<jupyter_start><jupyter_text>Trop to Tokyou using data Lets import the required libraries<jupyter_code>import pandas as pd
import numpy as np
import matplotlib.pyplot as plt<jupyter_output><empty_output><jupyter_text>bring in the data<jupyter_code>df = pd.read_csv('AB_TYO_2019.csv')
df.head()
df.shape<jupyter_output><empty_output><jupyter_text>Remove useless columns<jupyter_code># Columns with all Nan values 
df.columns[df.isnull().all()]
# We are going to drop the neighbourhood_group column
df.drop('neighbourhood_group', axis=1, inplace=True)
df.head()<jupyter_output><empty_output><jupyter_text>Fill missing values<jupyter_code>df.columns[df.isnull().any()]
fill_with_mode = lambda col: col.fillna(method = 'ffill')
df = df.apply(fill_with_mode)
df.columns[df.isnull().any()]<jupyter_output><empty_output><jupyter_text>Look at data<jupyter_code>df.hist(figsize=(12, 8))<jupyter_output><empty_output><jupyter_text>Find answers to our questions  Lets see which type of rooms are most popular in Tokyo<jupyter_code>df['room_type'].value_counts().plot.bar();<jupyter_output><empty_output><jupyter_text>So it looks like entire home/apartment is the most popular choice in tokyo  Which room type is the most costly on an average<jupyter_code>df[['room_type', 'price']].groupby('room_type').mean().plot.bar();<jupyter_output><empty_output><jupyter_text>It looks like Entire home/apartment are the most costly on an average  Dose the location of room mater when it comes to pricing ?<jupyter_code>unstacked_df = df[['room_type', 'price', 'neighbourhood']].groupby(['neighbourhood', 'room_type']).mean().unstack()
unstacked_df.plot.bar(figsize=(17, 10));<jupyter_output><empty_output><jupyter_text>Where are you most likemy to find a room<jupyter_code>df['neighbourhood'].value_counts()[:30].plot.bar(figsize=(14, 10));<jupyter_output><empty_output>
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