执行代码后Jupyter Kernel忙

Question

I'm using Jupyter Notebook with conda and Python 3. Recently, the kernel is busy even after the code execution is finished and the execution time is way longer than usual. I have been searching around but there is no result. Any suggestions?

Edit: I'm sorry for being too general. I am trying to identify the problem myself so any direction will be appreciated. After re-running the code a few times, it seems like whenever I run the following block of code, it happens:

train_X = np.array(train_X)
train_Y = np.array(train_Y)

The previous code is as following:

# In[1]:    
import pandas as pd
from collections import OrderedDict    

# In[2]:   
df = pd.read_csv('df.csv')
people_list = df['ID'].unique()
product_list = df['product'].unique() 

# Out[2]:
    ID  product     M1  M2  M3  class
0   0   A           1   2   6   1
1   1   B           2   3   7   1
2   2   C           3   4   3   0
3   0   C           4   3   2   1
4   1   A           5   4   3   1
5   2   B           6   6   1   0  

# In[3]:    
people_dict = {}
target_dict = {}

for i in range(len(people_list)):
    key = people_list[i]
    new_df = df[df['ID'] == people_list[i]]
    new_df = new_df.transpose()
    new_df.columns = new_df.iloc[1]
    new_df = new_df[2:-1]   
    people_dict[key] = new_df
    target_dict[key] = df.iat[i, 5]

for key in people_dict.keys():
    for i in product_list:
        if i not in people_dict[key].columns:
            people_dict[key][i] = [0]*3
    people_dict[key] = people_dict[key].reindex(sorted(people_dict[key].columns), axis = 1)

# In[5]:    
people_values = OrderedDict()
target_values = OrderedDict()

# extract the value of the dataframe
for key in people_dict.keys():
    people_values[key] = people_dict[key].values
    target_values[key] = target_dict[key]

# In[6]:
n_samples = 1
timestes = 3
n_features = 3

train_input = list(people_values.values())
train_target = list(target_values.values())

train_X = []
train_Y = []

for i in range(len(train_input)):
    train_X.append(train_input[i])
    train_Y.append(train_target[i])

# In[7]:
train_X = np.array(train_X)
train_Y = np.array(train_Y)

Essentially, I am trying to do some classification with Keras LSTM and the input is historical sales of 1 person, the output is their class, 'good' or 'bad'.

The real dataset has 60k rows but I simplified the dataset so everyone can follow more easily. When I worked with this dataset previously, I never encountered this issue.

Any suggestions are greatly appreciated, thank you.

Answer 1

It turns out that it is just an issue with converting a variable to a numpy array, as it is shown here . I just worked around it.