我需要计算目录中包含多个电子表格的n个excel文件的时差。首先,我根据日期将数据框拆分为电子表格,然后检查该列Door Name
中的两个连续行是否不同,最后,如果数据框的长度是偶数,则我计算了时间差。
第1步:
第2步:
我的代码:
import pandas as pd
import glob
import datetime
from tkinter import filedialog
pathEmp=Employees + "/*.xlsx" # Select directory using tkinter
for femp in glob.glob(pathEmp):
print('******\n')
name_file=os.path.split(femp)[-1]
print('Employee ',name_file)
xl = pd.ExcelFile(femp)
print('Sheet name: ',xl.sheet_names)
for sh in xl.sheet_names:
df = xl.parse(sh)
print('Processing: [{}] ...'.format(sh))
print('length : ',len(df))
df['Time'] = pd.to_datetime(df['Time'])
df['value'] = (df[['Door Name']] != df[['Door Name']].shift()).any(axis=1)
print('My df\n',df)
for i in range (len(df)):
if (len(df)) %2 == 0:
if (df.value.nunique() == 1):
df['Working hours'] = df['Time'].iloc[1::2].to_numpy() - df['Time'].iloc[::2]
Total = df['Working hours'].sum()
Total = '%02d:%02d:%02d' % (Total.days*24 + Total.seconds // 3600, (Total.seconds % 3600) // 60, Total.seconds // 60)
print('Working hours', Total)
预期产量:
如何Working hours
在目录中每个Excel文件的每个电子表格中保存该列?
这是一个例子。
# your input
df = pd.DataFrame({
'DoorName': ('RDC_IN-1', 'RDC_OUT-1', 'RDC_IN-1', 'RDC_OUT-1', 'RDC_IN-1', 'RDC_OUT-1',
'RDC_IN-1', 'RDC_OUT-1', 'RDC_IN-1', 'RDC_OUT-1', 'RDC_IN-1'),
'Time': (datetime(2019, 9, 30, 17, 49, 6), datetime(2019, 9, 30, 17, 45, 51),
datetime(2019, 9, 30, 17, 45, 28), datetime(2019, 9, 30, 16, 37, 53),
datetime(2019, 9, 30, 15, 59, 53), datetime(2019, 9, 30, 9, 15, 0),
datetime(2019, 9, 27, 18, 25, 39), datetime(2019, 9, 27, 18, 27, 9),
datetime(2019, 9, 27, 12, 10, 33),
datetime(2019, 9, 27, 8, 42, 50), datetime(2019, 9, 27, 18, 24, 34)),
})
df['name'] = 'Arya Stark'
# generate date column from Time column
df['date'] = df['Time'].dt.strftime('%Y-%m-%d')
# open file for writing
with pd.ExcelWriter('output.xlsx') as writer:
# for each unique date
for u_date in df['date'].unique(): # type: str
# sub DataFrame from main DataFrame by date
df_by_date = df[df['date'] == u_date]
# date column is no longer needed
df_by_date = df_by_date.drop(columns=['date'])
# DoorName Cumulative sum + group by name (Arya Stark)
s = df_by_date['DoorName'].eq('RDC_IN-1').iloc[::].cumsum()
con = df_by_date.name.groupby(s).transform('nunique') == 1
# diff in seconds between RDC_IN and RDC_OUT for each couple
sec_df = df_by_date[con].groupby(s).agg({
'Time': lambda x: (x.iloc[0] - x.iloc[-1]).seconds
})
df_by_date = df_by_date.reset_index()
df_by_date = df_by_date.drop(columns=['index'])
df_by_date['WorkingHours'] = ''
# sum all seconds and convert to timedelta
working_hours = str(timedelta(seconds=int(sec_df['Time'].sum())))
# insert only in first row of sheet(as in your example)
df_by_date['WorkingHours'].loc[0] = working_hours
# append sheet by unique date
df_by_date.to_excel(writer, sheet_name=u_date, index=False)
您会看到预期的文件。查看评论-如果您需要一些更改,我确定可以自定义它。希望这可以帮助。
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