gaqdaqi_321_890.py

# -*- 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()