Return Home

Return to Python Projects Page

Gapminder World Population Exporatory Data Analysis¶

Research Question : Is there any correlation between life expectancy and standard of living through the years?¶

Datasource : https://www.kaggle.com/datasets/tklimonova/gapminder-datacamp-2007/data

In [1]:
# Load in some packages
import calendar
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")

population_df = pd.read_csv(r"C:\Users\jki\Downloads\gapminder_full.csv")
population_df
Out[1]:
country year population continent life_exp gdp_cap
0 Afghanistan 1952 8425333 Asia 28.801 779.445314
1 Afghanistan 1957 9240934 Asia 30.332 820.853030
2 Afghanistan 1962 10267083 Asia 31.997 853.100710
3 Afghanistan 1967 11537966 Asia 34.020 836.197138
4 Afghanistan 1972 13079460 Asia 36.088 739.981106
... ... ... ... ... ... ...
1699 Zimbabwe 1987 9216418 Africa 62.351 706.157306
1700 Zimbabwe 1992 10704340 Africa 60.377 693.420786
1701 Zimbabwe 1997 11404948 Africa 46.809 792.449960
1702 Zimbabwe 2002 11926563 Africa 39.989 672.038623
1703 Zimbabwe 2007 12311143 Africa 43.487 469.709298

1704 rows × 6 columns

After getting a sense of the data's structure, it is a good idea to look at a statistical summary of the variables with df.describe()

In [2]:
population_df.describe()
Out[2]:
year population life_exp gdp_cap
count 1704.00000 1.704000e+03 1704.000000 1704.000000
mean 1979.50000 2.960121e+07 59.474439 7215.327081
std 17.26533 1.061579e+08 12.917107 9857.454543
min 1952.00000 6.001100e+04 23.599000 241.165876
25% 1965.75000 2.793664e+06 48.198000 1202.060309
50% 1979.50000 7.023596e+06 60.712500 3531.846988
75% 1993.25000 1.958522e+07 70.845500 9325.462346
max 2007.00000 1.318683e+09 82.603000 113523.132900
In [3]:
# lets  check for missing 
missing_values =  population_df.isna().sum()
print(missing_values)

country       0
year          0
population    0
continent     0
life_exp      0
gdp_cap       0
dtype: int64
In [4]:
# lets check on data types
population_df.info()

<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   population  1704 non-null   int64  
 3   continent   1704 non-null   object 
 4   life_exp    1704 non-null   float64
 5   gdp_cap     1704 non-null   float64
dtypes: float64(2), int64(2), object(2)
memory usage: 80.0+ KB
In [5]:
population_df.head(3)
Out[5]:
country year population continent life_exp gdp_cap
0 Afghanistan 1952 8425333 Asia 28.801 779.445314
1 Afghanistan 1957 9240934 Asia 30.332 820.853030
2 Afghanistan 1962 10267083 Asia 31.997 853.100710

1 Display top 10 Countries with the highest population¶

In [6]:
# Plot the Population size for each  country

population_size = population_df.groupby('country').sum()['population'] 

# Sort the values in descending order and select the top ten countries
top_ten_population_size= population_size.sort_values(ascending=False).head(10)

# Display the result
print(top_ten_population_size)

# Plot the top ten Countries
top_ten_population_size.plot(kind='bar', color='green', figsize=(10, 6))
plt.title('Top Ten Countries based on popluation ')
plt.xlabel('Country')
plt.ylabel('population')
plt.show()

country
China            11497920623
India             8413568878
United States     2738534790
Indonesia         1779874000
Brazil            1467745520
Japan             1341105696
Pakistan          1124200629
Bangladesh        1089064744
Germany            930564520
Nigeria            884496214
Name: population, dtype: int64

2 Display top 10 Countries with the highest life expectancy¶

In [7]:
population_df['life_exp'] = population_df['life_exp'].astype(int)
In [8]:
# Plot the life expectancy  for each  country

life_expectancy = population_df.groupby('country').sum()['life_exp'] 

# Sort the values in descending order and select the top ten
top_ten_life_expectancy= life_expectancy.sort_values(ascending=False).head(10)

# Display the result
print(top_ten_life_expectancy)

# Plot the top ten Country
top_ten_life_expectancy.plot(kind='bar', color='blue', figsize=(10, 6))
plt.title('Top Ten Countries based on life expectancy ')
plt.xlabel('Country')
plt.ylabel('life expectancy ')
plt.show()

country
Iceland        911
Sweden         909
Norway         905
Netherlands    902
Switzerland    901
Japan          893
Canada         891
Australia      890
Denmark        886
France         885
Name: life_exp, dtype: int32

3 Display top 10 Countries with the highest GDP (Incomes)¶

In [9]:
# Plot the GDP (income)  for each  country
income_amount = population_df.groupby('country').sum()['gdp_cap'] 

# Sort the values in descending order and select the top ten
top_ten_income_amount= income_amount.sort_values(ascending=False).head(10)

# Display the result
print(top_ten_income_amount)

# Pltop_ten_income_amount the top ten Country
top_ten_income_amount.plot(kind='bar', color='brown', figsize=(10, 6))
plt.title('Top Ten Countries based on Income ')
plt.xlabel('Country')
plt.ylabel('GDP Income')
plt.show()

country
Kuwait           783994.925660
Switzerland      324892.012860
Norway           320967.678650
United States    315133.816160
Canada           268928.956080
Netherlands      260986.226498
Denmark          260061.898655
Germany          246680.213193
Iceland          246377.067270
Austria          244942.995352
Name: gdp_cap, dtype: float64

Case Study 1: Kuwait¶

In [10]:
population_df
Out[10]:
country year population continent life_exp gdp_cap
0 Afghanistan 1952 8425333 Asia 28 779.445314
1 Afghanistan 1957 9240934 Asia 30 820.853030
2 Afghanistan 1962 10267083 Asia 31 853.100710
3 Afghanistan 1967 11537966 Asia 34 836.197138
4 Afghanistan 1972 13079460 Asia 36 739.981106
... ... ... ... ... ... ...
1699 Zimbabwe 1987 9216418 Africa 62 706.157306
1700 Zimbabwe 1992 10704340 Africa 60 693.420786
1701 Zimbabwe 1997 11404948 Africa 46 792.449960
1702 Zimbabwe 2002 11926563 Africa 39 672.038623
1703 Zimbabwe 2007 12311143 Africa 43 469.709298

1704 rows × 6 columns

In [28]:
# Selecting data for Kuwait
kuwait_data = population_df[population_df['country'] == "Kuwait"]

# Extracting specific columns
f1 = kuwait_data['life_exp']
f2 = kuwait_data['gdp_cap']

# Checking if both conditions hold true
kuwait_filtered_data = kuwait_data[(f1.notnull()) & (f2.notnull())]

kuwait_filtered_data.head(5)
Out[28]:
country year population continent life_exp gdp_cap
852 Kuwait 1952 160000 Asia 55 108382.35290
853 Kuwait 1957 212846 Asia 58 113523.13290
854 Kuwait 1962 358266 Asia 60 95458.11176
855 Kuwait 1967 575003 Asia 64 80894.88326
856 Kuwait 1972 841934 Asia 67 109347.86700
In [27]:
kuwait_filtered_data.describe()
Out[27]:
year population life_exp gdp_cap
count 12.000000 1.200000e+01 12.000000 12.000000
mean 1979.500000 1.206496e+06 68.500000 65332.910472
std 18.027756 7.836823e+05 7.692972 33882.139536
min 1952.000000 1.600000e+05 55.000000 28118.429980
25% 1965.750000 5.208188e+05 63.000000 35065.809143
50% 1979.500000 1.279226e+06 70.000000 53286.233460
75% 1993.250000 1.796880e+06 75.250000 98689.172045
max 2007.000000 2.505559e+06 77.000000 113523.132900
In [29]:
import plotly.express as px

# Plot histogram
fig = px.histogram(kuwait_filtered_data, x='life_exp', height=320, labels={'life_exp':'Life Expectancy'}, title='Life Expectancy Histogram - Kuwait')

# Show the plot
fig.show()
In [32]:
import plotly.express as px

# Plot relationship between life expectancy and years
fig = px.bar(kuwait_filtered_data, x='year', y='life_exp', height=320, labels={'life_exp':'Life Expectancy'}, title='Relationship between Life Expectancy and Years - Kuwait')
fig.update_layout(xaxis_tickangle=-45)  # Rotate x-axis labels for better readability

# Show the plot
fig.show()
In [34]:
import plotly.express as px

# Create a bar plot
fig = px.bar(kuwait_filtered_data, x='year', y='population', color='life_exp', height=320, labels={'population':'Population Kuwait'}, title='Life Expectancy with Respect to Population Growth per Year - Kuwait')

# Show the plot
fig.show()

Case Study 2 : China¶

In [35]:
population_df
Out[35]:
country year population continent life_exp gdp_cap
0 Afghanistan 1952 8425333 Asia 28 779.445314
1 Afghanistan 1957 9240934 Asia 30 820.853030
2 Afghanistan 1962 10267083 Asia 31 853.100710
3 Afghanistan 1967 11537966 Asia 34 836.197138
4 Afghanistan 1972 13079460 Asia 36 739.981106
... ... ... ... ... ... ...
1699 Zimbabwe 1987 9216418 Africa 62 706.157306
1700 Zimbabwe 1992 10704340 Africa 60 693.420786
1701 Zimbabwe 1997 11404948 Africa 46 792.449960
1702 Zimbabwe 2002 11926563 Africa 39 672.038623
1703 Zimbabwe 2007 12311143 Africa 43 469.709298

1704 rows × 6 columns

In [36]:
# Selecting data for China
China_data = population_df[population_df['country'] == "China"]
# Extracting specific columns
Chinaf1 = China_data['life_exp']
Chinaf2 = China_data['gdp_cap']

# Checking if both conditions hold true
China_filtered_data = China_data[(Chinaf1.notnull()) & (Chinaf2.notnull())]

China_filtered_data.head(5)
Out[36]:
country year population continent life_exp gdp_cap
288 China 1952 556263527 Asia 44 400.448611
289 China 1957 637408000 Asia 50 575.987001
290 China 1962 665770000 Asia 44 487.674018
291 China 1967 754550000 Asia 58 612.705693
292 China 1972 862030000 Asia 63 676.900092
In [37]:
China_filtered_data.describe()
Out[37]:
year population life_exp gdp_cap
count 12.000000 1.200000e+01 12.000000 12.000000
mean 1979.500000 9.581601e+08 61.333333 1488.307694
std 18.027756 2.643949e+08 10.174240 1370.628333
min 1952.000000 5.562635e+08 44.000000 400.448611
25% 1965.750000 7.323550e+08 56.000000 603.526020
50% 1979.500000 9.718680e+08 64.000000 851.829425
75% 1993.250000 1.181246e+09 68.500000 1814.146653
max 2007.000000 1.318683e+09 72.000000 4959.114854
In [44]:
import plotly.express as px

# Assuming population_df is defined elsewhere
# Selecting data for China
china_data = population_df[population_df['country'] == "China"]

# Extracting specific columns
f1 = china_data['life_exp']
f2 = china_data['gdp_cap']

# Checking if both conditions hold true
china_filtered_data = china_data[(f1.notnull()) & (f2.notnull())]

# Plot histogram
fig = px.histogram(china_filtered_data, x='life_exp', height=320, labels={'life_exp':'Life Expectancy'}, title='Life Expectancy Histogram - China')

# Show the plot
fig.show()
In [51]:
import plotly.express as px

# Plot relationship between life expectancy and years
fig = px.bar(china_filtered_data, x='year', y='life_exp', height=320, labels={'life_exp':'Life Expectancy'}, title='Relationship between Life Expectancy and Years - China')
fig.update_layout(xaxis_tickangle=-45)  # Rotate x-axis labels for better readability

# Show the plot
fig.show()
In [52]:
import plotly.express as px

# Create a bar plot
fig = px.bar(china_filtered_data, x='year', y='population', color='life_exp', height=320, labels={'population':'Population Kuwait'}, title='Life Expectancy with Respect to Population Growth per Year - China')

# Show the plot
fig.show()

Case Study 3 : Iceland¶

In [83]:
population_df
Out[83]:
country year population continent life_exp gdp_cap
0 Afghanistan 1952 8425333 Asia 28 779.445314
1 Afghanistan 1957 9240934 Asia 30 820.853030
2 Afghanistan 1962 10267083 Asia 31 853.100710
3 Afghanistan 1967 11537966 Asia 34 836.197138
4 Afghanistan 1972 13079460 Asia 36 739.981106
... ... ... ... ... ... ...
1699 Zimbabwe 1987 9216418 Africa 62 706.157306
1700 Zimbabwe 1992 10704340 Africa 60 693.420786
1701 Zimbabwe 1997 11404948 Africa 46 792.449960
1702 Zimbabwe 2002 11926563 Africa 39 672.038623
1703 Zimbabwe 2007 12311143 Africa 43 469.709298

1704 rows × 6 columns

In [55]:
# Selecting data for Iceland
Iceland_data = population_df[population_df['country'] == "Iceland"]
# Extracting specific columns
Icelandf1 = Iceland_data['life_exp']
Icelandf2 = Iceland_data['gdp_cap']

# Checking if both conditions hold true
Iceland_filtered_data = Iceland_data[(Icelandf1.notnull()) & (Icelandf2.notnull())]

Iceland_filtered_data.head(5)
Out[55]:
country year population continent life_exp gdp_cap
684 Iceland 1952 147962 Europe 72 7267.688428
685 Iceland 1957 165110 Europe 73 9244.001412
686 Iceland 1962 182053 Europe 73 10350.159060
687 Iceland 1967 198676 Europe 73 13319.895680
688 Iceland 1972 209275 Europe 74 15798.063620
In [56]:
Iceland_filtered_data.describe()
Out[56]:
year population life_exp gdp_cap
count 12.000000 12.000000 12.000000 12.000000
mean 1979.500000 226978.083333 75.916667 20531.422273
std 18.027756 48541.684217 2.968267 9373.245893
min 1952.000000 147962.000000 72.000000 7267.688428
25% 1965.750000 194520.250000 73.000000 12577.461525
50% 1979.500000 227910.000000 76.000000 21462.284985
75% 1993.250000 262057.000000 78.000000 27207.679625
max 2007.000000 301931.000000 81.000000 36180.789190
In [57]:
import plotly.express as px

# Plot histogram
fig = px.histogram(Iceland_filtered_data, x='life_exp', height=320, labels={'life_exp':'Life Expectancy'}, title='Life Expectancy Histogram - Iceland')

# Show the plot
fig.show()
In [58]:
import plotly.express as px

# Plot relationship between life expectancy and years
fig = px.bar(Iceland_filtered_data, x='year', y='life_exp', height=320, labels={'life_exp':'Life Expectancy'}, title='Relationship between Life Expectancy and Years - Iceland')
fig.update_layout(xaxis_tickangle=-45)  # Rotate x-axis labels for better readability

# Show the plot
fig.show()
In [59]:
import plotly.express as px

# Create a bar plot
fig = px.bar(Iceland_filtered_data, x='year', y='population', color='life_exp', height=320, labels={'pop':'Population Kuwait'}, title='Life Expectancy with Respect to population Growth per Year - Iceland')

# Show the plot
fig.show()
In [ ]: