pandas return 具有多个 'NA' 值的行的索引

Question

我的代码：

import pandas as pd
from sklearn.preprocessing import LabelEncoder
column_names = ["age","workclass","fnlwgt","education","education-num","marital-status","occupation","relationship","race","sex","capital-gain","capital-loss","hrs-per-week","native-country","income"]

adult_train = pd.read_csv("adult.data",header=None,sep=',\s',na_values=["?"])
adult_train.columns=column_names
adult_train.fillna('NA',inplace=True)

我想要在多个列中具有值 'NA' 的行的索引。是否有内置方法，或者我必须逐行迭代并检查每一列的值？这是数据的快照：

我想要 398,409 行的索引（B 和 G 列缺失值），而不是 394 行（仅 N 列缺失值）的索引

Answer 1

使用 isnull.any(1) 或 sum 获取布尔掩码，然后 select 行获取索引，即

df = pd.DataFrame({'A':[1,2,3,4,5],
               'B' :[np.nan,4,5,np.nan,8],
               'C' :[2,4,np.nan,3,5],
               'D' :[np.nan,np.nan,np.nan,np.nan,5]})

   A    B    C    D
0  1  NaN  2.0  NaN
1  2  4.0  4.0  NaN
2  3  5.0  NaN  NaN
3  4  NaN  3.0  NaN
4  5  8.0  5.0  5.0

# If you want to select rows with nan value from Columns B and C 
df.loc[df[['B','C']].isnull().any(1)].index
Int64Index([0, 2, 3], dtype='int64')

# If you want to rows with more than one nan then
df.loc[df.isnull().sum(1)>1].index
Int64Index([0, 2, 3], dtype='int64')

pandas return 具有多个 'NA' 值的行的索引

pandas return index of rows having more than one 'NA' value

python

python-2.7

python-3.x

pandas

sklearn-pandas