| |
| """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() |
