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我在数据框中有许多记录，其中到期日期列是 31-12-9999 12:00:00 AM，因为债券永远不会到期。这自然会引发错误：

Out of bounds nanosecond timestamp: 9999-12-31 00:00:00

我看到最大日期是：

pd.Timestamp.max
Timestamp('2262-04-11 23:47:16.854775807')

我只是想澄清清除 datframe 中所有日期列并修复我的错误的最佳方法是什么？我的代码模仿了文档：

df_Fix_Date = df_Date['maturity_date'].head(8)
display(df_Fix_Date)
display(df_Fix_Date.dtypes)

0    2020-08-15 00:00:00.000
1    2022-11-06 00:00:00.000
2    2019-03-15 00:00:00.000
3    2025-01-15 00:00:00.000
4    2035-05-29 00:00:00.000
5    2027-06-01 00:00:00.000
6    2021-04-01 00:00:00.000
7    2022-04-03 00:00:00.000
Name: maturity_date, dtype: object

def conv(x):
        return pd.Period(day = x%100, month = x//100 % 100, year = x // 10000, freq='D')

df_Fix_Date['maturity_date'] = pd.to_datetime(df_Fix_Date['maturity_date'])               # convert to datetype
df_Fix_Date['maturity_date'] = pd.PeriodIndex(df_Fix_Date['maturity_date'].apply(conv))   # fix error
display(df_Fix_Date)

输出：

KeyError: 'maturity_date'
4

1 回答 1

1

存在无法转换为越界日期时间的问题。

一种解决方案是替换9999为2261：

df_Fix_Date['maturity_date'] = df_Fix_Date['maturity_date'].replace('^9999','2261',regex=True)
df_Fix_Date['maturity_date'] = pd.to_datetime(df_Fix_Date['maturity_date']) 
print (df_Fix_Date)
  maturity_date
0    2020-08-15
1    2022-11-06
2    2019-03-15
3    2025-01-15
4    2035-05-29
5    2027-06-01
6    2021-04-01
7    2261-04-03

另一种解决方案是将所有日期替换为更高的2261年份2261：

m = df_Fix_Date['maturity_date'].str[:4].astype(int) > 2261
df_Fix_Date['maturity_date'] = df_Fix_Date['maturity_date'].mask(m, '2261' + df_Fix_Date['maturity_date'].str[4:])
df_Fix_Date['maturity_date'] = pd.to_datetime(df_Fix_Date['maturity_date']) 
print (df_Fix_Date)
  maturity_date
0    2020-08-15
1    2022-11-06
2    2019-03-15
3    2025-01-15
4    2035-05-29
5    2027-06-01
6    2021-04-01
7    2261-04-03

或者NaT通过参数将有问题的日期替换为 s errors='coerce'：

df_Fix_Date['maturity_date'] = pd.to_datetime(df_Fix_Date['maturity_date'], errors='coerce') 
print (df_Fix_Date)
  maturity_date
0    2020-08-15
1    2022-11-06
2    2019-03-15
3    2025-01-15
4    2035-05-29
5    2027-06-01
6    2021-04-01
7           NaT
于 2018-03-22T06:18:21.390 回答