Python pandas dataframe fill NaN with other Series
dataframe, pandas, python
Solution
you can use boolean indexing in conjunction with .map() method:
In [178]: fillna.set_index('var1', inplace=True)
In [179]: df.loc[df.var4.isnull(), 'var4'] = df.loc[df.var4.isnull(), 'var1'].map(fillna['mean'])
In [180]: df
Out[180]:
var1 var2 var3 var4
0 a 0 40 1.0
1 a 1 97 2.0
2 a 2 34 1.0
3 b 3 6 3.0
4 b 4 19 2.0
5 c 5 47 6.5
6 c 6 65 1.0
7 c 7 29 34.0
8 c 8 48 6.5
9 d 9 88 10.0
10 d 10 40 12.0
11 d 11 23 12.0
Explanation:
In [184]: df.loc[df.var4.isnull()]
Out[184]:
var1 var2 var3 var4
2 a 2 75 NaN
5 c 5 75 NaN
8 c 8 44 NaN
9 d 9 34 NaN
In [185]: df.loc[df.var4.isnull(), 'var1']
Out[185]:
2 a
5 c
8 c
9 d
Name: var1, dtype: object
In [186]: df.loc[df.var4.isnull(), 'var1'].map(fillna['mean'])
Out[186]:
2 1.0
5 6.5
8 6.5
9 10.0
Name: var1, dtype: float64
UPDATE: starting from Pandas 0.20.1 the .ix indexer is deprecated, in favor of the more strict .iloc and .loc indexers.
Problem
I want to fill NaN values in a DataFrame (df) column (var4) based on a control table (fillna_mean) using column mean, and var1 as index.In the dataframe I want them to match on var1. I have tried doing this with fillna but I dont get it to work all the way. How do I do this in a smart way, using df.var1 as index matching fillna_mean.var1? df: ``` df = pd.DataFrame({'var1' : list('a' * 3) + list('b' * 2) + list('c' * 4) + list('d' * 3) ,'var2' : [i for i in range(12)] ,'var3' : list(np.random.randint(100, size = 12)) ,'var4' : [1, 2, np.nan, 3, 2, np.nan, 1, 34, np.nan, np.nan, 12, 12] }) ``` fillna_mean: ``` fillna = pd.DataFrame({'var1' : ['a', 'b', 'c', 'd'], 'mean' : [1, 3.5, 6.5, 10]}) ``` End result is this: ``` var1 var2 var3 var4 a 0 69 1.0 a 1 17 2.0 a 2 83 1.0 b 3 12 3.0 b 4 36 2.0 c 5 68 6.5 c 6 13 1.0 c 7 30 34.0 c 8 23 6.5 d 9 82 10.0 d 10 32 12.0 d 11 19 12.0 ``` Thanks in advance for input! /swepab