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Category:Indian filmsQ:
How to remove NaN and negative values from a large dataframe (Pandas)
I am new to using pandas and would like to understand the best way to remove NaN and negative values. My df looks like:
print(df)
0 1 2
0 0.8392851 0.000000 0.000000
1 0.000000 0.978050 0.000000
2 0.000000 0.000000 0.982813
3 0.000000 0.000000 0.000000
4 0.000000 0.000000 0.000000
5 0.000000 0.000000 0.000000
6 0.000000 0.000000 0.000000
7 0.000000 0.000000 0.000000
8 0.000000 0.002480 0.972813
9 0.000000 0.000000 0.000000
10 0.000000 0.000000 0.000000
11 0.000000 0.000000 0.000000
12 0.000000 0.000000 0.000000
13 0.754585 0.000000 0.000000
14 0.000000 0.000000 0.696511
15 0.000000 0.000000 0.000000
16 0.734400 0.000000 0.000000
17 0.000000 0.000000 0.000000
18 0.000000 0.000000 0.000000
19 0.000000 0.000000 0.000000
I would like to remove the NaN and negative values from the dataframe.
A:
Use replace:
df = df.replace(to_replace=np.NaN, value=None, regex=True, inplace=True)
print(df)
0
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