CODEWITHSUNDEEP
pythonarraysnumpytime-series
ABC
ABC

Reputation: 141

Creating time series data in python

I have data in two numpy arrays:

data = [0.1, 0.3, 0.4, 0.6, 0.7]
time = [10,25, 27, 35, 42]

data corresponds to a value at the equivalent index of time

I want to create another array based a new time array that looks like this:

newtime = [10, 20, 30, 40, 50]

The new data array should look like this:

[0.1, 0.1, 0.4,0.6, 0.7]

Which corresponds to the value of data at each of the new times. For example, at 10s, the value was 0.1, and at 20 seconds the value had not changed, so both should be 0.1

The question is what's the easiest possible way to create the "new data" numpy from the data array and time array in python? I would be happy to try other python libraries as well other than numpy.

Upvotes: 1

Views: 1564

Answers (2)

Кирилл Солодских
Кирилл Солодских

Reputation: 94

For this tasks you can use RedBlackPy, it is supported for Python 3 on linux and macosx. RedBalckPy.Series class supports convenient functionality for time series, it keeps keys always sorted. You don't need to create new Series in your case, because interpolation is built into getitem operator(Series[key]). You have access at any key using interpolation(in your case floor). See the following code example:

import redblackpy as rb

# yours data and time
data = [0.1, 0.3, 0.4, 0.6, 0.7]
time = [10,25, 27, 35, 42]

# create Series object with floor interpolation type 
series = rb.Series( index=time, values=data, dtype='float64',
                    interpolate='floor' )

# now you can access at any key using interpolation,
# it does not create new element, it just use neighbours (t_1 < key < t_2)
# in our case we set floor interpolation, so we get value for t_1
print(series[10]) # prints 0.1
print(series[20]) # prints 0.1
print(series[30]) # prints 0.4
print(series[40]) # prints 0.6
print(series[50]) # prints 0.7

# print Series, Series does not insert this keys
print(Series)

Last print gives the following:

Series object Untitled
10: 0.1
25: 0.3
27: 0.4
35: 0.6
42: 0.7

For more code examples, see article on TowardsDataScience.

Upvotes: 0

klim
klim

Reputation: 1269

Using searchsorted probably is one of the fastest ways

import numpy as np

data = np.array([0.1, 0.3, 0.4, 0.6, 0.7])
time = np.array([10,25, 27, 35, 42])
newtime =np.array([10, 20, 30, 40, 50])

newdata = data[ np.searchsorted(time, newtime, side="right") - 1 ]

print(newdata)

This prints out

[ 0.1  0.1  0.4  0.6  0.7]

Upvotes: 1

Related Questions