How to apply LR over gridded time series datasets ?

[shalu.ashu50 at gmail.com]

Hello all,

This code is written for multivariate (multiple independent variables x1,x2,x3..xn and a dependent variable y) time series analysis using logistic regression (correlation and prediction). 

#Import Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#Import Dataset
dataset = pd.read_csv(‘precipitation.csv’)
x = dataset.iloc[:,[2,3]].values
y =dataset.iloc[:,4].values

#Split Training Set and Testing Set
from sklearn.cross_validation import train_test_split
x_train, x_test, y_train, y_test =train_test_split(x,y,test_size=0.25)

#Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X=StandardScaler()
x_train=sc_X.fit_transform(x_train)
x_test=sc_X.transform(x_test)

#Training the Logistic Model
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(x_train, y_train)

#Predicting the Test Set Result
y_pred = classifier.predict(x_test)


This code is based on one point location (one lat/long) datasets. Suppose, I am having gridded datasets (which has many points/locations, lat/long, varying in space and time) then How I will implement this code. I am not expertise in python. If somebody can help me in this? If somebody can give me an example or idea so I can implement this code as per my requirement.

Thank you in advance. 

Vishu