Compare rows and columns to add result in new dataframe column-CodePudding

I've need to compare data in diferents columns and row and I've a complex for and if python code. I'm thinking for a bether solution but I not found any more with my limited python knowhow.

I need to compare the "Open" value with previous row "Close" and if the diference is more than X value, 3 for this example I need to add a GapUp or GapDown text in "Gap" column.

I've this code and I need to add the first "df["Gap"] = None" line to solve the for exception error:

Any ideas to make it more simple and functionally?

df["Gap"] = None
N = 3
for i in range(1,len(df)):
    if df['Open'][i] > df['Close'][i-1] and df['Open'][i] - df['Close'][i-1] > N:
        df['Gap'][i] = 'GapUp'
    elif df['Open'][i] < df['Close'][i-1] and df['Close'][i-1] - df['Open'][i] > N:
        df['Gap'][i] = 'GapDown'
    else:
        df['Gap'][i] = None

Date	Open	Close	Gap
2018-01-17	178,90	184,36	NaN
2018-01-18	187,36	192,14	GapUp
2018-01-19	184,27	180,24	GapDown
2018-01-20	181,17	182,45	NaN
2018-01-21	183,14	187,47	NaN
2018-01-22	188,14	191,78	NaN
2018-01-23	205,41	209,17	GapUp
2018-01-24	211,02	215,27	NaN
2018-01-25	219,89	223,17	GapUp
2018-01-26	223,08	219,21	NaN
2018-01-27	212,32	206,21	GapDown
2018-01-28	205,14	203,12	NaN
2018-01-29	204,32	201,93	NaN
2018-01-30	199,45	195,15	NaN
2018-01-31	191,23	189,16	GapDown
2018-02-01	188,42	184,67	NaN
2018-02-02	183,12	186,74	NaN

CodePudding user response：

One idea would be to create a temporary column with the Previous Close then use a function to generate the Gap indicator:

def func(open, close):
    gap = open - close
    if gap >= 3.0:
        return 'GapUp'
    elif gap <= -3.0:
        return 'GapDown'
    else:
        return None
    
df['PrevClose'] = df['Close'].shift(1)
df['Gap'] = df.apply(lambda x: func(x['Open'], x['PrevClose']), axis = 1)
df.drop(['PrevClose'], axis = 1, inplace = True)