Can pandas automatically read dates from a CSV file?

Question

Today I was positively surprised by the fact that while reading data from a data file (for example) pandas is able to recognize types of values:

df = pandas.read_csv('test.dat', delimiter=r"\s+", names=['col1','col2','col3'])

For example it can be checked in this way:

for i, r in df.iterrows():
    print type(r['col1']), type(r['col2']), type(r['col3'])

In particular integer, floats and strings were recognized correctly. However, I have a column that has dates in the following format: 2013-6-4. These dates were recognized as strings (not as python date-objects). Is there a way to "learn" pandas to recognized dates?

Rutger Kassies · Accepted Answer · 2020-09-25T07:12:24.673

421

You should add parse_dates=True, or parse_dates=['column name'] when reading, thats usually enough to magically parse it. But there are always weird formats which need to be defined manually. In such a case you can also add a date parser function, which is the most flexible way possible.

Suppose you have a column 'datetime' with your string, then:

from datetime import datetime
dateparse = lambda x: datetime.strptime(x, '%Y-%m-%d %H:%M:%S')

df = pd.read_csv(infile, parse_dates=['datetime'], date_parser=dateparse)

This way you can even combine multiple columns into a single datetime column, this merges a 'date' and a 'time' column into a single 'datetime' column:

dateparse = lambda x: datetime.strptime(x, '%Y-%m-%d %H:%M:%S')

df = pd.read_csv(infile, parse_dates={'datetime': ['date', 'time']}, date_parser=dateparse)

You can find directives (i.e. the letters to be used for different formats) for strptime and strftime in this page.

edited Sep 25 '20 at 07:12

answered Jul 04 '13 at 10:32

Rutger Kassies

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11

Did not work for me, I got the following error: `TypeError: strptime() argument 1 must be str, not float` – Jean Paul May 04 '17 at 12:36
7

I got this error because there were nan in my data frame. – Jean Paul May 04 '17 at 13:10
can you add an item that also NaTs the non-parsable material or NaN or /Ns. cause it seems this parser totally skips the whole column if anything like that is present – Amir Oct 17 '17 at 20:31
2

There's an option `infer_datetime_format`: "pandas will attempt to infer the format of the datetime strings in the columns". This can be used instead of `date_parser`. – Winand Oct 12 '18 at 11:09
1

Note that if your dates are in `ISO 8601` format you should not pass `infer_datetime_format` or a parser function - it's much slower than letting pandas handle it (especially the latter). The dateformat in this answer falls into this category also – Mr_and_Mrs_D Dec 04 '18 at 00:25
1

Is a lambda function necessary here? I think we can just directly apply the function to the date column. – lstodd Feb 12 '19 at 13:45
As mentioned in the post, it's necessary if you want to deal with a specific date format. Not specifying anything makes assumptions about the format you provide. – Rutger Kassies Feb 13 '19 at 09:55
I am using the same format for parsing my date column (only difference is that I use ''%Y-%m-%d'). It does the parsing and reads the csv into dataframe. But still my column is of type string instead of datetime. What am I missing? \ – BobbyF Feb 19 '19 at 22:39
I stopped the environment and restarted and it worked!! – BobbyF Feb 19 '19 at 22:55
1

Adding `parse_dates=True` doesn't work, where 'date' is the column and the dates are stored as `YYYY-MM-DD`. – ifly6 Jun 05 '19 at 20:03
Getting this error: dateparse takes 1 positional argument but 2 were given, I am trying to do it for multiple columns. – royalyadnesh Jan 20 '20 at 09:05
@royalyadnesh You need something like dateparse = lambda x,y : ... – Septacle May 06 '20 at 01:17
2

`pd.datetime` is currently deprecated, replace `pd.datetime` with just `datetime` after `import datetime from datetime`. – Ébe Isaac Sep 24 '20 at 08:51
Maybe add a remark on `dtype`, the type you need to specify for `read_csv` argument is the input type and not the output type. So its `datetime={'datetime': 'string'}` and not `datetime={'datetime': 'datetime64'}`. If you do that the output type will be correct (`datetime64[ns]`). – cglacet Sep 29 '20 at 09:26
Why need to use a function instead of date_parser=datetime.strptime(x, '%Y-%m-%d %H:%M:%S') directly? – saga Nov 22 '20 at 23:10
Can I use multiple date format check with or condition? – Raman Joshi Aug 19 '21 at 11:44
What if I don't know the column name when uploading csv file. I want some generic solution to auto detect date column when uploading csv file with some pre-defined date formats. – Raman Joshi Aug 19 '21 at 11:54
It works fine, thanks. Note : `dateparse = lambda x: datetime.strptime(x, '%Y-%m-%d %H:%M:%S')`. Lambdas are anonymous functions. If you name an anonymous function, you might as well just use a function. So either pass a lambda directly as argument to `read_csv`, or define a `dateparse` function the usual way. – Eric Duminil Feb 01 '22 at 21:30

Sean · Answer 2 · 2021-08-19T19:40:03.780

Perhaps the pandas interface has changed since @Rutger answered, but in the version I'm using (0.15.2), the date_parser function receives a list of dates instead of a single value. In this case, his code should be updated like so:

from datetime import datetime
import pandas as pd

dateparse = lambda dates: [datetime.strptime(d, '%Y-%m-%d %H:%M:%S') for d in dates]
    
df = pd.read_csv('test.dat', parse_dates=['datetime'], date_parser=dateparse)

Since the original question asker said he wants dates and the dates are in 2013-6-4 format, the dateparse function should really be:

dateparse = lambda dates: [datetime.strptime(d, '%Y-%m-%d').date() for d in dates]

score 19 · Answer 3 · answered Sep 24 '17 at 12:52

You could use pandas.to_datetime() as recommended in the documentation for pandas.read_csv():

If a column or index contains an unparseable date, the entire column or index will be returned unaltered as an object data type. For non-standard datetime parsing, use pd.to_datetime after pd.read_csv.

Demo:

>>> D = {'date': '2013-6-4'}
>>> df = pd.DataFrame(D, index=[0])
>>> df
       date
0  2013-6-4
>>> df.dtypes
date    object
dtype: object
>>> df['date'] = pd.to_datetime(df.date, format='%Y-%m-%d')
>>> df
        date
0 2013-06-04
>>> df.dtypes
date    datetime64[ns]
dtype: object

it is converting other columns to date too, which are of object type — ratnesh, Jun 19 '20 at 10:56

score 13 · Answer 4 · edited Oct 25 '17 at 09:19

13

When merging two columns into a single datetime column, the accepted answer generates an error (pandas version 0.20.3), since the columns are sent to the date_parser function separately.

The following works:

def dateparse(d,t):
    dt = d + " " + t
    return pd.datetime.strptime(dt, '%d/%m/%Y %H:%M:%S')

df = pd.read_csv(infile, parse_dates={'datetime': ['date', 'time']}, date_parser=dateparse)

edited Oct 25 '17 at 09:19

DaFois

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answered Oct 25 '17 at 08:54

IamTheWalrus

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1

I'm using pandas 0.22 and agree that the accepted answer no longer works. – Dai May 06 '18 at 23:31
1

This creates a "TypeError: can only concatenate str (not "float") to str" for me. Date column is d/m/y and time column is H:M:00 – IceQueeny Oct 27 '18 at 11:56

score 10 · Answer 5 · answered Jul 04 '13 at 10:38

pandas read_csv method is great for parsing dates. Complete documentation at http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.parsers.read_csv.html

you can even have the different date parts in different columns and pass the parameter:

parse_dates : boolean, list of ints or names, list of lists, or dict
If True -> try parsing the index. If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a
separate date column. If [[1, 3]] -> combine columns 1 and 3 and parse as a single date
column. {‘foo’ : [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’

The default sensing of dates works great, but it seems to be biased towards north american Date formats. If you live elsewhere you might occasionally be caught by the results. As far as I can remember 1/6/2000 means 6 January in the USA as opposed to 1 Jun where I live. It is smart enough to swing them around if dates like 23/6/2000 are used. Probably safer to stay with YYYYMMDD variations of date though. Apologies to pandas developers,here but i have not tested it with local dates recently.

you can use the date_parser parameter to pass a function to convert your format.

date_parser : function
Function to use for converting a sequence of string columns to an array of datetime
instances. The default uses dateutil.parser.parser to do the conversion.

You can specify `dayfirst` as True for European/international dates. https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html — Will Gordon, Nov 15 '17 at 10:16

score 8 · Answer 6 · answered Apr 10 '17 at 02:46

8

Yes - according to the pandas.read_csv documentation:

Note: A fast-path exists for iso8601-formatted dates.

So if your csv has a column named datetime and the dates looks like 2013-01-01T01:01 for example, running this will make pandas (I'm on v0.19.2) pick up the date and time automatically:

df = pd.read_csv('test.csv', parse_dates=['datetime'])

Note that you need to explicitly pass parse_dates, it doesn't work without.

Verify with:

df.dtypes

You should see the datatype of the column is datetime64[ns]

answered Apr 10 '17 at 02:46

Gaurav

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10
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I think you misunderstand the question. The user is curious whether the option could be enabled for his format of string. – Arya McCarthy Apr 10 '17 at 02:51
@AryaMcCarthy umm, he basically wants the date to be recognized correctly, so I am mentioning how can he transform the source data so that it is naturally recognized by pandas. Nowhere does he mention he cannot change the format of the source data. – Gaurav Sep 02 '17 at 00:14

score 4 · Answer 7 · answered Sep 20 '19 at 19:30

While loading csv file contain date column.We have two approach to to make pandas to recognize date column i.e

Pandas explicit recognize the format by arg date_parser=mydateparser
Pandas implicit recognize the format by agr infer_datetime_format=True

Some of the date column data

01/01/18

01/02/18

Here we don't know the first two things It may be month or day. So in this case we have to use Method 1:- Explicit pass the format

    mydateparser = lambda x: pd.datetime.strptime(x, "%m/%d/%y")
    df = pd.read_csv(file_name, parse_dates=['date_col_name'],
date_parser=mydateparser)

Method 2:- Implicit or Automatically recognize the format

df = pd.read_csv(file_name, parse_dates=[date_col_name],infer_datetime_format=True)

score 2 · Answer 8 · answered Aug 10 '20 at 17:30

In addition to what the other replies said, if you have to parse very large files with hundreds of thousands of timestamps, date_parser can prove to be a huge performance bottleneck, as it's a Python function called once per row. You can get a sizeable performance improvements by instead keeping the dates as text while parsing the CSV file and then converting the entire column into dates in one go:

# For a data column
df = pd.read_csv(infile, parse_dates={'mydatetime': ['date', 'time']})

df['mydatetime'] = pd.to_datetime(df['mydatetime'], exact=True, cache=True, format='%Y-%m-%d %H:%M:%S')

# For a DateTimeIndex
df = pd.read_csv(infile, parse_dates={'mydatetime': ['date', 'time']}, index_col='mydatetime')

df.index = pd.to_datetime(df.index, exact=True, cache=True, format='%Y-%m-%d %H:%M:%S')

# For a MultiIndex
df = pd.read_csv(infile, parse_dates={'mydatetime': ['date', 'time']}, index_col=['mydatetime', 'num'])

idx_mydatetime = df.index.get_level_values(0)
idx_num = df.index.get_level_values(1)
idx_mydatetime = pd.to_datetime(idx_mydatetime, exact=True, cache=True, format='%Y-%m-%d %H:%M:%S')
df.index = pd.MultiIndex.from_arrays([idx_mydatetime, idx_num])

For my use case on a file with 200k rows (one timestamp per row), that cut down processing time from about a minute to less than a second.

Have you tried the ```infer_datetime_format``` parameter for read_csv. It infers the dates which is fine if your dates are consistently formatted. It speeds up the process. — Cam, Aug 30 '21 at 21:49
I would have, but my dates were in a weird format with the month first and the milliseconds part being separated by a third colon instead of a dot. At any rate, I figured it would still be useful to have this method written somewhere, since most sources either use inference or date_parser, with no fast alternative to date_format being given. IIRC the doc is also vague on how multiple date columns are combined when passed as an array to parse_dates (they get separated by spaces). — VLRoyrenn, Aug 31 '21 at 20:07

score 1 · Answer 9 · answered Dec 04 '18 at 22:32

If performance matters to you make sure you time:

import sys
import timeit
import pandas as pd

print('Python %s on %s' % (sys.version, sys.platform))
print('Pandas version %s' % pd.__version__)

repeat = 3
numbers = 100

def time(statement, _setup=None):
    print (min(
        timeit.Timer(statement, setup=_setup or setup).repeat(
            repeat, numbers)))

print("Format %m/%d/%y")
setup = """import pandas as pd
import io

data = io.StringIO('''\
ProductCode,Date
''' + '''\
x1,07/29/15
x2,07/29/15
x3,07/29/15
x4,07/30/15
x5,07/29/15
x6,07/29/15
x7,07/29/15
y7,08/05/15
x8,08/05/15
z3,08/05/15
''' * 100)"""

time('pd.read_csv(data); data.seek(0)')
time('pd.read_csv(data, parse_dates=["Date"]); data.seek(0)')
time('pd.read_csv(data, parse_dates=["Date"],'
     'infer_datetime_format=True); data.seek(0)')
time('pd.read_csv(data, parse_dates=["Date"],'
     'date_parser=lambda x: pd.datetime.strptime(x, "%m/%d/%y")); data.seek(0)')

print("Format %Y-%m-%d %H:%M:%S")
setup = """import pandas as pd
import io

data = io.StringIO('''\
ProductCode,Date
''' + '''\
x1,2016-10-15 00:00:43
x2,2016-10-15 00:00:56
x3,2016-10-15 00:00:56
x4,2016-10-15 00:00:12
x5,2016-10-15 00:00:34
x6,2016-10-15 00:00:55
x7,2016-10-15 00:00:06
y7,2016-10-15 00:00:01
x8,2016-10-15 00:00:00
z3,2016-10-15 00:00:02
''' * 1000)"""

time('pd.read_csv(data); data.seek(0)')
time('pd.read_csv(data, parse_dates=["Date"]); data.seek(0)')
time('pd.read_csv(data, parse_dates=["Date"],'
     'infer_datetime_format=True); data.seek(0)')
time('pd.read_csv(data, parse_dates=["Date"],'
     'date_parser=lambda x: pd.datetime.strptime(x, "%Y-%m-%d %H:%M:%S")); data.seek(0)')

prints:

Python 3.7.1 (v3.7.1:260ec2c36a, Oct 20 2018, 03:13:28) 
[Clang 6.0 (clang-600.0.57)] on darwin
Pandas version 0.23.4
Format %m/%d/%y
0.19123052499999993
8.20691274
8.143124389
1.2384357139999977
Format %Y-%m-%d %H:%M:%S
0.5238807110000039
0.9202787830000005
0.9832778819999959
12.002349824999996

So with iso8601-formatted date (%Y-%m-%d %H:%M:%S is apparently an iso8601-formatted date, I guess the T can be dropped and replaced by a space) you should not specify infer_datetime_format (which does not make a difference with more common ones either apparently) and passing your own parser in just cripples performance. On the other hand, date_parser does make a difference with not so standard day formats. Be sure to time before you optimize, as usual.

score 0 · Answer 10 · answered May 20 '21 at 15:54

You can use the parameter date_parser with a function for converting a sequence of string columns to an array of datetime instances:

parser = lambda x: pd.to_datetime(x, format='%Y-%m-%d %H:%M:%S')
pd.read_csv('path', date_parser=parser, parse_dates=['date_col1', 'date_col2'])

score 0 · Answer 11 · answered Jul 19 '21 at 16:19

No, there is no way in pandas to automatically recognize date columns.

Pandas does a poor job at type inference. It basically puts most columns as the generic object type, unless you manually work around it eg. using the abovementioned parse_dates parameter.

If you want to automatically detect columns types, you'd have to use a separate data profiling tool, eg. visions, and then cast or feed the inferred types back into your DataFrame constructor (eg. for dates and from_csv, using the parse_dates parameter).

score 0 · Answer 12 · answered Apr 10 '22 at 11:02

0

Yes, this code works like breeze. Here index 0 refers to the index of the date column.

df = pd.read_csv(filepath, parse_dates=[0], infer_datetime_format = True)

answered Apr 10 '22 at 11:02

user13934743

31
6

Can pandas automatically read dates from a CSV file?

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