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This rule raises an issue when the argument dayfirst or yearfirst is set to True on
pandas.to_datetime function with an incorrect string format.
Why is this an issue?
The pandas.to_datetime function transforms a string to a date object. The string representation of the date can take multiple formats.
To correctly parse these strings, pandas.to_datetime provides several arguments to setup the parsing, such as dayfirst or
yearfirst. For example setting dayfirst to True indicates to pandas.to_datetime that the date and
time will be represented as a string with the shape day month year time. Similarly with yearfirst, the string should have
the following shape year month day time.
These two arguments are not strict, meaning if the shape of the string is not the one expected by pandas.to_datetime, the function
will not fail and try to figure out which part of the string is the day, month or year.
In the following example the dayfirst argument is set to True but we can clearly see that the month part of
the date would be incorrect. In this case pandas.to_datetime will ignore the dayfirst argument, and parse the date as the
22nd of January.
import pandas as pd
pd.to_datetime(["01-22-2000 10:00"], dayfirst=True)
No issue will be raised in such a case, which could lead to bugs later in the program. Either the user made a mistake by setting
dayfirst to True or the month part of the date is incorrect.
How to fix it
To fix this issue either correct the string representation of the date to match the expected format, or remove the arguments dayfirst
or yearfirst.
Code examples
Noncompliant code example
import pandas as pd
pd.to_datetime(["01-22-2000 10:00"], dayfirst=True) # Noncompliant: the second part of the date (22) is not a valid month
pd.to_datetime(["02/03/2000 12:00"], yearfirst=True) # Noncompliant: the year is not the first part of the date
pd.to_datetime(["03-14-2000 10:00"], dayfirst=True) # Noncompliant
Compliant solution
import pandas as pd
pd.to_datetime(["01-12-2000 10:00"], dayfirst=True) # Compliant: the date will be parsed as expected
pd.to_datetime(["2000/02/28 12:00"], yearfirst=True) # Compliant
pd.to_datetime(["03-14-2000 10:00"]) # Compliant
Resources
Documentation
- Pandas documentation - Converting to
timestamps