Change Data Type Transform

The Change Data Type transform allows you to manually convert columns in your dataset to specific data types. This is useful for ensuring your data is represented in the most appropriate format for analysis or to correct any misidentified data types.

Basic Usage

To change data types in your dataset:

1. Select the Change Data Type transform from the transform menu.
2. For each column you want to change: a. Choose the column from the "Select Column" dropdown. b. Select the new data type from the "New Data Type" dropdown.
3. Apply the transformation.

Configuration Options

Basic Options

• Select Column: Choose the column you want to change. You can select multiple columns and specify a new data type for each.
• New Data Type: For each selected column, choose the new data type. Options include:
  • Numeric
  • Text
  • DateTime
  • Categorical

tip

You can change multiple columns at once by repeating the column selection and new data type specification for each column you want to change.

Data Type Explanations

Numeric

Suitable for numbers that you want to perform calculations on. This includes integers and floating-point numbers.

Best for: Quantities, measurements, counts, etc.

Text

Used for strings of characters. This type is suitable for data that should be treated as text, even if it contains numbers.

Best for: Names, descriptions, codes, etc.

DateTime

Used for dates and times. This type allows for easy manipulation and analysis of temporal data.

Best for: Dates, timestamps, time series data, etc.

Categorical

Used for data with a limited number of distinct values. This type can be more memory-efficient and allows for specific types of analysis.

Best for: Classifications, groups, yes/no data, etc.

Examples

Here's an example of how to use the Change Data Type transform:

Example: Adjusting Data Types in a Sales Dataset

Input Dataset:

date_column	season_column	sales_column
October 10, 2023	Fall	123.45
10/31/2023	Fall	234.56
November 15, 2023	Fall	345.67
12/31/2023	Winter	678.90

Initial Data Types:

• date_column: object (string)
• season_column: object (string)
• sales_column: object (string)

Configuration:

• date_column: DateTime
• season_column: Categorical
• sales_column: Numeric

Result:

date_column	season_column	sales_column
2023-10-10	Fall	123.45
2023-10-31	Fall	234.56
2023-11-15	Fall	345.67
2023-12-31	Winter	678.90

New Data Types:

• date_column: datetime64[ns]
• season_column: category
• sales_column: float64

The transform has converted the date strings to datetime objects, recognized the season column as categorical, and converted the sales values to numeric type.

Best Practices

1. Understand Your Data: Before changing data types, make sure you understand the nature of the data in each column.

2. Preserve Information: Be cautious when changing to a less precise data type (e.g., float to int) as it may lead to loss of information.

3. Check for Errors: After changing data types, check for any errors or unexpected NaN values that might indicate issues with the conversion.

4. Consider Performance: Remember that certain data types (like categorical) can improve performance for large datasets.

5. Datetime Formatting: When converting to datetime, ensure your date strings are in a consistent format across the column.

Troubleshooting

• If a column fails to convert to numeric, check for non-numeric characters or inconsistent formatting in the data.
• For datetime conversions, ensure all date strings in the column are in a format that can be parsed. You might need to clean the data first.
• If categorical conversion results in too many categories, consider if the column is truly categorical or if some values should be grouped.