as() and save_book_as() reads all data into memory and will make all rows the same width.

Cookbook

merge_csv_to_a_book(filelist[, outfilename]) merge a list of csv files into a excel book
merge_all_to_a_book(filelist[, outfilename]) merge a list of excel files into a excel book
split_a_book(file_name[, outfilename]) Split a file into separate sheets
extract_a_sheet_from_a_book(file_name, sheetname) Extract a sheet from a excel book

Book

Here’s the entity relationship between Book, Sheet, Row and Column

_images/entity-relationship-diagram.png
Constructor
Book([sheets, filename, path]) Read an excel book that has one or more sheets
Attribute
Book.number_of_sheets() Return the number of sheets
Book.sheet_names() Return all sheet names
Conversions
Book.bookdict Get/Set data in/from bookdict format
Book.url Set data in url format
Book.csv Get/Set data in/from csv format
Book.tsv Get/Set data in/from tsv format
Book.csvz Get/Set data in/from csvz format
Book.tsvz Get/Set data in/from tsvz format
Book.xls Get/Set data in/from xls format
Book.xlsm Get/Set data in/from xlsm format
Book.xlsx Get/Set data in/from xlsx format
Book.ods Get/Set data in/from ods format
Book.stream Return a stream in which the content is properly encoded
Save changes
Book.save_as(filename, **keywords) Save the content to a new file
Book.save_to_memory(file_type[, stream]) Save the content to a memory stream
Book.save_to_database(session, tables[, …]) Save data in sheets to database tables
Book.save_to_django_models(models[, …]) Save to database table through django model

Sheet

Constructor
Sheet([sheet, name, name_columns_by_row, …]) Two dimensional data container for filtering, formatting and iteration
Attributes
Sheet.content Plain representation without headers
Sheet.number_of_rows() The number of rows
Sheet.number_of_columns() The number of columns
Sheet.row_range() Utility function to get row range
Sheet.column_range() Utility function to get column range
Iteration
Sheet.rows() Returns a top to bottom row iterator
Sheet.rrows() Returns a bottom to top row iterator
Sheet.columns() Returns a left to right column iterator
Sheet.rcolumns() Returns a right to left column iterator
Sheet.enumerate() Iterate cell by cell from top to bottom and from left to right
Sheet.reverse() Opposite to enumerate
Sheet.vertical() Default iterator to go through each cell one by one from leftmost column to rightmost row and from top to bottom example.
Sheet.rvertical() Default iterator to go through each cell one by one from rightmost column to leftmost row and from bottom to top example.
Cell access
Sheet.cell_value(row, column[, new_value]) Random access to table cells
Sheet.__getitem__(aset) By default, this class recognize from top to bottom from left to right
Row access
Sheet.row_at(index) Gets the data at the specified row
Sheet.set_row_at(row_index, data_array) Update a row data range
Sheet.delete_rows(row_indices) Delete one or more rows
Sheet.extend_rows(rows) Take ordereddict to extend named rows
Column access
Sheet.column_at(index) Gets the data at the specified column
Sheet.set_column_at(column_index, data_array) Updates a column data range
Sheet.delete_columns(column_indices) Delete one or more columns
Sheet.extend_columns(columns) Take ordereddict to extend named columns
Data series
Any column as row name
Sheet.name_columns_by_row(row_index) Use the elements of a specified row to represent individual columns
Sheet.rownames Return row names if any
Sheet.named_column_at(name) Get a column by its name
Sheet.set_named_column_at(name, column_array) Take the first row as column names
Sheet.delete_named_column_at(name) Works only after you named columns by a row
Any row as column name
Sheet.name_rows_by_column(column_index) Use the elements of a specified column to represent individual rows
Sheet.colnames Return column names if any
Sheet.named_row_at(name) Get a row by its name
Sheet.set_named_row_at(name, row_array) Take the first column as row names
Sheet.delete_named_row_at(name) Take the first column as row names
Conversion
Sheet.array Get/Set data in/from array format
Sheet.records Get/Set data in/from records format
Sheet.dict Get/Set data in/from dict format
Sheet.url Set data in url format
Sheet.csv Get/Set data in/from csv format
Sheet.tsv Get/Set data in/from tsv format
Sheet.csvz Get/Set data in/from csvz format
Sheet.tsvz Get/Set data in/from tsvz format
Sheet.xls Get/Set data in/from xls format
Sheet.xlsm Get/Set data in/from xlsm format
Sheet.xlsx Get/Set data in/from xlsx format
Sheet.ods Get/Set data in/from ods format
Sheet.stream Return a stream in which the content is properly encoded
Formatting
Sheet.format(formatter) Apply a formatting action for the whole sheet
Filtering
Sheet.filter([column_indices, row_indices]) Apply the filter with immediate effect
Transformation
Sheet.transpose() Rotate the data table by 90 degrees
Sheet.map(custom_function) Execute a function across all cells of the sheet
Sheet.region(topleft_corner, bottomright_corner) Get a rectangle shaped data out
Sheet.cut(topleft_corner, bottomright_corner) Get a rectangle shaped data out and clear them in position
Sheet.paste(topleft_corner[, rows, columns]) Paste a rectangle shaped data after a position
Save changes
Sheet.save_as(filename, **keywords) Save the content to a named file
Sheet.save_to_memory(file_type[, stream]) Save the content to memory
Sheet.save_to_database(session, table[, …]) Save data in sheet to database table
Sheet.save_to_django_model(model[, …]) Save to database table through django model

Internal API reference

This is intended for developers and hackers of pyexcel.

Data sheet representation

In inheritance order from parent to child

Matrix(array) The internal representation of a sheet data.
SheetStream(name, payload) Memory efficient sheet representation
BookStream([sheets, filename, path]) Memory efficient book representation

Row representation

Row(matrix) Represent row of a matrix

Column representation

Column(matrix) Represent columns of a matrix

Developer’s guide

Developer’s guide

Architecture

pyexcel uses loosely couple plugins to fullfil the promise to access various file formats. lml is the plugin management library that provide the specialized support for the loose coupling.

The components of pyexcel is designed as building blocks. For your project, you can cherry-pick the file format support without affecting the core functionality of pyexcel. Each plugin will bring in additional dependences. For example, if you choose pyexcel-xls, xlrd and xlwt will be brought in as 2nd level depndencies.

Looking at the following architectural diagram, pyexcel hosts plugin interfaces for data source, data renderer and data parser. pyexel-pygal, pyexcel-matplotlib, and pyexce-handsontable extend pyexcel using data renderer interface. pyexcel-io package takes away the responsibilities to interface with excel libraries, for example: xlrd, openpyxl, ezodf.

As in A list of file formats supported by external plugins, there are overlapping capabilities in reading and writing xlsx, ods files. Because each third parties express different personalities although they may read and write data in the same file format, you as the pyexcel is left to pick which suit your task best.

Dotted arrow means the package or module is loaded later.

_images/architecture.svg

Development steps for code changes

  1. git clone https://github.com/pyexcel/pyexcel.git
  2. cd pyexcel

Upgrade your setup tools and pip. They are needed for development and testing only:

  1. pip install –upgrade setuptools pip

Then install relevant development requirements:

  1. pip install -r rnd_requirements.txt # if such a file exists
  2. pip install -r requirements.txt
  3. pip install -r tests/requirements.txt

Once you have finished your changes, please provide test case(s), relevant documentation and update CHANGELOG.rst.

Note

As to rnd_requirements.txt, usually, it is created when a dependent library is not released. Once the dependecy is installed (will be released), the future version of the dependency in the requirements.txt will be valid.

How to test your contribution

Although nose and doctest are both used in code testing, it is adviable that unit tests are put in tests. doctest is incorporated only to make sure the code examples in documentation remain valid across different development releases.

On Linux/Unix systems, please launch your tests like this:

$ make

On Windows systems, please issue this command:

> test.bat

How to update test environment and update documentation

Additional steps are required:

  1. pip install moban
  2. git clone https://github.com/moremoban/setupmobans.git # generic setup
  3. git clone https://github.com/pyexcel/pyexcel-commons.git commons
  4. make your changes in .moban.d directory, then issue command moban

What is pyexcel-commons

Many information that are shared across pyexcel projects, such as: this developer guide, license info, etc. are stored in pyexcel-commons project.

What is .moban.d

.moban.d stores the specific meta data for the library.

Acceptance criteria

  1. Has Test cases written
  2. Has all code lines tested
  3. Passes all Travis CI builds
  4. Has fair amount of documentation if your change is complex
  5. Please update CHANGELOG.rst
  6. Please add yourself to CONTRIBUTORS.rst
  7. Agree on NEW BSD License for your contribution

How to log pyexcel

When developing source plugins, it becomes necessary to have log trace available. It helps find out what goes wrong quickly.

The basic step would be to set up logging before pyexcel import statement.

import logging
import logging.config
logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    level=logging.DEBUG)

import pyexcel

And if you would use a complex configuration, you can use the following code.

import logging
import logging.config
logging.config.fileConfig('log.conf')

import pyexcel

And then save the following content as log.conf in your directory:

[loggers]
keys=root, sources, renderers

[handlers]
keys=consoleHandler

[formatters]
keys=custom

[logger_root]
level=INFO
handlers=consoleHandler

[logger_sources]
level=DEBUG
handlers=consoleHandler
qualname=pyexcel.sources.factory
propagate=0

[logger_renderers]
level=DEBUG
handlers=consoleHandler
qualname=pyexcel.renderers.factory
propagate=0

[handler_consoleHandler]
class=StreamHandler
level=DEBUG
formatter=custom
args=(sys.stdout,)

[formatter_custom]
format=%(asctime)s - %(name)s - %(levelname)s - %(message)s
datefmt=

Packaging with PyInstaller

With pyexcel v0.5.0, the way to package it has been changed because it uses lml for all plugins.

And you need to do the same for pyexcel-io plugins too.

Built-in plugins of pyexcel

In order to package every built-in plugins of pyexcel-io, you need to specify:

--hidden-import pyexcel.plugins.renderers.sqlalchemy
--hidden-import pyexcel.plugins.renderers.django
--hidden-import pyexcel.plugins.renderers.excel
--hidden-import pyexcel.plugins.renderers._texttable
--hidden-import pyexcel.plugins.parsers.excel
--hidden-import pyexcel.plugins.parsers.sqlalchemy
--hidden-import pyexcel.plugins.sources.http
--hidden-import pyexcel.plugins.sources.file_input
--hidden-import pyexcel.plugins.sources.memory_input
--hidden-import pyexcel.plugins.sources.file_output
--hidden-import pyexcel.plugins.sources.output_to_memory
--hidden-import pyexcel.plugins.sources.pydata.bookdict
--hidden-import pyexcel.plugins.sources.pydata.dictsource
--hidden-import pyexcel.plugins.sources.pydata.arraysource
--hidden-import pyexcel.plugins.sources.pydata.records
--hidden-import pyexcel.plugins.sources.django
--hidden-import pyexcel.plugins.sources.sqlalchemy
--hidden-import pyexcel.plugins.sources.querysets

How to write a plugin for pyexcel

Note

Under writing. Stay tuned.

There are three types of plugins for pyexcel: data parser, data renderer and data source.

Tutorial

Let me walk you through the process of creating pyexcel-pdfr package.

Prerequisites:

  1. pip install moban yehua
  2. git clone https://github.com/moremoban/setupmobans.git # generic setup
  3. git clone https://github.com/pyexcel/pyexcel-commons.git

Let me assume that you have the work directory as:

setupmobans pyexcel-commons

and YOUR_WORK_DIRECTORY points to the base directory for both.

And then please export an environment variable:

export YEHUA_FILE=$YOUR_WORK_DIRECTORY/pyexcel-commons/yehua/yehua.yml

Now let’s get started.

Step 1

Call yehua to get the basic scaffolding:

$ yehua
Yehua will walk you through creating a pyexcel package.
Press ^C to quit at any time.

What is your project name? pyexcel-pdfr
What is the description? parses tables in pdf file as tabular data
What is project type?
1. pyexcel plugins
2. command line interface
3. python's C externsion
(1,2,3): 1
What is the nick name? pdf
$
Step 2

Call moban to inflate all project files:

$ cd pyexcel-pdfr/
$ ln -s ../pyexcel-commons/ commons
$ ln -s ../setupmobans/ setupmobans
$ moban
Templating README.rst to README.rst
Templating setup.py to setup.py
Templating requirements.txt to requirements.txt
Templating NEW_BSD_LICENSE.jj2 to LICENSE
Templating MANIFEST.in.jj2 to MANIFEST.in
Templating tests/requirements.txt to tests/requirements.txt
Templating test.script.jj2 to test.sh
Templating test.script.jj2 to test.bat
Templating travis.yml.jj2 to .travis.yml
Templating gitignore.jj2 to .gitignore
Templating docs/source/conf.py.jj2 to docs/source/conf.py
Step 3 - Coding

Please put your code in pyexcel_pdfr

Change log

Migrate away from 0.4.3

get_{{file_type}}_stream functions from pyexcel.Sheet and pyexcel.Book were introduced since 0.4.3 but were removed since 0.4.4. Please be advised to use save_to_memory functions, Sheet.io.{{file_type}} or Book.io.{{file_type}}.

Migrate from 0.2.x to 0.3.0+

Filtering and formatting behavior of pyexcel.Sheet are simplified. Soft filter and soft formatter are removed. Extra classes such as iterator, formatter, filter are removed.

Most of formatting tasks could be achieved using format() and map(). and Filtering with filter(). Formatting and filtering on row and/or column can be found with row() and column()

1. Updated filter function

There is no alternative to replace the following code:

sheet.filter(pe.OddRowFilter())

You will need to remove odd rows by yourself:

>>> import pyexcel as pe
>>> data = [
...  ['1'],
...  ['2'],
...  ['3'],
... ]
>>> sheet = pe.Sheet(data)
>>> to_remove = []
>>> for index in sheet.row_range():
...     if index % 2 == 0:
...         to_remove.append(index)
>>> sheet.filter(row_indices=to_remove)
>>> sheet
pyexcel sheet:
+---+
| 2 |
+---+

Or, you could do this:

>>> data = [
...  ['1'],
...  ['2'],
...  ['3'],
... ]
>>> sheet = pe.Sheet(data)
>>> def odd_filter(row_index, _):
...    return row_index % 2 == 0
>>> del sheet.row[odd_filter]
>>> sheet
pyexcel sheet:
+---+
| 2 |
+---+

And the same applies to EvenRowFilter, OddColumnFilter, EvenColumnFilter.

2. Updated format function

2.1 Replacement of sheetformatter

The following formatting code:

sheet.apply_formatter(pe.sheets.formatters.SheetFormatter(int))

can be replaced by:

sheet.format(int)
2.2 Replacement of row formatters

The following code:

row_formatter = pe.sheets.formatters.RowFormatter([1, 2], str)
sheet.add_formatter(row_formatter)

can be replaced by:

sheet.row.format([1, 2], str)
2.3 Replacement of column formatters

The following code:

f = NamedColumnFormatter(["Column 1", "Column 3"], str)
sheet.apply_formatter(f)

can be replaced by:

sheet.column.format(["Column 1", "Column 3"], str)

Migrate from 0.2.1 to 0.2.2+

1. Explicit imports, no longer needed

Please forget about these statements:

import pyexcel.ext.xls
import pyexcel.ext.ods
import pyexcel.ext.xlsx

They are no longer needed. As long as you have pip-installed them, they will be auto-loaded. However, if you do not want some of the plugins, please use pip to uninstall them.

What if you have your code as it is? No harm but a few warnings shown:

Deprecated usage since v0.2.2! Explicit import is no longer required. pyexcel.ext.ods is auto imported.

2. Invalid environment marker: platform_python_implementation==”PyPy”

Yes, it is a surprise. Please upgrade setuptools in your environment:

pip install --upgrade setuptools

At the time of writing, setuptools (18.0.1) or setuptools-21.0.0-py2.py3-none-any.whl is installed on author’s computer and worked.

3. How to keep both pyexcel-xls and pyexcel-xlsx

As in Issue 20, pyexcel-xls was used for xls and pyexcel-xlsx had to be used for xlsx. Both must co-exist due to requirements. The workaround would failed when auto-import are enabled in v0.2.2. Hence, user of pyexcel in this situation shall use ‘library’ parameter to all signature functions, to instruct pyexcel to use a named library for each function call.

4. pyexcel.get_io is no longer exposed

pyexcel.get_io was passed on from pyexcel-io. However, it is no longer exposed. Please use pyexcel_io.manager.RWManager.get_io if you have to.

You are likely to use pyexcel.get_io when you do pyexcel.Sheet.save_to_memory() or pyexcel.Book.save_to_memory() where you need to put in a io stream. But actually, with latest code, you could put in a None.

Migrate from 0.1.x to 0.2.x

1. “Writer” is gone, Please use save_as.

Here is a piece of legacy code:

w = pyexcel.Writer("afile.csv")
data=[['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 1.1, 1]]
w.write_array(table)
w.close()

The new code is:

>>> data=[['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 1.1, 1]]
>>> pyexcel.save_as(array=data, dest_file_name="afile.csv")

Here is another piece of legacy code:

content = {
    "X": [1,2,3,4,5],
    "Y": [6,7,8,9,10],
    "Z": [11,12,13,14,15],
}
w = pyexcel.Writer("afile.csv")
w.write_dict(self.content)
w.close()

The new code is:

>>> content = {
...     "X": [1,2,3,4,5],
...     "Y": [6,7,8,9,10],
...     "Z": [11,12,13,14,15],
... }
>>> pyexcel.save_as(adict=content, dest_file_name="afile.csv")

Here is yet another piece of legacy code:

data = [
    [1, 2, 3],
    [4, 5, 6]
]
io = StringIO()
w = pyexcel.Writer(("csv",io))
w.write_rows(data)
w.close()

The new code is:

>>> data = [
...     [1, 2, 3],
...     [4, 5, 6]
... ]
>>> io = pyexcel.save_as(dest_file_type='csv', array=data)
>>> for line in io.readlines():
...     print(line.rstrip())
1,2,3
4,5,6

2. “BookWriter” is gone. Please use save_book_as.

Here is a piece of legacy code:

import pyexcel
content = {
         "Sheet1": [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]],
         "Sheet2": [[4, 4, 4, 4], [5, 5, 5, 5], [6, 6, 6, 6]],
         "Sheet3": [[u'X', u'Y', u'Z'], [1, 4, 7], [2, 5, 8], [3, 6, 9]]
     }
w = pyexcel.BookWriter("afile.csv")
w.write_book_from_dict(content)
w.close()

The replacement code is:

>>> import pyexcel
>>> content = {
...          "Sheet1": [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]],
...          "Sheet2": [[4, 4, 4, 4], [5, 5, 5, 5], [6, 6, 6, 6]],
...          "Sheet3": [[u'X', u'Y', u'Z'], [1, 4, 7], [2, 5, 8], [3, 6, 9]]
...      }
>>> pyexcel.save_book_as(bookdict=content, dest_file_name="afile.csv")

Change log

0.6.0 - unreleased

Planned
  1. investigate if hidden columns could be supported
  2. update cookbook.py using 0.5.0 api
  3. refactor test code
  4. suppert missing pandas io features: use custom boolean values, write stylish spreadsheets.
Added
  1. implemented __len__ for pyexcel.Book and pyexcel.Sheet. Suddenly, Sheet could be a argument of Sheet. So Sheet(Sheet([[1, 2]])) works.
  2. pyexcel.Sheet.group_rows_by_column(), helps categorize similiar fields into a book.
  3. __version__ at module level is finally available
  4. Presentation won’t break if there is an object (list, dict). Un-recognized type will be json dumped.
  5. Sheet.column.column_name, Sheet.row.row_name is added to provide a short cut for Sheet.column[‘column name’], Sheet.row[‘row name’]. In total, 4 characters less in typing.
  6. plotting functions using echarts are integrated.
Updated
  1. custom_headers for iget_records could have extra headers that does not exist in the current excel sheet. It helps header normalization across multiple excel sheets that has various headers. In this situation, what you may have done is to get a union of all headers in all excel sheets, then try get records from each sheet and merge all.
  2. pyexcel.Sheet.to_records(), pyexcel.get_records, pyexcel.Sheet.records returns a generator instead of a static array of dictionary.

0.5.6 - 23.10.2017

Removed
  1. #105, remove gease from setup_requires, introduced by 0.5.5.
  2. removed testing against python 2.6

0.5.5 - 20.10.2017

Added
  1. #103, include LICENSE file in MANIFEST.in, meaning LICENSE file will appear in the released tar ball.

0.5.4 - 27.09.2017

Updated
  1. #100, Sheet.to_dict() gets out of range error because there is only one row.
  2. Updated the baseline of pyexcel-io to 0.5.1.

0.5.3 - 01-08-2017

Updated
  1. #95, respect the order of records in iget_records, isave_as and save_as.
  2. #97, new feature to allow intuitive initialization of pyexcel.Book.

0.5.2 - 26-07-2017

Updated
  1. embeded the enabler for pyexcel-htmlr. http source does not support text/html as mime type.

0.5.1 - 12.06.2017

Updated
  1. support saving SheetStream and BookStream to database targets. This is needed for pyexcel-webio and its downstream projects.

0.5.0 - 19.06.2017

Added
  1. Sheet.top() and Sheet.top_left() for data browsing
  2. add html as default rich display in Jupyter notebook when pyexcel-text and pyexcel-chart is installed
  3. add svg as default rich display in Jupyter notebook when pyexcel-chart and one of its implementation plugin(pyexcel-pygal, etc.) are is installed
  4. new dictionary source supported: a dictionary of key value pair could be read into a sheet.
  5. added dynamic external plugin loading. meaning if a pyexcel plugin is installed, it will be loaded implicitly. And this change would remove unnecessary info log for those who do not use pyexcel-text and pyexcel-gal
  6. save_book_as before 0.5.0 becomes isave_book_as and save_book_as in 0.5.0 convert BookStream to Book before saving.
  7. #83, file closing mechanism is enfored. free_resource is added and it should be called when iget_array, iget_records, isave_as and/or isave_book_as are used.
Updated
  1. array is passed to pyexcel.Sheet as reference. it means your array data will be modified.
Removed
  1. pyexcel.Writer and pyexcel.BookWriter were removed
  2. pyexcel.load_book_from_sql and pyexcel.load_from_sql were removed
  3. pyexcel.deprecated.load_from_query_sets, pyexcel.deprecated.load_book_from_django_models and pyexcel.deprecated.load_from_django_model were removed
  4. Removed plugin loading code and lml is used instead

0.4.5 - 17.03.2017

Updated
  1. #80: remove pyexcel-chart import from v0.4.x

0.4.4 - 06.02.2017

Updated
  1. #68: regression save_to_memory() should have returned a stream instance which has been reset to zero if possible. The exception is sys.stdout, which cannot be reset.
  2. #74: Not able to handle decimal.Decimal
Removed
  1. remove get_{{file_type}}_stream functions from pyexcel.Sheet and pyexcel.Book introduced since 0.4.3.

0.4.3 - 26.01.2017

Added
  1. ‘.stream’ attribute are attached to ~pyexcel.Sheet and ~pyexcel.Book to get direct access the underneath stream in responding to file type attributes, such as sheet.xls. it helps provide a custom stream to external world, for example, Sheet.stream.csv gives a text stream that contains csv formatted data. Book.stream.xls returns a xls format data in a byte stream.
Updated
  1. Better error reporting when an unknown parameters or unsupported file types were given to the signature functions.

0.4.2 - 17.01.2017

Updated
  1. Raise exception if the incoming sheet does not have column names. In other words, only sheet with column names could be saved to database. sheet with row names cannot be saved. The alternative is to transpose the sheet, then name_columns_by_row and then save.
  2. fix iget_records where a non-uniform content should be given, e.g. [[“x”, “y”], [1, 2], [3]], some record would become non-uniform, e.g. key ‘y’ would be missing from the second record.
  3. skip_empty_rows is applicable when saving a python data structure to another data source. For example, if your array contains a row which is consisted of empty string, such as [‘’, ‘’, ‘’ … ‘’], please specify skip_empty_rows=False in order to preserve it. This becomes subtle when you try save a python dictionary where empty rows is not easy to be spotted.
  4. #69: better documentation for save_book_as.

0.4.1 - 23.12.2016

Updated
  1. #68: regression save_to_memory() should have returned a stream instance.

0.4.0 - 22.12.2016

Added
  1. Flask-Excel#19 allow sheet_name parameter
  2. pyexcel-xls#11 case-insensitive for file_type. xls and XLS are treated in the same way
Updated
  1. #66: export_columns is ignored
  2. Update dependency on pyexcel-io v0.3.0

0.3.3 - 07.11.2016

Updated
  1. #63: cannot display empty sheet(hence book with empty sheet) as texttable

0.3.2 - 02.11.2016

Updated
  1. #62: optional module import error become visible.

0.3.0 - 28.10.2016

Added:
  1. file type setters for Sheet and Book, and its documentation
  2. iget_records returns a generator for a list of records and should have better memory performance, especially dealing with large csv files.
  3. iget_array returns a generator for a list of two dimensional array and should have better memory performance, especially dealing with large csv files.
  4. Enable pagination support, and custom row renderer via pyexcel-io v0.2.3
Updated
  1. Take isave_as out from save_as. Hence two functions are there for save a sheet as
  2. #60: encode ‘utf-8’ if the console is of ascii encoding.
  3. #59: custom row renderer
  4. #56: set cell value does not work
  5. pyexcel.transpose becomes pyexcel.sheets.transpose
  6. iterator functions of pyexcel.Sheet were converted to generator functions
    • pyexcel.Sheet.enumerate()
    • pyexcel.Sheet.reverse()
    • pyexcel.Sheet.vertical()
    • pyexcel.Sheet.rvertical()
    • pyexcel.Sheet.rows()
    • pyexcel.Sheet.rrows()
    • pyexcel.Sheet.columns()
    • pyexcel.Sheet.rcolumns()
    • pyexcel.Sheet.named_rows()
    • pyexcel.Sheet.named_columns()
  7. ~pyexcel.Sheet.save_to_memory and ~pyexcel.Book.save_to_memory return the actual content. No longer they will return a io object hence you cannot call getvalue() on them.
Removed:
  1. content and out_file as function parameters to the signature functions are no longer supported.
  2. SourceFactory and RendererFactory are removed
  3. The following methods are removed
    • pyexcel.to_array
    • pyexcel.to_dict
    • pyexcel.utils.to_one_dimensional_array
    • pyexcel.dict_to_array
    • pyexcel.from_records
    • pyexcel.to_records
  4. pyexcel.Sheet.filter has been re-implemented and all filters were removed:
    • pyexcel.filters.ColumnIndexFilter
    • pyexcel.filters.ColumnFilter
    • pyexcel.filters.RowFilter
    • pyexcel.filters.EvenColumnFilter
    • pyexcel.filters.OddColumnFilter
    • pyexcel.filters.EvenRowFilter
    • pyexcel.filters.OddRowFilter
    • pyexcel.filters.RowIndexFilter
    • pyexcel.filters.SingleColumnFilter
    • pyexcel.filters.RowValueFilter
    • pyexcel.filters.NamedRowValueFilter
    • pyexcel.filters.ColumnValueFilter
    • pyexcel.filters.NamedColumnValueFilter
    • pyexcel.filters.SingleRowFilter
  5. the following functions have been removed
    • add_formatter
    • remove_formatter
    • clear_formatters
    • freeze_formatters
    • add_filter
    • remove_filter
    • clear_filters
    • freeze_formatters
  6. pyexcel.Sheet.filter has been re-implemented and all filters were removed:
    • pyexcel.formatters.SheetFormatter

0.2.5 - 31.08.2016

Updated:
  1. #58: texttable should have been made as compulsory requirement

0.2.4 - 14.07.2016

Updated:
  1. For python 2, writing to sys.stdout by pyexcel-cli raise IOError.

0.2.3 - 11.07.2016

Updated:
  1. For python 3, do not seek 0 when saving to memory if sys.stdout is passed on. Hence, adding support for sys.stdin and sys.stdout.

0.2.2 - 01.06.2016

Updated:
  1. Explicit imports, no longer needed
  2. Depends on latest setuptools 18.0.1
  3. NotImplementedError will be raised if parameters to core functions are not supported, e.g. get_sheet(cannot_find_me_option=”will be thrown out as NotImplementedError”)

0.2.1 - 23.04.2016

Added:
  1. add pyexcel-text file types as attributes of pyexcel.Sheet and pyexcel.Book, related to #31
  2. auto import pyexcel-text if it is pip installed
Updated:
  1. code refactoring done for easy addition of sources.
  2. bug fix #29, Even if the format is a string it is displayed as a float
  3. pyexcel-text is no longer a plugin to pyexcel-io but to pyexcel.sources, see pyexcel-text#22
Removed:
  1. pyexcel.presentation is removed. No longer the internal decorate @outsource is used. related to #31

0.2.0 - 17.01.2016

Updated
  1. adopt pyexcel-io yield key word to return generator as content
  2. pyexcel.save_as and pyexcel.save_book_as get performance improvements

0.1.7 - 03.07.2015

Added
  1. Support pyramid-excel which does the database commit on its own.

0.1.6 - 13.06.2015

Added
  1. get excel data from a http url

0.0.13 - 07.02.2015

Added
  1. Support django
  2. texttable as default renderer

0.0.12 - 25.01.2015

Added
  1. Added sqlalchemy support

0.0.10 - 15.12.2015

Added
  1. added csvz and tsvz format

0.0.4 - 12.10.2014

Updated
  1. Support python 3

0.0.1 - 14.09.2014

Added
  1. read and write csv, ods, xls, xlsx and xlsm files(which are referred later as excel files)
  2. various iterators for the reader
  3. row and column filters for the reader
  4. utilities to get array and dictionary out from excel files.
  5. cookbok receipes for some common and simple usage of this library.

Indices and tables