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?

Answer 1

Pandas 2.0 update

As part of a broad attempt to make datetime handling in Pandas 2.0 more reliable, date parsing (especially on CSV files) has seen a number of backwards-incompatible changes and deprecations. The infer_datetime_format and date_parser parameters reccomended by many other answers are now both deprecated (see PDEP-4 and this issue for the reasons why).

The proper way of parsing dates of known format now is now to use the parse_dates and date_format parameters of pd.read_csv().

Single column with auto-detected format

df = pd.read_csv(
    infile,
    parse_dates=['My DateTime']
)

Single column with known format

df = pd.read_csv(
    infile,
    parse_dates=['My DateTime'],
    date_format={'My DateTime': '%Y-%m-%d %H:%M:%S'}
)

Merged columns with known format

df = pd.read_csv(
    infile,
    parse_dates={'mydatetime': ['My Date', 'My Time']},
    # mydatetime will contain my_date and my_time separated by a single space
    date_format={'mydatetime': '%Y-%m-%d %H:%M:%S'}
)

If your date parsing logic is more complex than what can be done with static format strings, such as if you have multiple date formats on the same column, or were making advanced use of date_parser, you are encouraged by the documentation for read_csv (look at the parse_dates parameter) to leave your column as the default object type while reading the file and to do the date conversion in a second pass with pd.to_datetime, as shown below.

Pandas v1 answer

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.

Answer 2

• Read the existing string columns in date and time format respectively

---

pd.read_csv('CGMData.csv', parse_dates=['Date', 'Time'])

Resulted Columns

---

---

• Concat string columns of date and time and add new column of datetype object - Remove Original columns

1. if want to rename the new column name then pass as dictionary as show in below example and the new column name will be the key name,
2. if pass as list of column, new column name will be concate of column name passed in the list separated by _ e.g Date_Time

---

    # parse_dates={'given_name': ['Date', 'Time']}    
    pd.read_csv("InsulinData.csv",low_memory=False, 
                                 parse_dates=[['Date', 'Time']])

    pd.read_csv("InsulinData.csv",low_memory=False, 
                                 parse_dates={'date_time': ['Date', 'Time']})

---

• Concat string columns of date and time and add new column of datetype object and Keep the Original columns

---

pd.read_csv("InsulinData.csv",low_memory=False, 
          parse_dates=[['Date', 'Time']], keep_date_col=True)

---

• Want to change the format of date and time when read from csv

---

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

---

Answer 3

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)

Answer 4

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]

Answer 5

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).

Answer 6

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'])

Answer 7

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.

Answer 8

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

1. Pandas explicit recognize the format by arg date_parser=mydateparser

2. 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)

Answer 9

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.

Answer 10

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)

Answer 11

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

Answer 12

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]

Answer 13

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.