In [1]:
import pandas as pd
import numpy as np
import scipy as sp
import plotly.express as px
import seaborn as sns
Working with High Dimensional Data¶
In the following cells we will use visualization tools to push as far as we can in visualizing the MPG dataset in high-dimensional space:
In [2]:
mpg = sns.load_dataset("mpg").dropna()
mpg.head()
Out[2]:
| mpg | cylinders | displacement | horsepower | weight | acceleration | model_year | origin | name | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 18.0 | 8 | 307.0 | 130.0 | 3504 | 12.0 | 70 | usa | chevrolet chevelle malibu |
| 1 | 15.0 | 8 | 350.0 | 165.0 | 3693 | 11.5 | 70 | usa | buick skylark 320 |
| 2 | 18.0 | 8 | 318.0 | 150.0 | 3436 | 11.0 | 70 | usa | plymouth satellite |
| 3 | 16.0 | 8 | 304.0 | 150.0 | 3433 | 12.0 | 70 | usa | amc rebel sst |
| 4 | 17.0 | 8 | 302.0 | 140.0 | 3449 | 10.5 | 70 | usa | ford torino |
Visualizing 1 Dimensional Data¶
In [3]:
px.histogram(mpg, x="displacement")