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Charts using Python



"A picture is worth a thousand words - Python and Charts"

Data visualization is the graphical representation of  data. Python provides an easy way (charts, graphs, and maps) to see and understand trends, outliers, and patterns in data.

  • Transform data into visualizations
  • Extract information and better understand the data
  • Customize and organize visualizations
  • Add interactivity to charts
Matplotlib is a plotting library which produces charts in a variety of formats and interactive environments across platforms.

Installing packages


The following examples uses 2 basic charting libraries
How to install plotlib and seaborn charting libraries

pip install plotlib
pip install seaborn

Example-1
A simple chart using Randon numbers

c1.py
import matplotlib.pyplot as plt
import numpy as np
x = np.random.random((10, 1))
print(x)
plt.plot(x, '*-')
plt.show()

Output

Example-2

 # Example-2.py

import matplotlib.pyplot as plt

# Sales in M U.S $
height = [3, 12, 5, 18, 45]
bars = ('Jan', 'Feb', 'March', 'April', 'May')

# Choose the position of each barplots on the x-axis
y_pos = [0,1,2,3,4]

# Create bars
plt.bar(y_pos, height)

# Create names on the x-axis
plt.xticks(y_pos, bars)

# Show graphic
plt.show()

Output



There are many charts and formats available and is one of the best and easy to use charting library available today.

Word clouds are a great way to visualize sentiment, get a handle on a big chunk of text, and turn an otherwise drab article into a work of art. A Wordcloud is a visual representation of text Data. It displays a list of Words, the importance of each being shown with Font size or Color.

Let us look into the picture and its python code




#Examplie-3.py

from wordcloud import WordCloud
import matplotlib.pyplot as plt
# Create a list of word

text=("Emergys SAP  SAP-BusinessOne  S/4-HANA https://emergys.com/ Rpaide Emergys Software Private Limited Enterprise Resource Planning Emergys LLC Manufacturing Execution System Customer Relationship Manager Product Life Cycle Management S4H HCM SCM Business Intelligence Analytics")

# Create the wordcloud object
wordcloud = WordCloud(width=480, height=480, margin=0).generate(text)

# Display the generated image:
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.margins(x=0, y=0)
plt.show()



#Examplie-4.py
# python charts

from matplotlib import pyplot as plt
import numpy as np

# create data
x = np.random.rand(15)
y = x+np.random.rand(15)
z = x+np.random.rand(15)
z=z*z

# Use it with a call in cmap
#plt.scatter(x, y, s=z*2000, c=x, cmap="BuPu", alpha=0.4, edgecolors="grey", linewidth=2)

# You can reverse it:
#plt.scatter(x, y, s=z*2000, c=x, cmap="BuPu_r", alpha=0.4, edgecolors="grey", linewidth=2)

# OTHER: viridis / inferno / plasma / magma
plt.scatter(x, y, s=z*2000, c=x, cmap="plasma", alpha=0.4, edgecolors="grey", linewidth=2)
plt.show()

Output




Examplie-4.py

# 3D chart
from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import

from matplotlib.collections import PolyCollection
import matplotlib.pyplot as plt
from matplotlib import colors as mcolors
import numpy as np

# Fixing random state for reproducibility
np.random.seed(19680801)


def cc(arg):
 
    return mcolors.to_rgba(arg, alpha=0.6)


def polygon_under_graph(xlist, ylist):
 
    return [(xlist[0], 0.), *zip(xlist, ylist), (xlist[-1], 0.)]


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

verts = []

xs = np.linspace(0., 10., 26)

zs = range(4)

for i in zs:
    ys = np.random.rand(len(xs))
    verts.append(polygon_under_graph(xs, ys))

poly = PolyCollection(verts, facecolors=[cc('r'), cc('g'), cc('b'), cc('y')])
ax.add_collection3d(poly, zs=zs, zdir='y')

ax.set_xlabel('Temp')
ax.set_ylabel('Pressure')
ax.set_zlabel('Time')
ax.set_xlim(0, 10)
ax.set_ylim(-1, 4)
ax.set_zlim(0, 1)

plt.show()


Output



M S SQL and python

#
# dash1.py
#
import dash
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import dash_table
import pandas as pd

#import sqlite3
#conn = sqlite3.connect("DESKTOP-7KAT3LB\SQLEXPRESS")

import pyodbc
conn = pyodbc.connect('Driver={mssqlodbc};'
                      'DESKTOP-7KAT3LB\SQLEXPRESS;'
                      'Database=RB;'
                      'Trusted_Connection=yes;')

import plotly.graph_objs as go
c = conn.cursor()
df = pd.read_sql("SELECT  *  FROM npa1 where qtr='Qtr-1'", conn)
df = df[['Year_month', 'Loan_Category', 'Approved_amountt', 'Collected_amount']]
df.head(1)

Ploty and Dash

Plotly is a free and open-source graphing library for Python
Built on top of the Plotly JavaScript library (plotly.js), plotly.py enables Python users to create beautiful interactive web-based visualizations that can be displayed in Jupyter notebooks, saved to standalone HTML files, or served as part of pure Python-built web applications using Dash.

#plot0.py

import plotly.graph_objects as go
fig = go.Figure(data=go.Bar(y=[2, 3, 1]))
fig.write_html('first_figure.html', auto_open=True)


#plot1.py

import plotly.express as px

df = px.data.gapminder()

fig = px.bar(df,
             x='continent',
             y='gdpPercap',
             color='continent',
             animation_frame='year',
             animation_group='country',
             range_y=[0, 1000000])
fig.show()





#plot2.py

import plotly.express as px
df = px.data.gapminder()
fig=px.scatter(df, x="gdpPercap", y="lifeExp", animation_frame="year", animation_group="country",
           size="pop", color="continent", hover_name="country",
           log_x=True, size_max=55, range_x=[100,100000], range_y=[25,90])
fig.show()



Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.


  Next is Computer vision

Visit https://python-graph-gallery.com/all-charts/


Hari Nair

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