Uni-Variate Analysis:

• Uni-variate analysis is the simplest form of analyzing data.
• It doesnt deal with causes or relationships.
• We can analyse the variables one by one.
• It takes data, summarizes that data and finds patterns in the data.

Steps:

• Read the data set.
• Take only one variable for uni-variate analysis.
• Take summary of variable.
• Visualize the uni-variate using appropriate plots.
• Check normality.

#import libraries
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
import seaborn as sns
sns.set(style=”whitegrid”)
import numpy as np

#load data set URL
url = “https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data”
names = [‘sepal-length’, ‘sepal-width’, ‘petal-length’, ‘petal-width’, ‘class’]
data = pd.read_csv(url, names=names)

df = pd.DataFrame(data)

var = df[‘sepal-width’]

print(“Summary of Uni-variate\n”,var.describe())

df1 = df.sample(50)

count = len(var)

one_way = pd.crosstab(var,count,margins=True)
print(one_way)
print(“\n”)

#Data visualization
sns.countplot(x=”sepal-width”,data=df1)
plt.show()

plt.hist(var, density=True, bins=30)
plt.show()

sns.distplot(var)
plt.show()

sns.boxplot(var)
plt.show()

sns.countplot(var)
plt.show()

#Normality test
s, p = stats.shapiro(var)
print(“Ststistics:”,s)
print(“p-value:”,p)

alpha = 0.05
if p > alpha:
print(“Passed normality test”)
else:
print(“Fails to pass normality test”)
print(“Data does not follows normal distribution”)