I have a data table:
Index Value
0 NaN
1 1.15
2 2.25
3 2.33
Condition: First check wherever previous row value is not NaN then replace current row value with previous row value.
Desired output:
Index Value
0 NaN
1 1.15
2 1.15
3 1.15
Compare values for missing values, then get first consecutive value and replace another by DataFrame.where, forward filling missing values and last replace original missing values:
df = pd.DataFrame({'Value':[np.nan,1.15,2.15,3.15,np.nan,2.1,2.2,2.3]})
m = df.notna()
df1 = df.where(m.ne(m.shift())).ffill().where(m)
print (df1)
Value
0 NaN
1 1.15
2 1.15
3 1.15
4 NaN
5 2.10
6 2.10
7 2.10
Details:
print (m.ne(m.shift()))
Value
0 True
1 True
2 False
3 False
4 True
5 True
6 False
7 False
print (df.where(m.ne(m.shift())))
Value
0 NaN
1 1.15
2 NaN
3 NaN
4 NaN
5 2.10
6 NaN
7 NaN
print (df.where(m.ne(m.shift())).ffill())
Value
0 NaN
1 1.15
2 1.15
3 1.15
4 1.15
5 2.10
6 2.10
7 2.10
df is pd.DataFrame({'Value':[np.nan,1.15,2.15,3.15,np.nan,2.1,2.2,2.3]}) the expected output is NaN 1.15 1.15 1.15 NaN 2.1 2.1 2.1 but this gives NaN 1.15 1.15 1.15... all 1.15