我認爲你需要boolean indexing
與notnull
創建面膜:
df_features[df_features['sexo'].notnull()]
看來你需要:
df_features[(df_features['sexo'].notnull()) & (df_features['age'] != 'NA')]
樣品:
df_features = pd.DataFrame({'sexo':[np.nan,2,3],
'age':['10','20','NA']})
print (df_features)
age sexo
0 10 NaN
1 20 2.0
2 NA 3.0
a = df_features[(df_features['sexo'].notnull()) & (df_features['age'] != 'NA')]
print (a)
age sexo
1 20 2.0
但似乎你與01共謀值不是數字,而是字符串。
如果需要一些列轉換爲數字,嘗試to_numeric
,參數errors='coerce'
方式轉變,不能再見解析爲數字的所有值NaN
:
df_features = pd.DataFrame({'sexo':[np.nan,2,3],
'age':['10','20','NA']})
print (df_features)
age sexo
0 10 NaN
1 20 2.0
2 NA 3.0
df_features['age'] = pd.to_numeric(df_features['age'], errors='coerce')
print (df_features)
age sexo
0 10.0 NaN
1 20.0 2.0
2 NaN 3.0
a = df_features[(df_features['sexo'].notnull()) & (df_features['age'].notnull())]
print (a)
age sexo
1 20.0 2.0
呀,我找到NA是字符串和數據他們是' NA'和'NA'。 – yanachen