How to change values in a pandas DataFrame column based on a condition in Python

Question

I wanted to change the value of my row where if the column "state" had the value of "CANCELLED" then it would change the row value from column "Activity" into "Cancelled"

It should be like this:

ID	Activity	state
1	created	CANCELLED
1	completed	CANCELLED
2	created	FINNISHED
2	completed	FINISHED
3	created	REJECTED
3	rejected	REJECTED

what i tired using df.loc[df.state == "CANCELLED", "Activity"] = "cancelled"

It did changed the Activity but i still wanted the created activity to be the same.

There's also an error

C:\Users\aldev\miniconda3\lib\site-packages\pandas\core\indexing.py:1720: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(loc, value, pi)

If anyone has an answer thank you in advance

Answer 1

df.loc[(df.state == "CANCELLED") & (df.Activity != "created"), "Activity"] = "cancelled"

The code was missed () , in the (df.state == "CANCELLED")

Answer 2

We need to find the rows where the state = 'cancelled'. We can use the .loc(row, column) operator to select the entries where state = 'cancelled' and return the 'Activity' column we then just replace those values with 'cancelled'

import pandas as pd

data = {"ID": [1, 1, 2, 2, 3, 3], "Activity": ['created', 'completed', 'created', 'completed', 'created',
                                               'rejected'], "state": ['cancelled', 'cancelled', 'finished', 'finished', 'rejected', 'rejected']}
df = pd.DataFrame(data)
df.loc[df['state'] == 'cancelled', 'Activity'] = 'cancelled'


print(df)

ID	Activity	State
1	cancelled	cancelled
1	cancelled	cancelled
2	created	finished
2	completed	finished
3	created	rejected
3	rejected	rejected