如何更新/追加 Csv 文件中的模型名称列相对于值为 0 的另一列（Epoch）

Question

I am writing model history in csv in which later on I am adding a new column model name where I need to update model name everytime when I run with timestamp.But currently what I am facing is that it is updating the model name not just for the latest one but for all wherever it finds Epoch value as 0. Kindly Help if anybody have any idea.

Training CSV Contains the given data and model_na varible basically conatains FileName_Time stamp which you can see in model name column---

 epoch accuracy loss val_accuracy val_loss 0 0.5991348 0.954963956 0.61725134 0.928029025 1 0.6101555 0.934882281 0.61725134 0.926564478 2 0.6101555 0.933647258 0.61725134 0.925897177 3 0.6101555 0.933986878 0.61725134 0.925625712 4 0.6101555 0.93378263 0.61725134 0.92587147 5 0.6101555 0.933501081 0.61725134 0.925496578 6 0.6101555 0.934000334 0.61725134 0.925522646 7 0.6101555 0.933863895 0.61725134 0.926479578 8 0.6101555 0.933888928 0.61725134 0.926321284 0 0.59779584 0.954151712 0.61725134 0.925566993 1 0.6101555 0.935797452 0.61725134 0.926761304 2 0.6101555 0.935356209 0.61725134 0.926412033 3 0.6101555 0.933948885 0.61725134 0.925769904 0 0.6020188 0.956590114 0.61725134 0.928246004 1 0.6101555 0.935971673 0.61725134 0.925614129 2 0.6101555 0.934430503 0.61725134 0.926334595 3 0.6101555 0.933712303 0.61725134 0.927097352 4 0.6101555 0.933796334 0.61725134 0.925467268 5 0.6101555 0.93388608 0.61725134 0.925654634 6 0.6101555 0.933794685 0.61725134 0.925489133 7 0.6101555 0.933911282 0.61725134 0.926129941

I am using given code write now--- ''' df = pd.read_csv('\\History\\training.csv')

Type_new = pd.Series([])

for i in range(len(df)):

if df["epoch"][i] == 0: 

    Type_new[i]=model_na

df.insert(0, "Model_Name", Type_new)

df.to_csv('\\History\\cleaned_twitter_final.csv',index=False)

''' Current Output----

 Model_Name epoch accuracy loss val_accuracy val_loss LSTM_Model_19_Feb_2020_122031 0 0.5991348 0.954963956 0.61725134 0.928029025 1 0.6101555 0.934882281 0.61725134 0.926564478 2 0.6101555 0.933647258 0.61725134 0.925897177 3 0.6101555 0.933986878 0.61725134 0.925625712 4 0.6101555 0.93378263 0.61725134 0.92587147 5 0.6101555 0.933501081 0.61725134 0.925496578 6 0.6101555 0.934000334 0.61725134 0.925522646 7 0.6101555 0.933863895 0.61725134 0.926479578 8 0.6101555 0.933888928 0.61725134 0.926321284 LSTM_Model_19_Feb_2020_122031 0 0.59779584 0.954151712 0.61725134 0.925566993 1 0.6101555 0.935797452 0.61725134 0.926761304 2 0.6101555 0.935356209 0.61725134 0.926412033 3 0.6101555 0.933948885 0.61725134 0.925769904

Expected Output---

 Model_Name epoch accuracy loss val_accuracy val_loss LSTM_Model_19_Feb_2020_122031 0 0.5991348 0.954963956 0.61725134 0.928029025 1 0.6101555 0.934882281 0.61725134 0.926564478 2 0.6101555 0.933647258 0.61725134 0.925897177 3 0.6101555 0.933986878 0.61725134 0.925625712 4 0.6101555 0.93378263 0.61725134 0.92587147 5 0.6101555 0.933501081 0.61725134 0.925496578 6 0.6101555 0.934000334 0.61725134 0.925522646 7 0.6101555 0.933863895 0.61725134 0.926479578 8 0.6101555 0.933888928 0.61725134 0.926321284 LSTM_Model_19_Feb_2020_132031 0 0.59779584 0.954151712 0.61725134 0.925566993 1 0.6101555 0.935797452 0.61725134 0.926761304 2 0.6101555 0.935356209 0.61725134 0.926412033 3 0.6101555 0.933948885 0.61725134 0.925769904

Answer 1

得到了解决方案......可以使用 csv 记录器......