lets say i have the following dataframe
bb = pd.DataFrame(data = {'date' :['','','','2015-09-02', '2015-09-02', '2015-09-03','','2015-09-08', '', '2015-09-11','2015-09-14','','' ]})
bb['date'] = pd.to_datetime(bb['date'], format="%Y-%m-%d")
I want to interpolate and exptrapolate linearly to fill the missing date values . I used the following code but it doesn't change anything. I am new to pandas. please help
bb= bb.interpolate(method='time')
To extrapolate you have to use bfill() and ffill(). Missing values will be assigned by back- (or forward) values.
To linear interpolate you have to use function interpolate but dates need to convert to numbers:
import numpy as np
import pandas as pd
from datetime import datetime
bb = pd.DataFrame(data = {'date' :['','','','2015-09-02', '2015-09-02', '2015-09-03','','2015-09-08', '', '2015-09-11','2015-09-14','','' ]})
bb['date'] = pd.to_datetime(bb['date'], format="%Y-%m-%d")
# convert to seconds
tmp = bb['date'].apply(lambda t: (t-datetime(1970,1,1)).total_seconds())
# linear interpolation
tmp.interpolate(inplace=True)
# back convert to dates
bb['date'] = pd.to_datetime(tmp, unit='s')
bb['date'] = bb['date'].apply(lambda t: t.date())
# extrapolation for the first missing values
bb.bfill(inplace='True')
print bb
Result:
date
0 2015-09-02
1 2015-09-02
2 2015-09-02
3 2015-09-02
4 2015-09-02
5 2015-09-03
6 2015-09-05
7 2015-09-08
8 2015-09-09
9 2015-09-11
10 2015-09-14
11 2015-09-14
12 2015-09-14
