Upload gaqdaqi_321_890.py

gaqdaqi_321_890.py

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+# -*- coding: utf-8 -*-
+"""GAQDAQI.321.890
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/10yCiraevWJgKWVmJno07vSCNVIvsiA8q
+"""
+
+import pandas as pd
+
+file_path = '/content/global_air_quality_data_10000.csv'
+df_air_quality = pd.read_csv(file_path)
+
+df_air_quality.head()
+
+df_air_quality.info()
+
+df_air_quality.describe(include='all')
+
+df_air_quality.isnull().sum()
+
+df_air_quality.isnull().sum()
+
+df_air_quality.describe(include='all')
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+plt.figure(figsize=(10, 6))
+sns.histplot(df_air_quality['PM2.5'], bins=30, kde=True)
+plt.title('Distribution of PM2.5')
+plt.xlabel('PM2.5')
+plt.ylabel('Frequency')
+plt.show()
+
+plt.figure(figsize=(12, 6))
+plt.plot(df_air_quality['Date'], df_air_quality['PM2.5'])
+plt.title('PM2.5 Levels Over Time')
+plt.xlabel('Date')
+plt.ylabel('PM2.5')
+plt.show()
+
+plt.figure(figsize=(15, 8))
+sns.boxplot(x='City', y='PM2.5', data=df_air_quality)
+plt.title('PM2.5 Levels by City')
+plt.xlabel('City')
+plt.ylabel('PM2.5')
+plt.xticks(rotation=90)
+plt.show()
+
+numeric_cols = df_air_quality.select_dtypes(include=['number'])
+
+plt.figure(figsize=(12, 10))
+sns.heatmap(numeric_cols.corr(), annot=True, cmap='coolwarm', linewidths=0.5)
+plt.title('Correlation Heatmap')
+plt.show()
+
+plt.figure(figsize=(10, 6))
+sns.scatterplot(x='PM2.5', y='PM10', data=df_air_quality)
+plt.title('Scatter Plot of PM2.5 vs. PM10')
+plt.xlabel('PM2.5')
+plt.ylabel('PM10')
+plt.show()
+
+city_pollutants_mean = df_air_quality.groupby('City')[['PM2.5', 'PM10', 'NO2', 'SO2', 'CO', 'O3']].mean()
+city_pollutants_mean
+
+plt.figure(figsize=(15, 8))
+city_pollutants_mean['PM2.5'].plot(kind='bar')
+plt.title('Mean PM2.5 Levels by City')
+plt.xlabel('City')
+plt.ylabel('Mean PM2.5')
+plt.xticks(rotation=90)
+plt.show()
+
+sns.pairplot(df_air_quality[['PM2.5', 'PM10', 'NO2', 'SO2', 'CO', 'O3']])
+plt.suptitle('Pair Plot of Selected Pollutants', y=1.02)
+plt.show()
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+import numpy as np
+
+data = {
+    'Date': pd.date_range(start='1/1/2020', periods=100, freq='D'),
+    'PM2.5' : np.random.rand(100) * 100,
+    'OtherMetric': np.random.rand(100) * 50
+}
+df_air_quality = pd.DataFrame(data)
+
+df_air_quality['Date'] = pd.to_datetime(df_air_quality['Date'])
+df_air_quality.set_index('Date', inplace= True)
+
+seasonal_trends = df_air_quality.resample('M').mean(numeric_only=True)
+
+plt.figure(figsize=(12, 8))
+plt.plot(seasonal_trends)
+plt.title('Seasonal Trends in Air Quallity')
+plt.xlabel('Month')
+plt.ylabel('Mean Value')
+plt.legend(seasonal_trends.columns, loc='upper right')
+plt.show()
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+import numpy as np
+
+# Example DataFrame creation (Replace this with your actual data loading step)
+data = {
+    'Date': pd.date_range(start='1/1/2020', periods=100, freq='D'),
+    'Country': np.random.choice(['USA', 'China', 'India', 'Germany', 'Brazil'], 100),
+    'PM2.5': np.random.rand(100) * 100,
+    'PM10': np.random.rand(100) * 150,
+    'NO2': np.random.rand(100) * 50,
+    'SO2': np.random.rand(100) * 20,
+    'CO': np.random.rand(100) * 10,
+    'O3': np.random.rand(100) * 70,
+    'OtherMetric': np.random.rand(100) * 50
+}
+df_air_quality = pd.DataFrame(data)
+
+# Ensure the Date column is in datetime format and set it as the index
+df_air_quality['Date'] = pd.to_datetime(df_air_quality['Date'])
+df_air_quality.set_index('Date', inplace=True)
+
+# Group by country and compute mean of pollutants
+country_pollutants_mean = df_air_quality.groupby('Country')[['PM2.5', 'PM10', 'NO2', 'SO2', 'CO', 'O3']].mean()
+print(country_pollutants_mean)
+
+# Plot the mean values of pollutants by country
+fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(15, 15))
+
+pollutants = ['PM2.5', 'PM10', 'NO2', 'SO2', 'CO', 'O3']
+for i, pollutant in enumerate(pollutants):
+    row, col = divmod(i, 2)
+    sns.barplot(x=country_pollutants_mean.index, y=country_pollutants_mean[pollutant], ax=axes[row, col])
+    axes[row, col].set_title(f'Mean {pollutant} by Country')
+    axes[row, col].set_xlabel('Country')
+    axes[row, col].set_ylabel(f'Mean {pollutant}')
+
+plt.tight_layout()
+plt.show()
+
+plt.figure(figsize=(15, 10))
+sns.heatmap(country_pollutants_mean, annot=True, cmap= 'YlGnBu', linewidths=0.5)
+plt.title('Mean Pollutant Levels by Country')
+plt.xlabel('Pollutants')
+plt.ylabel('Country')
+plt.show()