I have a dataframe (df) that looks like this:
environment event time 2017-04-28 13:08:22 NaN add_rd 2017-04-28 08:58:40 NaN add_rd 2017-05-03 07:59:35 test add_env 2017-05-03 08:05:14 prod add_env ...
Now my goal is for each add_rd
in the event
column, the associated NaN
-value in the environment
column should be replaced with a string RD
.
environment event time 2017-04-28 13:08:22 RD add_rd 2017-04-28 08:58:40 RD add_rd 2017-05-03 07:59:35 test add_env 2017-05-03 08:05:14 prod add_env ...
What I did so far
I stumbled across df['environment'] = df['environment].fillna('RD')
which replaces every NaN
(which is not what I am looking for), pd.isnull(df['environment'])
which is detecting missing values and np.where(df['environment'], x,y)
which seems to be what I want but isn’t working. Furthermore did I try this:
import pandas as pd for env in df['environment']: if pd.isnull(env) and df['event'] == 'add_rd': env = 'RD'
The indexes are missing or some kind of iterator to access the equivalent value in the event
column.
And I tried this:
df['environment'] = np.where(pd.isnull(df['environment']), df['environment'] = 'RD', df['environment']) SyntaxError: keyword can't be an expression
which obviously didn’t worked.
I took a look at several questions but couldn’t build on the suggestions in the answers. Black’s question Simon’s question szli’s question Jan Willems Tulp’s question
So, how do I replace a value in a column based on another columns values?
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Answer
Now my goal is for each add_rd in the event column, the associated NaN-value in the environment column should be replaced with a string RD.
As per @Zero’s comment, use pd.DataFrame.loc
and Boolean indexing:
df.loc[df['event'].eq('add_rd') & df['environment'].isnull(), 'environment'] = 'RD'