如何使用 python 删除从 pandas 数据框转换而来的列表中包含空元素的行？

Question

So I try to convert a pandas data frame to my customized class function and here is the code for it:

import os
import pandas as pd
import math
cwd = os.path.abspath('') 
files = os.listdir(cwd)  
df = pd.DataFrame()
for file in files:
    if file.endswith('.XLSX'):
        df = df.append(pd.read_excel(file), ignore_index=True)
#print(df)
array = df.values.tolist()
print(array)

class Item():
    
    def __init__(self, name, cost, gender, prime):
        self.__name = name
        self.__cost = cost
        self.__gender = gender
        self.__prime = prime

    def __repr__(self):
        return f"Item({self.__name},{self.__cost},{self.__gender},{self.__prime})"
    


mylist = [Item(*k) for k in array if k[0] and k[1] and k[2] and k[3]]
#print(mylist)

However, there are missing elements in the data frame, so when converting it to the list using array = df.values.tolist() instead of being an "None" for the empty part, the result would produce "nan" instead. This, in fact will cause the filtering process in "mylist" not working.

So, can you should me the code to do instead. Thank you in advance.

Answer 1

Much easier to do while it's still a pandas DataFrame. If you insert a

df.dropna(inplace=True)

before you df.values.tolist() then any rows with missing values should be removed.

Answer 2

There are two ways

1. Use filter

import math
...
array = df.values.tolist()
array = filter(lambda e: all(map(lambda ee: not isinstance(ee, (float, int) or not math.isnan(ee), e)), array))
...

2. Use pandas

...
df = df.dropna()
array = df.values.tolist()
...