缺失数据的循环功能

Question

我想用列表解析方法用np.random.normal(mu,s,n)函数改变NaN值，但我做不到。

df_column_values = ["NaN","1","NaN","2","NaN","3","94","4","168","5","NaN"]

n, mu, sigma = 700, 155, 118
array = np.random.normal(mu, sigma, n)
for i in array:
    if i > 0 and i < 400:    
        data['Insulin'].replace(0,(i), inplace=True)  

此函数有效，但所有 NaN 值的输出都相同。 如何改进此代码？

来自Kaggle的原始数据

Answer 1

您似乎想用范围 (0, 400) 内的正态分布随机值替换缺失值。为此，您需要使用截断的正态分布。

然后，您应该创建一个与您可能要替换的数据长度相同的随机变量向量。

data = pd.DataFrame({'Insulin': ["NaN","1","NaN","2","NaN","3",
"94","4","168","5","NaN"]})
​
import scipy.stats as stats
​
lower, upper = 0, 400
mu, sigma = 155, 118
X = stats.truncnorm(
    (lower - mu) / sigma, 
    (upper - mu) / sigma, 
    loc=mu, scale=sigma)
​
data['Insulin'] = np.where(
     data['Insulin']=="NaN", 
     X.rvs(len(data)),
     data['Insulin'])

data['Insulin'] = np.where(
     data['Insulin'].isna(), 
     X.rvs(len(data)),
     data['Insulin'])

print(data)

       Insulin
0    59.069239
1            1
2   113.143013
3            2
4    63.488282
5            3
6           94
7            4
8          168
9            5
10  109.272469