{"id":79300,"date":"2020-07-27T10:58:50","date_gmt":"2020-07-27T05:28:50","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=79300"},"modified":"2021-05-09T13:13:30","modified_gmt":"2021-05-09T07:43:30","slug":"numpy-matplotlib-tutorial","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/numpy-matplotlib-tutorial\/","title":{"rendered":"Introduction to NumPy Matplotlib for Beginners"},"content":{"rendered":"

Python is a really useful tool for data science implementations. We prefer it because of the wide range of libraries and packages available in Python.<\/p>\n

NumPy along with Matplotlib is a fundamental feature of Python. It helps to ease data interpretation and visualization. We implement the plotting functions in NumPy with the use of Matplotlib.<\/p>\n

NumPy Matplotlib Tutorial<\/h2>\n

Let us firstly see what is Matplotlib?<\/p>\n

We use Matplotlib to ease the analysis of statistical data. Matplotlib is a visualization tool and hence provides a visual analysis of the data.<\/p>\n

Matplotlib is an effective replacement for the MatLab tool. It contains all the requirements for replacements. It is also effective when put to use with other GUI toolkits. We can use it alongside Tkinter and pyqt packages.<\/p>\n

We can access matplotlib by importing its subpackage pyplot.<\/p>\n

from matplotlib import pyplot as plt<\/strong><\/p>\n

The pyplot() function is very useful for demonstrating 2-D array plots.<\/p>\n

import numpy as np \r\nfrom matplotlib import pyplot as plt \r\n \r\nx = np.arange(10) \r\ny = 2 * x + 4\r\n \r\nplt.xlabel(\"x axis\") \r\nplt.ylabel(\"y axis\") \r\nplt.plot(x,y) \r\nplt.show()\r\n<\/pre>\n
Output<\/strong><\/p>\n
\"Matplotlib\"<\/a><\/p>\n
Matplotlib Basics<\/h2>\n
These are some basic functions available in Matplotlib :<\/p>\n
1. Labels<\/h3>\n
We use this function to provide label tags for the x and y-axis of the graph. We use plt.xlabel() <\/strong>and plt.ylabel() <\/strong>for labeling x and y-axis respectively.<\/p>\n
2. Title<\/h3>\n
We use plt.title()<\/strong> to provide a title to the plot.<\/p>\n
3. Ticks<\/h3>\n
It provides us with the choice of label position. It is to make the plots user friendly. We use the plt.xticks() <\/strong>for x-axis and plt.yticks()<\/strong> for y-axis.<\/p>\n
4. Plot size<\/h3>\n
We use the plt.figure()<\/strong> function to set the plot size. We can change the default size by providing a tuple value for the length of rows and columns.<\/p>\n
5. Subplot<\/h3>\n
We can make subplots in an existing plot. We use the plt.subplot()<\/strong> function in this case. Three arguments namely: nrows()<\/strong>, ncols()<\/strong>, and index()<\/strong>are provided for the number of rows, columns, and the index values of each respectively.<\/p>\n
6. Subplots<\/h3>\n
This is similar to subplots but is easier to understand. It has separate values for the figure and the axes.<\/p>\n
These functions map to the simple line plot. The plt.plot()<\/strong> function gives the basic line plot. It takes 2 arrays as input. We use the plt.show()<\/strong> method to display the graph. We have a range of plots available for graphical representation in matplotlib.<\/p>\n
Matplotlib Formatting Characters for plot<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Sr No.<\/strong><\/td>\nCharacter\u00a0<\/strong><\/td>\nDescription<\/strong><\/td>\n<\/tr>\n
1<\/span><\/td>\n\u2018-\u2019<\/span><\/td>\nSolid line<\/span><\/td>\n<\/tr>\n
2<\/span><\/td>\n\u2018–\u2019<\/span><\/td>\nDashed line<\/span><\/td>\n<\/tr>\n
3<\/span><\/td>\n\u2018-.\u2019<\/span><\/td>\nDash-dot line<\/span><\/td>\n<\/tr>\n
4<\/span><\/td>\n\u2018:\u2019<\/span><\/td>\nDotted line<\/span><\/td>\n<\/tr>\n
5<\/span><\/td>\n\u2018.\u2019<\/span><\/td>\nPoint marker\u00a0<\/span><\/td>\n<\/tr>\n
6<\/span><\/td>\n\u2018,\u2019<\/span><\/td>\nPixel marker<\/span><\/td>\n<\/tr>\n
7<\/span><\/td>\n\u2018o\u2019<\/span><\/td>\ncircle marker<\/span><\/td>\n<\/tr>\n
8<\/span><\/td>\n\u2018v\u2019<\/span><\/td>\nTriangle down marker<\/span><\/td>\n<\/tr>\n
9<\/span><\/td>\n\u2018^\u2019<\/span><\/td>\nTriangle up marker<\/span><\/td>\n<\/tr>\n
10<\/span><\/td>\n\u2018<\u2019<\/span><\/td>\nTriangle left marker<\/span><\/td>\n<\/tr>\n
11<\/span><\/td>\n\u2018>\u2019<\/span><\/td>\nTriangle right marker<\/span><\/td>\n<\/tr>\n
12<\/span><\/td>\n\u20181\u2019<\/span><\/td>\nTri down marker<\/span><\/td>\n<\/tr>\n
13<\/span><\/td>\n\u20182\u2019<\/span><\/td>\nTri up marker<\/span><\/td>\n<\/tr>\n
14<\/span><\/td>\n\u20183\u2019<\/span><\/td>\nTri left marker<\/span><\/td>\n<\/tr>\n
15<\/span><\/td>\n\u20184\u2019<\/span><\/td>\nTri right marker<\/span><\/td>\n<\/tr>\n
16<\/span><\/td>\n\u2018s\u2019<\/span><\/td>\nSquare marker<\/span><\/td>\n<\/tr>\n
17<\/span><\/td>\n\u2018p\u2019<\/span><\/td>\nPentagon marker<\/span><\/td>\n<\/tr>\n
18<\/span><\/td>\n\u2018*\u2019<\/span><\/td>\nStar marker<\/span><\/td>\n<\/tr>\n
19<\/span><\/td>\n\u2018h\u2019<\/span><\/td>\nHexagon1 marker<\/span><\/td>\n<\/tr>\n
20<\/span><\/td>\n\u2018H\u2019<\/span><\/td>\nHexagon2 marker<\/span><\/td>\n<\/tr>\n
21<\/span><\/td>\n\u2018+\u2019<\/span><\/td>\nPlus marker<\/span><\/td>\n<\/tr>\n
22<\/span><\/td>\n\u2018x\u2019<\/span><\/td>\nX marker<\/span><\/td>\n<\/tr>\n
23<\/span><\/td>\n\u2018d\u2019<\/span><\/td>\nThin diamond marker<\/span><\/td>\n<\/tr>\n
24<\/span><\/td>\n\u2018D\u2019<\/span><\/td>\nDiamond marker<\/span><\/td>\n<\/tr>\n
25<\/span><\/td>\n\u2018|\u2019<\/span><\/td>\nVline marker<\/span><\/td>\n<\/tr>\n
26<\/span><\/td>\n\u2018_\u2019<\/span><\/td>\nHline marker<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
Matplotlib Color abbreviations<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n
Sr no.<\/strong><\/td>\nAbbreviation<\/strong><\/td>\ncolor<\/strong><\/td>\n<\/tr>\n
1<\/span><\/td>\n‘b’<\/span><\/td>\nBlue<\/span><\/td>\n<\/tr>\n
2<\/span><\/td>\n\u2018g\u2019<\/span><\/td>\nGreen<\/span><\/td>\n<\/tr>\n
3<\/span><\/td>\n\u2018r\u2019<\/span><\/td>\nRed<\/span><\/td>\n<\/tr>\n
4<\/span><\/td>\n\u2018c\u2019<\/span><\/td>\nCyan<\/span><\/td>\n<\/tr>\n
5<\/span><\/td>\n\u2018m\u2019<\/span><\/td>\nMagenta<\/span><\/td>\n<\/tr>\n
6<\/span><\/td>\n\u2018y\u2019<\/span><\/td>\nYellow<\/span><\/td>\n<\/tr>\n
7<\/span><\/td>\n\u2018k\u2019<\/span><\/td>\nBlack<\/span><\/td>\n<\/tr>\n
8<\/span><\/td>\n\u2018w\u2019<\/span><\/td>\nWhite<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
Different Types of Plots in Matplotlib<\/h2>\n
There are different varieties of graphs available in matplotlib for providing a better understanding of the data set.<\/p>\n
1. Bar graph in matplotlib<\/h3>\n
It is a very useful type of plot when we have categories of the data. We use it to depict the values in each category by the height of the bars. We can use plt.bar()<\/strong> function for bar graph and plt.barh()<\/strong> for horizontal bar graph.<\/p>\n
There can be horizontal and vertical bar graphs. The horizontal bar graphs denote the width and index position. The vertical graphs denote a bottom argument.<\/p>\n
import matplotlib.pyplot as plt\r\nplot = plt.figure()\r\nchars = ['A','B','C']\r\nvalues = [7,9,3]\r\nplt.bar(chars,values)\r\nplt.show()\r\n<\/pre>\n
Output<\/strong><\/p>\n
 <\/p>\n
\"bar<\/a><\/p>\n
2. Histogram in Matplotlib<\/h3>\n
Histograms are useful when depicting the distribution of a single variable. It helps in the visualization of the variation of a single variable. The histogram can be plot using the plt.hist()<\/strong> function. We can customize Histograms accordingly using different arguments.<\/p>\n
import numpy as np\r\nimport matplotlib.pyplot as plt\r\n \r\nx = [20,40,60,90,40,50,20,70,30,20,10,90]\r\nnum_bins= 5\r\nplt.hist(x,num_bins)\r\nplt.show()\r\n<\/pre>\n
Output<\/strong><\/p>\n
\"histogram<\/a><\/p>\n
3. Scatter Plot in Matplotlib<\/h3>\n
It is also a useful type of plot. It is mainly useful when we want to perform data comparison. We use it mainly for making different comparisons amongst the observations. We use plt.scatter()<\/strong>for this plot.<\/p>\n
import matplotlib.pyplot as plt\r\narr1= [44,56,73,89,20,45,31]\r\narr2 = [10, 20, 30, 40, 50, 60, 70]\r\nfig=plt.figure()\r\nplt.scatter(arr2,arr1,color='r')\r\nplt.show()\r\n \r\n<\/pre>\n
Output<\/strong><\/p>\n
\"scatter<\/a><\/p>\n
4. Box Plot in Matplotlib<\/h3>\n
We use it to represent a summary of the data. It displays five things \u2013 the minimum value, the first quartile, the median, the third quartile, and the maximum value. We can plot it using the plt.boxplot() <\/strong>function.<\/p>\n
5. Sine wave Plot in Matplotlib<\/h3>\n
We use matplotlib to plot trigonometric functions. We can plot the sine wave using plotting functions.<\/p>\n
import numpy as np \r\nimport matplotlib.pyplot as plt  \r\n \r\n \r\nx = np.arange(0, 2 * np.pi, 0.2) \r\ny = np.sin(x) \r\nplt.title(\"sine wave \") \r\n \r\n# Plotting the sine wave\r\n \r\nplt.plot(x, y) \r\nplt.show() \r\n<\/pre>\n
Output<\/strong><\/p>\n
\"\"<\/a><\/p>\n
6. Subplot() in Matplotlib<\/h3>\n
We use subplot for plotting two different graphs. We can plot two different graphs in one figure. Here we take an example to plot both sine and cos graphs together.<\/p>\n
import numpy as np \r\nimport matplotlib.pyplot as plt  \r\n \r\nx = np.arange(0, 5 * np.pi, 0.5) \r\ny_sin = np.sin(x) \r\ny_cos = np.cos(x)  \r\nplt.subplot(2, 2, 2)\r\nplt.plot(x, y_sin) \r\n \r\n   \r\nplt.subplot(2, 2, 2) \r\nplt.plot(x, y_cos) \r\nplt.show()\r\n<\/pre>\n
Output<\/strong><\/p>\n
\"\"<\/a><\/p>\n
Customization of Graphs in Matplotlib<\/h2>\n
We can perform customization on graphs to make it more presentable and user friendly. We can make it more attractive by adding colors and lines.<\/p>\n
1. Color<\/h3>\n
We can specify the color of the graph as an argument. We can pass the name of the color as a string.<\/p>\n
import numpy as np \r\nfrom matplotlib import pyplot as plt \r\n \r\nx = np.arange(10) \r\ny = 2 * x + 4\r\n \r\nplt.xlabel(\"x axis\") \r\nplt.ylabel(\"y axis\") \r\nplt.plot(x,y, color='red') \r\nplt.show()\r\n<\/pre>\n
Output<\/strong><\/p>\n
\"bar<\/a><\/p>\n
2. Marker in Matplotlib<\/h3>\n
Markers represent the points of the plot. We can change the type of marker by passing a string parameter specifying the type.<\/p>\n
import numpy as np \r\nfrom matplotlib import pyplot as plt \r\n \r\nx = np.arange(10) \r\ny = 2 * x + 4\r\n \r\nplt.xlabel(\"x axis\") \r\nplt.ylabel(\"y axis\") \r\nplt.plot(x,y, marker='x') \r\nplt.show()\r\n<\/pre>\n
Output<\/strong><\/p>\n
\"marker<\/a><\/p>\n
3. Linestyle in Matplotlib<\/h3>\n
We can define the type of line of the plot. We can pass it the type as a string argument.<\/p>\n
import numpy as np \r\nfrom matplotlib import pyplot as plt \r\n \r\nx = np.arange(10) \r\ny = 2 * x + 4\r\n \r\nplt.xlabel(\"x axis\") \r\nplt.ylabel(\"y axis\") \r\nplt.plot(x,y, linestyle=':') \r\nplt.show()\r\n<\/pre>\n
Output<\/strong><\/p>\n
\"matplotlib\"<\/a><\/p>\n
Summary<\/h2>\n
Matplotlib is a very useful feature of the NumPy library. It is meant to ease data interpretation. The functions available allows for visual interpretation of the data. The visual representation makes the use of data more user-friendly.<\/p>\n","protected":false},"excerpt":{"rendered":"
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