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import numpy as np

import matplotlib.pyplot as plt

from scipy.stats import norm

import pandas as pd 

import seaborn as sns

from mpl_toolkits.mplot3d import Axes3D

df=pd.read_csv("iris.data")

df.columns = ['SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm','class']

df1=df['SepalLengthCm']

df2=df['SepalWidthCm']

df3=df['PetalLengthCm']

print('HEAD =\n',df1.dtype,df1.head())

#normal curve

mu = df1.mean()

sigma = df1.std()

print('mean, std devia',mu,sigma)

x = np.linspace(mu - 3*sigma, mu + 3*sigma, 1000)

plt.plot(x, norm.pdf(x, mu, sigma), color='red', linewidth=3)

plt.show()

#Density plots allow to visualize the distribution of a numeric variable for one or several groups.

df1.plot(kind = 'density')

plt.show()

#histogram

plt.hist(df['SepalLengthCm'], bins = 20, color = "green")

plt.title("Sepal Length in cm")

plt.xlabel("Sepal_Length_cm")

plt.ylabel("Count")

#correlation

corr = df.corr()

# According to the correlation matrix results PetalLengthCm and

#PetalWidthCm have possitive correlation which is proved by the plot above

print('Correlation amoung columns in iris dataset\n',corr)

#Scatterplot

df.plot(kind ="scatter",

          x ='SepalLengthCm',

          y ='PetalLengthCm')

plt.grid()

#Contour plots also called level plots are a tool for doing multivariate analysis and visualizing 3-D plots in 2-D space

# X, Y: 2-D numpy arrays with same shape as Z or 1-D arrays such that len(X)==M and len(Y)==N (where M and N are rows and columns of Z)

#Z: The height values over which the contour is drawn. Shape is (M, N)

# Creating 2-D grid of features

[X, Y] = np.meshgrid(df1,df2)

  

fig, ax = plt.subplots(1, 1)

  

Z = X ** 2 * Y ** 2

  

# plots filled contour plot

ax.contourf(X, Y, Z)

  

ax.set_title('Filled Contour Plot')

ax.set_xlabel('SepalLengthCm')

ax.set_ylabel('SepalWidthCm')

  

plt.show()

#3D Plotting

 

fig = plt.figure()

ax = fig.gca(projection='3d')

ax.plot_trisurf(df1, df2, df3, cmap=plt.cm.viridis, linewidth=0.2)

plt.show()