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