Python data analysis: two-color ball statistics method with a high proportion of a single red and blue ball, python Data Analysis

Source: Internet
Author: User

Python data analysis: two-color ball statistics method with a high proportion of a single red and blue ball, python Data Analysis

This article describes how to calculate the ratio of a single red ball to a blue ball by using the two-color ball in Python data analysis. We will share this with you for your reference. The details are as follows:

Count the combination of a single red ball and a blue ball. The first 19 groups of data are displayed.

#! /Usr/bin/python #-*-coding: UTF-8-*-import pandas as pdimport numpy as npimport matplotlib. pyplot as pltimport operatordf = pd.read_table('newdata.txt ', header = None, sep =', ') tdate = sorted (df. loc [:, 0]) # print tdateh1 = df. loc [:,]. values # Take the first red ball and blue ball # print h1h2 = df. loc [:,]. values # Take the second column of red ball and blue ball h3 = df. loc [:, :7:4]. valuesh4 = df. loc [:,]. valuesh5 = df. loc [:,]. valuesh6 = df. loc [:,]. values # tblue = df. loc [:, 7] # combine all the data split above into data = np. append (h1, h2, axis = 0) data = np. append (data, h3, axis = 0) data = np. append (data, h4, axis = 0) data = np. append (data, h5, axis = 0) data = np. append (data, h6, axis = 0) # print datadata1 = pd. dataFrame (data) # print data1#write into a file data1.to_csv('hldata.csv ', index = None, header = None) # Read the file, combine the statistics, and sort from large to small f = open ("hldata.csv ") count_dict ={} for line in f. readlines (): line = line. strip () count = count_dict.setdefault (line, 0) count + = 1 count_dict [line] = countsorted_count_dict = sorted (count_dict.iteritems (), key = operator. itemgetter (1), reverse = True) # for item in sorted_count_dict: # print "% s, % d" % (item [0], item [1]) # print sorted_count_dictfenzu = pd. dataFrame (sorted_count_dict ). set_index ([0]) # print fenzu # extract the first 19 data records from the first and second columns and put them in x y. x = list (fenzu. index [: 19]) y = list (fenzu. values [: 19]) print xprint y # returns x to the corresponding value. Otherwise, the drawing error s = pd. series (range (1, len (x) + 1), index = x) # Set the drawing attribute plt. figure (figsize = (12, 6), dpi = 80) plt. legend (loc = 'best') # plt. plot (fenzu, color = 'red') plt. bar (s, y, alpha =. 5, color = 'R', width = 0.8) plt. title ('the one red and one blue ball number') plt. xlabel ('one red and one blue number') plt. ylabel ('Times') # The label character # for I in range (): # plt. text (int (I + 1.4), 25, x [I], color = 'B', size = 10) # plt. text (1.4, 20, x [0], color = 'G', ha = 'center') # Replace ['1, 12', '26, 9 ', '5, 13. xticks (s, x, rotation = 10, size = 10, ha = 'left') plt. show ()

The result is as follows:

It can be seen that the red ball 1 and the blue ball 12 appear the most frequently, followed by the red ball 26 and the blue ball 9

Refer:

Import matplotlib. pyplot as pltimport numpy as npplt. rc ('font', family = 'simhei', size = 13) num = np. array ([13325,940 3, 9227,865 1]) ratio = np. array ([0.75, 0.76, 0.72, 0.75]) men = num * ratiowomen = num * (1-ratio) x = ['chat', 'pa ', 'Group buying \ n coupon ', 'Online video'] width = 0.5idx = np. arange (len (x) plt. bar (idx, men, width, color = 'red', label = 'male') plt. bar (idx, women, width, bottom = men, color = 'yellow', label = 'female user') plt. xlabel ('application categories') plt. ylabel ('male distribution ') plt. xticks (idx + width/2, x, rotation = 40) plt. legend ()

Contact Us

The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion; products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the content of the page makes you feel confusing, please write us an email, we will handle the problem within 5 days after receiving your email.

If you find any instances of plagiarism from the community, please send an email to: info-contact@alibabacloud.com and provide relevant evidence. A staff member will contact you within 5 working days.

A Free Trial That Lets You Build Big!

Start building with 50+ products and up to 12 months usage for Elastic Compute Service

  • Sales Support

    1 on 1 presale consultation

  • After-Sales Support

    24/7 Technical Support 6 Free Tickets per Quarter Faster Response

  • Alibaba Cloud offers highly flexible support services tailored to meet your exact needs.