Global Climate Change

Written by Akshay Chavan on #arccoder Blog

23 Mar 2016
Code, Javascript, Kaggle



Select Country


Select two countries to compare
First Country
Second Country


1990

Ipynb Script

Kaggle Script

# Calculate the average temperature per year for every country
# Collect that into data frame where the
# years take the index values & the
# countries take the column names

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

data = pd.read_csv('Data/GlobalLandTemperaturesByCountry.csv')

## There are 2 ways the dt variable is formatted
# 1. YYYY-MM-DD
# 2. MM/DD/YYYY

### Split the dt coulmn into 'year', 'month', and 'date' columns
def splitDT(datadt):
    l1 = datadt.str.split('-').tolist();
    l2 = data.dt.str.split('/').tolist();

    l = [];
    for index in range(len(l1)):
        if( len(l1[index]) > len(l2[index]) ):
            l.append(l1[index]);
        else:
            elel2 = l2[index];
            elel2.insert(0, elel2.pop())
            l.append(elel2);
    return l;

ymd = pd.DataFrame( splitDT(data.dt), columns = ['year','month','date'] )

### Concat with the original data
data = pd.concat([ymd, data], axis=1)

### Unique Countries
uCountry = data.Country.unique()
len(uCountry)

### Unique Years
uYear = data.year.unique()
len(uYear)

## Create a dataframe with
# - a column 'year'
# - one column for each country
#   with average temp for each year across it

uCountry = np.insert(uCountry, 0, 'year')
matdf = pd.DataFrame(columns=uCountry)
matdf.year = uYear
matdf = matdf.set_index('year')
matdf.describe()

### Loop through every country and find the average temperature from the data given for that country
# This loop is very slow.
# I am pretty new to **pandas**.
# *Would be happy to get suggestions on how calculate such a matrix efficiently.*

for country in uCountry:
    avgTemp = []
    for ind in range(len(uYear)):
        mCY = data.AverageTemperature[(data.Country == country) & (data.year == uYear[ind] )].mean()
        avgTemp.append(mCY)
    matdf[country] = avgTemp

matdf.tail()
matdf.to_csv('matYearCountry.csv')



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