import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

Exercise Pandas¶

For these exercices we are using a dataset provided by Airbnb for a Kaggle competition. It describes its offer for New York City in 2019, including types of appartments, price, location etc.

1. Create a dataframe¶

Create a dataframe of a few lines with objects and their poperties (e.g fruits, their weight and colour). Calculate the mean of your Dataframe.

2. Import¶

Import the table called AB_NYC_2019.csv as a dataframe. It is located in the Datasets folder. Have a look at the beginning of the table (head).
Create a histogram of prices

3. Operations¶

Create a new column in the dataframe by multiplying the "price" and "availability_365" columns to get an estimate of the maximum yearly income.

3b. Subselection and plotting¶

Create a new Dataframe by first subselecting yearly incomes between 1 and 100'000. Then make a scatter plot of yearly income versus number of reviews

4. Combine¶

We provide below and additional table that contains the number of inhabitants of each of New York's boroughs ("neighbourhood_group" in the table). Use merge to add this population information to each element in the original dataframe.

5. Groups¶

Using groupby calculate the average price for each type of room (room_type) in each neighbourhood_group. What is the average price for an entire home in Brooklyn ?
Unstack the multi-level Dataframe into a regular Dataframe with unstack() and create a bar plot with the resulting table

6. Advanced plotting¶

Using Seaborn, create a scatter plot where x and y positions are longitude and lattitude, the color reflects price and the shape of the marker the borough (neighbourhood_group). Can you recognize parts of new york ? Does the map make sense ?