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I have a function that returns False and True based on conditions defined in the function below:

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

I would like to see the exact amount of values returning True and False.

Is there a way to output this via print()?

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

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

by (36.8k points)
• (data['SMA']-data['SMA'].shift(1)) > 0 only creates a bool.
• create a variable for the value and for the bool and return both.
• data['SMA'] = data['close'].rolling(period).mean() will update the dataframe inplace, meaning a SMA column will get added to aapl.

def find_trend(data, period: int):

'''

Inputs: 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

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