<jupyter_start><jupyter_text>2.2<jupyter_code>df['house_type'].value_counts()<jupyter_output><empty_output><jupyter_text>2.3 avg price<jupyter_code>prc_more_2_bath=df.loc[df['bathroom']>2 ]['price']
print('avg price of house more than 2 bathroom is ${}'.format(prc_more_2_bath.mean()))<jupyter_output>avg price of house more than 2 bathroom is $383645.45454545453<jupyter_text>2.4<jupyter_code>print('mean unit price is ${}'.format(df['unit_price'].mean()))
print('median unit price is ${}'.format(df['unit_price'].median()))<jupyter_output>median unit price is $130.13392857142858<jupyter_text>2.5<jupyter_code>df.groupby('house_type').mean()['price']<jupyter_output><empty_output><jupyter_text>2.6<jupyter_code>from scipy import stats
result = stats.linregress(df['area'],df['price'])
print('slope is {}'.format(result.slope))
print('intercept is {}'.format(result.intercept))
print('r square is {}'.format(result.rvalue *result.rvalue))
print('p value is {}'.format(result.pvalue))<jupyter_output>slope is 79.95495729411489
intercept is 156254.76245096227
r square is 0.2343900121890692
p value is 0.001340065037461188<jupyter_text>2.7<jupyter_code>print('price of house with {}sqft is ${}'.format(2000,2000*result.slope+result.intercept))<jupyter_output>price of house with 2000sqft is $316164.67703919206
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