Can pandas automatically read dates from a CSV file?

Detailed explanation

Parsing dates in standard formats

To parse dates in standard formats, pandas offers the parse_dates parameter:

# "Let there be light!", said pandas, and there was light.
df = pd.read_csv('file.csv', parse_dates=['date_col']) 
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This function has a good understanding of standard ISO date formats. If the dates in your CSV file match common formats, you can trust the detective work to pandas ("Dear pandas, no need to pandar on this.").

Handling custom date formats

For non-standard date formats, it's necessary to define your own date parser function. For this, pandas gives in the date_parser parameter. This option is flexible and you can use a lambda function as your custom parser:

# "Lambda to the rescue!"
date_parse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S') 
df = pd.read_csv('file.csv', parse_dates=['date_col'], date_parser=date_parse)
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Now pandas will parse dates according to your script, like following a treasure map.

Combining date and time columns

If the dates and times are spread across multiple columns, you can combine them using the parse_dates parameter:

# "Where's my date? Oh, it's split across columns."
df = pd.read_csv('file.csv', parse_dates={'datetime': ['date_col', 'time_col']}) 
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Pandas will combine the date and time from multiple columns and create a single datetime64 column ["Yay, combination move!"].

Converting after reading the file

After reading the CSV file, you can also convert non-datetime columns into dates using pd.to_datetime() command as shown below:

# After questioning the suspect, the detective needs a makeover.
df['date_col'] = pd.to_datetime(df['date_col'], format='%Y-%m-%d') 
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This command changes the dtype to datetime64[ns] while preserving the date content.

All about compatibility

Validate that the desired datetime format is compatible with the CSV file. This can prevent potential errors during date conversions. Always check strptime and strftime directives for handling different date formats.

Peculiarities and Pitfalls

Updates in pandas versions

Newer versions of pandas can introduce changes in date parsing methods. Be sure to check the latest pandas documentation or related discussion threads on Stack Overflow.

Handling unparseable dates

If a date cannot be parsed and throws an error, handle it gracefully by converting the time column to object dtype. This ensures that the data's integrity is maintained.

Date format nuances

Depending on the locale, the date formats can vary. The formats like MM/DD/YY have been standardized to behave as North American MM/DD/YY.