计算 Pandas 中工作日与周末的使用情况答案

【问题标题】：Count Workdays vs Weekends usage in pandas计算 Pandas 中工作日与周末的使用情况
【发布时间】：2018-02-18 03:59:43
【问题描述】：

数据集

                  starttime   User Type
0         7/1/2015 00:00:03  Subscriber
1         7/1/2015 00:00:06  Subscriber
2         7/1/2015 00:00:17  Subscriber
3         7/1/2015 00:00:23  Subscriber
4         7/1/2015 00:00:44  Subscriber
5         7/1/2015 00:01:00  Subscriber
6         7/1/2015 00:01:03  Subscriber
7         7/1/2015 00:01:06  Subscriber
8         7/1/2015 00:01:25    Customer
9         7/1/2015 00:01:41  Subscriber
10        7/1/2015 00:01:50    Customer
11        7/1/2015 00:01:58  Subscriber
12        7/1/2015 00:02:06  Subscriber
13        7/1/2015 00:02:07  Subscriber
14        7/1/2015 00:02:26  Subscriber
15        7/1/2015 00:02:26  Subscriber
16        7/1/2015 00:02:35  Subscriber
17        7/1/2015 00:02:43    Customer
18        7/1/2015 00:02:47    Customer
19        7/1/2015 00:02:47  Subscriber
20        7/1/2015 00:03:05  Subscriber
21        7/1/2015 00:03:16    Customer
22        7/1/2015 00:03:27  Subscriber
23        7/1/2015 00:03:34  Subscriber
24        7/1/2015 00:03:48  Subscriber
25        7/1/2015 00:03:56  Subscriber
26        7/1/2015 00:03:57  Subscriber
27        7/1/2015 00:03:58    Customer
28        7/1/2015 00:04:03  Subscriber
29        7/1/2015 00:04:17  Subscriber
...                     ...         ...
1085646  7/31/2015 23:57:25  Subscriber
1085647  7/31/2015 23:57:29    Customer
1085648  7/31/2015 23:57:32  Subscriber
1085649  7/31/2015 23:57:33  Subscriber
1085650  7/31/2015 23:57:44  Subscriber
1085651  7/31/2015 23:57:54  Subscriber
1085652  7/31/2015 23:58:03  Subscriber
1085653  7/31/2015 23:58:08  Subscriber
1085654  7/31/2015 23:58:12    Customer
1085655  7/31/2015 23:58:15  Subscriber
1085656  7/31/2015 23:58:18    Customer
1085657  7/31/2015 23:58:24  Subscriber
1085658  7/31/2015 23:58:27  Subscriber
1085659  7/31/2015 23:58:42  Subscriber
1085660  7/31/2015 23:58:43  Subscriber
1085661  7/31/2015 23:58:51    Customer
1085662  7/31/2015 23:58:53  Subscriber
1085663  7/31/2015 23:58:58  Subscriber
1085664  7/31/2015 23:59:04  Subscriber
1085665  7/31/2015 23:59:10  Subscriber
1085666  7/31/2015 23:59:24  Subscriber
1085667  7/31/2015 23:59:23    Customer
1085668  7/31/2015 23:59:24  Subscriber
1085669  7/31/2015 23:59:24  Subscriber
1085670  7/31/2015 23:59:38  Subscriber
1085671  7/31/2015 23:59:40  Subscriber
1085672  7/31/2015 23:59:41  Subscriber
1085673  7/31/2015 23:59:42    Customer
1085674  7/31/2015 23:59:56  Subscriber
1085675  7/31/2015 23:59:59  Subscriber

问题

创建一个 pandas DataFrame，其中包含工作日和周末的小时数和用户类型的乘车次数。使用 starttime 确定每次骑行的小时数。

输出应该是这样的

    User 
    Type
    Hour    Customer    Subscriber

Weekday     0   124     2194
            1   120     1238
            2   53      716
            3   30      520
....    ....    ....    ....
Weekend     0   152     1879
            1   82      1222
            2   45      718
            3   34      431
            4   29      288
....    ....    ....    ....

图表应该是这样的

我的代码

def a11(rides):
    rides['starttime'] = pd.to_datetime(rides['starttime'], infer_datetime_format=True)

    hours_cats = ['12 AM', '01 AM', '02 AM', '03 AM', '04 AM', '05 AM', '06 AM', '07 AM', '08 AM', '09 AM', '10 AM', '11 AM', '12 PM', '01 PM', '02 PM', '03 PM', '04 PM', '05 PM', '06 PM', '07 PM', '08 PM', '09 PM', '10 PM', '11 PM']
    dates = pd.Categorical(rides.starttime.dt.strftime('%I %p'), categories=hours_cats, ordered=True)
    df = pd.crosstab(dates, rides['User Type'])

我不知道如何在工作日和周末分离数据框，如问题中所述。

【问题讨论】：

开始：根据骑行的开始时间，计算出每次骑行的星期几。这可能会帮助您指出正确的方向：stackoverflow.com/questions/30222533/…
使用pandas.DatetimeIndex.weekday 返回从0=星期一到6=星期日的每一天的数字。使用 [merge, join, concat] 将此数据添加到您的 df。(pandas.pydata.org/pandas-docs/stable/merging.html) 第三次使用 pandas.pivot_table 以所需形式压缩数据

标签： python python-2.7 pandas matplotlib

【解决方案1】：

对于所需的DataFrame，您需要创建新列：

def a11(rides):
    rides['starttime'] = pd.to_datetime(rides['starttime'], infer_datetime_format=True)
    rides['Type'] = np.where(rides['starttime'].dt.dayofweek < 5, 'Weekday', 'weekend')
    return pd.crosstab([rides['Type'], rides['starttime'].dt.dayofweek], rides['User Type'])

print (a11(rides))

User Type          Customer  Subscriber
Type    starttime                      
Weekday 2                 6          24
        4                 6          24

对于图形输出有点不同 - 更改为 dates 并使用 MultiIndex 的第一级选择 DataFrame.xs：

print (rides)
             starttime   User Type
0   7/01/2015 10:00:03    Customer
1   7/02/2015 02:00:06  Subscriber
2   7/02/2015 02:00:17    Customer
3   7/02/2015 04:00:23  Subscriber
4   7/03/2015 05:00:44    Customer
5   7/11/2015 07:01:00  Subscriber
6   7/12/2015 07:01:03    Customer
7   7/12/2015 08:01:06  Subscriber
8   7/12/2015 00:01:25    Customer
9   7/12/2015 09:01:41  Subscriber
10  7/12/2015 10:01:50    Customer

def a11(rides):
    rides['starttime'] = pd.to_datetime(rides['starttime'], infer_datetime_format=True)
    types = np.where(rides['starttime'].dt.dayofweek < 5, 'Weekday', 'Weekend')
    hours_cats = ['12 AM', '01 AM', '02 AM', '03 AM', '04 AM', '05 AM', '06 AM', '07 AM', '08 AM', '09 AM', '10 AM', '11 AM', '12 PM', '01 PM', '02 PM', '03 PM', '04 PM', '05 PM', '06 PM', '07 PM', '08 PM', '09 PM', '10 PM', '11 PM']
    dates = pd.Categorical(rides.starttime.dt.strftime('%I %p'), categories=hours_cats, ordered=True)
    return pd.crosstab([types, dates], rides['User Type']).assign(All=lambda x: x.sum(axis=1))

a11(rides).xs('Weekday').plot()
a11(rides).xs('Weekend').plot()

【讨论】：