Exploratory Data Analysis on Iris dataset
The data analyst has become the hottest entry-level data science job of the century. With help of tools and applications without coding knowledge, non-techie bees can effectively manage such analytical positions. But you need to own the basic concepts of data analysis. Exploratory Data Analysis on Iris dataset is such a crucial concept. This blog will help you to gather basic ideas on this aspect.
What is EDA?
Exploratory Data Analysis refers to the critical process of performing initial investigations on data so as to discover patterns, spot anomalies, test hypotheses and check assumptions with the help of summary statistics and graphical representations.
It is always good to explore and compare a data set with multiple exploratory techniques. After the exploratory data analysis, you will get confidence in your data to the point where you’re ready to engage a machine learning algorithm and another benefit of EDA is the selection of feature variables that will be used later for Machine Learning.
In this blog, we take Iris Dataset to get the process of EDA.
import numpy as np import pandas as pd import matplotlib.pyplot as pltLoading the Iris data
iris_data= pd.read_csv("Iris.csv")Understand the data:
iris_data.shape (150,5) iris_data['Species'].value_counts()1D scatter plot of the iris data:
setosa 50 virginica 50 versicolor 50 Name: species, dtype: int64iris_data.columns() Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width','species'],dtype='object')
iris_setso = iris.loc[iris["species"] == "setosa"]; iris_virginica = iris.loc[iris["species"] == "virginica"]; iris_versicolor = iris.loc[iris["species"] == "versicolor"]; plt.plot(iris_setso["petal_length"],np.zeros_like(iris_setso["petal_length"]), 'o') plt.plot(iris_versicolor["petal_length"],np.zeros_like(iris_versicolor["petal_length"]), 'o') plt.plot(iris_virginica["petal_length"],np.zeros_like(iris_virginica["petal_length"]), 'o') plt.grid() plt.show()2D scatter plot:
iris.plot(kind="scatter",x="sepal_length",y="sepal_width") plt.show()2D scatter plot with the seaborn library :
import seaborn as sns sns.set_style("whitegrid"); sns.FacetGrid(iris,hue="species",size=4) \ .map(plt.scatter,"sepal_length","sepal_width") \ .add_legend() plt.show()
- Blue points can be easily separated from red and green by drawing a line.
- But red and green data points cannot be easily separated.
- Using sepal_length and sepal_width features, we can distinguish Setosa flowers from others.
- Separating Versicolor from Viginica is much harder as they have considerable overlap.
A pairs plot allows us to see both the distribution of single variables and relationships between two variables. For example, let’s say we have four features ‘sepal length’, ‘sepal width’, ‘petal length’ and ‘petal width’ in our iris dataset. In that case, we will have 4C2 plots i.e. 6 unique plots. The pairs, in this case, will be :
- Sepal length, sepal width
- sepal length, petal length
- sepal length, petal width
- sepal width, petal length
- sepal width, petal width
- petal length, petal width
So, here instead of trying to visualize four dimensions which are not possible. We will look into 6 2D plots and try to understand the 4-dimensional data in the form of a matrix.
sns.set_style("whitegrid"); sns.pairplot(iris,hue="species",size=3); plt.show()
- petal length and petal width are the most useful features to identify various flower types.
- While Setosa can be easily identified (linearly separable), virginica and Versicolor have some overlap (almost linearly separable).
- We can find “lines” and “if-else” conditions to build a simple model to classify the flower types.
Cumulative distribution function:
iris_setosa = iris.loc[iris["species"] == "setosa"]; iris_virginica = iris.loc[iris["species"] == "virginica"]; iris_versicolor = iris.loc[iris["species"] == "versicolor"]; counts, bin_edges = np.histogram(iris_setosa['petal_length'], bins=10, density = True) pdf = counts/(sum(counts)) print(pdf); >>>[0.02 0.02 0.04 0.14 0.24 0.28 0.14 0.08 0. 0.04] print(bin_edges); >>>[1. 1.09 1.18 1.27 1.36 1.45 1.54 1.63 1.72 1.81 1.9 ] cdf = np.cumsum(pdf) plt.grid() plt.plot(bin_edges[1:],pdf); plt.plot(bin_edges[1:], cdf)
Mean, Median, and Std-Dev:
print("Means:") print(np.mean(iris_setosa["petal_length"])) print(np.mean(np.append(iris_setosa["petal_length"],50))); print(np.mean(iris_virginica["petal_length"])) print(np.mean(iris_versicolor["petal_length"])) print("\nStd-dev:"); print(np.std(iris_setosa["petal_length"])) print(np.std(iris_virginica["petal_length"])) print(np.std(iris_versicolor["petal_length"]))OutPut: - Means: 1.464 2.4156862745098038 5.5520000000000005 4.26
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