# 2D Interpolation With Datetime Format X Values

## 14 May 2018 - 1 answer

I have a dataframe like this:

``````import pandas as pd
import numpy as np

time = pd.date_range('2018-05-14 00:00:00','2018-05-14 01:00:00',freq='5T')
mile = np.linspace(0,100,10)
x = list(time)*len(mile)
y = np.repeat(mile,len(time))
z = []
for i in range(0,10,1):
z.extend(np.random.normal(loc=i*5, scale=5, size=13))
origin_data = pd.DataFrame({'x':x, 'y':y ,'z':z})
``````

`origin_data` contains original points' positions(x and y) and their values(z). I want to interpolate the `z` values at these new positions: `x = pd.date_range('2018-05-14 00:00:00','2018-05-14 01:00:00',freq='1T')` with `y = np.linspace(0,91,1)` just using bilinear interpolation.

I learned about the official document about `scipy.interpolate.interp2d`. But its x type is numeric, mine is datetime. Also, the tutorial's `z` values are calculated while mine are already given so I don't know how to handle the order of input `z` value. Could anyone give me an example that contains an interpolation result plot based on the dataframe I provided above? Thank you for your attention!

This is the way I found to this question:

``````import pandas as pd
import numpy as np
from scipy import interpolate
import itertools

time = pd.date_range('2018-05-14 00:00:00','2018-05-14 01:00:00',freq='5T')
mile = np.arange(0,100,10)
x = list(time)*len(mile)
y = np.repeat(mile,len(time))
z = []

for i in range(0,10,1):
z.extend(np.random.normal(loc=i*5, scale=5, size=13))
origin_data = pd.DataFrame({'x':x, 'y':y ,'z':z})

from ggplot import *

ggplot(aes(x = 'x', y = 'y', colour = 'z'), data = origin_data) +\
geom_point(size = 100) +\
scale_x_date(labels = date_format("%Y-%m-%d %H:%M:S"))

x_numeric = [x.timestamp() for x in origin_data['x']]

x_cors = pd.unique(x_numeric)
y_cors = pd.unique(origin_data['y'])

cors = list(itertools.product(x_cors,y_cors))

interp_func = interpolate.LinearNDInterpolator(cors, z)
interp_func = interpolate.CloughTocher2DInterpolator(cors, z)

new_x = [x.timestamp() for x in pd.date_range('2018-05-14 00:00:00','2018-05-14 01:00:00',freq='1T')]
new_y = np.arange(0,91,1)

new_cors = list(itertools.product(new_x,new_y))

new_z = interp_func(new_cors)

new_data = pd.DataFrame({'x':[x for x in new_cors],
'y':[x for x in new_cors],
'z':new_z})

import datetime

new_data['x'] = [pd.Timestamp(x,unit = 's') for x in new_data['x']]

ggplot(aes(x='x',y='y',colour='z'),data=new_data) +\
geom_point(size=100) +\
scale_x_date(labels = date_format("%Y-%m-%d %H:%M:S"))
``````