Codes for Information Visualisation using python.

Histogram_using_plotly.py

import plotly.plotly as py
import plotly.graph_objs as go
import pandas as pd

iris = pd.read_csv("iris.csv")

data = [go.Histogram(x=iris['sepal_length'].tolist())]

layout = go.Layout(title='Iris Dataset - Sepal.Length',
xaxis=dict(title='Sepal.Length'),
yaxis=dict(title='Count')
)

fig = go.Figure(data=data, layout=layout)
py.iplot(fig)

Scatter_plot_bokeh.py

from bokeh.plotting import figure, show, output_file
import pandas as pd

data = pd.read_csv('iris.csv')

colormap = {'setosa': 'red', 'versicolor': 'green', 'virginica': 'blue'}
colors = [colormap[x] for x in data['species']]

p = figure(title = "Iris Morphology")
p.xaxis.axis_label = 'Petal Length'
p.yaxis.axis_label = 'Petal Width'

p.circle(data["petal_length"], data["petal_width"],
        color=colors, fill_alpha=0.2, size=10)

output_file("iris.html", title="iris.py example")

show(p)

Viz_1.py

import matplotlib.pyplot as plt
import numpy as np

t = np.arange(0.0, 2.0, 0.01)
s = 1 + np.sin(2 * np.pi * t)

fig, ax = plt.subplots()
ax.plot(t, s)

ax.set(xlabel = 'time (s)', ylabel = 'voltage (mV)', title = 'Simple Line Plot')
ax.grid()

plt.show()

Viz_2.py

import matplotlib.pyplot as plt
import pandas as pd

iris = pd.read_csv('iris.csv')

p_length = iris['petal_length'].tolist()

bins = [0,1,2,3,4,5,6,7,8,9]

plt.hist(p_length, bins, histtype = 'bar', rwidth=0.8)

plt.xlabel("Petal Length")
plt.ylabel("Count")
plt.title("Petal Length Distribution")
plt.legend()
plt.show()

Viz_3.py

import matplotlib.pyplot as plt

# Data to plot
labels = 'Python', 'C++', 'Ruby', 'Java'
sizes = [215, 130, 245, 210]
colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']
explode = (0.1, 0, 0, 0)  # explode 1st slice

# Plot
plt.pie(sizes, explode=explode, labels=labels, colors=colors,
        autopct='%1.1f%%', shadow=True, startangle=140)

plt.axis('equal')
plt.show()

Viz_4.py

import plotly.plotly as py
import plotly.graph_objs as go
import pandas as pd

iris = pd.read_csv("iris.csv")

data = [go.Bar(x=['setosa','versicolor','virginica'],
y=[iris.loc[iris['species']=='setosa'].shape[0],
   iris.loc[iris['species']=='versicolor'].shape[0],iris.loc[iris['species']=='virginica'].shape[0]]

)]


layout = go.Layout(title='Iris Dataset - Species',
xaxis=dict(title='Iris Dataset - Species'),
yaxis=dict(title='Count')
)

fig = go.Figure(data=data, layout=layout)
py.iplot(fig)

Viz_5.py

import matplotlib.pyplot as plt
import pandas as pd

iris = pd.read_csv('iris.csv')

for n in range(0,150):
 if iris['species'][n] == 'setosa':
    plt.scatter(iris['sepal_length'][n],iris['sepal_width'][n],color='red')
    plt.xlabel('sepal_length')
    plt.ylabel('sepal_width')
    
 elif iris['species'][n] == 'versicolor':
    plt.scatter(iris['sepal_length'][n],iris['sepal_width'][n],color='blue')
    
 else:
    plt.scatter(iris['sepal_length'][n],iris['sepal_width'][n],color='green')

Viz_6.py

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

sns.set(style='white', color_codes=True)
iris = pd.read_csv("iris.csv")

sns.boxplot(x="species", y="petal_length", data=iris)