如果值不在Python范围内

如何解决如果值不在Python范围内

我正在尝试根据值将数据分为几类。因此，将高于-25的值指定为熊级别0，依此类推。但是，当我查看数据时，可以看到有低于-25的值被指定为熊级别0。所有类别都是如此。

###############################################################
Bear_level = ['high','medium-high','medium','medium-low','low','very-low']

Level=[]
for value in data_shifted[k]['SCI300max [um]']:
    if value >= -25:
        Level.append(Bear_level[0])
    elif value < -25 and value >= -50:
        Level.append(Bear_level[1])
    elif value < -50 and value >= -75:
        Level.append(Bear_level[2])
    elif value < -75 and value >= -100:
        Level.append(Bear_level[3])
    elif value < -100 and value >= -150:
        Level.append(Bear_level[4])
    else:
        Level.append(Bear_level[5])

Amount = 0
for i in Bear_level:
    for m in range(int(len(Level))):
        if Level[m] ==i:
            Amount += 1
    print(Amount)
    Amount = 0    

for k in data_shifted:
    data_shifted[k]['Bear Level']= Level


data_interp={k:[] for k in progression}   
for k in data_interp:
    data_interp[k]=data_shifted[k][['Chainage [m]','Driving Speed [m/s]','Latitude','Longitude','Road temperature [C]','Air temperature [C]','Temp corrected Bells2_50','Load Left [kg]','Load Right [kg]','Dmax [um]','D0 [um]','D300 [um]','D600 [um]','D900 [um]','D1200 [um]','D1500 [um]','SCI300max [um]','SCI300 [um]','SCI300diff [um]','SCI600max [um]','SCI600 [um]','SCI900max [um]','SCI900 [um]','SCI600max-SCI300max [um]','SCI900max-SCI600max [um]','speedfilter','Bear Level','Traffic [ESALs x day]','Layer1 Thickness [m]']]
    data_interp[k]= data_interp[k][data_interp[k]['speedfilter']=='Pass']    

all_data_list = [ v for k,v in data_interp.items()] 
all_data = pd.concat(all_data_list,axis=0)

all_data.to_csv("all_data.csv",index=True,header=True)##### generate csv file with all data

H_bear_section = all_data[all_data['Bear Level'] == Bear_level[0]]
MH_bear_section = all_data[all_data['Bear Level'] == Bear_level[1]]
M_bear_section = all_data[all_data['Bear Level'] == Bear_level[2]]
ML_bear_section = all_data[all_data['Bear Level'] == Bear_level[3]]
L_bear_section = all_data[all_data['Bear Level'] == Bear_level[4]]
VL_bear_section = all_data[all_data['Bear Level'] == Bear_level[5]]

####### Correlations analysis##################

我希望有人能够看到问题，因为我迷失了主意。

解决方法

您需要翻转逻辑。所有值都在-25以下： 所以您需要先检查最低的数字

for value in data_shifted[k]['SCI300max [um]']:
    if value <= -150:
        Level.append(Bear_level[5])
    elif value < -100 and value >= -150:
        Level.append(Bear_level[4])
    elif value < -75 and value >= -100:
        Level.append(Bear_level[3])
    elif value < -50 and value >= -75:
        Level.append(Bear_level[2])
    elif value < -25 and value >= -50:
        Level.append(Bear_level[1])
    else:
        Level.append(Bear_level[0])

您确实不需要and子句：

for value in data_shifted[k]['SCI300max [um]']:
    if value <= -150:
        Level.append(Bear_level[5])
    elif value < -100:
        Level.append(Bear_level[4])
    elif value < -75:
        Level.append(Bear_level[3])
    elif value <:
        Level.append(Bear_level[2])
    elif value < -25:
        Level.append(Bear_level[1])
    else:
        Level.append(Bear_level[0])