Python data visualizations

This notebook demos Python data visualizations on the Iris dataset

This Python 3 environment comes with many helpful analytics libraries installed. It is defined by the kaggle/python docker image

We'll use three libraries for this tutorial: pandas, matplotlib, and seaborn.

Press "Fork" at the top-right of this screen to run this notebook yourself and build each of the examples.

In [1]:

# First, we'll import pandas, a data processing and CSV file I/O library
import pandas as pd

# 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
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("../input/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

Out[1]:

	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

In [2]:

# Let's see how many examples we have of each species
iris["Species"].value_counts()

Out[2]:

Iris-virginica     50
Iris-versicolor    50
Iris-setosa        50
Name: Species, dtype: int64

In [3]:

# The first way we can plot things is using the .plot extension from Pandas dataframes
# We'll use this to make a scatterplot of the Iris features.
iris.plot(kind="scatter", x="SepalLengthCm", y="SepalWidthCm")

Out[3]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f87d034b0f0>

In [4]:

# We can also use the seaborn library to make a similar plot
# A seaborn jointplot shows bivariate scatterplots and univariate histograms in the same figure
sns.jointplot(x="SepalLengthCm", y="SepalWidthCm", data=iris, size=5)

Out[4]:

<seaborn.axisgrid.JointGrid at 0x7f87d02f0630>

In [5]:

# One piece of information missing in the plots above is what species each plant is
# We'll use seaborn's FacetGrid to color the scatterplot by species
sns.FacetGrid(iris, hue="Species", size=5) \
   .map(plt.scatter, "SepalLengthCm", "SepalWidthCm") \
   .add_legend()

Out[5]:

<seaborn.axisgrid.FacetGrid at 0x7f87c6a86eb8>

In [6]:

# We can look at an individual feature in Seaborn through a boxplot
sns.boxplot(x="Species", y="PetalLengthCm", data=iris)

Out[6]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f87c68072e8>

In [7]:

# One way we can extend this plot is adding a layer of individual points on top of
# it through Seaborn's striplot
# 
# We'll use jitter=True so that all the points don't fall in single vertical lines
# above the species
#
# Saving the resulting axes as ax each time causes the resulting plot to be shown
# on top of the previous axes
ax = sns.boxplot(x="Species", y="PetalLengthCm", data=iris)
ax = sns.stripplot(x="Species", y="PetalLengthCm", data=iris, jitter=True, edgecolor="gray")

In [8]:

# A violin plot combines the benefits of the previous two plots and simplifies them
# Denser regions of the data are fatter, and sparser thiner in a violin plot
sns.violinplot(x="Species", y="PetalLengthCm", data=iris, size=6)

Out[8]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f87c6626668>

In [9]:

# A final seaborn plot useful for looking at univariate relations is the kdeplot,
# which creates and visualizes a kernel density estimate of the underlying feature
sns.FacetGrid(iris, hue="Species", size=6) \
   .map(sns.kdeplot, "PetalLengthCm") \
   .add_legend()

Out[9]:

<seaborn.axisgrid.FacetGrid at 0x7f87c3482898>

In [10]:

# Another useful seaborn plot is the pairplot, which shows the bivariate relation
# between each pair of features
# 
# From the pairplot, we'll see that the Iris-setosa species is separataed from the other
# two across all feature combinations
sns.pairplot(iris.drop("Id", axis=1), hue="Species", size=3)

Out[10]:

<seaborn.axisgrid.PairGrid at 0x7f87c3325588>

In [11]:

# The diagonal elements in a pairplot show the histogram by default
# We can update these elements to show other things, such as a kde
sns.pairplot(iris.drop("Id", axis=1), hue="Species", size=3, diag_kind="kde")

Out[11]:

<seaborn.axisgrid.PairGrid at 0x7f87c24c0e10>

In [12]:

# Now that we've covered seaborn, let's go back to some of the ones we can make with Pandas
# We can quickly make a boxplot with Pandas on each feature split out by species
iris.drop("Id", axis=1).boxplot(by="Species", figsize=(12, 6))

Out[12]:

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f87c1fbd780>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f87bb3b9da0>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f87bb039048>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f87bafb4278>]], dtype=object)

In [13]:

# One cool more sophisticated technique pandas has available is called Andrews Curves
# Andrews Curves involve using attributes of samples as coefficients for Fourier series
# and then plotting these
from pandas.tools.plotting import andrews_curves
andrews_curves(iris.drop("Id", axis=1), "Species")

Out[13]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f87baf6f198>

In [14]:

# Another multivariate visualization technique pandas has is parallel_coordinates
# Parallel coordinates plots each feature on a separate column & then draws lines
# connecting the features for each data sample
from pandas.tools.plotting import parallel_coordinates
parallel_coordinates(iris.drop("Id", axis=1), "Species")

Out[14]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f87ba94ec88>

In [15]:

# A final multivariate visualization technique pandas has is radviz
# Which puts each feature as a point on a 2D plane, and then simulates
# having each sample attached to those points through a spring weighted
# by the relative value for that feature
from pandas.tools.plotting import radviz
radviz(iris.drop("Id", axis=1), "Species")

Out[15]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f87ba6b84a8>

Wrapping Up

I hope you enjoyed this quick introduction to some of the quick, simple data visualizations you can create with pandas, seaborn, and matplotlib in Python!

I encourage you to run through these examples yourself, tweaking them and seeing what happens. From there, you can try applying these methods to a new dataset and incorprating them into your own workflow!

See Kaggle Datasets for other datasets to try visualizing. The World Food Facts data is an especially rich one for visualization.