import pandas as pd
data = [['a',1],['b',2],['c',3]]
df = pd.DataFrame(data, columns = ['letter', 'number']
exclude_list = [2, 4, 6]
I want to change row 2 in df, where "number" == 2, to empty/nan. I want to do this by comparing the "number" column to the exclude list, and if there is a match, exclude that row.
Check these useful functions:
mask : Lets you replace values where a condition is met, we use the inplace=True flag to perform the operation without using no auxiliary data structure (or significant extra storage in simple words).isin : To check if values are in another list.
df.number.mask(df.number.isin(exclude_list),inplace=True)
df
Out[200]:
letter number
0 a 1.0
1 b NaN
2 c 3.0
df.loc[df['number'].isin(exclude_list), 'number'] = None
letter number
0 a 1.0
1 b NaN
2 c 3.0
For the whole row
df.loc[df['number'].isin(exclude_list), :] = None
letter number
0 a 1.0
1 None NaN
2 c 3.0