Adding Static and Computed Columns to a Pandas DataFrame in Python

To add extra columns to a DataFrame in pandas, where the values of these columns are either static strings or derived from existing values (like multiplying by a factor), you can use the following methods:

1. Adding Static Value Columns: You can directly assign a static value to a new column in the DataFrame.

2. Adding Derived Value Columns: For columns where the value is derived from existing columns (e.g., multiplying a column value by 3), you can use vectorized operations.

Here's an example based on your description:

Let's say your initial DataFrame looks like this:

import pandas as pd

data = {
    'tag': ['ABC', 'GHQ'],
    'ts': ['2020-10-05 05:00:00.535000', '2020-10-05 05:00:00.535000'],
    'value': [2.9, 2.7]
}

df = pd.DataFrame(data)

This will create a DataFrame df with columns ['tag', 'ts', 'value'].

Adding Static and Derived Columns:

# Adding a static value column
df['new_static_column'] = 'static_value'

# Adding a derived value column by multiplying 'value' by 3
df['derived_value'] = df['value'] * 3

After running this code, your DataFrame df will have two new columns:

• new_static_column with all rows having the value 'static_value'.

• derived_value where each row is the value from the value column multiplied by 3.

Result:

The DataFrame will now look like this:

tag	ts	value	new_static_column	derived_value
ABC	2020-10-05 05:00:00	2.9	static_value	8.7
GHQ	2020-10-05 05:00:01	2.7	static_value	8.1

Additional Notes:

• Data Types: Be mindful of data types when performing arithmetic operations. Ensure that the columns involved in such operations are of numeric types.

• Vectorized Operations: Pandas is optimized for vectorized operations, so multiplying a column by a scalar (like 3) is a very efficient operation.

• Derived Values: If you need to derive values in a more complex way, you can use .apply() with a custom function, but for simple arithmetic, direct operations are more efficient.

This approach is versatile and can be adjusted to add various types of static or dynamically calculated columns to your DataFrame as per your requirements.