Upload 676_252_1434_72.py

676_252_1434_72.py

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+# -*- coding: utf-8 -*-
+"""676_252_1434_72
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1FniZJX1OfI1PltPCXhpw50znN1aYMFcP
+"""
+
+import numpy as np
+import pandas as pd
+
+import os
+for dirname, _, filenames in os.walk('/content/world_bank_data_2025.csv'):
+  for filename in filenames:
+    print(os.path.join(dirname, filename))
+
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+df = pd.read_csv('/content/world_bank_data_2025.csv')
+df.head()
+
+print("Shape of dataset:", df.shape)
+print("COlumns:\n", df.columns.tolist())
+print("\nMissing values:\n", df.isnull().sum())
+df.dtypes
+
+indicators = df.columns.difference(['country_name', 'country_id', 'year'])
+df_clean = df.dropna(subset=indicators, how='all')
+
+top_countries = df_clean.groupby('country_name')['GDP (Current USD)'].mean().nlargest(10).index
+gdp_plot = df_clean[df_clean['country_name'].isin(top_countries)]
+
+plt.figure(figsize=(12, 6))
+sns.lineplot(data=gdp_plot, x='year', y='GDP (Current USD)', hue='country_name')
+plt.title('GDP Trends (Top 10 Countries by Avg GDP)')
+plt.ylabel('GDP in USD')
+plt.xticks(rotation=45)
+plt.grid(True)
+plt.tight_layout()
+plt.show()
+
+numeric_df = df_clean.select_dtypes(include=['number']).drop(columns=['year'])
+plt.figure(figsize=(10, 8))
+sns.heatmap(numeric_df.corr(), annot=True, cmap='coolwarm', fmt='.2f')
+plt.title('Correlation Between Economic Indicators')
+plt.show()
+
+inflation_2020 = df_clean[df_clean['year'] == 2020]
+plt.figure(figsize=(12, 5))
+sns.histplot(inflation_2020['Inflation (CPI %)'].dropna(), bins=30, kde=True, color='orange')
+plt.title('Inflation Rate Distribution - 2020')
+plt.xlabel('Inflation (CPI %)')
+plt.grid(True)
+plt.show()