# We'll also import seaborn, a Python graphing library
import warnings # current version of seaborn generates a bunch of warnings that we'll ignore
warnings.filterwarnings("ignore")
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
sns.set(style="white", color_codes=True)
# Next, we'll load the Iris flower dataset, which is in the "../input/" directory
iris = pd.read_csv("Iris.csv") # the iris dataset is now a Pandas DataFrame
# Let's see what's in the iris data - Jupyter notebooks print the result of the last thing you do
iris.head()
# Press shift+enter to execute this cell
|
Id |
SepalLengthCm |
SepalWidthCm |
PetalLengthCm |
PetalWidthCm |
Species |
| 0 |
1 |
5.1 |
3.5 |
1.4 |
0.2 |
Iris-setosa |
| 1 |
2 |
4.9 |
3.0 |
1.4 |
0.2 |
Iris-setosa |
| 2 |
3 |
4.7 |
3.2 |
1.3 |
0.2 |
Iris-setosa |
| 3 |
4 |
4.6 |
3.1 |
1.5 |
0.2 |
Iris-setosa |
| 4 |
5 |
5.0 |
3.6 |
1.4 |
0.2 |
Iris-setosa |
from pandas.plotting import andrews_curves
andrews_curves(iris.drop("Id", axis=1), "Species")
<AxesSubplot:>
from pandas.plotting import parallel_coordinates
parallel_coordinates(iris.drop("Id", axis=1), "Species")
<AxesSubplot:>
from pandas.plotting import radviz
radviz(iris.drop("Id", axis=1), "Species")
<AxesSubplot:>
