 import pandas as pd

	import matplotlib.pyplot as plt
	df = pd.read_csv('weatherAUS.csv')
	df['RainToday'].replace({'No': 0, 'Yes': 1},inplace = True)
	df['RainTomorrow'].replace({'No': 0, 'Yes': 1},inplace = True)
	def possible_rain():
	    
	    fig = plt.figure(figsize = (8,5))
	    df.RainTomorrow.value_counts(normalize = True).plot(kind='bar', color= ['skyblue','navy'], alpha = 0.9, rot=0)
	    plt.title('RainTomorrow Indicator No(0) and Yes(1) in the Imbalanced Dataset')
	    plt.show()
	

	def datatype():
	    fig, axarr = plt.subplots(1, 2, figsize=(20, 8))
	

	    df.dtypes.value_counts().plot.pie(explode=[0.1,0.1],autopct='%1.1f%%',shadow=True,ax=axarr[1])
	    axarr[1].set_title("type of our data ", fontsize=18)
	

	    df.dtypes.value_counts().plot(kind='bar',ax=axarr[0])
	    plt.title('type of our data');
	    axarr[0].set_title("type of our data ", fontsize=18)
	    plt.show()
	    
	def analyze():
	    data = pd.read_csv("Weather Data in India from 1901 to 2017.csv")
	        # Average monthly rainfall in India
	    ax=data[['JAN', 'FEB', 'MAR', 'APR','MAY', 'JUN', 'AUG', 'SEP', 'OCT','NOV','DEC']].mean().plot.bar(width=0.5, linewidth=2, figsize=(16,10))
	    plt.xlabel('Month',fontsize=30)
	    plt.ylabel('Monthly Rainfall (in mm)', fontsize=30)
	    plt.title('Monthly Rainfall in Subdivisions of India', fontsize=25)
	    ax.tick_params(labelsize=10)
	    plt.grid()
	    plt.show()

 import pandas as pd

	import matplotlib.pyplot as plt
	df = pd.read_csv('weatherAUS.csv')
	df['RainToday'].replace({'No': 0, 'Yes': 1},inplace = True)
	df['RainTomorrow'].replace({'No': 0, 'Yes': 1},inplace = True)
	def possible_rain():
	    
	    fig = plt.figure(figsize = (8,5))
	    df.RainTomorrow.value_counts(normalize = True).plot(kind='bar', color= ['skyblue','navy'], alpha = 0.9, rot=0)
	    plt.title('RainTomorrow Indicator No(0) and Yes(1) in the Imbalanced Dataset')
	    plt.show()
	

	def datatype():
	    fig, axarr = plt.subplots(1, 2, figsize=(20, 8))
	

	    df.dtypes.value_counts().plot.pie(explode=[0.1,0.1],autopct='%1.1f%%',shadow=True,ax=axarr[1])
	    axarr[1].set_title("type of our data ", fontsize=18)
	

	    df.dtypes.value_counts().plot(kind='bar',ax=axarr[0])
	    plt.title('type of our data');
	    axarr[0].set_title("type of our data ", fontsize=18)
	    plt.show()
	    
	def analyze():
	    data = pd.read_csv("Weather Data in India from 1901 to 2017.csv")
	        # Average monthly rainfall in India
	    ax=data[['JAN', 'FEB', 'MAR', 'APR','MAY', 'JUN', 'AUG', 'SEP', 'OCT','NOV','DEC']].mean().plot.bar(width=0.5, linewidth=2, figsize=(16,10))
	    plt.xlabel('Month',fontsize=30)
	    plt.ylabel('Monthly Rainfall (in mm)', fontsize=30)
	    plt.title('Monthly Rainfall in Subdivisions of India', fontsize=25)
	    ax.tick_params(labelsize=10)
	    plt.grid()
	    plt.show()

# Average monthly rainfall in India