計數工作日VS週末使用在熊貓

數據集計數工作日VS週末使用在熊貓

    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

問題

創建的遊樂設施由小時和工作日和週末的數量用戶鍵入大熊貓數據幀。使用starttime來確定每個小時的乘車時間。

輸出應該像

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 
.... .... .... ....

圖表應該是這樣的

我的代碼

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'])

我不知道如何在平日和週末分開數據框架，如問題中提到的那樣。

來源

2017-09-09 RaThOd

開始：根據騎行的開始時間，計算出每次騎行如何獲得一週中的某一天。這可能有助於讓你指出正確的方向：https://stackoverflow.com/questions/30222533/create-a-day-of-week-column-in-a-pandas-dataframe-using-python – payne

使用[pandas .DatetimeIndex.weekday]（https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DatetimeIndex.weekday.html），它返回從0 =星期一到6 =星期日每天的數字。（https://pandas.pydata.org/pandas-docs/stable/merging.html）使用[pandas.pivot_table]（https：// pandas）將這些數據添加到您的df中.pydata.org/pandas-docs/stable/generated/pandas.pivot_table.html）以期望的形式對數據進行sqeeze – 2Obe

對於期望DataFrame你需要創建新的列：

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

對於圖形的輸出是一個有點不同 - 改變dates並選擇通過多指標的第一級使用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()

來源

2017-09-10 05:57:16 jezrael

計數工作日VS週末使用在熊貓

回答

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