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  • Post last modified:March 27, 2024
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You are currently viewing Pandas.to_datetime() – Examples

2. Pandas.to_datetime() Syntax & Examples

Below is the syntax of the Pandas.to_datetime() method.

# Pandas.to_datetime() syntax
Pandas.to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, 
     utc=None, format=None, exact=True, unit=None, 
     infer_datetime_format=False, origin='unix', cache=True)
  • arg – An integer, string, float, list, or DataFrame/dict_like object to convert into a Datetime object.
  • errors – Take values raise, ignore or coerce. if ‘raise’ is used, raise a KeyError when a dict-like mapper, index, or column contains labels that are not present in the Index being transformed. Default set to ignore.
  • dayfirst – default set False, Boolean value places day first if True.
  • yearfirst – Boolean value places year first if True, the default set False.
  • utc – Boolean value, Returns the time in UTC DatetimeIndex if True.
  • format – String input to tell the position of the day, month, and year. default set None.
  • exact – Boolean value, If True, requires an exact format match. – If False, allow the format to match anywhere in the target string.
  • infer_datetime_formatbool – If True and no format is given, attempt to infer the format of the datetime strings based on the first non-NaN element. the default set False.

Now, let’s create a DataFrame with a few rows and columns, execute the above examples and validate results. Our DataFrame contains column names Courses, Fee, Duration, Discount and Inserted.

# Pandas.to_datetime() Syntax & Examples
import pandas as pd
from datetime import datetime, timedelta
from Pandas import DataFrame
df = DataFrame.from_dict(
     'Fee' :[20000,25000,30000],
     'Inserted': ["11/22/2021, 10:39:24","11/22/2021, 10:39:24","11/22/2021, 10:39:24"]},

Yields below output. Note that Inserted column on the DataFrame has datetime in the format of "%m/%d/%Y, %H:%M:%S"

# Output:
  Courses    Fee Duration Discount              Inserted
A   Spark  20000   30days     1000  11/22/2021, 10:39:24
B  Hadoop  25000   40days     2500  11/22/2021, 10:39:24
C  Pandas  30000   35days     1500  11/22/2021, 10:39:24

Naveen Nelamali

Naveen Nelamali (NNK) is a Data Engineer with 20+ years of experience in transforming data into actionable insights. Over the years, He has honed his expertise in designing, implementing, and maintaining data pipelines with frameworks like Apache Spark, PySpark, Pandas, R, Hive and Machine Learning. Naveen journey in the field of data engineering has been a continuous learning, innovation, and a strong commitment to data integrity. In this blog, he shares his experiences with the data as he come across. Follow Naveen @ LinkedIn and Medium