Eager to get started? This page gives a good introduction in how to get started with Tablib. This assumes you already have Tablib installed. If you do not, head over to the Installation section.

First, make sure that:

Let’s get started with some simple use cases and examples.

Creating a Dataset

A Dataset is nothing more than what its name implies—a set of data.

Creating your own instance of the tablib.Dataset object is simple.

data = tablib.Dataset()

You can now start filling this Dataset object with data.

Example Context

From here on out, if you see data, assume that it’s a fresh Dataset object.

Adding Rows

Let’s say you want to collect a simple list of names.

# collection of names
names = ['Kenneth Reitz', 'Bessie Monke']

for name in names:
    # split name appropriately
    fname, lname = name.split()

    # add names to Dataset
    data.append([fname, lname])

You can get a nice, Pythonic view of the dataset at any time with Dataset.dict:

>>> data.dict
[('Kenneth', 'Reitz'), ('Bessie', 'Monke')]

Adding Headers

It’s time to enhance our Dataset by giving our columns some titles. To do so, set Dataset.headers.

data.headers = ['First Name', 'Last Name']

Now our data looks a little different.

>>> data.dict
[{'Last Name': 'Reitz', 'First Name': 'Kenneth'},
 {'Last Name': 'Monke', 'First Name': 'Bessie'}]

Adding Columns

Now that we have a basic Dataset in place, let’s add a column of ages to it.

data.append_col([22, 20], header='Age')

Let’s view the data now.

>>> data.dict
[{'Last Name': 'Reitz', 'First Name': 'Kenneth', 'Age': 22},
 {'Last Name': 'Monke', 'First Name': 'Bessie', 'Age': 20}]

It’s that easy.

Importing Data

Creating a tablib.Dataset object by importing a pre-existing file is simple.

with open('data.csv', 'r') as fh:
    imported_data = Dataset().load(fh)

This detects what sort of data is being passed in, and uses an appropriate formatter to do the import. So you can import from a variety of different file types.

Source without headers

When the format is csv, tsv, dbf, xls or xlsx, and the data source does not have headers, the import should be done as follows

with open(‘data.csv’, ‘r’) as fh:

imported_data = Dataset().load(fh, headers=False)

Exporting Data

Tablib’s killer feature is the ability to export your Dataset objects into a number of formats.

Comma-Separated Values

>>> data.export('csv')
Last Name,First Name,Age

JavaScript Object Notation

>>> data.export('json')
[{"Last Name": "Reitz", "First Name": "Kenneth", "Age": 22}, {"Last Name": "Monke", "First Name": "Bessie", "Age": 20}]

YAML Ain’t Markup Language

>>> data.export('yaml')
- {Age: 22, First Name: Kenneth, Last Name: Reitz}
- {Age: 20, First Name: Bessie, Last Name: Monke}

Microsoft Excel

>>> data.export('xls')
<redacted binary data>

Pandas DataFrame

>>> data.export('df')
  First Name Last Name  Age
0    Kenneth     Reitz   22
1     Bessie     Monke   21

Selecting Rows & Columns

You can slice and dice your data, just like a standard Python list.

>>> data[0]
('Kenneth', 'Reitz', 22)
>>> data[0:2]
[('Kenneth', 'Reitz', 22), ('Bessie', 'Monke', 20)]

You can also access a row using its index without slicing.

>>> data.get(0)
('Kenneth', 'Reitz', 22)

If we had a set of data consisting of thousands of rows, it could be useful to get a list of values in a column. To do so, we access the Dataset as if it were a standard Python dictionary.

>>> data['First Name']
['Kenneth', 'Bessie']

You can also access the column using its index.

>>> data.headers
['Last Name', 'First Name', 'Age']
>>> data.get_col(1)
['Kenneth', 'Bessie']

Let’s find the average age.

>>> ages = data['Age']
>>> float(sum(ages)) / len(ages)

Removing Rows & Columns

It’s easier than you could imagine. Delete a column:

>>> del data['Col Name']

Delete a range of rows:

>>> del data[0:12]

Advanced Usage

This part of the documentation services to give you an idea that are otherwise hard to extract from the API Documentation.

And now for something completely different.

Dynamic Columns

Thanks to Josh Ourisman, Tablib now supports adding dynamic columns. A dynamic column is a single callable object (e.g. a function).

Let’s add a dynamic column to our Dataset object. In this example, we have a function that generates a random grade for our students.

import random

def random_grade(row):
    """Returns a random integer for entry."""
    return (random.randint(60,100)/100.0)

data.append_col(random_grade, header='Grade')

Let’s have a look at our data.

>>> data.export('yaml')
- {Age: 22, First Name: Kenneth, Grade: 0.6, Last Name: Reitz}
- {Age: 20, First Name: Bessie, Grade: 0.75, Last Name: Monke}

Let’s remove that column.

>>> del data['Grade']

When you add a dynamic column, the first argument that is passed in to the given callable is the current data row. You can use this to perform calculations against your data row.

For example, we can use the data available in the row to guess the gender of a student.

def guess_gender(row):
    """Calculates gender of given student data row."""
    m_names = ('Kenneth', 'Mike', 'Yuri')
    f_names = ('Bessie', 'Samantha', 'Heather')

    name = row[0]

    if name in m_names:
        return 'Male'
    elif name in f_names:
        return 'Female'
        return 'Unknown'

Adding this function to our dataset as a dynamic column would result in:

>>> data.export('yaml')
- {Age: 22, First Name: Kenneth, Gender: Male, Last Name: Reitz}
- {Age: 20, First Name: Bessie, Gender: Female, Last Name: Monke}

When you add new rows to a dataset that contains dynamic columns, you should either provide all values in the row, or only the non-dynamic values and then the dynamic values will be automatically generated using the function initially provided for the column calculation.

..versionchanged:: 3.6.0

In older versions, you could only add new rows with fully-populated rows, including dynamic columns.

Filtering Datasets with Tags

When constructing a Dataset object, you can add tags to rows by specifying the tags parameter. This allows you to filter your Dataset later. This can be useful to separate rows of data based on arbitrary criteria (e.g. origin) that you don’t want to include in your Dataset.

Let’s tag some students.

students = tablib.Dataset()

students.headers = ['first', 'last']

students.rpush(['Kenneth', 'Reitz'], tags=['male', 'technical'])
students.rpush(['Daniel', 'Dupont'], tags=['male', 'creative' ])
students.rpush(['Bessie', 'Monke'], tags=['female', 'creative'])

Now that we have extra meta-data on our rows, we can easily filter our Dataset. Let’s just see Female students.

>>> students.filter(['female']).yaml
- {first: Bessie, Last: Monke}

By default, when you pass a list of tags you get filter type or.

>>> students.filter(['female', 'creative']).yaml
- {first: Daniel, Last: Dupont}
- {first: Bessie, Last: Monke}

Using chaining you can get a filter type and.

>>> students.filter(['female']).filter(['creative']).yaml
- {first: Bessie, Last: Monke}

It’s that simple. The original Dataset is untouched.

Open an Excel Workbook and read first sheet

Open an Excel 2007 and later workbook with a single sheet (or a workbook with multiple sheets but you just want the first sheet).

data = tablib.Dataset()
with open('my_excel_file.xlsx', 'rb') as fh:
    data.load(fh, 'xlsx')

Excel Workbook With Multiple Sheets

When dealing with a large number of Datasets in spreadsheet format, it’s quite common to group multiple spreadsheets into a single Excel file, known as a Workbook. Tablib makes it extremely easy to build workbooks with the handy Databook class.

Let’s say we have 3 different Datasets. All we have to do is add them to a Databook object…

book = tablib.Databook((data1, data2, data3))

… and export to Excel just like Datasets.

with open('students.xls', 'wb') as f:

The resulting students.xls file will contain a separate spreadsheet for each Dataset object in the Databook.

Binary Warning

Make sure to open the output file in binary mode.


When constructing a spreadsheet, it’s often useful to create a blank row containing information on the upcoming data. So,

daniel_tests = [
    ('11/24/09', 'Math 101 Mid-term Exam', 56.),
    ('05/24/10', 'Math 101 Final Exam', 62.)

suzie_tests = [
    ('11/24/09', 'Math 101 Mid-term Exam', 56.),
    ('05/24/10', 'Math 101 Final Exam', 62.)

# Create new dataset
tests = tablib.Dataset()
tests.headers = ['Date', 'Test Name', 'Grade']

# Daniel's Tests
tests.append_separator('Daniel\'s Scores')

for test_row in daniel_tests:

# Susie's Tests
tests.append_separator('Susie\'s Scores')

for test_row in suzie_tests:

# Write spreadsheet to disk
with open('grades.xls', 'wb') as f:

The resulting tests.xls will have the following layout:

Daniel’s Scores:
  • ‘11/24/09’, ‘Math 101 Mid-term Exam’, 56.

  • ‘05/24/10’, ‘Math 101 Final Exam’, 62.

Suzie’s Scores:
  • ‘11/24/09’, ‘Math 101 Mid-term Exam’, 56.

  • ‘05/24/10’, ‘Math 101 Final Exam’, 62.

Format Support

At this time, only Excel output supports separators.

Now, go check out the API Documentation or begin Tablib Development.