Single API for reading, manipulating and writing data in csv, ods, xls, xlsx and xlsm files
OTHER License
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Fonts, colors and charts are not supported.
Nor to read password protected xls, xlsx and ods files.
.. table:: A list of supported file formats
============ =======================================================
file format definition
============ =======================================================
csv comma separated values
tsv tab separated values
csvz a zip file that contains one or many csv files
tsvz a zip file that contains one or many tsv files
xls a spreadsheet file format created by
MS-Excel 97-2003
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
============ =======================================================
.. image:: https://github.com/pyexcel/pyexcel/raw/dev/docs/source/_static/images/architecture.svg
One application programming interface(API) to handle multiple data sources:
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.
You can install pyexcel via pip:
.. code-block:: bash
$ pip install pyexcel
or clone it and install it:
.. code-block:: bash
$ git clone https://github.com/pyexcel/pyexcel.git
$ cd pyexcel
$ python setup.py install
This section shows you how to get data from your excel files and how to export data to excel files in one line
Get a list of dictionaries
Suppose you want to process History of Classical Music <https://www.naxos.com/education/brief_history.asp>
_:
History of Classical Music:
=============== ============= ==================================== Name Period Representative Composers Medieval c.1150-c.1400 Machaut, Landini Renaissance c.1400-c.1600 Gibbons, Frescobaldi Baroque c.1600-c.1750 JS Bach, Vivaldi Classical c.1750-c.1830 Joseph Haydn, Wolfgan Amadeus Mozart Early Romantic c.1830-c.1860 Chopin, Mendelssohn, Schumann, Liszt Late Romantic c.1860-c.1920 Wagner,Verdi Modernist 20th century Sergei Rachmaninoff,Calude Debussy =============== ============= ====================================
Let's get a list of dictionary out from the xls file:
.. code-block:: python
records = p.get_records(file_name="your_file.xls")
And let's check what do we have:
.. code-block:: python
for row in records: ... print(f"{row['Representative Composers']} are from {row['Name']} period ({row['Period']})") Machaut, Landini are from Medieval period (c.1150-c.1400) Gibbons, Frescobaldi are from Renaissance period (c.1400-c.1600) JS Bach, Vivaldi are from Baroque period (c.1600-c.1750) Joseph Haydn, Wolfgan Amadeus Mozart are from Classical period (c.1750-c.1830) Chopin, Mendelssohn, Schumann, Liszt are from Early Romantic period (c.1830-c.1860) Wagner,Verdi are from Late Romantic period (c.1860-c.1920) Sergei Rachmaninoff,Calude Debussy are from Modernist period (20th century)
Get two dimensional array
Instead, what if you have to use pyexcel.get_array
to do the same:
.. code-block:: python
for row in p.get_array(file_name="your_file.xls", start_row=1): ... print(f"{row[2]} are from {row[0]} period ({row[1]})") Machaut, Landini are from Medieval period (c.1150-c.1400) Gibbons, Frescobaldi are from Renaissance period (c.1400-c.1600) JS Bach, Vivaldi are from Baroque period (c.1600-c.1750) Joseph Haydn, Wolfgan Amadeus Mozart are from Classical period (c.1750-c.1830) Chopin, Mendelssohn, Schumann, Liszt are from Early Romantic period (c.1830-c.1860) Wagner,Verdi are from Late Romantic period (c.1860-c.1920) Sergei Rachmaninoff,Calude Debussy are from Modernist period (20th century)
where start_row
skips the header row.
Get a dictionary
You can get a dictionary too:
.. code-block:: python
my_dict = p.get_dict(file_name="your_file.xls", name_columns_by_row=0)
And let's have a look inside:
.. code-block:: python
from pyexcel._compact import OrderedDict isinstance(my_dict, OrderedDict) True for key, values in my_dict.items(): ... print(key + " : " + ','.join([str(item) for item in values])) Name : Medieval,Renaissance,Baroque,Classical,Early Romantic,Late Romantic,Modernist Period : c.1150-c.1400,c.1400-c.1600,c.1600-c.1750,c.1750-c.1830,c.1830-c.1860,c.1860-c.1920,20th century Representative Composers : Machaut, Landini,Gibbons, Frescobaldi,JS Bach, Vivaldi,Joseph Haydn, Wolfgan Amadeus Mozart,Chopin, Mendelssohn, Schumann, Liszt,Wagner,Verdi,Sergei Rachmaninoff,Calude Debussy
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:
Top Violinist:
================= ========= ================ Name Period Nationality Antonio Vivaldi 1678-1741 Italian Niccolo Paganini 1782-1840 Italian Pablo de Sarasate 1852-1904 Spainish Eugene Ysaye 1858-1931 Belgian Fritz Kreisler 1875-1962 Astria-American Jascha Heifetz 1901-1987 Russian-American David Oistrakh 1908-1974 Russian Yehundi Menuhin 1916-1999 American Itzhak Perlman 1945- Israeli-American Hilary Hahn 1979- American ================= ========= ================
Noteable Violin Makers:
====================== ========= ================ Maker Period Country Antonio Stradivari 1644-1737 Cremona, Italy Giovanni Paolo Maggini 1580-1630 Botticino, Italy Amati Family 1500-1740 Cremona, Italy Guarneri Family 1626-1744 Cremona, Italy Rugeri Family 1628-1719 Cremona, Italy Carlo Bergonzi 1683-1747 Cremona, Italy Jacob Stainer 1617-1683 Austria ====================== ========= ================
Most Expensive Violins:
===================== =============== =================================== Name Estimated Value Location Messiah Stradivarious $ 20,000,000 Ashmolean Museum in Oxford, England Vieuxtemps Guarneri $ 16,000,000 On loan to Anne Akiko Meyers Lady Blunt $ 15,900,000 Anonymous bidder ===================== =============== ===================================
Here is the code to obtain those sheets as a single dictionary:
.. code-block:: python
book_dict = p.get_book_dict(file_name="book.xls")
And check:
.. code-block:: python
isinstance(book_dict, OrderedDict) True import json for key, item in book_dict.items(): ... print(json.dumps({key: item})) {"Most Expensive Violins": [["Name", "Estimated Value", "Location"], ["Messiah Stradivarious", "$ 20,000,000", "Ashmolean Museum in Oxford, England"], ["Vieuxtemps Guarneri", "$ 16,000,000", "On loan to Anne Akiko Meyers"], ["Lady Blunt", "$ 15,900,000", "Anonymous bidder"]]} {"Noteable Violin Makers": [["Maker", "Period", "Country"], ["Antonio Stradivari", "1644-1737", "Cremona, Italy"], ["Giovanni Paolo Maggini", "1580-1630", "Botticino, Italy"], ["Amati Family", "1500-1740", "Cremona, Italy"], ["Guarneri Family", "1626-1744", "Cremona, Italy"], ["Rugeri Family", "1628-1719", "Cremona, Italy"], ["Carlo Bergonzi", "1683-1747", "Cremona, Italy"], ["Jacob Stainer", "1617-1683", "Austria"]]} {"Top Violinist": [["Name", "Period", "Nationality"], ["Antonio Vivaldi", "1678-1741", "Italian"], ["Niccolo Paganini", "1782-1840", "Italian"], ["Pablo de Sarasate", "1852-1904", "Spainish"], ["Eugene Ysaye", "1858-1931", "Belgian"], ["Fritz Kreisler", "1875-1962", "Astria-American"], ["Jascha Heifetz", "1901-1987", "Russian-American"], ["David Oistrakh", "1908-1974", "Russian"], ["Yehundi Menuhin", "1916-1999", "American"], ["Itzhak Perlman", "1945-", "Israeli-American"], ["Hilary Hahn", "1979-", "American"]]}
Export an array
Suppose you have the following array:
.. code-block:: python
data = [['G', 'D', 'A', 'E'], ['Thomastik-Infield Domaints', 'Thomastik-Infield Domaints', 'Thomastik-Infield Domaints', 'Pirastro'], ['Silver wound', '', 'Aluminum wound', 'Gold Label Steel']]
And here is the code to save it as an excel file :
.. code-block:: python
p.save_as(array=data, dest_file_name="example.xls")
Let's verify it:
.. code-block:: python
>>> p.get_sheet(file_name="example.xls")
pyexcel_sheet1:
+----------------------------+----------------------------+----------------------------+------------------+
| G | D | A | E |
+----------------------------+----------------------------+----------------------------+------------------+
| Thomastik-Infield Domaints | Thomastik-Infield Domaints | Thomastik-Infield Domaints | Pirastro |
+----------------------------+----------------------------+----------------------------+------------------+
| Silver wound | | Aluminum wound | Gold Label Steel |
+----------------------------+----------------------------+----------------------------+------------------+
And here is the code to save it as a csv file :
.. code-block:: python
p.save_as(array=data, ... dest_file_name="example.csv", ... dest_delimiter=':')
Let's verify it:
.. code-block:: python
>>> with open("example.csv") as f:
... for line in f.readlines():
... print(line.rstrip())
...
G:D:A:E
Thomastik-Infield Domaints:Thomastik-Infield Domaints:Thomastik-Infield Domaints:Pirastro
Silver wound::Aluminum wound:Gold Label Steel
Export a list of dictionaries
.. code-block:: python
>>> records = [
... {"year": 1903, "country": "Germany", "speed": "206.7km/h"},
... {"year": 1964, "country": "Japan", "speed": "210km/h"},
... {"year": 2008, "country": "China", "speed": "350km/h"}
... ]
>>> p.save_as(records=records, dest_file_name='high_speed_rail.xls')
Export a dictionary of single key value pair
.. code-block:: python
>>> henley_on_thames_facts = {
... "area": "5.58 square meters",
... "population": "11,619",
... "civial parish": "Henley-on-Thames",
... "latitude": "51.536",
... "longitude": "-0.898"
... }
>>> p.save_as(adict=henley_on_thames_facts, dest_file_name='henley.xlsx')
Export a dictionary of single dimensonal array
.. code-block:: python
>>> ccs_insights = {
... "year": ["2017", "2018", "2019", "2020", "2021"],
... "smart phones": [1.53, 1.64, 1.74, 1.82, 1.90],
... "feature phones": [0.46, 0.38, 0.30, 0.23, 0.17]
... }
>>> p.save_as(adict=ccs_insights, dest_file_name='ccs.csv')
Export a dictionary of two dimensional array as a book
Suppose you want to save the below dictionary to an excel file :
.. code-block:: python
a_dictionary_of_two_dimensional_arrays = { ... 'Sheet 1': ... [ ... [1.0, 2.0, 3.0], ... [4.0, 5.0, 6.0], ... [7.0, 8.0, 9.0] ... ], ... 'Sheet 2': ... [ ... ['X', 'Y', 'Z'], ... [1.0, 2.0, 3.0], ... [4.0, 5.0, 6.0] ... ], ... 'Sheet 3': ... [ ... ['O', 'P', 'Q'], ... [3.0, 2.0, 1.0], ... [4.0, 3.0, 2.0] ... ] ... }
Here is the code:
.. code-block:: python
p.save_book_as( ... bookdict=a_dictionary_of_two_dimensional_arrays, ... dest_file_name="book.xls" ... )
If you want to preserve the order of sheets in your dictionary, you have to pass on an ordered dictionary to the function itself. For example:
.. code-block:: python
data = OrderedDict() data.update({"Sheet 2": a_dictionary_of_two_dimensional_arrays['Sheet 2']}) data.update({"Sheet 1": a_dictionary_of_two_dimensional_arrays['Sheet 1']}) data.update({"Sheet 3": a_dictionary_of_two_dimensional_arrays['Sheet 3']}) p.save_book_as(bookdict=data, dest_file_name="book.xls")
Let's verify its order:
.. code-block:: python
book_dict = p.get_book_dict(file_name="book.xls") for key, item in book_dict.items(): ... print(json.dumps({key: item})) {"Sheet 2": [["X", "Y", "Z"], [1, 2, 3], [4, 5, 6]]} {"Sheet 1": [[1, 2, 3], [4, 5, 6], [7, 8, 9]]} {"Sheet 3": [["O", "P", "Q"], [3, 2, 1], [4, 3, 2]]}
Please notice that "Sheet 2" is the first item in the book_dict, meaning the order of sheets are preserved.
.. note::
Please note that pyexcel-cli
can perform file transcoding at command line.
No need to open your editor, save the problem, then python run.
The following code does a simple file format transcoding from xls to csv:
.. code-block:: python
p.save_as(file_name="birth.xls", dest_file_name="birth.csv")
Again it is really simple. Let's verify what we have gotten:
.. code-block:: python
sheet = p.get_sheet(file_name="birth.csv") sheet birth.csv: +-------+--------+----------+ | name | weight | birth | +-------+--------+----------+ | Adam | 3.4 | 03/02/15 | +-------+--------+----------+ | Smith | 4.2 | 12/11/14 | +-------+--------+----------+
.. NOTE::
Please note that csv(comma separate value) file is pure text file. Formula, charts, images and formatting in xls file will disappear no matter which transcoding tool you use. Hence, pyexcel is a quick alternative for this transcoding job.
Let use previous example and save it as xlsx instead
.. code-block:: python
p.save_as(file_name="birth.xls", ... dest_file_name="birth.xlsx") # change the file extension
Again let's verify what we have gotten:
.. code-block:: python
sheet = p.get_sheet(file_name="birth.xlsx") sheet pyexcel_sheet1: +-------+--------+----------+ | name | weight | birth | +-------+--------+----------+ | Adam | 3.4 | 03/02/15 | +-------+--------+----------+ | Smith | 4.2 | 12/11/14 | +-------+--------+----------+
Merge all excel files in directory into a book where each file become a sheet
The following code will merge every excel files into one file, say "output.xls":
.. code-block:: python
from pyexcel.cookbook import merge_all_to_a_book
import glob
merge_all_to_a_book(glob.glob("your_csv_directory\*.csv"), "output.xls")
You can mix and match with other excel formats: xls, xlsm and ods. For example, if you are sure you have only xls, xlsm, xlsx, ods and csv files in your_excel_file_directory
, you can do the following:
.. code-block:: python
from pyexcel.cookbook import merge_all_to_a_book
import glob
merge_all_to_a_book(glob.glob("your_excel_file_directory\*.*"), "output.xls")
Split a book into single sheet files
Suppose you have many sheets in a work book and you would like to separate each into a single sheet excel file. You can easily do this:
.. code-block:: python
from pyexcel.cookbook import split_a_book split_a_book("megabook.xls", "output.xls") import glob outputfiles = glob.glob("*_output.xls") for file in sorted(outputfiles): ... print(file) ... Sheet 1_output.xls Sheet 2_output.xls Sheet 3_output.xls
for the output file, you can specify any of the supported formats
Extract just one sheet from a book
Suppose you just want to extract one sheet from many sheets that exists in a work book and you would like to separate it into a single sheet excel file. You can easily do this:
.. code-block:: python
>>> from pyexcel.cookbook import extract_a_sheet_from_a_book
>>> extract_a_sheet_from_a_book("megabook.xls", "Sheet 1", "output.xls")
>>> if os.path.exists("Sheet 1_output.xls"):
... print("Sheet 1_output.xls exists")
...
Sheet 1_output.xls exists
for the output file, you can specify any of the supported formats
Most pyexcel users do not know, but other library users were requesting partial read <https://github.com/jazzband/tablib/issues/467>
_
When you are dealing with huge amount of data, e.g. 64GB, obviously you would not like to fill up your memory with those data. What you may want to do is, record data from Nth line, take M records and stop. And you only want to use your memory for the M records, not for beginning part nor for the tail part.
Hence partial read feature is developed to read partial data into memory for processing.
You can paginate by row, by column and by both, hence you dictate what portion of the data to read back. But remember only row limit features help you save memory. You can 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.
This feature depends heavily on the implementation details.
pyexcel-xls
_ (xlrd), pyexcel-xlsx
_ (openpyxl), pyexcel-ods
_ (odfpy) and
pyexcel-ods3
_ (pyexcel-ezodf) will read all data into memory. Because xls,
xlsx and ods file are effective a zipped folder, all four will unzip the folder
and read the content in xml format in full, so as to make sense of all details.
Hence, during the partial data is been returned, the memory consumption won't differ from reading the whole data back. Only after the partial data is returned, the memory comsumption curve shall jump the cliff. So pagination code here only limits the data returned to your program.
With that said, pyexcel-xlsxr
, pyexcel-odsr
and pyexcel-htmlr
_ DOES read
partial data into memory. Those three are implemented in such a way that they
consume the xml(html) when needed. When they have read designated portion of the
data, they stop, even if they are half way through.
In addition, pyexcel's csv readers can read partial data into memory too.
Let's assume the following file is a huge csv file:
.. code-block:: python
import datetime import pyexcel as pe data = [ ... [1, 21, 31], ... [2, 22, 32], ... [3, 23, 33], ... [4, 24, 34], ... [5, 25, 35], ... [6, 26, 36] ... ] pe.save_as(array=data, dest_file_name="your_file.csv")
And let's pretend to read partial data:
.. code-block:: python
pe.get_sheet(file_name="your_file.csv", start_row=2, row_limit=3) your_file.csv: +---+----+----+ | 3 | 23 | 33 | +---+----+----+ | 4 | 24 | 34 | +---+----+----+ | 5 | 25 | 35 | +---+----+----+
And you could as well do the same for columns:
.. code-block:: python
pe.get_sheet(file_name="your_file.csv", start_column=1, column_limit=2) your_file.csv: +----+----+ | 21 | 31 | +----+----+ | 22 | 32 | +----+----+ | 23 | 33 | +----+----+ | 24 | 34 | +----+----+ | 25 | 35 | +----+----+ | 26 | 36 | +----+----+
Obvious, you could do both at the same time:
.. code-block:: python
pe.get_sheet(file_name="your_file.csv", ... start_row=2, row_limit=3, ... start_column=1, column_limit=2) your_file.csv: +----+----+ | 23 | 33 | +----+----+ | 24 | 34 | +----+----+ | 25 | 35 | +----+----+
The pagination support is available across all pyexcel plugins.
.. note::
No column pagination support for query sets as data source.
If you are transcoding a big data set, conventional formatting method would not help unless a on-demand free RAM is available. However, there is a way to minimize the memory footprint of pyexcel while the formatting is performed.
Let's continue from previous example. Suppose we want to transcode "your_file.csv" to "your_file.xls" but increase each element by 1.
What we can do is to define a row renderer function as the following:
.. code-block:: python
def increment_by_one(row): ... for element in row: ... yield element + 1
Then pass it onto save_as function using row_renderer:
.. code-block:: python
pe.isave_as(file_name="your_file.csv", ... row_renderer=increment_by_one, ... dest_file_name="your_file.xlsx")
.. note::
If the data content is from a generator, isave_as has to be used.
We can verify if it was done correctly:
.. code-block:: python
pe.get_sheet(file_name="your_file.xlsx") your_file.csv: +---+----+----+ | 2 | 22 | 32 | +---+----+----+ | 3 | 23 | 33 | +---+----+----+ | 4 | 24 | 34 | +---+----+----+ | 5 | 25 | 35 | +---+----+----+ | 6 | 26 | 36 | +---+----+----+ | 7 | 27 | 37 | +---+----+----+
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.
Get a list of dictionaries
Suppose you want to process the following coffee data again:
Top 5 coffeine drinks:
===================================== =============== ============= Coffees Serving Size Caffeine (mg) Starbucks Coffee Blonde Roast venti(20 oz) 475 Dunkin' Donuts Coffee with Turbo Shot large(20 oz.) 398 Starbucks Coffee Pike Place Roast grande(16 oz.) 310 Panera Coffee Light Roast regular(16 oz.) 300 ===================================== =============== =============
Let's get a list of dictionary out from the xls file:
.. code-block:: python
records = p.iget_records(file_name="your_file.xls")
And let's check what do we have:
.. code-block:: python
for r in records: ... print(f"{r['Serving Size']} of {r['Coffees']} has {r['Caffeine (mg)']} mg") venti(20 oz) of Starbucks Coffee Blonde Roast has 475 mg large(20 oz.) of Dunkin' Donuts Coffee with Turbo Shot has 398 mg grande(16 oz.) of Starbucks Coffee Pike Place Roast has 310 mg regular(16 oz.) of Panera Coffee Light Roast has 300 mg
Please do not forgot the second line to close the opened file handle:
.. code-block:: python
p.free_resources()
Get two dimensional array
Instead, what if you have to use pyexcel.get_array
to do the same:
.. code-block:: python
for row in p.iget_array(file_name="your_file.xls", start_row=1): ... print(f"{row[1]} of {row[0]} has {row[2]} mg") venti(20 oz) of Starbucks Coffee Blonde Roast has 475 mg large(20 oz.) of Dunkin' Donuts Coffee with Turbo Shot has 398 mg grande(16 oz.) of Starbucks Coffee Pike Place Roast has 310 mg regular(16 oz.) of Panera Coffee Light Roast has 300 mg
Again, do not forgot the second line:
.. code-block:: python
p.free_resources()
where start_row
skips the header row.
Export an array
Suppose you have the following array:
.. code-block:: python
data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
And here is the code to save it as an excel file :
.. code-block:: python
p.isave_as(array=data, dest_file_name="example.xls")
But the following line is not required because the data source are not file sources:
.. code-block:: python
p.free_resources()
Let's verify it:
.. code-block:: python
>>> p.get_sheet(file_name="example.xls")
pyexcel_sheet1:
+---+---+---+
| 1 | 2 | 3 |
+---+---+---+
| 4 | 5 | 6 |
+---+---+---+
| 7 | 8 | 9 |
+---+---+---+
And here is the code to save it as a csv file :
.. code-block:: python
p.isave_as(array=data, ... dest_file_name="example.csv", ... dest_delimiter=':')
Let's verify it:
.. code-block:: python
with open("example.csv") as f: ... for line in f.readlines(): ... print(line.rstrip()) ... 1:2:3 4:5:6 7:8:9
Export a list of dictionaries
.. code-block:: python
>>> records = [
... {"year": 1903, "country": "Germany", "speed": "206.7km/h"},
... {"year": 1964, "country": "Japan", "speed": "210km/h"},
... {"year": 2008, "country": "China", "speed": "350km/h"}
... ]
>>> p.isave_as(records=records, dest_file_name='high_speed_rail.xls')
Export a dictionary of single key value pair
.. code-block:: python
>>> henley_on_thames_facts = {
... "area": "5.58 square meters",
... "population": "11,619",
... "civial parish": "Henley-on-Thames",
... "latitude": "51.536",
... "longitude": "-0.898"
... }
>>> p.isave_as(adict=henley_on_thames_facts, dest_file_name='henley.xlsx')
Export a dictionary of single dimensonal array
.. code-block:: python
>>> ccs_insights = {
... "year": ["2017", "2018", "2019", "2020", "2021"],
... "smart phones": [1.53, 1.64, 1.74, 1.82, 1.90],
... "feature phones": [0.46, 0.38, 0.30, 0.23, 0.17]
... }
>>> p.isave_as(adict=ccs_insights, dest_file_name='ccs.csv')
>>> p.free_resources()
Export a dictionary of two dimensional array as a book
Suppose you want to save the below dictionary to an excel file :
.. code-block:: python
a_dictionary_of_two_dimensional_arrays = { ... 'Sheet 1': ... [ ... [1.0, 2.0, 3.0], ... [4.0, 5.0, 6.0], ... [7.0, 8.0, 9.0] ... ], ... 'Sheet 2': ... [ ... ['X', 'Y', 'Z'], ... [1.0, 2.0, 3.0], ... [4.0, 5.0, 6.0] ... ], ... 'Sheet 3': ... [ ... ['O', 'P', 'Q'], ... [3.0, 2.0, 1.0], ... [4.0, 3.0, 2.0] ... ] ... }
Here is the code:
.. code-block:: python
p.isave_book_as( ... bookdict=a_dictionary_of_two_dimensional_arrays, ... dest_file_name="book.xls" ... )
If you want to preserve the order of sheets in your dictionary, you have to pass on an ordered dictionary to the function itself. For example:
.. code-block:: python
from pyexcel._compact import OrderedDict data = OrderedDict() data.update({"Sheet 2": a_dictionary_of_two_dimensional_arrays['Sheet 2']}) data.update({"Sheet 1": a_dictionary_of_two_dimensional_arrays['Sheet 1']}) data.update({"Sheet 3": a_dictionary_of_two_dimensional_arrays['Sheet 3']}) p.isave_book_as(bookdict=data, dest_file_name="book.xls") p.free_resources()
Let's verify its order:
.. code-block:: python
import json book_dict = p.get_book_dict(file_name="book.xls") for key, item in book_dict.items(): ... print(json.dumps({key: item})) {"Sheet 2": [["X", "Y", "Z"], [1, 2, 3], [4, 5, 6]]} {"Sheet 1": [[1, 2, 3], [4, 5, 6], [7, 8, 9]]} {"Sheet 3": [["O", "P", "Q"], [3, 2, 1], [4, 3, 2]]}
Please notice that "Sheet 2" is the first item in the book_dict, meaning the order of sheets are preserved.
.. note::
Please note that the following file transcoding could be with zero line. Please install pyexcel-cli and you will do the transcode in one command. No need to open your editor, save the problem, then python run.
The following code does a simple file format transcoding from xls to csv:
.. code-block:: python
import pyexcel p.save_as(file_name="birth.xls", dest_file_name="birth.csv")
Again it is really simple. Let's verify what we have gotten:
.. code-block:: python
sheet = p.get_sheet(file_name="birth.csv") sheet birth.csv: +-------+--------+----------+ | name | weight | birth | +-------+--------+----------+ | Adam | 3.4 | 03/02/15 | +-------+--------+----------+ | Smith | 4.2 | 12/11/14 | +-------+--------+----------+
.. note::
Please note that csv(comma separate value) file is pure text file. Formula, charts, images and formatting in xls file will disappear no matter which transcoding tool you use. Hence, pyexcel is a quick alternative for this transcoding job.
Let use previous example and save it as xlsx instead
.. code-block:: python
import pyexcel p.isave_as(file_name="birth.xls", ... dest_file_name="birth.xlsx") # change the file extension
Again let's verify what we have gotten:
.. code-block:: python
sheet = p.get_sheet(file_name="birth.xlsx") sheet pyexcel_sheet1: +-------+--------+----------+ | name | weight | birth | +-------+--------+----------+ | Adam | 3.4 | 03/02/15 | +-------+--------+----------+ | Smith | 4.2 | 12/11/14 | +-------+--------+----------+
.. _file-format-list: .. _a-map-of-plugins-and-file-formats:
.. table:: A list of file formats supported by external plugins
======================== ======================= =================
Package name Supported file formats Dependencies
======================== ======================= =================
pyexcel-io
_ csv, csvz [#f1], tsv,
tsvz [#f2]
pyexcel-xls
_ xls, xlsx(read only), xlrd
,
xlsm(read only) xlwt
pyexcel-xlsx
_ xlsx openpyxl
_
pyexcel-ods3
_ ods pyexcel-ezodf
,
lxml
pyexcel-ods
ods odfpy
_
======================== ======================= =================
.. table:: Dedicated file reader and writers
======================== ======================= =================
Package name Supported file formats Dependencies
======================== ======================= =================
pyexcel-xlsxw
_ xlsx(write only) XlsxWriter
_
pyexcel-libxlsxw
_ xlsx(write only) libxlsxwriter
_
pyexcel-xlsxr
_ xlsx(read only) lxml
pyexcel-xlsbr
_ xlsb(read only) pyxlsb
pyexcel-odsr
_ read only for ods, fods lxml
pyexcel-odsw
_ write only for ods loxun
pyexcel-htmlr
_ html(read only) lxml,html5lib
pyexcel-pdfr
_ pdf(read only) camelot
======================== ======================= =================
Since 2020, all pyexcel-io plugins have dropped the support for python versions which are lower than 3.6. If you want to use any of those Python versions, please use pyexcel-io and its plugins versions that are lower than 0.6.0.
Except csv files, xls, xlsx and ods files are a zip of a folder containing a lot of xml files
The dedicated readers for excel files can stream read
In order to manage the list of plugins installed, you need to use pip to add or remove a plugin. When you use virtualenv, you can have different plugins per virtual environment. In the situation where you have multiple plugins that does the same thing in your environment, you need to tell pyexcel which plugin to use per function call. For example, pyexcel-ods and pyexcel-odsr, and you want to get_array to use pyexcel-odsr. You need to append get_array(..., library='pyexcel-odsr').
.. _pyexcel-io: https://github.com/pyexcel/pyexcel-io .. _pyexcel-xls: https://github.com/pyexcel/pyexcel-xls .. _pyexcel-xlsx: https://github.com/pyexcel/pyexcel-xlsx .. _pyexcel-ods: https://github.com/pyexcel/pyexcel-ods .. _pyexcel-ods3: https://github.com/pyexcel/pyexcel-ods3 .. _pyexcel-odsr: https://github.com/pyexcel/pyexcel-odsr .. _pyexcel-odsw: https://github.com/pyexcel/pyexcel-odsw .. _pyexcel-pdfr: https://github.com/pyexcel/pyexcel-pdfr
.. _pyexcel-xlsxw: https://github.com/pyexcel/pyexcel-xlsxw .. _pyexcel-libxlsxw: https://github.com/pyexcel/pyexcel-libxlsxw .. _pyexcel-xlsxr: https://github.com/pyexcel/pyexcel-xlsxr .. _pyexcel-xlsbr: https://github.com/pyexcel/pyexcel-xlsbr .. _pyexcel-htmlr: https://github.com/pyexcel/pyexcel-htmlr
.. _xlrd: https://github.com/python-excel/xlrd .. _xlwt: https://github.com/python-excel/xlwt .. _openpyxl: https://bitbucket.org/openpyxl/openpyxl .. _XlsxWriter: https://github.com/jmcnamara/XlsxWriter .. _pyexcel-ezodf: https://github.com/pyexcel/pyexcel-ezodf .. _odfpy: https://github.com/eea/odfpy .. _libxlsxwriter: http://libxlsxwriter.github.io/getting_started.html
.. table:: Other data renderers
======================== ======================= ================= ==================
Package name Supported file formats Dependencies Python versions
======================== ======================= ================= ==================
pyexcel-text
_ write only:rst, tabulate
_ 2.6, 2.7, 3.3, 3.4
mediawiki, html, 3.5, 3.6, pypy
latex, grid, pipe,
orgtbl, plain simple
read only: ndjson
r/w: json
pyexcel-handsontable
_ handsontable in html handsontable
_ same as above
pyexcel-pygal
_ svg chart pygal
_ 2.7, 3.3, 3.4, 3.5
3.6, pypy
pyexcel-sortable
_ sortable table in html csvtotable
_ same as above
pyexcel-gantt
_ gantt chart in html frappe-gantt
_ except pypy, same
as above
======================== ======================= ================= ==================
.. _pyexcel-text: https://github.com/pyexcel/pyexcel-text .. _tabulate: https://bitbucket.org/astanin/python-tabulate .. _pyexcel-handsontable: https://github.com/pyexcel/pyexcel-handsontable .. _handsontable: https://cdnjs.com/libraries/handsontable .. _pyexcel-pygal: https://github.com/pyexcel/pyexcel-chart .. _pygal: https://github.com/Kozea/pygal .. _pyexcel-matplotlib: https://github.com/pyexcel/pyexcel-matplotlib .. _matplotlib: https://matplotlib.org .. _pyexcel-sortable: https://github.com/pyexcel/pyexcel-sortable .. _csvtotable: https://github.com/vividvilla/csvtotable .. _pyexcel-gantt: https://github.com/pyexcel/pyexcel-gantt .. _frappe-gantt: https://github.com/frappe/gantt
.. rubric:: Footnotes
.. [#f1] zipped csv file .. [#f2] zipped tsv file
All great work have been done by odf, ezodf, xlrd, xlwt, tabulate and other individual developers. This library unites only the data access code.
New BSD License