pyexcel - Let you focus on data, instead of file formats
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.
Nor to read password protected xls, xlsx and ods files.
Introduction
Feature Highlights
| file format | definition |
|---|---|
| csv | comma separated values |
| tsv | tab separated values |
| csvz | a zip file that contains one or many csv files |
| tsvz | a zip file that contains one or many tsv files |
| xls | a spreadsheet file format created by MS-Excel 97-2003 [] |
| xlsx | MS-Excel Extensions to the Office Open XML SpreadsheetML File Format. [] |
| xlsm | an MS-Excel Macro-Enabled Workbook file |
| ods | open document spreadsheet |
| fods | flat open document spreadsheet |
| json | java script object notation |
| html | html table of the data structure |
| simple | simple presentation |
| rst | rStructured Text presentation of the data |
| mediawiki | media wiki table |
| [f3] | quoted from whatis.com. Technical details can be found at MSDN XLS |
| [f4] | xlsx is used by MS-Excel 2007, more information can be found at MSDN XLSX |
- 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
- One API to read and write data in various excel file formats.
- 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
| Package name | Supported file formats | Dependencies |
|---|---|---|
| pyexcel-io | csv, csvz [1], tsv, tsvz [2] | |
| pyexcel-xls | xls, xlsx(read only), xlsm(read only) | xlrd, xlwt |
| pyexcel-xlsx | xlsx | openpyxl |
| pyexcel-ods3 | ods | pyexcel-ezodf, lxml |
| pyexcel-ods | ods | odfpy |
| Package name | Supported file formats | Dependencies |
|---|---|---|
| pyexcel-xlsxw | xlsx(write only) | XlsxWriter |
| pyexcel-libxlsxw | xlsx(write only) | libxlsxwriter |
| pyexcel-xlsxr | xlsx(read only) | lxml |
| pyexcel-xlsbr | xlsb(read only) | pyxlsb |
| pyexcel-odsr | read only for ods, fods | lxml |
| pyexcel-odsw | write only for ods | loxun |
| pyexcel-htmlr | html(read only) | lxml,html5lib |
| pyexcel-pdfr | pdf(read only) | camelot |
Plugin shopping guide
Since 2020, all pyexcel-io plugins have dropped the support for python version lower than 3.6. If you want to use any python verions, please use pyexcel-io and its plugins version lower than 0.6.0.
Except csv files, xls, xlsx and ods files are a zip of a folder containing a lot of xml files
The dedicated readers for excel files can stream read
In order to manage the list of plugins installed, you need to use pip to add or remove a plugin. When you use virtualenv, you can have different plugins per virtual environment. In the situation where you have multiple plugins that does the same thing in your environment, you need to tell pyexcel which plugin to use per function call. For example, pyexcel-ods and pyexcel-odsr, and you want to get_array to use pyexcel-odsr. You need to append get_array(..., library='pyexcel-odsr').
| 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
4.2k Dec 30, 2022
1k Dec 24, 2022
448 Dec 28, 2022
3.1k Dec 29, 2022
30 Jan 8, 2023
0 Feb 26, 2022
5.2k Dec 31, 2022
1 Nov 13, 2021
37 Apr 16, 2022
2.5k Jan 6, 2023
7 Jun 14, 2021
1 Dec 7, 2021
247 Dec 27, 2022
3 Jan 25, 2022
2 Feb 18, 2022
1 Nov 7, 2021
2.9k Jan 1, 2023