Data Exploration prj 2.py

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

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import pandas as pd


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data = pd.read_csv(r"C:\Users\nanakjyoth\Downloads\Weather.csv")


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data


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data.head()


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data.shape


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data.index


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data.columns


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data.dtypes


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data['Weather'].unique()


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data.nunique()


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data.count()


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data['Weather'].value_counts()


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data.info()


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#now exploration


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data.head(2)


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data.nunique()


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data['Wind Speed_km/h'].nunique()


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data['Wind Speed_km/h'].unique()


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data.head(3)


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data.Weather.value_counts()


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#data.head(2)
data[data.Weather=='Clear']


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data.groupby('Weather').get_group('Clear')


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data [data['Wind Speed_km/h']==4]


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data.isnull().sum()


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data.notnull().sum()


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data.rename(columns = {'Weather' :'weather cnd' }, inplace = True)


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data.head(1)


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data.Visibility_km.mean()


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data.Press_kPa.std()


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data['Rel Hum_%'].var()


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data['weather cnd'].value_counts()


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#filtering
data[data['weather cnd'] == 'Snow']


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#str.contain

data[data['weather cnd'].str.contains('Snow')].tail(20)


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#wind above 24

data[(data['Wind Speed_km/h']>24)&(data['Visibility_km'] ==25)] 


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#mean for ech column in wthr cnd
#data.head(1)

data.groupby('weather cnd').mean()


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#mx and Min vlue for wthr cnd
data.groupby('weather cnd').min()
data.groupby('weather cnd').max()



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#wthr cnd is fog

data[data['weather cnd'] == 'Fog']


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#wthr clr or visblty > 40
data[(data['weather cnd'] == 'Clear') | (data['Visibility_km']>40)]
#data.head(1)
         


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#all instance clear and rltive humidty >50
#and vblity >40


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data.head(1)


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data[(data['weather cnd'] == 'Clear') &(data['Rel Hum_%']> 50) |(data['Visibility_km']> 40)]



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