如何返回在函数中标识的结果计数？ 真假

如何解决如何返回在函数中标识的结果计数？ 真假

我有一个函数根据以下函数中定义的条件返回False和True：

def find_trend(data,period:int):
    '''
    Inputs:
    takes in a dataframe and an interger
    Outputs:
    returns True if the trend of the simple moving average over given period is positive,else returns False
    '''
    data['SMA'] = data['close'].rolling(period).mean()
    return (data['SMA']-data['SMA'].shift(1)) > 0

我希望看到返回True和False的值的确切数量。

是否可以通过print()输出此内容？

aapl['trend'] = find_trend(aapl,4)
aapl.trend.head()

0   False
1   False
2   False
3   False
4   False
5   False
6   False
7   True
8   True
9   True
10  False
11  False
12  False
13  False
14  True
15  True
16  True
17  True
18  False
19  False
20  False
21  True
22  False
23  False
24  False
25  True
26  False
27  False
28  False
29  True

解决方法

• (data['SMA']-data['SMA'].shift(1)) > 0仅创建一个布尔值。
• 为值和bool创建一个变量，并同时返回两者。
• data['SMA'] = data['close'].rolling(period).mean()将就地更新数据框，这意味着SMA列将被添加到aapl。

def find_trend(data,period: int):
    '''
    Inputs:
    takes in a dataframe and an interger
    Outputs:
    returns True if the trend of the simple moving average over given period is positive,else returns False
    '''
    sma = data['Close'].rolling(period).mean()  # creates a series with the rolling mean
    diff = sma - sma.shift(1)  # calculates a series of values
    greater_than_0 = diff > 0  # creates a series of bools
    return diff,greater_than_0


aapl['value'],aapl['trend'] = find_trend(aapl,4)

# display(aapl)
         Date        High         Low        Open       Close      Volume   Adj Close ticker     value  trend
0  2019-01-31  169.000000  164.559998  166.110001  166.440002  40739600.0  162.852737   aapl       NaN  False
1  2019-02-01  168.979996  165.929993  166.960007  166.520004  32668100.0  162.931030   aapl       NaN  False
2  2019-02-04  171.660004  167.279999  167.410004  171.250000  31495500.0  167.559082   aapl       NaN  False
3  2019-02-05  175.080002  172.350006  172.860001  174.179993  36101600.0  170.425934   aapl       NaN  False
4  2019-02-06  175.570007  172.850006  174.649994  174.240005  28239600.0  170.484650   aapl  1.950001   True
5  2019-02-07  173.940002  170.339996  172.399994  170.940002  31741700.0  167.255768   aapl  1.105000   True
6  2019-02-08  170.660004  168.419998  168.990005  170.410004  23820000.0  167.452316   aapl -0.209999  False
7  2019-02-11  171.210007  169.250000  171.050003  169.429993  20993400.0  166.489288   aapl -1.187500  False
8  2019-02-12  171.000000  169.699997  170.100006  170.889999  22283500.0  167.923965   aapl -0.837502  False