visualize iris dataset using python
This notebook demos Python data visualizations on the Iris dataset
from: https://www.kaggle.com/benhamner/d/uciml/iris/python-data-visualizations
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.
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# 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 |
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| 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 |
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# Let's see how many examples we have of each species iris["Species"].value_counts() |
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Iris-virginica 50 Iris-setosa 50 Iris-versicolor 50 Name: Species, dtype: int64 |
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# 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") |
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<matplotlib.axes._subplots.AxesSubplot at 0x7f288ef713c8> |
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# 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) |
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<seaborn.axisgrid.JointGrid at 0x7f288e8687f0> |
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# 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() |
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<seaborn.axisgrid.FacetGrid at 0x7f288bc98668> |
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# We can look at an individual feature in Seaborn through a boxplot sns.boxplot(x="Species", y="PetalLengthCm", data=iris) |
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<matplotlib.axes._subplots.AxesSubplot at 0x7f288bd179e8> |
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# 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") |
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# 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) |
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<matplotlib.axes._subplots.AxesSubplot at 0x7f288bb7df98> |
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# 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() |
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<seaborn.axisgrid.FacetGrid at 0x7f288ba86b38> |
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# 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) |
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<seaborn.axisgrid.PairGrid at 0x7f288b9fa6a0> |
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# 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") |
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<seaborn.axisgrid.PairGrid at 0x7f288b54cc50> |
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# 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)) |
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array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f288926d940>, <matplotlib.axes._subplots.AxesSubplot object at 0x7f28890e7a20>], [<matplotlib.axes._subplots.AxesSubplot object at 0x7f28890adfd0>, <matplotlib.axes._subplots.AxesSubplot object at 0x7f28890699e8>]], dtype=object) |
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# 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") |
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<matplotlib.axes._subplots.AxesSubplot at 0x7f2888e49c18> |
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# 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") |
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<matplotlib.axes._subplots.AxesSubplot at 0x7f2888bdaf28> |
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# 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") |
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<matplotlib.axes._subplots.AxesSubplot at 0x7f28889c0d68> |
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.
