如果NaN和Pandas在一起，我如何检查手机？

Question

我是熊猫的初学者。 我想处理一个Excel文件，并计算尺寸（D）=160mm的建筑对象（R-R）的米数

如何从for切片行中的单元格中获取“IsoOf”列中的值？ df.loc[filt, 'IsoOf'].isnull().values.any() == True

范例

“R-R”为160的行=索引10,12,15,65,70。。。。 df.loc[filt, 'IsoOf'].isnull().values.any() == True每次检查行0时，它都没有到for切片的链接

在哪里可以设置“row”（i）元素来检查正确的索引？ 像df.loc[filt, 'IsoOf'].isnull(row).values.any() == True

import pandas as pd

#Open file
df = pd.read_excel('Bauteilliste.xlsx')

#edit the display option on jupyter
pd.set_option('display.max_columns', 75)

#Filter 
# 1. All Elements with the ID R-R and the dimension 160mm
filt = (df['KZ'] == 'R-R') & (df['D'] == 160)
#Calculate all the Elements
counter_lenght = 0  #Without Isaltion
counter_lenght_isolation = 0 #With Isaltion

#Get throut every row with the filt filter
for row in df.loc[filt, 'L']:
       #PROBLEM: What todo taht .isnull get the same id from row??
       #It only checks the value .isnull from the index 0 not from the filtered row 
   if df.loc[filt, 'IsoOf'].isnull().values.any() == True:
       counter_lenght = counter_lenght + row
   else:
       counter_lenght_isolation = counter_lenght_isolation + row

print(counter_lenght)
print(counter_lenght_isolation)

Screenshot from Jupyter Notebook

Answer 1

试着这样做：

import pandas as pd

#Open file
df = pd.read_excel('Bauteilliste.xlsx')

#edit the display option on jupyter
pd.set_option('display.max_columns', 75)

#Filter 
# 1. All Elements with the ID R-R and the dimension 160mm
filt = (df['KZ'] == 'R-R') & (df['D'] == 160)
#Calculate all the Elements
counter_lenght = 0  #Without Isaltion
counter_lenght_isolation = 0 #With Isaltion

#Get throut every row with the filt filter
for row in df.loc[filt, 'L'].iterrows():
       #PROBLEM: What todo taht .isnull get the same id from row??
       #It only checks the value .isnull from the index 0 not from the filtered row 
   if not row[1]['IsoOf']:
       counter_lenght = counter_lenght + row
   else:
       counter_lenght_isolation = counter_lenght_isolation + row

print(counter_lenght)
print(counter_lenght_isolation)

Answer 2

我已经找到了解决我问题的办法。我将用两个不同的过滤器过滤这些行

import pandas as pd


df = pd.read_excel('Bauteilliste.xlsx')

pd.set_option('display.max_columns', 75)

# Filter settings
filt_with_isolation = (df['KZ'] == 'R-R') & (df['D'] == 160) & (df['IsoOf'].isna() == False)
filt_without_isolation = (df['KZ'] == 'R-R') & (df['D'] == 160) & (df['IsoOf'].isna() == True)

# counting the meters
counter_with_isolation = 0
counter_without_isolation = 0 

# for-Slice, get Elements with isolation
for row in df.loc[filt_with_isolation, 'L']:
    counter_with_isolation = counter_with_isolation + row

for row in df.loc[filt_without_isolation, 'L']:
    counter_without_isolation = counter_without_isolation + row

print(counter_with_isolation)
print(counter_without_isolation)

Output:

6030.0
41050.0