{"id":3201,"date":"2019-08-28T08:33:48","date_gmt":"2019-08-28T06:33:48","guid":{"rendered":"https:\/\/pythonprogramming.altervista.org\/?p=3201"},"modified":"2019-08-30T17:29:44","modified_gmt":"2019-08-30T15:29:44","slug":"matplotlib-charts-1","status":"publish","type":"post","link":"https:\/\/pythonprogramming.altervista.org\/matplotlib-charts-1\/","title":{"rendered":"Matplotlib charts 1"},"content":{"rendered":"

\"\"<\/a>Let’s start a series of posts about matplotlib<\/strong>, a module to create charts and other visual<\/strong> data rappresentation tool.<\/p>\n

Data on the screen<\/h2>\n

The first thing we are going to take a look at is the simplest chart ever with a sequence of data of my blog views. Here is the example from the matplot lib site.<\/p>\n

import numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\nN = 5\r\nmenMeans = (20, 35, 30, 35, 27)\r\nwomenMeans = (25, 32, 34, 20, 25)\r\nmenStd = (2, 3, 4, 1, 2)\r\nwomenStd = (3, 5, 2, 3, 3)\r\nind = np.arange(N)    # the x locations for the groups\r\nwidth = 0.35       # the width of the bars: can also be len(x) sequence\r\n\r\np1 = plt.bar(ind, menMeans, width, yerr=menStd)\r\np2 = plt.bar(ind, womenMeans, width,\r\n             bottom=menMeans, yerr=womenStd)\r\n\r\nplt.ylabel('Scores')\r\nplt.title('Scores by group and gender')\r\nplt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))\r\nplt.yticks(np.arange(0, 81, 10))\r\nplt.legend((p1[0], p2[0]), ('Men', 'Women'))\r\n\r\nplt.show()<\/pre>\n
And this is the output<\/p>\n
\"\"<\/a><\/h2>\n
Let’s transform this into the most simple chart ever<\/h2>\n
I want to get a basic chart with just data across months for an imaginary blog views. So, after some deletings and some changes we have this code:<\/p>\n
import numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\nN = 10\r\nind = np.arange(N)    # the x locations for the groups\r\nviews = (634, 754, 937, 1300, 2200, 3000, 2800, 3600, 4200, 4600)\r\nwidth = 0.35       # the width of the bars: can also be len(x) sequence\r\np1 = plt.bar(ind, views, width)\r\nplt.ylabel('Views')\r\nplt.title('My blog views')\r\nplt.xticks(ind, ('N\\n18', 'D\\n18', 'G\\n19', 'F\\n19', 'M\\n19', 'A\\n19', 'M\\n19', 'J\\n19', 'J\\n19', 'A\\n19'))\r\nplt.yticks(np.arange(0, 81, 10))\r\nplt.show()<\/pre>\n
And this brought me to this chart<\/p>\n
\"\"<\/a><\/p>\n
Now I thing that I miss is the number of views on the left… right?<\/p>\n
We need to adjust this<\/p>\n
plt.yticks(np.arange(0, 81, 10))<\/pre>\n
to this<\/p>\n
plt.yticks(np.arange(0, 5000, 1000))<\/pre>\n
The values on the vertical<\/strong> axe starts from 0 and ends at 5000 (because the max value is 4600) and the interval is set to 1.000.<\/p>\n
So, now the result is this:<\/p>\n
\"\"<\/a><\/p>\n
In this video the live coding of the most basic chart ever<\/h2>\n
Let’s give a look at the live coding<\/strong> of a basic bar chart, even more basic than the one in the code above. It’s a short video, a little more than 6 minutes, to see how easy it easy to create charts with this very famouse tool: matplotlib<\/strong> for Python.<\/p>\n