Fully automated processing of monthly card shortage data and output of card shortage personnel information

Whether it is school or work, attendance statistics will be made , Some schools or companies will have too many missing cards every month ( For example, more than three times ) To punish the personnel . Some companies also stipulate that basic level employees should submit logs on weekdays 、 Management personnel shall submit weekly or monthly reports , Those who submit less shall be punished . If the company HR Handle the personnel log or card shortage data one by one , It will be a time-consuming and boring job .
  
This article provides a method to automatically handle attendance and missing logs . No need to install Python, No need to learn Python grammar , As long as you can create new folders on your computer , Click the file to realize the statistical output of attendance and log missing list . Next, let's take a look at the implementation steps .

  
  

One 、 Effect display

  

1 Realization effect

  
First, let's take a look at the implementation effect .

The general implementation steps are as follows ：
  
step 1： stay D New disk “ Monthly card shortage data processing “ Folder ( Fixed in the code , This folder must be created ).
  
step 2： Deal with the missing attendance exe Files and raw data files are put into step1 New folder .
  
step 3： Click on exe file , Will automatically come out csv Results file , The specific format is as follows ：
  

  
  

2 Raw data template

  
The original data file needs to be ” Determine whether to submit the log 2.xlsx“, The raw data used in this article are as follows ( The header shall be named as follows )：
  

  
notes ： If you need the original data of this article 、 And running directly to get the results exe file , You can reply to this official account “ Missing card ”, Free access to .
  
The person filling in refers to the name of the student or employee , If the Department is a student, you can fill in a class . Filling time refers to the filling time of the log , Date refers to the actual date of the log . In case of clock in , For both dates, you can fill in the actual punch in date . In case of clock in , Today's completion column can be left blank .
  
If you want to store the annual data of employees' clocking in the original file , But I want to count the card shortage data of a certain month . Just put the month you want to count on the first line of the date , The code has filtered the data sub box of the same month and year according to the first line of the date . To set a scheduled task , Send the running results to relevant personnel by regular email , You can trust me in the official account. .

  
  

Two 、 Code details

  
For partial understanding Python To a friend , If there is a personalized need , You can fine tune your code to meet your requirements . Next, the code to realize the above functions is described in detail .

  

1 Import library

  
First, import the library to be loaded in this article , If you don't have some libraries installed , Cause the running code to report an error , Can be in Anaconda Prompt of use pip Method installation .

# -*- coding: UTF-8 -*-
'''  Code usage  ： Processing card missing data   author  ： Ali Yiyang   official account  :  The code of Ali Yiyang  '''
import os
import calendar
import numpy as np
import pandas as pd
from datetime import datetime
from xlrd import xldate_as_tuple
from chinese_calendar import is_workday
from chinese_calendar import is_holiday
from chinese_calendar import get_holiday_detail

This paper applies to os、calendar、numpy、pandas、datetime、xlrd、chinese_calendar library .
  
os The library can set the location where files are read .
  
calendar and chinese_calendar Library is a date processing library .
  
numpy and pandas The library handles data frames .
  
xlrd and datetime Library processing time .

  

2 Define time processing functions

  
Then apply xlrd and datetime The function in the library defines the time processing function , Convert the time stamp or the time with hour, minute and second into the time containing only the month, year and day .

def num_to_date(x):   
    '''  Date processing function   Convert the time stamp or the time with hour, minute and second into the time containing only the month, year and day  '''
    try:
        x1 = datetime(*xldate_as_tuple(x, 0)).strftime('%Y-%m-%d')
    except:
        x1 = datetime.date(x).strftime('%Y-%m-%d')
    return x1

The purpose of defining the unified time of adult month and day is to facilitate the operation of subsequent code .

  

3 Read data to adjust date format

  
Then read the data , Use the time processing function defined in Section 2 to process the filling time and date .

# Reading data 
os.chdir(r'D:\ Monthly card shortage data processing ')
date = pd.read_excel(' Determine whether to submit the log 2.xlsx', sheet_name='Sheet1')

# Adjust date format 
date[' Filling time '] = date[' Filling time '].apply(num_to_date)
date[' date '] = date[' date '].apply(num_to_date)

The original partial data are as follows ：
  

  
Some data obtained by calling the time processing function are as follows ：
  

  

4 Calculate the number of working days

  
Then take the first value of the date column of the data frame , Get the year / month information to be counted . Obtain the number of working days of the month according to the information of the month .

# Take out the year and year you want to see the missing card information 
y_m1 = date[' date '][0][0:7] 

def sele_ym(x, y_m=y_m1):
    '''  Judge whether the date in the data frame is a month  '''
    if x.find(y_m)>=0:
        return True

# Find the working days of the month , Find the number of working days 
days = calendar.Calendar().itermonthdates(int(y_m1.split('-')[0]), int(y_m1.split('-')[1]))    
mth_nwkdays = []  # Non working days 
mth_wkdays = []   # Working day 
mth_days = []     # All dates 
for day in days:  
    if str(day).find(y_m1)>=0:
        #print(str(day))
        mth_days.append(str(day))
        if is_workday(day)>0:
            mth_wkdays.append(str(day))
        else:
            mth_nwkdays.append(str(day))
work_days = len(mth_wkdays)    # Working days 

Compare the working days with the actual clock in or log in days of the current month , If the actual value is less than the theoretical value , It means that the employee is missing a card or asking for leave . Because most of the employees clock in or write logs normally , At this time, the manual screening of the employees who lack cards has greatly reduced the screening area . If there are special code requirements , Need help , You can trust me in the official account. .

  

5 Get the missing card list

  
Finally, call the function to get the missing card list , It mainly compares each filling date with the actual working date .

# Define the function to obtain card shortage information 
def stat_dail_short(date, y_m1, work_days):
    ''' date： Large data table for storing logs  y_m1： month  work_days: Number of working days in the month  '''
    qk_file = []
    date_m = date[date[' date '].apply(sele_ym)==True]
    for i in set(date_m[' Filled by ']):
        sub_date = date_m[date_m[' Filled by '] == i]
        if len(sub_date[' date '])<work_days:
            qk = str(set(sub_date[' Filled by '])) + str(set(sub_date[' department '])) + ' Missing '+ str((work_days-len(sub_date[' date ']))) + ' Second card ' + '; The card shortage date is ：'+ str(set(mth_wkdays)^set(sub_date[' date ']))
            qk_file.append(qk)
            print(set(sub_date[' Filled by ']), set(sub_date[' department ']), ' Missing %d Second card '%(work_days-len(sub_date[' date '])), '; The card shortage date is ：', set(mth_wkdays)^set(sub_date[' date ']),sep='')
    qk_file_1 = pd.DataFrame(qk_file)
    qk_file_1.columns = [' Missing card information ']
    qk_file_1.to_csv(y_m1+'  Missing card list '+'.csv', encoding='gbk') 
    
# Call the function to get the missing card list 
stat_dail_short(date, y_m1, work_days)

Get the results ：

{
    ' Zhang Jike '}{
    ' Ministry of sports '} Missing 5 Second card ; The card shortage date is ：{
    '2022-04-11', '2022-04-29', '2022-04-22', '2022-04-18', '2022-04-21'}
{
    ' Andy '}{
    ' Robbery Department '} Missing 1 Second card ; The card shortage date is ：{
    '2022-04-20'}
{
    ' Liu shiwen '}{
    ' Ministry of sports '} Missing 2 Second card ; The card shortage date is ：{
    '2022-04-18', '2022-04-28'}

The data in the results are reported by 、 Filling Department 、 Number of card missing 、 The specific card missing date is displayed by splicing . Will csv In the specified folder . If you need to put your name 、 department 、 The number of missing cards and other information shall be separated , Can be in excel By specific conditions , Or adjust the code for implementation .
  
At the beginning of this article exe File generation method , You can refer to Pinstaller(Python Packaging for exe file ) One article . I'm generating exe There have been errors in the process of , Later, I saw the method on the Internet and said that it was in cmd Run in pip uninstall matplotlib, Run the build again exe The statement will not report an error . It's really successful to follow the online method , Although I don't understand the principle , But thank you very much ！ If you don't report an error when packing , It is not recommended to delete matplotlib library .
  
thus , Fully automated processing of monthly card shortage data , The output of the information of the missing card personnel has been explained , Use your hands to share what you need ta Well .
  
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