I have a pandas dataframe (let’s call it df), and one of the columns of this df (let’s say column 10) specifies if a sensor has acquired data (1 it did, 0 it didn’t). In the rows where the sensor acquired data the columns from 11 to 20 are filled with sensor’s data, otherwise they are filled with zeroes.
What I would like to do is to turn the zeroes in columns from 11 to 20 into nans and do that on the basis of column 10 (i.e.: sensor’s acquisition status) to avoid eliminating “true” zeroes measured by the sensor.
I’m struggling on how to do it, though.
0
It’s best to provide an example of your dataframe so that the answer works for you.
The trick it to use .loc, listing the condition and the columns you would like to change:
import pandas as pd
import numpy as np
df = pd.DataFrame({
"id": [0, 1, 2, 3, 4, 5],
"10": [True, True, True, False, True, False],
"11": [39.5, 0, -34, 0, 0, 0],
"12": [40, 5, 12, 0, 0, 0],
})
df.loc[~df["10"], ["11", "12"]] = np.nan
print(df)
Output:
id 10 11 12
0 0 True 39.5 40.0
1 1 True 0.0 5.0
2 2 True -34.0 12.0
3 3 False NaN NaN
4 4 True 0.0 0.0
5 5 False NaN NaN