4D To exploring various commands for doing description analysis on the Iris data set

import pandas as pd

import matplotlib.pyplot as plt

from pandas.api.types import is_numeric_dtype

data = pd.read_csv("Iris.csv ")

print(data.sample(5))

print(data.columns)

#The first one is the number of rows and

# the other one is the number of columns.

print(data.shape)

print(data[10:14])

print('INDEX :',data.index)

# sorting by an axis

print(data.sort_index(axis=1, ascending=False).head(10))

for col in data.columns:

    if is_numeric_dtype(data[col]):

        print('%s:' % (col))

        print('\t Mean = %.2f' % data[col].mean())

        print('\t Standard deviation = %.2f' % data[col].std())

        print('\t Minimum = %.2f' % data[col].min())

        print('\t Maximum = %.2f' % data[col].max())

print('value count\n',data['Species'].value_counts())

print('DESCRIBE\n',data.describe(include='all'))

boxplot = data.boxplot(column=['SepalWidthCm', 'PetalWidthCm'])  

plt.show()

data.hist()

data.plot(subplots=True)

data.plot(y='SepalWidthCm');