Python 如何更新列值忽略（如果存在）

我有一个熊猫数据框和一个我想更新熊猫列的列表 如果值已经存在，则使用该列表，然后忽略该行
（e.x）

代码：

my_list = ["2018-11-01 00:00:02", "2018-11-01 00:00:07", "2018-11-01 00:00:12", "2018-11-01 00:00:17", "2018-11-01 00:00:22", "2018-11-01 00:00:27", "2018-11-01 00:00:32", "2018-11-01 00:00:37", "2018-11-01 00:00:42", "2018-11-01 00:00:47"]
df["date_time"] = pd.Series(my_list).astype(str)

当我执行上述代码时，它会产生以下输出：

---

如果

date\u time

列是从

list

创建的

datetimes

，则创建

DatetimeIndex

并使用：

或者创建帮助程序

DataFrame

并与left join一起使用：

df['date_time'] = pd.to_datetime(df['date_time'])

df = pd.DataFrame({'date_time': pd.to_datetime(my_list)}).merge(df, how='left')
print (df)
            date_time  value
0 2018-11-01 00:00:02  100.0
1 2018-11-01 00:00:07    NaN
2 2018-11-01 00:00:12  150.0
3 2018-11-01 00:00:17    NaN
4 2018-11-01 00:00:22   56.0
5 2018-11-01 00:00:27    NaN
6 2018-11-01 00:00:32   95.0
7 2018-11-01 00:00:37    NaN
8 2018-11-01 00:00:42  700.0
9 2018-11-01 00:00:47    NaN

---

如果DatetimeIndex：

df.index = pd.to_datetime(df.index)
df = df.reindex(pd.to_datetime(my_list).rename('date_time'))
print (df)
                     value
date_time                 
2018-11-01 00:00:02  100.0
2018-11-01 00:00:07    NaN
2018-11-01 00:00:12  150.0
2018-11-01 00:00:17    NaN
2018-11-01 00:00:22   56.0
2018-11-01 00:00:27    NaN
2018-11-01 00:00:32   95.0
2018-11-01 00:00:37    NaN
2018-11-01 00:00:42  700.0
2018-11-01 00:00:47    NaN

或：

---

当我使用这段代码时，它会生成``float（）参数必须是字符串或数字，而不是'NaTType'，``并且``您正在尝试合并datetime64[ns]和object列。如果您希望继续，您应该使用pd.concat``@arunkumar-您是否忘记了使用

df['date\u time']=pd.to\u datetime（df['date\u time']）

？因为错误意味着列/索引或列表未转换为日期时间。

df['date_time'] = pd.to_datetime(df['date_time'])
df = (df.set_index('date_time')
        .reindex(pd.to_datetime(my_list)
        .rename('date_time'))
        .reset_index())
print (df)
            date_time  value
0 2018-11-01 00:00:02  100.0
1 2018-11-01 00:00:07    NaN
2 2018-11-01 00:00:12  150.0
3 2018-11-01 00:00:17    NaN
4 2018-11-01 00:00:22   56.0
5 2018-11-01 00:00:27    NaN
6 2018-11-01 00:00:32   95.0
7 2018-11-01 00:00:37    NaN
8 2018-11-01 00:00:42  700.0
9 2018-11-01 00:00:47    NaN

df['date_time'] = pd.to_datetime(df['date_time'])

df = pd.DataFrame({'date_time': pd.to_datetime(my_list)}).merge(df, how='left')
print (df)
            date_time  value
0 2018-11-01 00:00:02  100.0
1 2018-11-01 00:00:07    NaN
2 2018-11-01 00:00:12  150.0
3 2018-11-01 00:00:17    NaN
4 2018-11-01 00:00:22   56.0
5 2018-11-01 00:00:27    NaN
6 2018-11-01 00:00:32   95.0
7 2018-11-01 00:00:37    NaN
8 2018-11-01 00:00:42  700.0
9 2018-11-01 00:00:47    NaN

df.index = pd.to_datetime(df.index)
df = df.reindex(pd.to_datetime(my_list).rename('date_time'))
print (df)
                     value
date_time                 
2018-11-01 00:00:02  100.0
2018-11-01 00:00:07    NaN
2018-11-01 00:00:12  150.0
2018-11-01 00:00:17    NaN
2018-11-01 00:00:22   56.0
2018-11-01 00:00:27    NaN
2018-11-01 00:00:32   95.0
2018-11-01 00:00:37    NaN
2018-11-01 00:00:42  700.0
2018-11-01 00:00:47    NaN

df.index = pd.to_datetime(df.index)
df = pd.DataFrame({'date_time': pd.to_datetime(my_list)}).join(df, on='date_time')