flat-table 1.0.0

A broader implementation of pandas json_normalize function.

Flat-Table: Dictionary and List Normalizer

This package is a normalizer for pandas dataframe objects that has dictionary or list objects within it's columns. The library will expand all of the columns that has data types in (list, dict) into individual seperate rows and columns.

To Install

To install, use pip.

pip install flat_table

How to Use It

From a given pandas dataframe, the index of the dataframe will be used to create seperate columns and rows.

# some dataframe contains dicts and lists in it's columns
df = ...

import flat_table

flat_table.normalize(df)

This will give you all the keys in dictionaries as columns, and all the lists as seperate rows.

Example Illustration

Lets assume that you have a dataframe of the followings shape.

id	user_info	address
1001	{ 'first_name': 'john', 'last_name': 'smith', 'phones': {'mobile': '201-..', 'home': '978-..'} }	[{ 'zip': '07014', 'city': 'clifton' }]
1002	NaN	[{'zip': '07014', 'address1': '1 Journal Square'}]
1003	{ 'first_name': 'marry', 'last_name': 'kate', 'gender': 'female' }	[{ 'zip': '10001', 'city': 'new york' }, { 'zip': '10008', 'city': 'brooklyn' }]

This table given above has some dictionaries and lists in it's columns. Normally, what you would do is to use pd.io.json.json_normalize function to expand dictionaries. However, in cases you have NaN values in your column, pd.io.json.json_normalize end up throwing an AttributeError error for NaN values because they are not of the same type. flat_table is a wraper around the json_normalize function where it expands it's abilities to be more robust for NaN values and also, it expands lists rowwise so that it will be more clear to see the information.

For the above table, the flatten table after applying flat_table.normalize will look like the following.

	index	id	gender	phones.home	phones.mobile	last_name	first_name	address1	city	zip
0	0	1001	nan	978-..	201-..	smith	john	nan	clifton	07014
1	1	1002	nan	nan	nan	nan	nan	1 Journal Square	nan	07014
2	2	1003	female	nan	nan	kate	marry	nan	new york	10001
3	2	1003	female	nan	nan	kate	marry	nan	brooklyn	10008

How it Works?

Basically, flat_table will look for each of the series in a dataframe to understand what type of data it contains.

For every series, it creates a list of information on how to expand it. It will go into all dictionaries and all lists in all levels and expand them as rows and columns. Dictionary keys will be used for column names, and The index of the giden dataframe will be used for row expansion.

If you want to see how the columns are mapped, you can use flat_table.mapper function to get all information about your columns in your original dataframe. For example, for the above table, the mapper function will provide the following table.

	parent	child	type	obj
0	.	id	int	Series
1	.	user_info	dict	Series
2	.user_info	gender	str	Series
3	.user_info	phones.home	str	Series
4	.user_info	phones.mobile	str	Series
5	.user_info	last_name	str	Series
6	.user_info	first_name	str	Series
7	.	address	list	Series
8	.address	address	dict	Series
9	.address.address	address1	str	Series
10	.address.address	city	str	Series
11	.address.address	zip	str	Series

Licence

Licence is use it at your own will, with whatever way you want it to use :smiley:. Anyways, Licence is in here.

Author

Build by @metinsenturk