The "Convert Pandas Series to Datetime.Date" project provides a straightforward solution for converting a Pandas Series containing string representations of dates into the more versatile datetime.date format. This functionality is crucial in data science, economics, and statistics, where working with accurate and standardized date formats is essential.

Key Features:

• Converts various date formats to datetime.date.
• Handles multiple compatible formats or lets you specify the format.
• Detects and suggests compatible date formats.

Use Cases:

1. Data Cleaning: Ensure consistency in date formats for datasets, facilitating downstream analysis.
2. Time Series Analysis: Proper date formatting is critical for time-based insights and predictions.
3. Data Integration: Streamline data integration from diverse sources with different date representations.

Why It Matters: Inaccurate or inconsistent date formats can lead to errors in analysis and misinterpretation of data. This project simplifies the process of standardizing dates, contributing to more reliable and meaningful data-driven insights.

Explore the demo and integrate this tool into your data processing pipeline for improved date handling and analysis!

• Converts a Pandas Series with string values to datetime.date.
• Useful for datasets and timeseries in data science, economics, and statistics.

• 'dd-mm-yyyy'
• 'dd/mm/yyyy'
• 'mm-dd-yyyy'
• 'mm/dd/yyyy'
• 'yyyy-mm-dd'
• 'yyyy/mm/dd'
• 'dd-mm-yy'
• 'dd/mm/yy'
• 'mm-dd-yy'
• 'mm/dd/yy'

# input
date_series = pd.Series(['04/03/00', '05/10/00', '04/01/01', '05/10/01', '06/04/02', '11/05/03', '01/01/06', '09/10/09', '05/10/11'])

# convert
convert_series_to_date(date_series, format='dd/mm/yy')

• Output:

pd.Series([datetime.date(2000, 3, 4),
 datetime.date(2000, 10, 5),
 datetime.date(2001, 1, 4),
 datetime.date(2001, 10, 5),
 datetime.date(2002, 4, 6),
 datetime.date(2003, 5, 11),
 datetime.date(2006, 1, 1),
 datetime.date(2009, 10, 9),
 datetime.date(2011, 10, 5)])

or

0    2000-03-04
1    2000-10-05
2    2001-01-04
3    2001-10-05
4    2002-04-06
5    2003-05-11
6    2006-01-01
7    2009-10-09
8    2011-10-05
Name: Standardized_dates, dtype: object

1. Clone the repository to your local machine:

   git clone https://github.com/Edamas/pandas_string_to_datetime.git

2. Navigate to the project directory:

   cd your-repository

3. Run the main script:

   python project.py

4. Enjoy the standardized dates output!

• Name: Elysio Damasceno da Silva Neto
• Date: 17th december, 2023
• City: São Paulo
• State: São Paulo
• Country: Brazil