Understanding Pandas Datetime Conversion Using Dataframe

Pandas provides an efficient way to handle data, including datetime objects. In this article, we’ll explore how to convert a specific format of date stored in a pandas DataFrame into a datetime object and then use it to calculate the days since a reference time.

The Problem: Converting a Custom Date Format to Datetime

When working with dates in pandas DataFrames, it’s common to encounter dates in non-standard formats. In this case, we have dates stored in the format YYYYMMDDhhmm, such as 200902110403. We want to convert these dates into datetime objects for further analysis.

The Solution: Using pd.to_datetime with infer_datetime_format=True

One way to achieve this conversion is by using the pd.to_datetime function. This function takes a string or array-like object representing the date and attempts to infer the format of the date from it.

temp_date = (pd.to_datetime(indexed_data.index.str[0:12], infer_datetime_format=True)).to_pydatetime()

In this code snippet, we’re using indexed_data.index.str[0:12] to extract a substring representing the first 12 characters of each index value. We then pass this string to pd.to_datetime, which attempts to convert it into a datetime object.

Understanding Datetime and Timedelta Objects

When working with datetime objects, it’s essential to understand the differences between them and their related concepts:

• Datetime Object: A datetime object represents a specific point in time. It consists of year, month, day, hour, minute, and second components.
• Timedelta Object: A timedelta object represents a duration or interval between two points in time. It can be used to calculate the difference between two datetime objects.

In the provided solution, we create a datetime object using pd.to_datetime, which returns a Series of datetime objects representing the dates in our DataFrame.

Converting Days Since Reference Time

To convert these datetime objects into days since a reference time, we use the date2num function from the netcdf4 library. This function takes two arguments: the first is the input series containing the datetime objects, and the second is the format string representing the desired output (in this case, ‘days since 2009-01-01’).

days = date2num(temp_date, 'days since 2009-01-01')

However, there’s a problem with this approach. The date2num function requires both input and output formats to be the same, but we’re trying to convert from one format to another.

Solving the Problem: Assigning Converted Dates Directly

The issue arises because of how we assign the converted datetime objects back to our DataFrame’s index and column. Instead of using date2num, which expects both input and output formats to be consistent, we can directly assign the converted datetime objects to a new column in our DataFrame.

indexed_data['date'] = (pd.to_datetime(indexed_data.index.str[0:12], infer_datetime_format=True)).to_pydatetime()
indexed_data['days'] = indexed_data['date'].dt.days

In this revised approach, we create a new column date in our DataFrame by assigning the converted datetime objects directly. We then calculate the days since the reference time using the dt.days accessor.

Understanding DatetimeIndex Objects

A datetimeindex object is simply a datetime object that is set as the index of your DataFrame. It represents the dates themselves, rather than the values being associated with those dates.

In summary, to convert custom date formats stored in pandas DataFrames into datetime objects and calculate days since a reference time:

• Use pd.to_datetime with infer_datetime_format=True to infer the format of the date from the string.
• Create a new column in your DataFrame by assigning the converted datetime objects directly.
• Calculate the days since the reference time using the dt.days accessor.

Example Code

Here’s an example code snippet that demonstrates the conversion:

import pandas as pd

# Sample data
date_strs = ['200902110403', '200902120403', '200902130403', '200902140403', '200902150403']
df = pd.DataFrame(date_strs, columns=['Date'])

# Convert dates to datetime objects
df['Date'] = pd.to_datetime(df['Date'], infer_datetime_format=True)

# Calculate days since reference time
reference_date = pd.to_datetime('2009-01-01')
df['Days'] = (df['Date'] - reference_date).dt.days

print(df)

This code snippet creates a sample DataFrame with dates in the format YYYYMMDDhhmm, converts them into datetime objects, and calculates the days since a reference time (2009-01-01). The resulting DataFrame is printed to the console.

Last modified on 2023-05-26