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A wrapper library that provides one API to read, manipulate and writedata in different excel formats

Project description

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Support the project

If your company has embedded pyexcel and its components into a revenue generating product, please support me on github, patreon or bounty source to maintain the project and develop it further.

If you are an individual, you are welcome to support me too and for however long you feel like. As my backer, you will receive early access to pyexcel related contents.

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

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

Known constraints

Fonts, colors and charts are not supported.

Introduction

Feature Highlights

https://github.com/pyexcel/pyexcel/raw/dev/docs/source/_static/images/architecture.svg
  1. One application programming interface(API) to handle multiple data sources:
    • physical file
    • memory file
    • SQLAlchemy table
    • Django Model
    • Python data structures: dictionary, records and array
  2. One API to read and write data in various excel file formats.
  3. For large data sets, data streaming are supported. A genenerator can be returned to you. Checkout iget_records, iget_array, isave_as and isave_book_as.

Installation

You can install pyexcel via pip:

$ pip install pyexcel

or clone it and install it:

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

One liners

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

Read from the excel files

Get a list of dictionaries

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

Top 5 coffeine drinks:

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

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

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

And let’s check what do we have:

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

Get two dimensional array

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

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

where start_row skips the header row.

Get a dictionary

You can get a dictionary too:

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

>>> 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:

pyexcel:Sheet 1:

1 2 3
4 5 6
7 8 9

pyexcel:Sheet 2:

X Y Z
1 2 3
4 5 6

pyexcel:Sheet 3:

O P Q
3 2 1
4 3 2

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

>>> 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]]}

Write data

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.

Transcoding

Note

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

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

>>> 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

Hidden feature: partial read

Most pyexcel users do not know, but other library users were requesting the similar features

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

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

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

Why did not I see above benefit?

This feature depends heavily on the implementation details.

pyexcel-xls (xlrd), pyexcel-xlsx (openpyxl), pyexcel-ods (odfpy) and pyexcel-ods3 (pyexcel-ezodf) will read all data into memory. Because xls, xlsx and ods file are effective a zipped folder, all four will unzip the folder and read the content in xml format in full, so as to make sense of all details.

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

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

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

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

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

And let’s pretend to read partial data:

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

And you could as well do the same for columns:

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

Obvious, you could do both at the same time:

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

The pagination support is available across all pyexcel plugins.

Note

No column pagination support for query sets as data source.

Formatting while transcoding a big data file

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

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

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

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

Then pass it onto save_as function using row_renderer:

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

Note

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

We can verify if it was done correctly:

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

Stream APIs for big file : A set of two liners

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

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

Two liners for get data from big excel files

Get a list of dictionaries

Suppose you want to process the following coffee data again:

Top 5 coffeine drinks:

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

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

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

And let’s check what do we have:

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

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

>>> 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(f"{row[1]} of {row[0]} has {row[2]} mg")
venti(20 oz) of Starbucks Coffee Blonde Roast has 475 mg
large(20 oz.) of Dunkin' Donuts Coffee with Turbo Shot has 398 mg
grande(16 oz.) of Starbucks Coffee Pike Place Roast has 310 mg
regular(16 oz.) of Panera Coffee Light Roast has 300 mg

Again, do not forgot the second line:

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

Available Plugins

A list of file formats supported by external plugins
Package name Supported file formats Dependencies Python versions
pyexcel-io >=v0.6.0 csv, csvz [1], tsv, tsvz [2]   3.6+
pyexcel-io <=0.5.20 same as above   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-xlsbr xlsx(read only) pyxlsb same as above
pyexcel-odsr read only for ods, fods lxml same as above
pyexcel-odsw write only for ods loxun same as above
pyexcel-htmlr html(read only) lxml,html5lib same as above
pyexcel-pdfr pdf(read only) pdftables Python 2 only.

Plugin shopping guide

Except csv files, xls, xlsx and ods files are a zip of a folder containing a lot of xml files

The dedicated readers for excel files can stream read

In order to manage the list of plugins installed, you need to use pip to add or remove a plugin. When you use virtualenv, you can have different plugins per virtual environment. In the situation where you have multiple plugins that does the same thing in your environment, you need to tell pyexcel which plugin to use per function call. For example, pyexcel-ods and pyexcel-odsr, and you want to get_array to use pyexcel-odsr. You need to append get_array(…, library=’pyexcel-odsr’).

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

Footnotes

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

Acknowledgement

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

License

New BSD License

Change log

0.6.5 - 8.10.2020

Updated

  1. update queryset source to work with pyexcel-io 0.6.0

0.6.4 - 18.08.2020

Updated

  1. #219: book created from dict no longer discards order.

0.6.3 - 01.08.2020

fixed

  1. #214: remove leading and trailing whitespace for column names

removed

  1. python 2 compatibility have been permanently removed.

0.6.2 - 8.06.2020

fixed

  1. #109: Control the column order when write the data output

0.6.1 - 02.05.2020

fixed

  1. #203: texttable was dropped out in 0.6.0 as compulsary dependency. end user may experience it when a sheet/table is printed in a shell. otherwise, new user of pyexcel won’t see it. As of release date, no issues were created

0.6.0 - 21.04.2020

updated

  1. #199: += in place; = + shall return new instance
  2. #195: documentation update. however small is welcome

removed

  1. Dropping the test support for python version lower than 3.6. v0.6.0 should work with python 2.7 but is not guaranteed to work. Please upgrade to python 3.6+.

0.5.15 - 07.07.2019

updated

  1. #185: fix a bug with http data source. The real fix lies in pyexcel-io v0.5.19. this release just put the version requirement in.

0.5.14 - 12.06.2019

updated

  1. #182: support dest_force_file_type on save_as and save_book_as

0.5.13 - 12.03.2019

updated

  1. #176: get_sheet {IndexError}list index out of range // XLSX can’t be opened

0.5.12 - 25.02.2019

updated

  1. #174: include examples in tarbar

0.5.11 - 22.02.2019

updated

  1. #169: remove pyexcel-handsontalbe in test
  2. add tests, and docs folder in distribution

0.5.10 - 3.12.2018

updated

  1. #157: Please use scan_plugins_regex, which lml 0.7 complains about
  2. updated dependency on pyexcel-io to 0.5.11

0.5.9.1 - 30.08.2018

updated

  1. to require pyexcel-io 0.5.9.1 and use lml at least version 0.0.2

0.5.9 - 30.08.2018

added

  1. support __len__. len(book) returns the number of sheets and len(sheet) returns the number of rows
  2. #144: memory-efficient way to read sheet names.
  3. #148: force_file_type is introduced. When reading a file on a disk, this parameter allows you to choose a reader. i.e. csv reader for a text file. xlsx reader for a xlsx file but with .blob file suffix.
  4. finally, pyexcel got import pyexcel.__version__

updated

  1. Sheet.to_records() returns a generator now, saving memory
  2. #115, Fix set membership test to run faster in python2
  3. #140, Direct writes to cells yield weird results

0.5.8 - unreleased

added

  1. #125, sort book sheets

updated

  1. #126, dest_sheet_name in save_as will set the sheet name in the output
  2. #115, Fix set membership test to run faster in python2

0.5.7 - 11.01.2018

added

  1. pyexcel-io#46, expose bulk_save to developer.

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
  3. #103, include LICENSE file in MANIFEST.in, meaning LICENSE file will appear in the released tar ball.

0.5.5 - 20.10.2017

removed

  1. #105, remove gease from setup_requires, introduced by 0.5.5.
  2. removed testing against python 2.6
  3. #103, include LICENSE file in MANIFEST.in, meaning LICENSE file will appear in the released tar ball.

0.5.4 - 27.09.2017

fixed

  1. #100, Sheet.to_dict() gets out of range error because there is only one row.

updated

  1. Updated the baseline of pyexcel-io to 0.5.1.

0.5.3 - 01-08-2017

added

  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

Features:

  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.

Project details


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