熊猫用条件填充了另一列中的nan值？

Question

我有以下数据框：

data = {'feature1_in_use?': [0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1],
     'feature1_available?': [0, 1, 1, 'NA', 1, 'NA', 1, 1, 'NA', 1, 1]}

df = pd.DataFrame(data)

在“ feature1_available？”列中我想用'feature1_in_use中的值填充NAN？ '仅在1时，否则用'x'或任何有效的字符串填充它。

看起来应该这样：

data = {'feature1_in_use?': [0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1],
     'feature1_available?': [0, 1, 1, 'X', 1, 1, 1, 1, 1, 1, 1]}

df = pd.DataFrame(data)

Answer 1

您可以使用的灵活选项是np.Select（）

data = {'feature1_in_use?': [0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1],
     'feature1_available?': [0, 1, 1, np.nan, 1, np.nan, 1, 1, np.nan, 1, 1]}

df = pd.DataFrame(data)
df['feature1_available?'] = df['feature1_available?'].replace({np.nan : None})
condition_list = [
    ((df['feature1_available?'].values == None) & (df['feature1_in_use?'] == 1)),
    ((df['feature1_available?'].values == None) & (df['feature1_in_use?'] == 0))
]
choice_list = [df['feature1_in_use?'], 'X']
df['feature1_available?'] = np.select(condition_list, choice_list, df['feature1_available?'])
df

Answer 2

假设您希望您的空白值为“ x”，然后执行以下操作：

df['feature1_available'] = df['feature1_available'].fillna(df['feature1_in_use'])

df['feature1_in_use'] = df['feature1_in_use'].fillna('x')

步骤1填充 farture1_available 的空白值来自 features1_in_use 。步骤2用“ x”填充所有剩余的NAN值（或您选择的任何其他值

Answer 3

第一行创建了一个系列，其中除1以外的其他所有内容都是'x'。然后，“ na”被np.nan替换，该使我们能够用创建的输出的系列填充缺失值

s = df['feature1_in_use?'].where(df['feature1_in_use?'].eq(1),'X')
df.replace('NA',np.NaN).fillna({'feature1_available?':s})

：

    feature1_in_use? feature1_available?
0                  0                 0.0
1                  0                 1.0
2                  1                 1.0
3                  0                   X
4                  1                 1.0
5                  1                   1
6                  1                 1.0
7                  0                 1.0
8                  1                   1
9                  0                 1.0
10                 1                 1.0