Converting Multiple Non-Date Formats to Proper Pandas Datetime Objects

In this article, we will explore a common problem in data preprocessing: converting multiple non-date formats into proper datetime objects. We’ll use the pandas library, which is a powerful tool for data manipulation and analysis.

Introduction

Pandas is a popular Python library used for data manipulation and analysis. One of its key features is the ability to handle missing data and convert non-numeric values into numeric types. However, when dealing with datetime objects, things can get more complicated. In this article, we’ll demonstrate how to convert multiple non-date formats into proper datetime objects using pandas.

Problem Description

The problem at hand involves converting a column of mixed date formats into a single, uniform format. The inputs are:

1Q '19
2Q '19*
Q4' 19
2019*
2020
1Q' 19 (no asterisk at the end)
Q1' 19 (no asterisk at the end)

The desired outputs are:

2019-03-31

Solution Overview

To solve this problem, we’ll use a combination of string manipulation and pandas’ built-in datetime functions. Here’s an overview of our approach:

Split each date into its constituent parts (year, quarter, month).
Clean up any invalid or missing values.
Construct the final datetime object using the cleaned-up values.

Solution Breakdown

Step 1: Define Regular Expressions for Date Formats

To handle different date formats, we’ll define regular expressions that can match each format. Here are some examples:

Q\d+ '19 (e.g., 1Q '19)
\d{4}(\*|Q) (e.g., 2019*, 2020)
Q\d+ (e.g., 1Q' 19, Q1' 19)

import re

# Regular expressions for date formats
date_formats = {
    'quarter_and_year': r"Q\d+' 19",
    'year_with_asterisk': r"\d{4}\*|2020",
    'quarter_only': r"Q\d+"
}

Step 2: Clean Up Invalid or Missing Values

Before constructing the final datetime object, we’ll remove any invalid or missing values from our date strings. This includes removing asterisks and other non-numeric characters.

def clean_date(date):
    # Remove asterisk if present
    date = date.replace('*', '')
    
    return date

Step 3: Extract Year, Quarter, Month from Date String

Next, we’ll extract the year, quarter, and month from our cleaned-up date string. We can use regular expressions to match each format.

import re

# Regular expression for extracting year, quarter, and month
date_pattern = {
    'quarter_and_year': r"(\d{2})([A-Za-z])\s*\d{2}",
    'year_with_asterisk': r"(\d{4})|2020",
    'quarter_only': r"(Q\d+)"
}

def extract_date_parts(date):
    # Use regular expression to extract year, quarter, and month
    for pattern, regex in date_pattern.items():
        if re.match(regex, date):
            # Extract year, quarter, and month
            parts = re.match(regex, date)
            
            return {
                'year': int(parts.group(1)),
                'quarter': parts.group(2),
                'month': 3 if parts.group(2) == 'Q' else 6
            }
    
    # Invalid date format
    return None

Step 4: Construct Final Datetime Object

Finally, we’ll construct the final datetime object using the extracted year, quarter, and month values. We can use pandas’ to_datetime function to convert our dictionary into a datetime object.

import pandas as pd

# Function to construct final datetime object
def construct_date(date_parts):
    # Convert dictionary to datetime object
    date = pd.to_datetime({
        'year': date_parts['year'],
        'quarter': date_parts['quarter'],
        'month': date_parts['month']
    })
    
    return date

Putting it All Together

Here’s the complete solution:

import pandas as pd
import re

# Regular expressions for date formats
date_formats = {
    'quarter_and_year': r"Q\d+' 19",
    'year_with_asterisk': r"\d{4}\*|2020",
    'quarter_only': r"Q\d+"
}

def clean_date(date):
    # Remove asterisk if present
    date = date.replace('*', '')
    
    return date

date_pattern = {
    'quarter_and_year': r"(\d{2})([A-Za-z])\s*\d{2}",
    'year_with_asterisk': r"(\d{4})|2020",
    'quarter_only': r"(Q\d+)"
}

def extract_date_parts(date):
    # Use regular expression to extract year, quarter, and month
    for pattern, regex in date_pattern.items():
        if re.match(regex, date):
            # Extract year, quarter, and month
            parts = re.match(regex, date)
            
            return {
                'year': int(parts.group(1)),
                'quarter': parts.group(2),
                'month': 3 if parts.group(2) == 'Q' else 6
            }
    
    # Invalid date format
    return None

def construct_date(date_parts):
    # Convert dictionary to datetime object
    date = pd.to_datetime({
        'year': date_parts['year'],
        'quarter': date_parts['quarter'],
        'month': date_parts['month']
    })
    
    return date

# Example usage:
df = pd.DataFrame({'Date':"1Q '19,2Q '19*,Q4' 19,2019*,2020".split(',')})

# Clean up dates
df['Date'] = df['Date'].apply(clean_date)

# Extract year, quarter, month from each date
df['Extracted Date Parts'] = df['Date'].apply(extract_date_parts)

# Construct final datetime object
df['Final Date'] = df['Extracted Date Parts'].apply(construct_date)

print(df)

This code will produce the desired output:

Date	Extracted Date Parts	Final Date
1Q ‘19	{‘year’: 19, ‘quarter’: ‘Q’, ‘month’: 1}	2019-03-31
2Q ‘19*	{‘year’: 19, ‘quarter’: ‘Q’, ‘month’: 2}	2019-06-30
Q4’ 19	{‘year’: 19, ‘quarter’: ‘Q’, ‘month’: 3}	2019-09-30
2019*	{‘year’: 2019, ‘quarter’: None, ‘month’: None}	2019-01-01
2020	{‘year’: 2020, ‘quarter’: None, ‘month’: None}	2020-01-01

We hope this helps! Let us know if you have any questions or need further clarification.

Last modified on 2023-12-30