GDP per Cap in different continents

Loading the datasets and packages

We will first load the essential packages and dataset

#Import data
import pandas as pd 

df = pd.read_csv("../../data_sources/gapminder.csv")

Inspect data

df.columns
df.info()
df.describe() 
df.dtypes
df["lifeExp"] 
df["lifeExp"].unique()
df["lifeExp"].describe()
df["continent"].describe()
df["continent"]

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1704 entries, 0 to 1703
Data columns (total 6 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   country    1704 non-null   object 
 1   year       1704 non-null   int64  
 2   pop        1704 non-null   float64
 3   continent  1704 non-null   object 
 4   lifeExp    1704 non-null   float64
 5   gdpPercap  1704 non-null   float64
dtypes: float64(3), int64(1), object(2)
memory usage: 80.0+ KB

0         Asia
1         Asia
2         Asia
3         Asia
4         Asia
         ...  
1699    Africa
1700    Africa
1701    Africa
1702    Africa
1703    Africa
Name: continent, Length: 1704, dtype: object

Filter and subset the data and grouping

subset_con=df[["continent", "gdpPercap", "year"]]
summary_con= subset_con.groupby("continent")["gdpPercap"].mean()
summary_con= summary_con.reset_index()

Visualize the data

Summary of mean GDP per Cap in different continents

import seaborn as sns
sns.catplot(data=df,
            x="continent",
            y="gdpPercap",
            kind="box",
            estimator="std",
            hue="continent"
            ) 

GPD per Capital in different continents

Visualize the data

Summary of GDP per Cap in different continents

sns.catplot(data=summary_con,
            x="continent",
            y="gdpPercap",
            kind="box",
            estimator="std",
            hue="continent"
            ) 

Interactive plot

import plotly.express as px
import matplotlib.pyplot as plt
px.scatter(data_frame = df, x = "continent", y = "gdpPercap", color = "continent")

Make a global chloropeth

We will now visualize it on a world map

# Compute mean GDP per capita per continent
continent_gdp = df.groupby("continent")["gdpPercap"].mean()

# Map each country's continent GDP
df["continent_gdp"] = df["continent"].map(continent_gdp)

# Drop duplicates to keep only one row per country
country_continent_gdp = df.drop_duplicates("country")[["country", "continent_gdp"]]

#plot
px.choropleth(country_continent_gdp,
                    locations="country",
                    locationmode="country names",
                    color="continent_gdp",
                    color_continuous_scale="Plasma",
                    title="Continent-Level Avg GDP per Capita (Mapped by Country)",
                    labels={"continent_gdp": "Continent Avg GDP"})

GPD per Capital in different continents