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import os
for dirname, _, filenames in os.walk('/content/Per Capita GDP of All Countries 1970 to 2022.csv'):
for filename in filenames:
print(os.path.join(dirname, filename))
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
import plotly.offline as pyo
import plotly.io as pio
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('/content/Per Capita GDP of All Countries 1970 to 2022.csv')
print('### first 5 lines ###', '\n')
df.head()
df.drop(["Sr.No"], axis=1, inplace=True)
rows = df.shape[0]
cols = df.shape[1]
print("Rows : " + str(rows))
print("Columns: " + str(cols))
print('### Dataframe information ###', '\n')
df.info()
print('### Total Null Data in DataFrame ###', '\n')
df.isnull().sum()
print("Number of duplicates: " + str(df.duplicated().sum()))
df['Growth_GDP_ 1970_2022_%'] = (((df['2022'] - df['1970'])/df['1970'])*100).round(2)
df.head()
df_country = df.dropna()
char_bar = df_country.groupby(['Country'])[['2022']].sum().reset_index()
char_bar = char_bar.sort_values(by=("2022"), ascending=False)
top = char_bar.head(10)
fig = go.Figure()
fig.add_trace(go.Bar(x=top['Country'], y=top["2022"]))
fig.update_layout(title='Highest Countries According to GDP 2022',
xaxis_title='Country',
yaxis_title= "2022",
plot_bgcolor='#F0EEED',
paper_bgcolor='#F0EEED',
font=dict(color='black'))
pyo.init_notebook_mode(connected=True)
pyo.iplot(fig)
char_bar = df_country.groupby(['Country'])[['2022']].sum().reset_index()
char_bar = char_bar.sort_values(by=("2022"), ascending=True)
top = char_bar.head(10)
fig = go.Figure()
fig.add_trace(go.Bar(x=top['Country'], y=top["2022"]))
fig.update_layout(title='Lowest Countries According to GDP 2022',
xaxis_title='Country',
yaxis_title= "2022",
plot_bgcolor='#F0EEED',
paper_bgcolor='#F0EEED',
font=dict(color='black'))
pyo.init_notebook_mode(connected=True)
pyo.iplot(fig)
char_bar = df_country.groupby(['Country'])[['Growth_GDP_ 1970_2022_%']].sum().reset_index()
char_bar = char_bar.sort_values(by=("Growth_GDP_ 1970_2022_%"), ascending=False)
top = char_bar.head(10)
fig = go.Figure()
fig.add_trace(go.Bar(x=top['Country'], y=top["Growth_GDP_ 1970_2022_%"]))
fig.update_layout(title='Highest Countries According to Growth_GDP) 1970_2022)%',
xaxis_title='Country',
yaxis_title='Growth_GDP_ 1970_2022)%',
plot_bgcolor='#F0EEED',
paper_bgcolor='#F0EEED',
font=dict(color='black'))
pyo.init_notebook_mode(connected=True)
pyo.iplot(fig)
char_bar = df_country.groupby(['Country'])[['Growth_GDP_ 1970_2022_%']].sum().reset_index()
char_bar = char_bar.sort_values(by=("Growth_GDP_ 1970_2022_%"), ascending=True)
top = char_bar.head(10)
fig = go.Figure()
fig.add_trace(go.Bar(x=top['Country'], y=top["Growth_GDP_ 1970_2022_%"]))
fig.update_layout(title='Lowest Countries According to Growth_GDP_ 1970_2022%',
xaxis_title='Country',
yaxis_title= "Growth_GPD_ 1970_2022)%",
plot_bgcolor='#F0EEED',
paper_bgcolor='#F0EEED',
font=dict(color='black'))
pyo.init_notebook_mode(connected=True)
pyo.iplot(fig)
df_europe = df.loc[df['Country'].isin(['Portugal', 'Spain', 'Italy', 'Germany', 'France'])]
dfy = df_europe.iloc[:,:-1]
dfy = dfy.transpose()
cols = dfy.iloc[0].to_list()
dfy.columns = cols
dfy = dfy.iloc[1:, :]
dfy.plot(figsize=(8, 4))
plt.title("Evolution of GDP - Europe", fontsize= 12)
plt.xlabel('Year', rotation=0, fontsize = 10)
plt.ylabel('GPD', rotation=90, fontsize = 10)
plt.grid()
plt.show();
df_eastern_euro = df.loc[df['Country'].isin(['Hungary', 'Poland', 'Romania', 'Albania'])]
dfy = df_eastern_euro.iloc[:,:-1]
dfy = dfy.transpose()
cols = dfy.iloc[0].to_list()
dfy.columns = cols
dfy = dfy.iloc[1:, :]
dfy.plot(figsize=(8, 4))
plt.title("Evolution of GPD - Eastern Europe", fontsize = 12)
plt.xlabel('Year', rotation=0, fontsize = 10)
plt.ylabel('Growth Rate (%)', rotation=90, fontsize = 10)
plt.grid()
plt.show();
df_top5 = df.loc[df['Country'].isin(['United States', 'China', 'Germany', 'Japan', 'India'])]
dfy = df_top5.iloc[:, :-1]
dfy = dfy.transpose()
cols = dfy.iloc[0].to_list()
dfy.columns = cols
dfy = dfy.iloc[1:, :]
dfy.plot(figsize=(8, 4))
plt.title("Evolution of GDP - Top 5 World Economies", fontsize= 12)
plt.xlabel('Year', rotation=0, fontsize = 10)
plt.ylabel('Growth Rate (%)', rotation=90, fontsize = 10)
plt.grid()
plt.show();
df_brics = df.loc[df['Country'].isin(['Brazil', 'USSR (Former)', 'India', 'China', 'South Africa'])]
dfy = df_brics.iloc[:, :-1]
dfy = dfy.transpose()
cols = dfy.iloc[0].to_list()
dfy.columns = cols
dfy = dfy.iloc[1:, :]
dfy.plot(figsize=(8, 4))
plt.title("Evolution of GDP - BRICS", fontsize = 12)
plt.xlabel('Year', rotation=0, fontsize = 10)
plt.ylabel('Growth Rate (%)', rotation=90, fontsize = 10)
plt.grid()
plt.show();
df_2_korea = df.loc[df['Country'].isin(['Republic of Korea', 'D.P.R. of Korea'])]
dfy = df_2_korea.iloc[:, :-1]
dfy = dfy.transpose()
cols = dfy.iloc[0].to_list()
dfy.columns = cols
dfy = dfy.iloc[1:, :]
dfy.plot(figsize=(8, 4))
plt.title("Evolution of the GDP - South Korea vs North Korea",
fontsize = 12)
plt.xlabel('Year', rotation=0, fontsize = 10)
plt.ylabel('Growth Rate (%)', rotation=90, fontsize = 10)
plt.grid()
plt.show();
df_70_22 = df[['Country', '1970', '2022']]
char_bar = df_70_22.groupby(['Country'])[['1970', '2022']].sum().reset_index()
char_bar = char_bar.sort_values(by=("2022"), ascending=False)
top = char_bar.head(20)
top.plot(x="Country", y=["1970", "2022"], kind="bar", figsize=(12, 5))
plt.title("Comparison between GDP 1970 and 2022 - Top 20 Countries", fontsize = 12)
plt.show()
fig = px.choropleth(df,
locations='Country', locationmode='country names',
color = '2022',hover_name="Country",
color_continuous_scale='Viridis_r')
fig.update_layout(margin={'r':0,'t':0,'l':0,'b':0}, coloraxis_colorbar=dict(
title = 'GDP - 2022',
ticks = 'outside',
tickvals = [0,50000,100000,150000,200000,250000],
dtick = 12))
fig.show() |