5 Best Ways to Convert Dates to Proleptic Gregorian Ordinal in Python Pandas

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💡 Problem Formulation: When working with time series data in Python’s Pandas library, you might need to convert dates to their proleptic Gregorian ordinal equivalent. This means translating a calendar date into an integer, which represents the number of days since January 1st, 1 AD. For instance, converting the date ‘2023-03-01’ should return the ordinal 737850.

Method 1: Using Timestamp.toordinal()

To convert a date to its proleptic Gregorian ordinal in Pandas, you can use the Timestamp.toordinal() method. This method is applied to a Timestamp object, which represents a point in time, and returns the date’s ordinal.

Here’s an example:

import pandas as pd

# Create a Timestamp object
timestamp = pd.Timestamp('2023-03-01')

# Get the proleptic Gregorian ordinal
ordinal = timestamp.toordinal()

print(ordinal)

The output of this code snippet will be:

737850

In this snippet, we have imported Pandas and created a Timestamp object with the specific date ‘2023-03-01’. Then, we utilize the toordinal() method to get the proleptic Gregorian ordinal for the date, which outputs the expected number of days since January 1st, 1 AD.

Method 2: Applying toordinal() on a Series

If you have a column of dates in a DataFrame, you can apply the toordinal() method directly to the Series of dates to convert all entries to ordinals.

Here’s an example:

import pandas as pd

# Creating a DataFrame with a column of dates
df = pd.DataFrame({'dates': pd.to_datetime(['2023-03-01', '2023-03-02'])})

# Convert the dates to ordinals
df['ordinals'] = df['dates'].apply(lambda x: x.toordinal())

print(df)

The output of this code snippet will be:

       dates  ordinals
0 2023-03-01    737850
1 2023-03-02    737851

This code demonstrates converting a Series of dates into their ordinal representation by applying lambda x: x.toordinal() to each element in the ‘dates’ column, creating a corresponding ‘ordinals’ column with the results.

Method 3: Using List Comprehension

You can perform the conversion using a more Pythonic approach with list comprehension, iterating over each date and applying the toordinal() method.

Here’s an example:

import pandas as pd

# List of dates
dates_list = [pd.Timestamp('2023-03-01'), pd.Timestamp('2023-03-02')]

# Convert list of dates to list of ordinals using list comprehension
ordinals_list = [date.toordinal() for date in dates_list]

print(ordinals_list)

The output of this code snippet will be:

[737850, 737851]

Using list comprehension, this example converts a Python list of Timestamp objects into a list of their corresponding ordinal values.

Method 4: Using DataFrame.applymap()

If your DataFrame contains multiple datetime columns and you need to convert all of them to ordinals, applymap() is a convenient method to achieve this at once.

Here’s an example:

import pandas as pd

# Creating a DataFrame with multiple columns of dates
df = pd.DataFrame({
    'start_dates': pd.to_datetime(['2023-03-01', '2023-03-03']),
    'end_dates': pd.to_datetime(['2023-03-02', '2023-03-04'])
})

# Convert all dates to ordinals
ordinal_df = df.applymap(lambda x: x.toordinal())

print(ordinal_df)

The output of this code snippet will be:

   start_dates  end_dates
0       737850     737851
1       737852     737853

With applymap(), the lambda function is applied to every entry in the DataFrame, converting each datetime object to its ordinal equivalent.

Bonus One-Liner Method 5: Using Series.dt.to_pydatetime() and a Lambda Function

For a concise one-liner, you can access the Python datetime objects in a Pandas Series and apply the toordinal() method using a lambda function.

Here’s an example:

import pandas as pd

# Create a Series of dates
dates_series = pd.Series(pd.to_datetime(['2023-03-01', '2023-03-02']))

# Convert the Series to ordinals
ordinals = dates_series.dt.to_pydatetime().map(lambda x: x.toordinal())

print(ordinals)

The output of this code snippet will be:

0    737850
1    737851
dtype: int64

In this compact version, we convert the Series of dates into an array of Python datetime objects and then map over it to apply the toordinal() method, returning a Series of ordinals.

Summary/Discussion

  • Method 1: Timestamp.toordinal(). Simple. Best for single date conversions. Limited to Timestamp objects.
  • Method 2: toordinal() on a Series. Streamlined for DataFrames. Automatically handles NaT values. Can be slower for large DataFrames.
  • Method 3: Using List Comprehension. Pythonic and flexible. Suitable for operations outside DataFrames. May not be the most Pandas-efficient method.
  • Method 4: DataFrame.applymap(). Efficient for whole DataFrames. Not suitable for Series. Can add unnecessary complexity for single column DataFrames.
  • Method 5: One-liner using lambda. Concise. Perfect for quick conversions. Requires understanding of chaining methods in Pandas.