Mikael Mengistu mikaelm12

## dataexploration6.py
bedroom_val_counts.sort_index().plot.bar()

## dataexploration5.py
bedroom_val_counts.plot.bar()

## dataexploration11.py
listings.groupby("neighbourhood")["price"].mean().plot.bar(figsize=(15,5))

## dataexploration10.py
neighborhoods = listings['neighbourhood'].value_counts()
ax = neighborhoods.plot.bar(x='neighbourhood',figsize=(15,5)) # Increased figsize so x axis values don't overlap

## dataexploration4.py
bedroom_val_counts = listings.bedrooms.value_counts()

## dataexploration9.py
bedrooms.mean().plot.bar(y='price')

## dataexploration7.py
listings.price = listings.price.replace('[\$,]', '', regex=True).astype(float)

## dataexploration8.py
bedrooms = listings.groupby('bedrooms')
bedrooms["price"].describe()

## dataexploration3.py
listings.shape
listings.head()

## dataexploration2.py
listings = pd.read_csv("../input/listings.csv")
	neighborhoods = listings['neighbourhood'].value_counts()
	ax = neighborhoods.plot.bar(x='neighbourhood',figsize=(15,5)) # Increased figsize so x axis values don't overlap
	bedrooms = listings.groupby('bedrooms')
	bedrooms["price"].describe()