Menu
I'm using Pandas and Numpy and I'm trying to replace all NaN values in a Series like this one:
date a
2017-04-24 01:00:00 [1,0,0]
2017-04-24 01:20:00 [1,0,0]
2017-04-24 01:40:00 NaN
2017-04-24 02:00:00 NaN
2017-04-24 02:20:00 [0,1,0]
2017-04-24 02:40:00 [1,0,0]
2017-04-24 03:00:00 NaN
2017-04-24 03:20:00 [0,0,1]
2017-04-24 03:40:00 NaN
2017-04-24 04:00:00 [1,0,0]
with the nearest objcet (a Numpy array in this case). The result is:
date a
2017-04-24 01:00:00 [1,0,0]
2017-04-24 01:20:00 [1,0,0]
2017-04-24 01:40:00 [1,0,0]
2017-04-24 02:00:00 [0,1,0]
2017-04-24 02:20:00 [0,1,0]
2017-04-24 02:40:00 [1,0,0]
2017-04-24 03:00:00 [1,0,0]
2017-04-24 03:20:00 [0,0,1]
2017-04-24 03:40:00 [0,0,1]
2017-04-24 04:00:00 [1,0,0]
Does someone know an efficient method to do it? Many thanks.
600
This can be done by using the fillna() method. The basic operation of this pandas series. fillna() method is used to replace missing values (Nan or NA) with a specified value. Initially, the method verifies all the Nan values and replaces them with the assigned replacement value.
fillna() from the pandas' library, we can easily replace the 'NaN' in the data frame. Procedure: To calculate the mean() we use the mean function of the particular column. Now with the help of fillna() function we will change all 'NaN' of that particular column for which we have its mean.
Convert Nan to Empty String in PandasUse df. replace(np. nan,'',regex=True) method to replace all NaN values to an empty string in the Pandas DataFrame column.
drop nulls then fill back up with reindex
df.set_index('date').a.dropna().reindex(df.date, method='nearest').reset_index()
date a
0 2017-04-24 01:00:00 [1, 0, 0]
1 2017-04-24 01:20:00 [1, 0, 0]
2 2017-04-24 01:40:00 [1, 0, 0]
3 2017-04-24 02:00:00 [0, 1, 0]
4 2017-04-24 02:20:00 [0, 1, 0]
5 2017-04-24 02:40:00 [1, 0, 0]
6 2017-04-24 03:00:00 [0, 0, 1]
7 2017-04-24 03:20:00 [0, 0, 1]
8 2017-04-24 03:40:00 [1, 0, 0]
9 2017-04-24 04:00:00 [1, 0, 0]
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
