import pandas as pd import seaborn as sns from matplotlib import pyplot as plt import io import requests from PIL import Image plt.figure(figsize=(15,8)) # Setting plot size

df = pd.read_csv('AB_NYC_2019.csv')

df.columns

df.shape

df.dtypes

df.info()

df.head()

df.tail()

df.describe()

df.isnull().sum()

columnsToDrop = ['id', 'host_name', 'last_review']

columnsToDrop = ['id', 'host_name', 'last_review'] df.drop(columnsToDrop, axis="columns", inplace=True) # inplace = True returns new df

df.fillna({'reviews_per_month': 0}, inplace=True) # {'column_name': value}

df['name'] # column_name, pandas series

df[['name', 'price']] # multiple columns, pandas df

df[0:111] # rows by index

df[['name', 'price']][0:111]

df['price'] < 100 # Boolean Indexing

booleanMask = df['price'] < 100

df[booleanMask]

df.nlargest(10, 'price')

df['neighbourhood_group'].unique()

df['neighbourhood_group'].value_counts()

df['neighbourhood'].value_counts().head(10)

df['neighbourhood'].value_counts().head(10).plot(kind="bar")

sns.countplot(data=df, x="neighbourhood_group" )

order = df['neighbourhood_group'].value_counts().index sns.countplot(data=df, x='neighbourhood_group', order=order)

sns.countplot(data=df, x='neighbourhood_group', order=order, hue="room_type")

sns.histplot(df['price'])

mask = df['price'] <= 500

df[mask]

sns.histplot(df[mask]['price'])

sns.histplot(df[mask]['price'], kde=True)

df[mask].price.mean() affordableDf = df[mask]

plt.figure(figsize=(15,8)) sns.violinplot(data=affordableDf, x="neighbourhood_group", y="price")

affordableDf.plot( kind='scatter', x='longitude', y='latitude', c='price', cmap='inferno', colorbar=True, alpha=0.8, figsize=(12,8))

# url ='https://upload.wikimedia.org/wikipedia/commons/e/ec/Neighbourhoods_New_York_City_Map.PNG' # im = Image.open(requests.get(url, stream=True).raw) # print(requests.get(url, stream=True).content) im = Image.open('Neighbourhoods_New_York_City_Map.png') plt.imshow(im, zorder=0, extent=[-74.258, -73.7, 40.49, 40.92]) ax = plt.gca() affordableDf.plot( ax=ax, zorder=1, kind='scatter', x='longitude', y='latitude', c='price', cmap='inferno', colorbar=True, alpha=0.8, figsize=(12,8) )