Python 如何在不更改数据帧中其他值的情况下替换行值？

我正在运行一个python脚本。我希望在不更改其他值的情况下更改特定的行值。 你能帮我怎么做吗

例如：

df1
Table           Count
case            20
recordtype      50
consumer        70
settlement      150
address         250
bridge          130

我只为“案例”和“消费者”工作运行该流程。现在新的计数在案件和消费者是80和150，但我得到的文件像这样

Table           Count
case            20
recordtype      50
consumer        70
settlement      150
address         250
case            80
consumer        150

它不会替换计数值。它只是在添加新的列。但我想要这样的结果：

df

Table           Count
case            80
recordtype      50
consumer        150
settlement      150
address         250

你能帮我根据表名怎么替换值吗

我正在使用以下代码：

if(os.path.isfile('/medaff/Scripts/python/count.txt')):
            df_s.to_csv('/medaff/Scripts/python/count.txt',mode='a', sep = '|', index= False, header=False)
else:
            df_s.to_csv('/medaff/Scripts/python/count.txt', sep = '|', index= False)

if(os.path.isfile('/medaff/Scripts/python/count.txt')):
            df_s1 = pd.read_csv('/medaff/Scripts/python/count.txt', delimiter='|')
            for index, row in df_s.iterrows():
                print(row)
                print(row['Master Job Name'])
                print(row['Current_Count'])
                idx1=(df_s1['Master Job Name'] == row['Master Job Name'])

                df_s1.at[idx1, 'Current_Count'] = row['Current_Count']
            set1 = set(list(df_s['Master Job Name'].unique()))
            set2 = set(list(df_s1['Master Job Name'].unique()))
            set1 = list(set1 - set2)

            df_s_new = df_s[df_s['Master Job Name'].isin(set1)]

            df_s1 = df_s1.append(df_s_new)
            df_s1.to_csv('/medaff/Scripts/python/count.txt', sep='|', index=False)

else:
        df_s.to_csv('/medaff/Scripts/python/count.txt', mode='a', sep = '|', index= False)

---

我使用以下代码修复了该问题：

if(os.path.isfile('/medaff/Scripts/python/count.txt')):
            df_s.to_csv('/medaff/Scripts/python/count.txt',mode='a', sep = '|', index= False, header=False)
else:
            df_s.to_csv('/medaff/Scripts/python/count.txt', sep = '|', index= False)

if(os.path.isfile('/medaff/Scripts/python/count.txt')):
            df_s1 = pd.read_csv('/medaff/Scripts/python/count.txt', delimiter='|')
            for index, row in df_s.iterrows():
                print(row)
                print(row['Master Job Name'])
                print(row['Current_Count'])
                idx1=(df_s1['Master Job Name'] == row['Master Job Name'])

                df_s1.at[idx1, 'Current_Count'] = row['Current_Count']
            set1 = set(list(df_s['Master Job Name'].unique()))
            set2 = set(list(df_s1['Master Job Name'].unique()))
            set1 = list(set1 - set2)

            df_s_new = df_s[df_s['Master Job Name'].isin(set1)]

            df_s1 = df_s1.append(df_s_new)
            df_s1.to_csv('/medaff/Scripts/python/count.txt', sep='|', index=False)

else:
        df_s.to_csv('/medaff/Scripts/python/count.txt', mode='a', sep = '|', index= False)

---

似乎主题是误导性的。请澄清这里的内容。您拥有什么以及您试图实现什么。另外

我只为“案例”和“消费者”作业运行了该流程。

？@iamklaus我正在基于Active\u flg运行该流程。因此，我将Active_flg='Y'仅用于case和consumer。所以只有两个作业将运行意味着运行sql查询并在dataframe中生成输出，然后我将进行计数。