Get differences between two pandas dataframes
Install pandas_diff with pip
pip install pandas_diffimport 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 smartIn 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.
For every item in a table, by using pandas_diff you will have an event log to audit listing how the resources are being consumed.
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.
Eg: You have a disaster recovery environment for your platform. You can synch two platforms, production and disaster recovery, using their APIs and pandas_diff to propagate the changes (in objects, users, permissions) from production to disaster recovery environments.- Key multicolum
- Blacklist of columns
- Support for stand alone app