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A broader implementation of pandas json_normalize function.

Project description

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.

PS: Flat table will use the current index of the dataframe as an identifier while expanding lists. The output will have an index column of your original dataframe. You can drop it later if you not plan to use it.

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 user_info.gender user_info.phones.home user_info.phones.mobile user_info.last_name user_info.first_name address.address1 address.city address.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

New in Version 1.1.0

The expansion for dicts and lists made optional. Now, you can choose to expand list types and dict types with normalize function.

flat_table.normalize(df, expand_dicts=False, expand_lists=True)

Normalized version of df will be following.

index id user_info address.address1 address.city address.zip
0 0 1001 {...} nan clifton 07014
1 1 1002 nan 1 Journal Square nan 07014
2 2 1003 {...} nan new york 10001
3 2 1003 {...} 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 ...
1 . user_info dict ...
2 user_info user_info.gender str ...
3 user_info user_info.phones.home str ...
4 user_info user_info.phones.mobile str ...
5 user_info user_info.last_name str ...
6 user_info user_info.first_name str ...
7 . address list ...
8 address dict ...
9 address address.address1 str ...
10 address address.city str ...
11 address address.zip str ...

Licence

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

Author

Build by @metinsenturk

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