Trying to check if value is in range with a counter

I have a df with the windspeed and the power. I calculated 6 new columns with ranges around the power. Now I want to check if the windspeed is smaller then 7 and check than if the power is smaller than the value my column '-10' or greater then the value in my column '+10'. If it is out of this range I want to count +1 if nothing schould happens and I want to check the next power value.

x=df.T.iloc[0]
y=df.T.iloc[1]
c=result[0]
a=result[1]
b=result[2]

df['soll'] = c / (1 + (a) * np.exp(-b*(x)))

df['+3'] = df['soll'] + (c * 0.03)
df['-3'] = df['soll'] - (c * 0.03)
df['+6'] = df['soll'] + (c * 0.06)
df['-6'] = df['soll'] - (c * 0.06)
df['+10'] = df['soll'] + (c * 0.1)
df['-10'] = df['soll'] - (c * 0.1)

counter = 0

for number in df['power']:
    if df['windspeed'] <= 7:
        np.where((df['+10'] >= df['power']) & (df['-10'] <= df['power'])
                   , counter+=1, 
               elif df['windspeed'] <= 15 & >7:
                    np.where((df['+6'] >= df['power']) & (df['-6'] <= df['power'])
                   , counter+=1,
                       else:
                          np.where((df['+3'] >= df['power']) & (df['-3'] <= df['power'])
                        , counter+=1
                        )
                          )
                            )

This is a part of my df:

    windspeed   power soll  +3  -3  +6  -6  +10 -10
0   6.1 230.0   210.29186534841182  266.3048877761675   154.27884292065613  322.31791020392313  98.26582049290047   397.0019401075974   23.581790589226216
1   6.4 271.0   245.18211360320805  301.19513603096374  189.16909117545237  357.20815845871937  133.1560687476967   431.89218836239365  58.47203884402245
2   6.4 270.0   245.18211360320805  301.19513603096374  189.16909117545237  357.20815845871937  133.1560687476967   431.89218836239365  58.47203884402245
3   6.0 219.0   199.6625381512033   255.675560578959    143.64951572344762  311.6885830067147   87.63649329569196   386.3726129103889   12.952463392017705
4   6.9 338.0   314.1478597411359   370.1608821688916   258.1348373133802   426.1739045966473   202.12181488562456  500.8579345003215   127.4377849819503
5   6.9 345.0   314.1478597411359   370.1608821688916   258.1348373133802   426.1739045966473   202.12181488562456  500.8579345003215   127.4377849819503
6   6.9 338.0   314.1478597411359   370.1608821688916   258.1348373133802   426.1739045966473   202.12181488562456  500.8579345003215   127.4377849819503
7   6.2 251.0   221.4119211836951   277.4249436114508   165.39889875593946  333.43796603920646  109.38587632818377  408.12199594288074  34.70184642450951