Analsis_of_Avg_temp_of_major_Indian_Cities.py

#!/usr/bin/env python
# coding: utf-8

# In[1]:


import pandas as pd
import numpy as np
import seaborn as sns
import plotly.express as px
from plotly.offline import init_notebook_mode
init_notebook_mode(connected=True)


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Cities_temp_data = pd.read_csv("E:/Python/Data/Global_Warming_Spatial_Analysis/GlobalLandTemperaturesByCity.csv")


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Cities_temp_data.head()


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Indian_cities = Cities_temp_data[Cities_temp_data['Country'] == 'India']


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Indian_cities['City'].unique()


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cities = ['New Delhi', 'Visakhapatnam', 'Bombay', 'Bangalore', 'Agra','Varanasi', 'Calcutta' ]


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cities = Indian_cities[Indian_cities['City'].isin(cities)]
cities.head()


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cities['Latitude'] = cities['Latitude'].str.strip('N')


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cities['Longitude'] = cities['Longitude'].str.strip('E')


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cities.head()


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cities['dt'] = pd.to_datetime(cities['dt'])


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cities['Month'] = cities['dt'].dt.month


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cities.head()


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cities_temp = cities.groupby(['Month','City'])['AverageTemperature'].mean().to_frame().reset_index()
cities_temp.head()


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cities_temp.columns = ['Month','City','Mean_temp']
cities_temp.head()


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df = cities_temp.merge(
                    cities,
                    on = 'City' 
                )
df.head()


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data = df.drop_duplicates(subset=['Month_x','City'])


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data.head()


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data = data.rename(columns = {'Month_x': 'Month'}, inplace = False)


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data.head()


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data2 = data[['Month', 'City', "Mean_temp", 'Country', 'Latitude', 'Longitude']]


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data2.head()


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import plotly.graph_objs as go


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heatmap_data = [go.Heatmap(
    x=data2['Month'],
    y=data2['City'],
    z=data2['Mean_temp']
)]


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layout = go.Layout(title='Avg temp of Cities by Month')


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fig = go.Figure(data = heatmap_data, layout = layout)
fig.show()


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import folium
from folium.plugins import HeatMap
basemap = folium.Map()


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basemap


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data2.head()


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for id,row in data2.iterrows():
    folium.Marker(location=[row['Latitude'], row['Longitude']], popup = row['Mean_temp']).add_to(basemap)


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basemap


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