{"id":5411,"date":"2020-10-07T19:27:26","date_gmt":"2020-10-07T13:57:26","guid":{"rendered":"https:\/\/www.h2kinfosys.com\/blog\/?p=5411"},"modified":"2020-10-07T19:27:29","modified_gmt":"2020-10-07T13:57:29","slug":"introduction-to-data-visualization-using-matplot","status":"publish","type":"post","link":"https:\/\/www.h2kinfosys.com\/blog\/introduction-to-data-visualization-using-matplot\/","title":{"rendered":"Introduction to data visualization using matplot"},"content":{"rendered":"\n

Matplotlib is one of the most popular Python packages used for data visualization. It is a cross-platform library <\/a>for making 2D plots from data in arrays. It provides an object-oriented API that helps in embedding plots in applications using Python GUI toolkits such as PyQt,  WxPython Tkinter. It can be used in Python and IPython shells,  Jupyter notebook, and web application servers also. <\/p>\n\n\n\n

How to install matplot <\/strong><\/p>\n\n\n\n

Matplotlib and its dependency packages are available in the form of wheel packages on the standard Python package<\/a> repositories and can be installed on Windows, Linux as well as macOS systems using the pip package manager. <\/p>\n\n\n\n

python -m pip install -U pip  \npython -m pip install -U matplotlib <\/pre>\n\n\n\n
matplotlib.pyplot <\/strong>is a collection of command style functions that make  Matplotlib work like MATLAB. Each pyplot function makes some changes to a figure. For example, a function creates a figure, a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. <\/p>\n\n\n\n
The following are the list of plots that are available in matplot  <\/strong><\/p>\n\n\n\n
Plot -It is used to  plot lines and\/or markers to the Axes <\/p>\n\n\n\n
Scatter Make a scatter plot of x vs y <\/p>\n\n\n\n
Bar Make a bar plot <\/p>\n\n\n\n
Barh Make a horizontal bar plot <\/p>\n\n\n\n
Boxplot Make a box and whisker plot <\/p>\n\n\n\n
Hist Plot a histogram <\/p>\n\n\n\n
Pie Plot a pie chart <\/p>\n\n\n\n
Step Make a step plot <\/p>\n\n\n\n
Stem Create a stem plot <\/p>\n\n\n\n
Stackplot Draw a stacked area plot<\/p>\n\n\n\n
The following are the list of Axis Functions that are available in  matplot  <\/strong><\/p>\n\n\n\n
Axes Add axes to the figure <\/p>\n\n\n\n
Text Add text to the axes. <\/p>\n\n\n\n
Title Set a title of the current axes <\/p>\n\n\n\n
X – label Set the x-axis label of the current axis <\/p>\n\n\n\n
Y – label Set the y-axis label of the current axis <\/p>\n\n\n\n
X – lim Get or set the x limits of the current axes <\/p>\n\n\n\n
Y – lim Get or set the y limits of the current axes <\/p>\n\n\n\n
X – ticks Get or set the x-limits of the current tick locations and labels. Y – ticks Get or set the y-limits of the current tick locations and labels.<\/p>\n\n\n\n
The following are the list of Figure Functions that are available in  matplot  <\/strong><\/p>\n\n\n\n
Figtext Add text to figure <\/p>\n\n\n\n
Figure Creates a new figure<\/p>\n\n\n\n
\"Figtext<\/figure>\n\n\n\n
Sample Plot using matplot<\/strong>  <\/strong><\/p>\n\n\n\n
Create a new untitled notebook with the .ipynb <\/strong>extension (stands for  the IPython notebook) <\/p>\n\n\n\n
We shall now display a simple line plot between 2 arrays To begin with, the Pyplot module from the Matplotlib package is imported,  with an alias plt as a matter of convention.<\/p>\n\n\n\n
import matplotlib.pyplot as plt  <\/code><\/p>\n\n\n\n
Next we need an array of numbers to plot. Various array functions are  defined in the NumPy library which is imported with the np alias.<\/p>\n\n\n\n
import numpy as np  <\/code><\/p>\n\n\n\n
Now we will obtain the ndarray objects for x and y <\/p>\n\n\n\n
x = np.random.randint(5, 50,10)<\/code><\/p>\n\n\n\n
## To generate random numbers for plotting<\/p>\n\n\n\n
y = 2*x<\/code><\/p>\n\n\n\n
The values from two arrays are plotted using the plot()<\/p>\n\n\n\n
function. plt.plot(x,y)  <\/code><\/p>\n\n\n\n
You can set the plot title, and labels for x and y axes.<\/p>\n\n\n\n
plt.xlabel(\"X-values\")
plt.ylabel(\"Y-values\")
plt.title('X vs Y\u2019)  <\/code><\/p>\n\n\n\n
The Plot viewer window is invoked by the show() function plt.show() <\/p>\n\n\n\n
The complete program is as follows  <\/p>\n\n\n\n
import numpy as np  \nimport matplotlib.pyplot as plt  \nx=np.random.randint(5,50,10)  \ny=2*x  \nplt.xlabel(\"X-values\")  \nplt.ylabel(\"Y-values\")  \nplt.title('X vs Y')  \nplt.plot(x,y) <\/code><\/pre>\n\n\n\n
Output:<\/strong>  <\/strong><\/p>\n\n\n\n
\"Sample<\/figure>\n\n\n\n
To display plot outputs inside the notebook itself and not in the  separate viewer , enter the following magic statement<\/p>\n\n\n\n
%matplotlib inline <\/code><\/p>\n\n\n\n
Matplotlib – PyLab module<\/strong>  <\/strong><\/p>\n\n\n\n
PyLab is a procedural interface to the Matplotlib object-oriented plotting library. Matplotlib is the whole package; matplotlib.pyplot is a  module in Matplotlib, and PyLab is a module that gets installed alongside Matplotlib. <\/p>\n\n\n\n
PyLab is a convenience module that imports matplotlib.pyplot (for plotting) and NumPy (for Mathematics and working with arrays) in a  single namespace. Although many examples use PyLab, it is no longer recommended. Now we will plot a curve and try to play with the color and markers style<\/p>\n\n\n\n
import numpy as np  \nimport matplotlib.pyplot as plt  \nx = np.linspace(-3, 3, 30)  \ny = x**2  \nplt.plot(x,y)\n<\/code><\/pre>\n\n\n\n
Output:<\/strong> <\/strong><\/p>\n\n\n\n
\"Matplotlib<\/figure>\n\n\n\n
Supported marker styles :<\/p>\n\n\n\n
 \u201c+<\/strong>\u201d , \u201c,<\/strong>\u201d , \u201c.<\/strong>\u201d , \u201c1<\/strong>\u201d , \u201c2<\/strong>\u201d , \u201c3<\/strong>\u201d , \u201c4<\/strong>\u201d  Supported line styles : <\/p>\n\n\n\n
\u201c–<\/strong>\u201d , \u201c– –<\/strong>\u201d , \u201c-.<\/strong>\u201d , \u201c:<\/strong>\u201d , \u201csteps<\/strong>\u201d  <\/p>\n\n\n\n
Supported colour formats : <\/p>\n\n\n\n
\u201cb<\/strong>\u201d , \u201cg<\/strong>\u201d , \u201cr<\/strong>\u201d , \u201cc<\/strong>\u201d , \u201cm<\/strong>\u201d , \u201cy<\/strong>\u201d , \u201ck<\/strong>\u201d , \u201cw<\/strong>\u201d  <\/p>\n\n\n\n
To plot symbols rather than lines and change colour , we need to  provide an additional string argument. <\/p>\n\n\n\n
import numpy as np  \nimport matplotlib.pyplot as plt  \nx = np.linspace(-3, 3, 30)  \ny = x**2  \n## \u201cg\u201d represents the colour of the graph  \nplt.plot(x,y,\u2019g\u2019)  \n<\/code><\/pre>\n\n\n\n
Output:<\/strong><\/p>\n\n\n\n
\"Supported<\/figure>\n\n\n\n
Now we will change the marker style of the above graph.<\/p>\n\n\n\n
import numpy as np  \nimport matplotlib.pyplot as plt  \nx = np.linspace(-3, 3, 30)  \ny = x**2  \n## \u201cr\u201d represents the colour and \u201c-o\u201d represents the marker style plt.plot(x,y,\u2019r-o\u2019)<\/code><\/pre>\n\n\n\n
Output:<\/strong>  <\/strong><\/p>\n\n\n\n
\"data<\/figure>\n\n\n\n
Plotting curves of given equation  <\/strong><\/p>\n\n\n\n
Here, we use NumPy which is a general-purpose array-processing  package in python. <\/p>\n\n\n\n
\u2022 To set the x-axis values, we use the np.arange() <\/strong>method in which the first two arguments are for range and the third one for step-wise increment. The result is a numpy array. <\/p>\n\n\n\n
\u2022 To get corresponding y-axis values, we simply use the predefined np.sin() <\/strong>method on the numpy array. <\/p>\n\n\n\n
\u2022 Finally, we plot the points by passing x and y arrays to the  plt.plot() <\/strong>function.<\/p>\n\n\n\n
x = np.arange(0, 2*(np.pi), 0.1)  y = np.sin(x)  
plt.plot(x, y)<\/code><\/p>\n\n\n\n
Output:<\/strong>  <\/strong><\/p>\n\n\n\n
\"Plotting<\/figure>\n\n\n\n
x = np.linspace(-3, 3, 30)  
y = np.linspace(-3, 3, 20)  
plt.plot(x, sin(x),'g--')  plt.scatter(y,sin(y),c='b',marker='o')  plt.show()<\/code><\/p>\n\n\n\n
Output:<\/strong> <\/strong><\/p>\n\n\n\n
\"data<\/figure>\n\n\n\n
Plots can be overlaid. Just use the multiple plot commands.<\/p>\n\n\n\n
x = np.linspace(-3, 3, 30)  \nplt.plot(x, sin(x),\u2019b\u2019)  \nplt.plot(x, cos(x), \u2018r\u2019)  \nplt.plot(x, -sin(x), \u2018g\u2019)  \nplt.legend([\u2018Sin\u2019,\u2019Cos','-Sin'])\n<\/code><\/pre>\n\n\n\n
Output:<\/strong>  <\/strong><\/p>\n\n\n\n
\"data<\/figure>\n\n\n\n
\u2022 Here, we plot three curves on the same graph. We differentiate between them by giving them a name(label<\/strong>) which is passed as an argument of  .plot() <\/strong>function. <\/p>\n\n\n\n
\u2022 The small rectangular box giving information about the type of line and its color is called a legend. We can add a legend to our plot  using .legend() <\/strong>function.<\/p>\n\n\n\n
Multiple Subplots <\/strong> <\/strong><\/p>\n\n\n\n
Sometimes it is helpful to compare different views of data side by side.  To this end, Matplotlib has the concept of subplots: groups of smaller axes that can exist together within a single figure. These subplots might be insets, grids of plots, or other more complicated layouts. In this section, we’ll explore four routines for creating subplots in Matplotlib. This is the general syntax for subplots  <\/p>\n\n\n\n
subplot(nrows, ncols, index)  <\/code><\/p>\n\n\n\n
Now we will try to create a empty 2 X 2 layout<\/p>\n\n\n\n
plt.subplots(2,2)<\/code><\/p>\n\n\n\n
Output:<\/strong><\/p>\n\n\n\n
\"Multiple<\/figure>\n\n\n\n
Now we will try to plot graphs using subplot function <\/p>\n\n\n\n
x = np.linspace(-3, 3, 30)  \nplt.subplot(2,2,1)  \nplt.plot(x, sin(x),\u2019b\u2019)  \nplt.subplot(2,2,2)  \nplt.plot(x, cos(x),\u2019r\u2019)  \nplt.subplot(2,2,3)  \nplt.plot(x, -sin(x),\u2019g\u2019)  \nplt.subplot(2,2,4)  \nplt.plot(x, -cos(x),\u2019y')  <\/code><\/pre>\n\n\n\n
Output:<\/strong> <\/strong><\/p>\n\n\n\n
\"data<\/figure>\n\n\n\n
In the next article, we will learn how to visualize the remaining types of plots.<\/p>\n","protected":false},"excerpt":{"rendered":"
Matplotlib is one of the most popular Python packages used for data visualization. It is a cross-platform library for making 2D plots from data in arrays. It provides an object-oriented API that helps in embedding plots in applications using Python GUI toolkits such as PyQt,  WxPython Tkinter. It can be used in Python and IPython […]<\/p>\n","protected":false},"author":1,"featured_media":5429,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[500],"tags":[],"class_list":["post-5411","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science-using-python-tutorials"],"_links":{"self":[{"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/posts\/5411","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/comments?post=5411"}],"version-history":[{"count":0,"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/posts\/5411\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/media\/5429"}],"wp:attachment":[{"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/media?parent=5411"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/categories?post=5411"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/tags?post=5411"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}