Pandas.to_datetime() - Examples

pandas.to_datetime() function in the Pandas library in Python used to convert arguments to DateTime. It's quite handy for converting strings, timestamps, or mixed-type data into datetime objects. In this article, I will explain the pandas.to_datetime() function, its syntax, parameters, and usage of how to to_datetime() is a versatile function used extensively in data analysis and manipulation tasks, especially when dealing with time-series data.

Key Points –

Pandas.to_datetime() converts input into datetime objects.
The function can infer datetime format or be explicitly provided with format specifications.
It supports handling of missing or erroneous data through optional parameters like errors and infer_datetime_format.
Pandas.to_datetime() is a versatile tool for data preprocessing, especially in time-series analysis and data cleaning tasks.
Supports options to handle errors gracefully, such as 'raise', 'ignore', and 'coerce'.
Converts various data types (strings, numbers, lists, etc.) into datetime objects.

Syntax of Pandas.to_datetime()

Following 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)

Parameters of the Pandas.to_datetime()

Following are the parameters of the Pandas.to_datetime() function.

arg – An integer, string, float, list, or DataFrame/dict_like object to convert into a Datetime object.
errors – Take values raise,ignoreor 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.

Usage of Pandas to_datetime() Method

The pandas.to_datetime() function is used to convert a wide variety of date and time formats to datetime64[ns] objects in Pandas. This is essential for performing time series analysis or date-based operations in data analysis workflows.

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(
    {'Courses':["Spark","Hadoop","Pandas"],
     'Fee' :[20000,25000,30000],
     'Duration':['30days','40days','35days'],
     'Discount':[1000,2500,1500],
     'Inserted': ["11/22/2021, 10:39:24","11/22/2021, 10:39:24","11/22/2021, 10:39:24"]},
     orient='index', 
     columns=['A','B','C']).T
print(df)

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

Convert a Pandas String to DateTime

Converting a Pandas string to DateTime is a common operation when dealing with time-series data. You can use the pd.to_datetime() function


import pandas as pd

# Example string
date_string = '2024-05-07 10:30:00'

# Convert string to datetime
date_time = pd.to_datetime(date_string)
print(date_time)

# Output:
# 2024-05-07 10:30:00

In the above example, the date_string variable contains a string representing a date and time. The pd.to_datetime() function converts this string into a Pandas datetime object date_time. You can now use date_time for various datetime operations and analyses in Pandas.

FAQ on Pandas.to_datetime()

The pandas.to_datetime() function is used to convert an argument (string, list, dictionary, or a column in a DataFrame) into a datetime object, allowing for easier date manipulation and analysis in Pandas.

To use pandas.to_datetime() to convert a column of date strings to datetime objects, you simply pass the column (which is typically a Series) to pd.to_datetime(). This will automatically convert the date strings to datetime64 type, which allows for easier date manipulation and analysis.

You can specify the format parameter to tell to_datetime() the exact format of the input strings, which can speed up the conversion process and avoid errors.

You can pass a Unix timestamp (integer or float) to to_datetime(), and it will automatically be converted to a datetime object.

to_datetime() can automatically infer date formats, even if the column has mixed formats, as long as they are recognizable by Pandas.

References

https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.astype.html