Working with Pivot table and Heatmap

My blog contains a step to step guide on working with a pivot table and heatmap on Python

#Importing pandas library
import pandas as pd
url = 'https://raw.githubusercontent.com/resbaz/r-novice-gapminder-files/master/data/gapminder-FiveYearData.csv'

#Defining dataFrame
df1=pd.read_csv(url)
df1.head()
country
year
pop
continent
lifeExp
gdpPercap
0
Afghanistan
1952
8425333.0
Asia
28.801
779.445314
1
Afghanistan
1957
9240934.0
Asia
30.332
820.853030
2
Afghanistan
1962
10267083.0
Asia
31.997
853.100710
3
Afghanistan
1967
11537966.0
Asia
34.020
836.197138
4
Afghanistan
1972
13079460.0
Asia
36.088
739.981106









#Creating a pivot table dataframe with year along x-axes, continent along y-axes and lifeExp filled within cells.

df1 = pd.pivot_table(df1,index = ["continent"], columns= ["year"], values =["lifeExp"])







#Importing seaborn library
import seaborn as sns

df= sns.heatmap(df1)













The life expectancy base on continents for about 50 decades.
The graph shows Africa with the least of about (40 -56)years while  Oceanic has about (72-82 )years.
analytics pythonprogramminglanguage Datasciences BigData AI machineLearning








Comments

  1. Unknown18 July 2019 at 20:40

    Thanks

    ReplyDelete
  2. LanreGiss11 July 2020 at 05:32

    Hello, I am not getting the last part of it which is the seaborn

    df=sns.heatmap(df1)
    Print df1

    It shows 'seaborn has no attribute' heatmap'

    ReplyDelete

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