python with pandas to parse dates like “0001-11-29 13:00:00 BC”

I am trying to read some sql data using pandas library and one of the column “customer_date” has values like “0001-11-29 13:00:00 BC”. My query fails with error

ValueError: year 0 is out of range

Please suggest a way to parse such date/timestamps.

Here is my code.

import pandas as pd
from datetime import datetime, timedelta

session = ah.get_boto_session()
db = get_connection() **### Custom method**

pd.set_option('display.max_rows', None)
query = "SELECT customer_date FROM customer"
df = pd.read_sql(query, db, parse_dates=["customer_date"])
print(df.head())

db.dispose()

Error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-2-d4e334c3f39c> in <module>()
 22 pd.set_option('display.max_rows', None)
 23 query = "SELECT customer_date FROM customer"
---> 24 df = pd.read_sql(query, db, parse_dates=["customer_date"])
 25 print(df.head())

ValueError: year 0 is out of range

Answer

This is what worked:

import pandas as pd
from datetime import datetime, timedelta

session = ah.get_boto_session()
db = get_connection() **### Custom method**

pd.set_option('display.max_rows', None)
query = "SELECT case when customer_date >= '1970-01-01' then customer_date else '1970-01-01' end AS customer_date FROM customer"
df = pd.read_sql(query, db)
print(df.head())

db.dispose()