Data Wrangling

Data wrangling, also known as data munging or data cleaning, is the process of transforming raw data from various sources into a clean, consistent, and usable format. This process involves several steps to ensure that the data is accurate, complete, and in a suitable form for analysis.

Steps Involved in Data Wrangling:

1. Data Collection: Gathering data from various sources such as files, databases, or APIs.
2. Data Cleaning: Identifying and correcting errors, inconsistencies, and missing values in the data.
3. Data Transformation: Converting the data into a suitable format for analysis, such as converting date formats or aggregating data.
4. Data Integration: Combining data from multiple sources into a single dataset.

Example:

Suppose we have a dataset of customer information with the following structure:

| Customer ID | Name | Email | Phone |
| --- | --- | --- | --- |
| 1 | John Smith | john.smith@example.com | 123-4567 |
| 2 | Jane Doe | jane.doe@example.com | 234-5678 |

The data is collected from various sources, but it contains some errors and inconsistencies:

• Missing values in the "Email" column
• Incorrect phone number format (e.g., 123-4567 instead of 1234567)
• Typos in the "Name" column

Data Cleaning:

We need to identify and correct these errors. For example, we can use data validation techniques to check for missing values, incorrect formats, or typos.

| Customer ID | Name | Email | Phone |
| --- | --- | --- | --- |
| 1 | John Smith | john.smith@example.com | 1234567 |
| 2 | Jane Doe | jane.doe@example.com | 2345678 |

Data Transformation:

We need to convert the phone number format from string to numeric.

| Customer ID | Name | Email | Phone (numeric) |
| --- | --- | --- | --- |
| 1 | John Smith | john.smith@example.com | 1234567 |
| 2 | Jane Doe | jane.doe@example.com | 2345678 |

Data Integration:

We need to combine this dataset with another dataset containing customer order information.

| Customer ID | Order Date | Product Name |
| --- | --- | --- |
| 1 | 2022-01-01 | iPhone |
| 1 | 2022-02-01 | Laptop |
| 2 | 2022-03-01 | Tablet |

After data wrangling, we have a clean and integrated dataset:

| Customer ID | Name | Email | Phone (numeric) | Order Date | Product Name |
| --- | --- | --- | --- | --- | --- |
| 1 | John Smith | john.smith@example.com | 1234567 | 2022-01-01 | iPhone |
| 1 | John Smith | john.smith@example.com | 1234567 | 2022-02-01 | Laptop |
| 2 | Jane Doe | jane.doe@example.com | 2345678 | 2022-03-01 | Tablet |

The data is now ready for analysis, and we can perform various statistical and machine learning tasks to gain insights from the data.