I’m reading in a csv file with multiple datetime columns. I’d need to set the data types upon reading in the file, but datetimes appear to be a problem. For instance:

headers = ['col1', 'col2', 'col3', 'col4']
dtypes = ['datetime', 'datetime', 'str', 'float']
pd.read_csv(file, sep='\t', header=None, names=headers, dtype=dtypes)

When run gives a error:

TypeError: data type “datetime” not understood

Converting columns after the fact, via pandas.to_datetime() isn’t an option I can’t know which columns will be datetime objects. That information can change and comes from whatever informs my dtypes list.

Alternatively, I’ve tried to load the csv file with numpy.genfromtxt, set the dtypes in that function, and then convert to a pandas.dataframe but it garbles the data. Any help is greatly appreciated!

Why it does not work

There is no datetime dtype to be set for read_csv as csv files can only contain strings, integers and floats.

Setting a dtype to datetime will make pandas interpret the datetime as an object, meaning you will end up with a string.

Pandas way of solving this

The pandas.read_csv() function has a keyword argument called parse_dates

Using this you can on the fly convert strings, floats or integers into datetimes using the default date_parser (dateutil.parser.parser)

headers = ['col1', 'col2', 'col3', 'col4']
dtypes = {'col1': 'str', 'col2': 'str', 'col3': 'str', 'col4': 'float'}
parse_dates = ['col1', 'col2']
pd.read_csv(file, sep='\t', header=None, names=headers, dtype=dtypes, parse_dates=parse_dates)

This will cause pandas to read col1 and col2 as strings, which they most likely are (“2016-05-05” etc.) and after having read the string, the date_parser for each column will act upon that string and give back whatever that function returns.

Defining your own date parsing function:

The pandas.read_csv() function also has a keyword argument called date_parser

Setting this to a lambda function will make that particular function be used for the parsing of the dates.

GOTCHA WARNING

You have to give it the function, not the execution of the function, thus this is Correct

date_parser = pd.datetools.to_datetime

This is incorrect:

date_parser = pd.datetools.to_datetime()

Pandas 0.22 Update

pd.datetools.to_datetime has been relocated to date_parser = pd.to_datetime

Thanks @stackoverYC

There is a parse_dates parameter for read_csv which allows you to define the names of the columns you want treated as dates or datetimes:

date_cols = ['col1', 'col2']
pd.read_csv(file, sep='\t', header=None, names=headers, parse_dates=date_cols)

You might try passing actual types instead of strings.

import pandas as pd
from datetime import datetime
headers = ['col1', 'col2', 'col3', 'col4'] 
dtypes = [datetime, datetime, str, float] 
pd.read_csv(file, sep='\t', header=None, names=headers, dtype=dtypes)

But it’s going to be really hard to diagnose this without any of your data to tinker with.

And really, you probably want pandas to parse the the dates into TimeStamps, so that might be:

pd.read_csv(file, sep='\t', header=None, names=headers, parse_dates=True)

I tried using the dtypes=[datetime, …] option, but

import pandas as pd
from datetime import datetime
headers = ['col1', 'col2', 'col3', 'col4'] 
dtypes = [datetime, datetime, str, float] 
pd.read_csv(file, sep='\t', header=None, names=headers, dtype=dtypes)

I encountered the following error:

TypeError: data type not understood

The only change I had to make is to replace datetime with datetime.datetime

import pandas as pd
from datetime import datetime
headers = ['col1', 'col2', 'col3', 'col4'] 
dtypes = [datetime.datetime, datetime.datetime, str, float] 
pd.read_csv(file, sep='\t', header=None, names=headers, dtype=dtypes)