How to delete rows in Pandas after a certain value?

Question

I replicated a Pandas series with the following code:

data = np.array([1, 2, 3, 4, 5, np.nan, np.nan, np.nan, 9,10,11,12,13,14])
  
ser = pd.Series(data)
print(ser)

I would like to select only the columns before the NaN values so that I only get the values 1,2,3,4,5. How should I do that?

Answer 1

Test missing values with Series.isna and add Series.cummax for repeat Trues after first match and last invert mask by ~, filter in boolean indexing:

a = ser[~ser.isna().cummax()]
print(a)
0    1.0
1    2.0
2    3.0
3    4.0
4    5.0
dtype: float64

Alternative solution with cumulative sum:

a = ser[ser.isna().cumsum().eq(0)]
print(a)
0    1.0
1    2.0
2    3.0
3    4.0
4    5.0
dtype: float64

Details:

print(ser.to_frame().assign(testna = ser.isna(), 
                            cummax = ser.isna().cumsum(),
                            invert = ser.isna().cumsum().eq(0)))
       0  testna  cummax  invert
0    1.0   False       0    True
1    2.0   False       0    True
2    3.0   False       0    True
3    4.0   False       0    True
4    5.0   False       0    True
5    NaN    True       1   False
6    NaN    True       2   False
7    NaN    True       3   False
8    9.0   False       3   False
9   10.0   False       3   False
10  11.0   False       3   False
11  12.0   False       3   False
12  13.0   False       3   False
13  14.0   False       3   False

---

print(ser.to_frame().assign(testna = ser.isna(), 
                            cummax = ser.isna().cummax(),
                            test0 = ~ser.isna().cummax()))

       0  testna  cummax   test0
0    1.0   False   False    True
1    2.0   False   False    True
2    3.0   False   False    True
3    4.0   False   False    True
4    5.0   False   False    True
5    NaN    True    True   False
6    NaN    True    True   False
7    NaN    True    True   False
8    9.0   False    True   False
9   10.0   False    True   False
10  11.0   False    True   False
11  12.0   False    True   False
12  13.0   False    True   False
13  14.0   False    True   False