Upload capita_gdp_of_all_countries.py

capita_gdp_of_all_countries.py

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+# -*- coding: utf-8 -*-
+"""Capita GDP of All Countries
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1miKbrdpnHpgvZ8b8vdLzi06jWrcedtbx
+"""
+
+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()