I have a series and df
s = pd.Series([1,2,3,5])
df = pd.DataFrame()
When I add columns to df like this
df.loc[:, "0-2"] = s.iloc[0:3]
df.loc[:, "1-3"] = s.iloc[1:4]
I get df
0-2 1-3
0 1 NaN
1 2 2.0
2 3 3.0
Why am I getting NaN? I tried create new series with correct idxs, but adding it to df still causes NaN.
What I want is
0-2 1-3
0 1 2
1 2 3
2 3 5
Try either of the following lines.
df.loc[:, "1-3"] = s.iloc[1:4].values
# -OR-
df.loc[:, "1-3"] = s.iloc[1:4].reset_index(drop=True)
Your original code is trying unsuccessfully to match the index of the data frame df to the index of the subset series s.iloc[1:4]. When it can't find the 0 index in the series, it places a NaN value in df at that location. You can get around this by only keeping the values so it doesn't try to match on the index or resetting the index on the subset series.
>>> s.iloc[1:4]
1 2
2 3
3 5
dtype: int64
Notice the index values since the original, unsubset series is the following.
>>> s
0 1
1 2
2 3
3 5
dtype: int64
The index of the first row in df is 0. By dropping the indices with the values call, you bypass the index matching which is producing the NaN. By resetting the index in the second option, you make the indices the same.
