The output of this program is:<\/p>\n\n\n\n And now for the slightly longer but much improved histogram code:<\/p>\n\n\n\n The result of this program is:<\/p>\n\n\n\n This second plot is easier to read, has less visual clutter thanks to the removal of the “filled” histograms, and has labeled axes. The choice of histogram bins is an important consideration that I am not going to touch on here. You can experiment yourself to see how adding, for example, bins=’fd’ to the plt.hist calls in the second program above changes the visual depiction of the results with all else held constant. <\/p>\n","protected":false},"excerpt":{"rendered":" Histograms are frequently used to visualize the distribution of a data set or to compare between multiple distributions. Python, via matplotlib.pyplot, contains convenient functions for plotting histograms; the default plots it generates, however, leave much to be desired in terms of visual appeal and clarity. The two code blocks below generate histograms of two normally […]<\/p>\n","protected":false},"author":69,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"nf_dc_page":"","wikipediapreview_detectlinks":true,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"ngg_post_thumbnail":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1],"tags":[721,723,722,256,720],"ppma_author":[542],"class_list":["post-9617","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-data-visualization","tag-general-programming","tag-making-figures","tag-plotting","tag-python3"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"authors":[{"term_id":542,"user_id":69,"is_guest":0,"slug":"dan","display_name":"Daniel Nissley","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/fd064a72579f11063ca36621317b744b6bc9df79116bc01af9b57f531bf10662?s=96&d=mm&r=g","0":null,"1":"","2":"","3":"","4":"","5":"","6":"","7":"","8":""}],"_links":{"self":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts\/9617","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/users\/69"}],"replies":[{"embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/comments?post=9617"}],"version-history":[{"count":5,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts\/9617\/revisions"}],"predecessor-version":[{"id":9658,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts\/9617\/revisions\/9658"}],"wp:attachment":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/media?parent=9617"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/categories?post=9617"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/tags?post=9617"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=9617"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"\"](\"https:\/\/i0.wp.com\/www.blopig.com\/blog\/wp-content\/uploads\/2023\/04\/default_hist.png?resize=625%2C417&ssl=1\")
<\/figure>\n\n\n\n## BETTER HISTOGRAMS\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# set random seed so we get reproducible behavior\nnp.random.seed(1)\n\n# generate two data series each containing 1,000 normally distributed values\nd1 = np.random.normal(5.0, 2.0, 1000)\nd2 = np.random.normal(6.0, 2.0, 1000)\n\n# make the plot\nplt.clf()\n\n# generate subplot object so we can modify axis lines easily\nax = plt.subplot(111)\n\n# updated histogram commands\n# use colors that can be differentiated by the colorblind from Paul Tol's notes\n# do not use \"filled\" histograms so all bin heights can be seen clearly\nplt.hist(d1, histtype='step', color='#EE8026', label='Data Set 1', alpha=0.7)\nplt.hist(d2, histtype='step', color='#BA8DB4', label='Data Set 2', alpha=0.7)\n\n# new things\nax.spines['top'].set_visible(False) # turn off top line\nax.spines['right'].set_visible(False) # turn off right line\nplt.ylabel('Counts') # label the y axis\nplt.xlabel('Values') # label the x axis\nplt.xlim(-2, 14) # set x limits that span full data range\nplt.ylim(-10, 300) # set y limits so that full range can be seen\nplt.legend(loc='best', fancybox=True) # add a legend\n\nplt.savefig('better_hist.png', dpi=300)<\/pre>\n\n\n\n![\"\"](\"https:\/\/i0.wp.com\/www.blopig.com\/blog\/wp-content\/uploads\/2023\/04\/better_hist.png?resize=625%2C417&ssl=1\")
<\/figure>\n\n\n\n