如何修改循环以便从 Pandas Python 中的 DataFrame 中的列中的值中获取 NaN 值？

Question

I have sample of my code in Python like below:

...

for col in df.columns.tolist():
    if val in df[f"{col}"].values:
       if val.isna():
          my_list.append(col)

So, if some column from my DataFrame contains NaN value add name of this column to "my_list".

I know that in my DF are columns with NaN values, but my code generate empty "my_list", probably the error is in line: if val.isna(): , how can I modify that? How can I "tell" Python to take NaN values from columns?

Answer 1

Just use a if col statement like this

for col in df.columns.tolist():
    if val in df[f"{col}"].values:
       if col == False:
          my_list.append(col)

I am not giving you the best way of doing it, just fixing your little list loop

Answer 2

By iterating over the values in the column, adding the column name to my_list and then breaking you get this:

my_list = ['col1','col3']

My code:

import pandas as pd
from numpy import NaN

df = pd.DataFrame(data={
    "col1":[10,2.5,NaN],
    "col2":[10,2.5,3.5],
    "col3":[5,NaN,1]})
my_list = []

for col in df.columns:
    for val in df[col].values:
        if pd.isna(val):
            my_list.append(col)
            break
print(f"{my_list=}")

Answer 3

You can fix your code with changes that @Orange mentioned. I'm just adding this as an alternative. When working with data you want to allow the data base/data analysis software to do the heavy lifting. Looping over a cursor is something you should try to avoid as best as you can.

The code you have can be changed to:

for col in df.columns:
    if df[col].hasnans:
        my_list.append(col)

The code below functionally does the same thing:

df.columns[[df[col].hasnans for col in df.columns]].to_list()

The code below calculates hasnans using isna and sum .

df.columns[df.isna().sum() > 0].to_list()