Quickstart¶
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:
Tablib is installed
Tablib is up-to-date
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
Reitz,Kenneth,22
Monke,Bessie,20
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)
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)
21.0
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¶
New in version 0.8.3.
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'
else:
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}
Separators¶
New in version 0.8.2.
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:
tests.append(test_row)
# Susie's Tests
tests.append_separator('Susie\'s Scores')
for test_row in suzie_tests:
tests.append(test_row)
# Write spreadsheet to disk
with open('grades.xls', 'wb') as f:
f.write(tests.export('xls'))
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.