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Fonts, colors and charts are not supported.
Nor to read password protected xls, xlsx and ods files.
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 |
- 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.
You can install pyexcel via pip:
$ pip install pyexcelor clone it and install it:
$ git clone https://github.com/pyexcel/pyexcel.git
$ cd pyexcel
$ python setup.py installThis section shows you how to get data from your excel files and how to export data to excel files in one line
Suppose you want to process History of Classical Music:
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:
>>> records = p.get_records(file_name="your_file.xls")And let's check what do we have:
>>> 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)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[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.
You can get a dictionary too:
>>> my_dict = p.get_dict(file_name="your_file.xls", name_columns_by_row=0)And let's have a look inside:
>>> 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 DebussyPlease note that my_dict is an OrderedDict.
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:
>>> 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}))
{"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"]]}Suppose you have the following array:
>>> 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 :
>>> p.save_as(array=data, dest_file_name="example.xls")Let's verify it:
>>> 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 :
>>> 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())
...
G:D:A:E
Thomastik-Infield Domaints:Thomastik-Infield Domaints:Thomastik-Infield Domaints:Pirastro
Silver wound::Aluminum wound:Gold Label Steel>>> 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')>>> 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')>>> 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')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.
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 extensionAgain 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 |
+-------+--------+----------+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")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.xlsfor the output file, you can specify any of the supported formats
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 existsfor 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 partial read
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.
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.
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 + 1Then 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 |
+---+----+----+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.
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 mgPlease do not forgot the second line to close the opened file handle:
>>> p.free_resources()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 mgAgain, do not forgot the second line:
>>> p.free_resources()where start_row skips the header row.
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>>> 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')>>> 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')>>> 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()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.
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 extensionAgain 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 |
+-------+--------+----------+| Package name | Supported file formats | Dependencies |
|---|---|---|
xls, xlsx(read only), xlsm(read only) |
||
| pyexcel-xlsx | xlsx | openpyxl |
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 |
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').
Other data renderers| Package name | Supported file formats | Dependencies | Python versions |
|---|---|---|---|
write only:rst, mediawiki, html, latex, grid, pipe, orgtbl, plain simple read only: ndjson r/w: json |
2.6, 2.7, 3.3, 3.4 3.5, 3.6, pypy |
||
| pyexcel-handsontable | handsontable in html | handsontable | same as above |
svg chart |
2.7, 3.3, 3.4, 3.5 3.6, pypy |
||
| pyexcel-sortable | sortable table in html | csvtotable | same as above |
gantt chart in html |
except pypy, same as above |
Footnotes
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
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