Crawler re-exploration (v) ——— crawl app data-Super curriculum "three"--date and time analysis

Then organize the relationship between time and date and posting volume.

　　　　PROJECT2: Date (select 2015 year)

The code is as follows: (The kebiao_all.xlsx here is all the data captured before)

Import Xlrdimport xlsxwriter# The time list to get the data----->dates_listfname = ' kebiao_all.xlsx ' BK = Xlrd.open_workbook (fname) #  Shxrange = range (bk.nsheets) sh = bk.sheet_by_name (' Sheet1 ') nrows = Sh.nrowsncols = sh.ncols# Get 2015 year-round data # actually here date_16_l You can't use it, it's written for logic. date_15_l = []date_16_l = []for i in range (1, nrows): Row_data_date = Sh.row_values (i) [3] #print (row_data_d ATE) Y_a_m = Row_data_date.split ('/') if y_a_m[0] = = ':d ate_15_l.append (y_a_m) Else:date_16_l.append (y_a_m) # Create a spreadsheet workbook = Xlsxwriter. Workbook (' statistic_date.xlsx ') worksheet = Workbook.add_worksheet () worksheet.set_column (' a:a ', ten) worksheet.set_ Column (' B:b ', 5) # takes out the date and groups it, then writes Def every_m (m):d t_15_ml = []for dt_15 in date_15_l:if dt_15[1] = = str (m):d T_15_ml.append ( DT_15) worksheet.write (m,0,dt_15[1]+ ' month ') Worksheet.write (M,1,len (DT_15_ML)) for M in Range (1,13): Every_m (m) Workbook.close ()

In the resulting Excel simple graphing, the output is as follows:

　　　　PROJECT3: Time

Import Xlrdimport xlsxwriter# Get 24 time periods----->time_listtime_list = []for i in Range (0,24): if i < 10:i = ' 0 ' +str (i) Else: i = str (i) time_list.append (i) #print (time_list) #获取数据的时间列表----->times_listfname = ' kebiao_all.xlsx ' BK = Xlrd.open_ Workbook (fname) #shxrange = range (bk.nsheets) sh = bk.sheet_by_name (' Sheet1 ') nrows = Sh.nrowsncols = Sh.ncolstimes_list = []for I in range (1, nrows): Row_data_time = Sh.row_values (i) [4] #print (row_data_time) Times_list.append (row_data_time) # Print (len (times_list)) workbook = Xlsxwriter. Workbook (' statistic_time.xlsx ') worksheet = Workbook.add_worksheet () worksheet.set_column (' a:a ', ten) worksheet.set_ Column (' B:b ', 5) def every_time (i): num = 0for times in Times_list:if str (times) [0]+str (Times) [1] = = I:num + = 1worksheet.wri Te (int (i), 0,str (i) + ' point ') worksheet.write (int (i), 1,num) print (i+ '----> ' +str (num)) for I in Time_list:every_time (i) Workbook.close ()

Look closely to find that this and the previous QQ chat data analysis is very similar.

The output is as follows:

At this point, after a simple data processing and analysis, has been able to do a simple visualization, these figures closely associative, or very interesting.

Crawler re-exploration (v) ——— crawl app data-Super curriculum "three"--date and time analysis