我有一个熊猫DataFrame。它包含一列“开始日期”和一列“结束日期”。我想计算开始和结束之间的工作日，将它们分成几个月，然后将它们添加到数据框中作为其他列。
目前，这就是我编写的代码。无论如何，我可以使它更快吗？
data = pd.DataFrame([
{'start_date': '2020-03-03', 'end_date' : '2020-06-18'},
{'start_date': '2020-06-03', 'end_date' : '2020-09-18'},
])
def days_month(df):
days_month = pd.DatetimeIndex(pd.bdate_range(df['start_date'], df['end_date'])).month.value_counts()
for i in range(1, 13):
try:
days_month[i]
except:
days_month[i] = 0
return days_month
data[['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']] = data.apply(days_month, axis=1, result_type="expand")
print(data)
end_date start_date Jan Feb Mar Apr May Jun Jul Aug Sep Oct \
0 2020-06-18 2020-03-03 0 0 21 22 21 14 0 0 0 0
1 2020-09-18 2020-06-03 0 0 0 0 0 20 23 21 14 0
Nov Dec
0 0 0
1 0 0
尝试实施Ethan建议的更新：
def countWeekDays(df):
fromDate=df['PO Creation Date']
toDate=df['PO Expected Delivery Date']
d = np.arange(fromDate, toDate, dtype=np.datetime64)
weekdays = d[np.is_busday(d, busdaycal=calendar())]
workDays = {m: np.array([i for i in weekdays if i.item().month==m]).size for m in range(1,13)}
return workDays
def calendar():
#set work week mask and optional holidays array
return np.busdaycalendar(weekmask='1111100', holidays=['2020-01-01','2020-01-20','2020-02-17','2020-05-25','2020-07-03','2020-09-07','2020-10-12','2020-11-11','2020-11-26','2020-12-25'])
data[['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']] = data.apply(countWeekDays, axis=1, result_type="expand")
但是，这给了我一个内存错误。不知道为什么...
您可以使用numpy datetime函数和一些包装：
import numpy as np
def countWeekDays(fromDate='2020-03-03', toDate='2020-06-03'):
d = np.arange(fromDate, toDate, dtype=np.datetime64)
weekdays = d[np.is_busday(d, busdaycal=calendar())]
workDays = [(m, np.array([i for i in weekdays if i.item().month==m]).size) for m in range(1,13)]
return workDays
def calendar():
#set work week mask and optional holidays array
return np.busdaycalendar(weekmask='1111100', holidays=['2020-01-01','2020-01-20','2020-02-17','2020-05-25','2020-07-03','2020-09-07','2020-10-12','2020-11-11','2020-11-26','2020-12-25'])
结果：
>>> countWeekDays()
[(1, 0), (2, 0), (3, 21), (4, 22), (5, 20), (6, 2), (7, 0), (8, 0), (9, 0), (10, 0), (11, 0), (12, 0)]
这是对您的代码的修改，以构建一个与我的函数一起使用的数据框，以获取工作日。我认为您收到的错误是由于数据帧的构建和修改方式引起的。我的经验是，修改数据框有点麻烦，最好将数据汇总在一起，然后从完整的数据集中创建数据框：
def applyDays():
data = [{'start_date': '2020-03-03', 'end_date' : '2020-06-18'},
{'start_date': '2020-06-03', 'end_date' : '2020-09-18'}]
return countWeekDays(data)
def countWeekDays(lst): #, result_type):
months = ['start_date','end_date', 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
data = []
for row in lst:
fromDate = row['start_date'] # df['PO Creation Date']
toDate = row['end_date'] #df['PO Expected Delivery Date']
d = np.arange(fromDate, toDate, dtype=np.datetime64)
weekdays = d[np.is_busday(d, busdaycal=calendar())]
data.append([fromDate, toDate] + [np.array([i for i in weekdays if i.item().month==m]).size for m in range(1,13)])
return pd.DataFrame(data, columns=months)
数据框结果：
applyDays()
Out[6]:
start_date end_date Jan Feb Mar Apr May Jun Jul Aug Sep Oct \
0 2020-03-03 2020-06-18 0 0 21 22 20 13 0 0 0 0
1 2020-06-03 2020-09-18 0 0 0 0 0 20 22 21 12 0
Nov Dec
0 0 0
1 0 0
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