Realization method of Python reading xlsx file

The script is as follows:

from openpyxl import load_workbook

workbook = load_workbook(u'/tmp/test.xlsx')  # Find the need xlsx Location of the file 
booksheet = workbook.active         # Gets the currently active sheet, The default is the first 1 A sheet

# If you want to get something else sheet Page takes the following approach, first getting all sheet Page name, after specifying the 1 Page. 
# sheets = workbook.get_sheet_names() #  Get from the name sheet
# booksheet = workbook.get_sheet_by_name(sheets[0])

# Get sheet Row data for a page 
rows = booksheet.rows
# Get sheet Column data of the page 
columns = booksheet.columns


i = 0
#  Iterate all rows 
for row in rows:
  i = i + 1
  line = [col.value for col in row]
  cell_data_1 = booksheet.cell(row=i, column=3).value        # Get the i Row 1  Column data 
  cell_data_2 = booksheet.cell(row=i, column=4).value        # Get the i Row  2  Column data 
  cell_data_3 = booksheet.cell(row=i, column=8).value          # Get the i Row  3  Column data 
  cell_data_4 = booksheet.cell(row=i, column=18).value          # Get the i Row  4  Column data 
  print (cell_data_1, cell_data_2, cell_data_3, cell_data_4)

Example: python reads excel data for classification statistics

A certain excel records a person's call record, the following program will count the call time according to the call place, call type, etc.

# -*- coding:utf-8 -*-
import xlrd
import datetime
infos=[]
info_file=xlrd.open_workbook('src.xls')# Open excel Documents 
info_sheet=info_file.sheets()[0]# Get worksheets by index order 
row_count=info_sheet.nrows# Get the number of rows and columns ncols
for row in range(1,row_count):
  time_string=info_sheet.cell(row,3).value
  time_s_sp=time_string.split(':')
  infos.append(# The array contains row_count A dictionary 
    {
      'type':info_sheet.cell(row,2).value,# Gets cell, call type, caller and called 
      'other_cellphone':info_sheet.cell(row,0).value,# Opposite number ,
      'timespan':datetime.timedelta(seconds=int(time_s_sp[2]),minutes=int(time_s_sp[1]),hours=int(time_s_sp[0])),
      'gpscity':info_sheet.cell(row,5).value# Is the call local or foreign 
    }
  )
time_all=datetime.timedelta(seconds=0)# Initialization 
time_types={}
time_classes={}
time_numbers={}
for infor in infos:# Take out the dictionary in the array 
  time_all +=infor['timespan']# Find the total number of calls 
  infor_type=infor['type']
  if infor_type in time_types:
    time_types[infor_type]+=infor['timespan']
  else:
    time_types[infor_type]=infor['timespan']# Talk time by call type 
  infor_class=infor['gpscity']
  if infor_class in time_classes:
    time_classes[infor_class]+=infor['timespan']
  else:
    time_classes[infor_class]=infor['timespan']# This is equivalent to classifying and assigning values first and then ++ According to the attribution, the call time is counted 
  infor_number=infor['other_cellphone']
  if infor_number in time_numbers:
    time_numbers[infor_number]+=infor['timespan']
  else:
    time_numbers[infor_number]=infor['timespan']# Count the call time according to the number 

print ' Total talk time: %s' % time_all
print
print ' Classification of total call mode '
for k,v in time_types.items():
  print k.encode('utf-8'),v
print
print ' Call type classification :'
for k,v in time_classes.items():
  print k.encode('utf-8'),v
print
print ' Counter number classification: '
for k,v in time_numbers.items():
  print k,v

Optimize the code again

# -*- coding:utf-8 -*-
import xlrd
from datetime import timedelta
def read_excel(file_excel):# Read excel And put the required data classification in the array 
  infos=[]
  info_file=xlrd.open_workbook(file_excel)
  info_sheet=info_file.sheets()[0]
  row_count=info_sheet.nrows
  for row in range(1,row_count):
    time_string=info_sheet.cell(row,3).value
    time_s_sp=time_string.split(':')
    infos.append(
      {
        'type':info_sheet.cell(row,2).value,
        'other_cellphone':info_sheet.cell(row,0).value,
        'timespan':timedelta(seconds=int(time_s_sp[2]),minutes=int(time_s_sp[1]),hours=int(time_s_sp[0])),
        'gpscity':info_sheet.cell(row,5).value

      }
    )
  return infos
def count_cell(list_dirs,infotype):# Statistical total calls and classified statistical results are stored in the dictionary 
  result_dir={}
  time_all=timedelta(seconds=0)
  for list_dir in list_dirs:
    time_all +=list_dir['timespan']
    info_type = list_dir[infotype]
    if info_type not in result_dir:
      result_dir[info_type]=list_dir['timespan']
    else:
      result_dir[info_type]+=list_dir['timespan']
  return time_all,result_dir
def print_result(result_dir):# Print data 
  for k,v in result_dir.items():
    print k.encode('utf-8'),v

if __name__=="__main__":
  list_dirs=read_excel('src.xls')
  time_all,result_type=count_cell(list_dirs,'type')
  result_cell=count_cell(list_dirs,'other_cellphone')
  result_gpscity = count_cell(list_dirs, 'gpscity')
  print ' Total talk time: %s' % time_all
  print ' Classified by call type: '
  print_result(result_type)
  print ' Classified by number: '
  print_result(result_cell[1])
  print ' Classification according to attribution: '
  print_result(result_gpscity[1])