Georg Heiler Georg Heiler - 1 month ago 18
Python Question

pandas concat generates nan values

I am curious why a simple concatenation of two data frames in pandas:

shape: (66441, 1)
dtypes: prediction int64
dtype: object
isnull().sum(): prediction 0
dtype: int64

shape: (66441, 1)
CUSTOMER_ID int64
dtype: object
isnull().sum() CUSTOMER_ID 0
dtype: int64


of the same shape and both without NaN values

foo = pd.concat([initId, ypred], join='outer', axis=1)
print(foo.shape)
print(foo.isnull().sum())


can result in a lot of NaN values if joined.

(83384, 2)
CUSTOMER_ID 16943
prediction 16943


How can I fix this problem and prevent NaN values being introduced?



Trying to reproduce it like

aaa = pd.DataFrame([0,1,0,1,0,0], columns=['prediction'])
print(aaa)
bbb = pd.DataFrame([0,0,1,0,1,1], columns=['groundTruth'])
print(bbb)
pd.concat([aaa, bbb], axis=1)


failed e.g. worked just fine as no NaN values were introduced.

Answer

I think there is problem with different index values, so where concat cannot align get NaN:

aaa  = pd.DataFrame([0,1,0,1,0,0], columns=['prediction'], index=[4,5,8,7,10,12])
print(aaa)
    prediction
4            0
5            1
8            0
7            1
10           0
12           0

bbb  = pd.DataFrame([0,0,1,0,1,1], columns=['groundTruth'])
print(bbb)
   groundTruth
0            0
1            0
2            1
3            0
4            1
5            1

print (pd.concat([aaa, bbb], axis=1))
    prediction  groundTruth
0          NaN          0.0
1          NaN          0.0
2          NaN          1.0
3          NaN          0.0
4          0.0          1.0
5          1.0          1.0
7          1.0          NaN
8          0.0          NaN
10         0.0          NaN
12         0.0          NaN

Solution is reset_index if indexes values are not necessary:

aaa.reset_index(drop=True, inplace=True)
bbb.reset_index(drop=True, inplace=True)

print(aaa)
   prediction
0           0
1           1
2           0
3           1
4           0
5           0

print(bbb)
   groundTruth
0            0
1            0
2            1
3            0
4            1
5            1

print (pd.concat([aaa, bbb], axis=1))
   prediction  groundTruth
0           0            0
1           1            0
2           0            1
3           1            0
4           0            1
5           0            1