如何根据数据帧的条件选择列中缺少值的行?

如果您已经填充了数据帧,那么您可以这样做

import numpy as np
import pandas as pd

## Generate Random Data
raw_data=np.random.choice([None,1], (50,8))
raw_data= np.r_[raw_data, np.random.choice([None, 1,2,3], (50,8))]

## Create dataframe from random data
df = pd.DataFrame(raw_data, columns="A, B, D, E, F, G, I, L".split(","))
notnull_counts = (~df.isnull()).sum(axis=1)

## filter rows with your condition
legit_rows = df[((notnull_counts==1) | (notnull_counts==2) | (notnull_counts==8))]
non_legit_rows = df[~((notnull_counts==1) | (notnull_counts==2) | (notnull_counts==8))]

display(legit_rows)

sum() 到 perform the sum for each row by using axis="columns" ,就像这样:

# This is a series indexed like df.
# It counts the number of null values in the given columns.
n_null = df[["A", "B", "D", "E", "F", "G", "I", "L"]].isnull().sum(axis="columns")

# This selects the rows where n_null has certain values.
df_notlegit = df.loc[n_null.isin([8, 5, 4, 3, 2])]

# This is another way to do it.
df_nonlegit = df.loc[(n_null > 1) & (n_null < 9)]

df.loc[(df.isna().sum(axis=1)==0) | (df.isna().sum(axis=1)==7) | (df.isna().sum(axis=1)==6)]