Flask-Excel - Let you focus on data, instead of file formats

Source code:http://github.com/pyexcel-webwares/Flask-Excel.git
License:New BSD License
Generated:Nov 10, 2020

Here is a typical conversation between the developer and the user:

User: "I have uploaded an excel file"
      "but your application says un-supported file format"
Developer: "Did you upload an xlsx file or a csv file?"
User: "Well, I am not sure. I saved the data using "
      "Microsoft Excel. Surely, it must be in an excel format."
Developer: "OK. Here is the thing. I were not told to support"
           "all available excel formats in day 1. Live with it"
           "or delay the project x number of days."

Flask-Excel is based on pyexcel and makes it easy to consume/produce information stored in excel files over HTTP protocol as well as on file system. This library can turn the excel data into a list of lists, a list of records(dictionaries), dictionaries of lists. And vice versa. Hence it lets you focus on data in Flask based web development, instead of file formats.

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

The highlighted features are:

  1. excel data import into and export from databases
  2. turn uploaded excel file directly into Python data structure
  3. pass Python data structures as an excel file download
  4. provide data persistence as an excel file in server side
  5. supports csv, tsv, csvz, tsvz by default and other formats are supported via the following plugins:
A list of file formats supported by external 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
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

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’).

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


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

This library makes information processing involving various excel files as easy as processing array, dictionary when processing file upload/download, data import into and export from SQL databases, information analysis and persistence. It uses pyexcel and its plugins:

  1. to provide one uniform programming interface to handle csv, tsv, xls, xlsx, xlsm and ods formats.
  2. to provide one-stop utility to import the data in uploaded file into a database and to export tables in a database as excel files for file download.
  3. to provide the same interface for information persistence at server side: saving a uploaded excel file to and loading a saved excel file from file system.


You can install Flask-Excel via pip:

$ pip install Flask-Excel

or clone it and install it:

$ git clone https://github.com/pyexcel-webwares/Flask-Excel.git
$ cd Flask-Excel
$ python setup.py install

Installation of individual plugins , please refer to individual plugin page. For example, if you need xlsx file support, please install pyexcel-xlsx:

$ pip install pyexcel-xlsx


In your application, you must import it before using it:

import flask_excel as excel

excel.init_excel(app) # required since version 0.0.7

Quick start

A minimal application may look like this:

# -*- coding: utf-8 -*-
:copyright: (c) 2015 by C. W.
:license: GPL v3 or BSD
from flask import Flask, request, jsonify
import flask_excel as excel

app = Flask(__name__)

@app.route("/upload", methods=['GET', 'POST'])
def upload_file():
    if request.method == 'POST':
        return jsonify({"result": request.get_array(field_name='file')})
    return '''
    <!doctype html>
    <title>Upload an excel file</title>
    <h1>Excel file upload (csv, tsv, csvz, tsvz only)</h1>
    <form action="" method=post enctype=multipart/form-data><p>
    <input type=file name=file><input type=submit value=Upload>

@app.route("/download", methods=['GET'])
def download_file():
    return excel.make_response_from_array([[1, 2], [3, 4]], "csv")

@app.route("/export", methods=['GET'])
def export_records():
    return excel.make_response_from_array([[1, 2], [3, 4]], "csv",

@app.route("/download_file_named_in_unicode", methods=['GET'])
def download_file_named_in_unicode():
    return excel.make_response_from_array([[1, 2], [3, 4]], "csv",

# insert database related code here
if __name__ == "__main__":

The tiny application exposes four urls:

  1. one for file upload
  2. three urls for file download.

The first url presents a simple file upload html and responds back in json with the content of the uploaded file. Here is an example file for testing but you can upload any other excel file. The file upload handler uses request.get_array to parse the uploaded file and gets an array back. The parameter file is coded in the html form:

<input ... name=file>


If ‘field_name’ was not specified, for example request.get_array(‘file’) in upload_file() function, your browser would display “Bad Request: The browser (or proxy) sent a request that this server could not understand.”

The rest of the links simply throw back a csv file whenever a http request is made to http://localhost:50000/download/. make_response_from_array() takes a list of lists and a file type as parameters and sets up the mime type of the http response. If you would like to give ‘tsvz’ a go, please change “csv” to “tsvz”.

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.

More excel file formats

The example application understands csv, tsv and its zipped variants: csvz and tsvz. If you would like to expand the list of supported excel file formats (see A list of file formats supported by external plugins) for your own application, you could install one or all of the following:

pip install pyexcel-xls
pip install pyexcel-xlsx
pip install pyexcel-ods


If you are using pyexcel <=0.2.1, you still need to import each plugin manually, e.g. import pyexcel.ext.xls and Your IDE or pyflakes may highlight it as un-used but it is used. The registration of the extra file format support happens when the import action is performed

Data import and export

Continue with the previous example, the data import and export will be explained. You can copy the following code in their own appearing sequence and paste them after the place holder:

# insert database related code here

Alternatively, you can find the complete example on github

Now let’s add the following imports first:

from flask_sqlalchemy import SQLAlchemy # sql operations

And please make sure that you have pyexcel-xls and pyexcel-handsontable installed:

pip install pyexcel-xls, pyexcel-handsontable

Now configure the database connection. Sqllite will be used and tmp.db will be used and can be found in your current working directory:

app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///tmp.db'
db = SQLAlchemy(app)

And paste some models from Flask-SQLAlchemy’s documentation:

class Post(db.Model):
    id = db.Column(db.Integer, primary_key=True)
    title = db.Column(db.String(80))
    body = db.Column(db.Text)
    pub_date = db.Column(db.DateTime)

    category_id = db.Column(db.Integer, db.ForeignKey('category.id'))
    category = db.relationship('Category',

    def __init__(self, title, body, category, pub_date=None):
        self.title = title
        self.body = body
        if pub_date is None:
            pub_date = datetime.utcnow()
        self.pub_date = pub_date
        self.category = category

    def __repr__(self):
        return '<Post %r>' % self.title

class Category(db.Model):
    id = db.Column(db.Integer, primary_key=True)
    name = db.Column(db.String(50))

    def __init__(self, name):
        self.name = name

    def __repr__(self):
        return '<Category %r>' % self.name

Now let us create the tables in the database:


Write up the view functions for data import:

@app.route("/import", methods=['GET', 'POST'])
def doimport():
    if request.method == 'POST':

        def category_init_func(row):
            c = Category(row['name'])
            c.id = row['id']
            return c

        def post_init_func(row):
            c = Category.query.filter_by(name=row['category']).first()
            p = Post(row['title'], row['body'], c, row['pub_date'])
            return p
            field_name='file', session=db.session,
            tables=[Category, Post],
            initializers=[category_init_func, post_init_func])
        return redirect(url_for('.handson_table'), code=302)
    return '''
    <!doctype html>
    <title>Upload an excel file</title>
    <h1>Excel file upload (xls, xlsx, ods please)</h1>
    <form action="" method=post enctype=multipart/form-data><p>
    <input type=file name=file><input type=submit value=Upload>

In the code, category_init_func and post_init_func are custom initialization functions for Category and Post respectively. In the situation where you want to skip certain rows, None should should be returned and flask_excel will ignore the row.

Write up the view function for data export:

@app.route("/export", methods=['GET'])
def doexport():
    return excel.make_response_from_tables(db.session, [Category, Post], "xls")

Then run the example again. Visit http://localhost:5000/import and upload sample-data.xls.

And it responds with


This result is rendered via pyexcel-handsontable. All you needed is to put ‘handsontable.html’ as file type:

@app.route("/handson_view", methods=['GET'])
def handson_table():
    return excel.make_response_from_tables(
        db.session, [Category, Post], 'handsontable.html')

Then visit http://localhost:5000/export to download the data back.

Export filtered query sets

Previous example shows you how to dump one or more tables over http protocol. Hereby, let’s look at how to turn a query sets into an excel sheet. You can pass a query sets and an array of selected column names to make_response_from_query_sets() and generate an excel sheet from it:

@app.route("/custom_export", methods=['GET'])
def docustomexport():
    query_sets = Category.query.filter_by(id=1).all()
    column_names = ['id', 'name']
    return excel.make_response_from_query_sets(query_sets, column_names, "xls")

Then visit http://localhost:5000/custom_export to download the data .. _data-types-and-its-conversion-funcs:

All supported data types

The example application likes to have array but it is not just about arrays. Here is table of functions for all supported data types:

data structure from file to data structures from data structures to response
dict get_dict() make_response_from_dict()
records get_records() make_response_from_records()
a list of lists get_array() make_response_from_array()
dict of a list of lists get_book_dict() make_response_from_book_dict()
pyexcel.Sheet get_sheet() make_response()
pyexcel.Book get_book() make_response()
database table save_to_database() isave_to_database() make_response_from_a_table()
a list of database tables save_book_to_database() isave_book_to_database() make_response_from_tables()
a database query sets   make_response_from_query_sets()
a generator for records iget_records()  
a generator of lists iget_array()  

See more examples of the data structures in pyexcel documentation

API Reference

Flask-Excel attaches pyexcel functions to flask.Request class.


flask_excel.ExcelRequest.get_sheet(field_name=None, sheet_name=None, **keywords)
  • field_name – the file field name in the html form for file upload
  • sheet_name – For an excel book, there could be multiple sheets. If it is left unspecified, the sheet at index 0 is loaded. For ‘csv’, ‘tsv’ file, sheet_name should be None anyway.
  • keywords – additional keywords to pyexcel.get_sheet()

A sheet object

The following html form, the field_name should be “file”:

<!doctype html>
<title>Upload an excel file</title>
<h1>Excel file upload (csv, tsv, csvz, tsvz only)</h1>
<form action="" method=post enctype=multipart/form-data><p>
<input type=file name=file><input type=submit value=Upload>
flask_excel.ExcelRequest.get_array(field_name=None, sheet_name=None, **keywords)

a two dimensional array, a list of lists

flask_excel.ExcelRequest.get_dict(field_name=None, sheet_name=None, name_columns_by_row=0, **keywords)
  • field_name – same as get_sheet()
  • sheet_name – same as get_sheet()
  • name_columns_by_row – uses the first row of the sheet to be column headers by default.
  • keywords – additional keywords to pyexcel.get_dict()

a dictionary of the file content

flask_excel.ExcelRequest.get_records(field_name=None, sheet_name=None, name_columns_by_row=0, **keywords)
  • field_name – same as get_sheet()
  • sheet_name – same as get_sheet()
  • name_columns_by_row – uses the first row of the sheet to be record field names by default.
  • keywords – additional keywords to pyexcel.get_records()

a list of dictionary of the file content

flask_excel.ExcelRequest.get_book(field_name=None, **keywords)

a two dimensional array, a list of lists

flask_excel.ExcelRequest.get_book_dict(field_name=None, **keywords)

a two dimensional array, a list of lists

flask_excel.ExcelRequest.save_to_database(field_name=None, session=None, table=None, initializer=None, mapdict=None **keywords)
  • field_name – same as get_sheet()
  • session – a SQLAlchemy session
  • table – a database table
  • initializer – a custom table initialization function if you have one
  • mapdict – the explicit table column names if your excel data do not have the exact column names
  • keywords – additional keywords to pyexcel.Sheet.save_to_database()
flask_excel.ExcelRequest.isave_to_database(field_name=None, session=None, table=None, initializer=None, mapdict=None **keywords)

similar to save_to_database(). But it requires less memory.

This requires column names must be at the first row.

flask_excel.ExcelRequest.save_book_to_database(field_name=None, session=None, tables=None, initializers=None, mapdicts=None, **keywords)
  • field_name – same as get_sheet()
  • session – a SQLAlchemy session
  • tables – a list of database tables
  • initializers – a list of model initialization functions.
  • mapdicts – a list of explicit table column names if your excel data sheets do not have the exact column names
  • keywords – additional keywords to pyexcel.Book.save_to_database()
flask_excel.ExcelRequest.isave_book_to_database(field_name=None, session=None, tables=None, initializers=None, mapdicts=None, **keywords)

similar to save_book_to_database(). But it requires less memory.

This requires column names must be at the first row in each sheets

Response methods

flask_excel.make_response(pyexcel_instance, file_type, status=200, file_name=None)
  • pyexcel_instancepyexcel.Sheet or pyexcel.Book
  • file_type

    one of the following strings:

    • ’csv’
    • ’tsv’
    • ’csvz’
    • ’tsvz’
    • ’xls’
    • ’xlsx’
    • ’xlsm’
    • ’ods’
  • status – unless a different status is to be returned.
  • file_name – provide a custom file name for the response, excluding the file extension
flask_excel.make_response_from_array(array, file_type, status=200, file_name=None)
flask_excel.make_response_from_dict(dict, file_type, status=200, file_name=None)
flask_excel.make_response_from_records(records, file_type, status=200, file_name=None)
flask_excel.make_response_from_book_dict(book_dict, file_type, status=200, file_name=None)
flask_excel.make_response_from_a_table(session, table, file_type, status=200, file_name=None)

Produce a single sheet Excel book of file_type

flask_excel.make_response_from_query_sets(query_sets, column_names, file_type, status=200, file_name=None)

Produce a single sheet Excel book of file_type from your custom database queries

  • query_sets – a query set
  • column_names – a nominated column names. It could not be None, otherwise no data is returned.
  • file_type – same as make_response()
  • status – same as make_response()
  • file_name – same as make_response()
flask_excel.make_response_from_tables(session, tables, file_type, status=200, file_name=None)

Produce a multiple sheet Excel book of file_type. It becomes the same as make_response_from_a_table() if you pass tables with an array that has a single table


Change log

0.0.7 - 20.07.2017


  1. the intialization method has been modified. please call init_excel(app) before you do anything else. This change was made in order to apply for approved flask extension status. And by doing this change, it will support multiple Flask apps and only the app that was initialized with init_excel gets Flask-Excel and other apps in your BIG app won’t get affected.

0.0.6 - 22.06.2017


  1. #22: support downloadfile name in unicode(including Chinese texts)

0.0.5 - 21.08.2016


  1. compatibility with pyexcel v0.2.2: automatic discovery of pyexcel plugins.
  2. #15: file name may have more than one dot

0.0.4 - 15.01.2016


  1. #8: set file name in response

0.0.3 - 01.07.2015


  1. code refactoring. less code lines in Flask-Excel and more reusable code in pyexcel-webio

0.0.2 - 21.05.2015


  1. turn query sets into a response

0.0.1 - 22.01.2015