Nan Values When Adding Two Columns

I have two dataframes with different indexing that I want to sum the same column from the two dataframes. I tried the following but gives NaN values result['Anomaly'] = df['Anomal

Solution 1:

Here is necessary align by datetimes, so first use DataFrame.set_index for DatetimeIndex and then use Series.add:

df = df.set_index('date')
tmp = tmp.set_index('date')
result = df['Anomaly'].add(tmp['Anomaly'], fill_value=0).reset_index()

Solution 2:

You can try this

pd.concat([df, tmp]).groupby('date', as_index=False)["Anomaly"].sum()

         date  Anomaly
0  2018-12-06        0
1  2019-01-07        1
2  2019-02-06        1
3  2019-03-06        0
4  2019-04-06        0

Solution 3:

combine_first():

res = pd.DataFrame({'date':df.date,'Anomaly':tmp.Anomaly.combine_first(df.Anomaly)})
print(res)

---

dateAnomaly02018-12-06      0.012019-01-07      1.022019-02-06      1.032019-03-06      0.042019-04-06      0.0

Solution 4:

You must first set correct indices on your dataframes, and then add using the date indices:

tmp1 = tmp.set_index('date')
result= df.set_index('date')
result.loc[tmp1.index] += tmp1
result.reset_index(inplace=True)