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I’m conducting a data analysis on a dataset which displays temperature data (from January to December) over the years.
By importing the dataset I found that it has some inaccurate temperature values in the range of 999.90. What I could do replace this value by the average of the months?
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You can first replace the value 999.90 for np.nan and then use pandas.Dataframe.fillna.
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: df = pd.DataFrame([[10,2,4,5], [20,7,8,9], [30, 999.9, 40, 50]], columns=list('ABCD'))
In [4]: df
Out[4]:
A B C D
0 10 2.0 4 5
1 20 7.0 8 9
2 30 999.9 40 50
In [5]: df = df.replace(999.90, np.nan)
In [6]: df
Out[6]:
A B C D
0 10 2.0 4 5
1 20 7.0 8 9
2 30 NaN 40 50
In [7]: df.fillna(df.mean())
Out[7]:
A B C D
0 10 2.0 4 5
1 20 7.0 8 9
2 30 4.5 40 50
In [8]: df
Out[8]:
A B C D
0 10 2.0 4 5
1 20 7.0 8 9
2 30 NaN 40 50
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I got it. The laptop I was using bugged! Replace the values of
999.90forNaNand then insert in place ofNaNthe average of the values by the following commanddf_rec2_nan.fillna(df_rec2_nan.mean(0),inplace=True)which resulted in the same values as the drec4s solution. Thank you!– Felipe Roque