Ellipse, Pie Charts, Tables and Scatter Plot in Matplotlib Using Python

import matplotlib.pyplot as plt 
import numpy as np 
from matplotlib.patches import Ellipse
 
# Fixing random stole for reproducibility 
np.random.seed(19680801)
 
NUM = 250
 
ells =[Ellipse(xy=np.random.rand(2) * 10,
          width=np.random.rand(), height=np.random.rand(), 
          angle=np.random.rand() * 360) 
           for i in range(NUM)] 
fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) 
for e in ells: 
       ax.add_artist(e) 
       e.set_clip_box(ax.bbox) 
       e.set_alpha(np.random.rand())  
       e.set_facecolor(np.random.rand(3)) 
 
ax.set_xlim(0,10)
ax.set_ylim(0,10) 
plt.show() 

import matplotlib.pyplot as plt 
# Pie chart,where the slices are ordered and counter-ctockwise plotted:
labels = 'Frogs', 'Hogs', 'Dogs', 'Logs' 
sizes = [20, 35, 30 ,15]
explode = (0, 0.1, 0, 0) # The 2nd slice (i.e. 'Hogs') just "explode"

fig1, ax1= plt.subplots() 
ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90) 
axl.axis('equal')  # The equal aspect ratio guarantees the drawing of the pie as a circle.
plt.show() 

import numpy as np
import matplotlib.pyplot as plt


data = [[ 65486, 174296,  75131, 577908,  32015],[ 58480, 381139,  78045,  99308, 160454],
[ 89135,  80552, 152558, 497981, 603535],[ 78415,  81238, 150656, 193763,  69638],
[139361, 331509, 343164, 785480,  52269]]

columns = ('Freeze', 'Wind', 'Flood', 'Quake', 'Hail')
rows = ['%d year' % x for x in (100, 50, 25, 15, 5)]

values = np.arange(0, 2500, 500)
value_increment = 1000

# Get some pastel shades for the colors
colors = plt.cm.BuPu(np.linspace(0, 0.5, len(rows)))
n_rows = len(data)

index = np.arange(len(columns)) + 0.3
bar_width = 0.4

# For the stacked bar chart, initialise the vertical-offset.
y_offset = np.zeros(len(columns))

# Plot bars and build table text labels
cell_text = []
for row in range(n_rows):
    plt.bar(index, data[row], bar_width, bottom=y_offset, color=colors[row])
    y_offset = y_offset + data[row]
    cell_text.append(['%1.2f' % (x / 1000.0) for x in y_offset])
# To show the last value at the top, reverse the colours and text labels.
colors = colors[::-1]
cell_text.reverse()

# At the bottom of the axis, add a table
the_table = plt.table(cellText=cell_text,
                      rowLabels=rows,
                      rowColours=colors,
                      colLabels=columns,
                      loc='bottom')

# To make space for the table, adjust the layout:
plt.subplots_adjust(left=0.2, bottom=0.2)

plt.ylabel("Loss in ${0}'s".format(value_increment))
plt.yticks(values * value_increment, ['%d' % val for val in values])
plt.xticks([])
plt.title('Loss by Disaster')

plt.show()

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook

# Load an array of numpy records from Yahoo csv data with date fields, open, close,
# volume, adj_close from the mpl-data/example directory. The record array
# In the date column, store the date as np.datetime64 with a day unit ('D').
price_data = (cbook.get_sample_data('goog.npz', np_load=True)['price_data']
              .view(np.recarray))
price_data = price_data[-250:]  # Get the most recent 250 days of trading
delta1 = np.diff(price_data.adj_close) / price_data.adj_close[:-1]

# Marker size in units of points^2
volume = (15 * price_data.volume[:-2] / price_data.volume[0])**2
close = 0.0031 * price_data.close[:-2] / 0.0031 * price_data.open[:-2]

fig, ax = plt.subplots()
ax.scatter(delta1[:-1], delta1[1:], c=close, s=volume, alpha=0.5)

ax.set_xlabel(r'$\Delta_i$', fontsize=15)
ax.set_ylabel(r'$\Delta_{i+1}$', fontsize=15)
ax.set_title('Volume and percent change')

ax.grid(True)
fig.tight_layout()

plt.show()