 # Your GEE data in Pandas

Your spatial GEE data in a pandas data-structure.

Step 1: install the Google earth engine python api.

Step 2: run the code below.

```import ee
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Initialize the GEE
ee.Initialize()

# import the RS products

# Define time range
startyear = 2000
endyear = 2001

# create list for years
years = range(startyear,endyear);

# make a list with months
months = range(1,2);

# Set date in ee date format
startdate = ee.Date.fromYMD(startyear,1,1)
enddate = ee.Date.fromYMD(endyear+1,12,31)

# Filter chirps
Pchirps = chirps.filterDate(startdate, enddate).sort('system:time_start', False).select(&quot;precipitation&quot;)

# Define geograpic domain
area = ee.Geometry.Rectangle(-20.0, 20.0, 20, 20.0)

# calculate the monthly mean
def calcMonthlyMean(imageCollection):
mylist = ee.List([])
for y in years:
for m in months:
w = imageCollection.filter(ee.Filter.calendarRange(y, y, 'year')).filter(ee.Filter.calendarRange(m, m, 'month')).sum();
mylist = mylist.add(w.set('year', y).set('month', m).set('date', ee.Date.fromYMD(y,m,1)).set('system:time_start',ee.Date.fromYMD(y,m,1)))
return ee.ImageCollection.fromImages(mylist)

# run the calcMonthlyMean function
monthlyChirps = ee.ImageCollection(calcMonthlyMean(Pchirps))

# select the region of interest, 25000 is the cellsize in meters
monthlyChirps = monthlyChirps.getRegion(area,25000,&quot;epsg:4326&quot;).getInfo()

# get january (index = 0)
January = pd.DataFrame(monthlyChirps, columns = monthlyChirps)
# remove the first line
January = January[1:]

# make sure unicode characters are removed
January['id'] = January['id'].str.decode('utf-8').replace(u'\xf1', 'n').astype('int')

# print the result for january
print January

# get the longitudes
lons = np.array(January.longitude)

# get the latitudes
lats = np.array(January.latitude)

# get the precipitation values
data = np.array(January.precipitation)

```