pandas-diff 1.4.7

Python utility to extract differences between two pandas dataframes.

Installation

Install pandas_diff with pip

pip install pandas_diff

Usage/Examples

import pandas_diff as pd_diff

import pandas as pd

# Create two example dataframes
df_infinity_war = pd.DataFrame([
                {"hero" : "hulk" , "power" : "strength"},
                {"hero" : "black_widow" , "power" : "spy"},
                {"hero" : "thor" , "hammers" : 0 },
                {"hero" : "thor" , "hammers" : 1 } ] )
df_endgame = pd.DataFrame([
                {"hero" : "hulk" , "power" : "smart"},
                {"hero" : "captain marvel" , "power" : "strength"},
                {"hero" : "thor" , "hammers" : 2 } ] )

# Get differences, using the key "hero"
df = pd_diff.get_diffs(df_infinity_war ,df_endgame ,"hero")

df

#operation object_keys  object_values                     object_json                     attribute_changed old_value new_value
#0   create     [hero]    captain marvel  {'hero': 'captain marvel', 'power': 'strength'...           NaN           NaN      NaN
#1   delete     [hero]       black_widow  {'hero': 'black_widow', 'power': 'spy', 'hamme...           NaN           NaN      NaN
#2   modify     [hero]              thor     {'hero': 'thor', 'power': nan, 'hammers': 2.0}       hammers             1        2
#3   modify     [hero]              hulk  {'hero': 'hulk', 'power': 'smart', 'hammers': ...         power      strength    smart

Why pandas diff ? Cases of use

Migrating from batch to an event driven architecture

In my work, we use a lot of data pipelines to get info from external platforms, (active directory, github, jira). We load the new data replacing the entire table.

By using pandas_diff we detect how the infraestructure changes between executions, and stream those change events into a kafka cluster, so other teams could suscribe to their favourite events. Also, by defining a pandas_diff step in the master pipeline, every item in our project has ther life cycle events controlled.

Events log

For every item in a table, by using pandas_diff you will have an event log to audit of how the resources are being consumed.

Conciliation

To conciliate one datasource against the source of truth. Eg: You have a CMDB controlling with info regarding virtual machines. As there are several methods for creating those VMs, you use pandas_diff to replicate state of the infraestructure against the CMDB.

Features

• Filtering of columns

Roadmap

• Support for stand alone app

Documentation

Documentation

History

0.7.18 (2021-12-05)

* Add codacy badge

0.7.19 (2021-12-05)

* Feat filter column

0.7.20 (2021-12-05)

* Feat filter column

0.7.21 (2021-12-05)

* Add filter fest

0.7.22 (2021-12-06)

* Add confition keys exist in df’s

1.1.0 (2021-12-06)

* Add confition keys exist in df’s 1.2.0 (2021-12-06) ——————

* Improve doc

1.2.0 (2021-12-06)

* Improve doc

1.3.0 (2021-12-06)

* Remove workflows

1.4.0 (2021-12-06)

* Remove workflows

1.4.0 (2023-09-01)

* Improve doc

1.4.1 (2023-09-01)

* Improve doc

1.4.2 (2023-09-17)

* Bugfix version string

1.4.3 (2023-09-17)

* bugfix version tag

1.4.4 (2023-09-17)

* bugfix version tag

1.4.5 (2023-09-17)

* bugfixx history string

1.4.6 (2023-09-17)

* bugfix history string

1.4.7 (2023-09-17)

* bugfix release description