Task: Transformations - Assign from column attribute

[cisaacstern]

Towards #39

[cisaacstern]

This PR follows the suggestions of ChatGPT (what a world we live in) which I thought were quite reasonable, as pasted in https://github.com/wildlife-dynamics/ecoscope-workflows/issues/39#issuecomment-2189954068, to use pd.DataFrame.assign in a limited way for addition of new columns based on the attributes of other columns in the dataframe.

Opening this now because I believe I will need it to address #28 / #45 ... to explain why: in #45, I want to group a dataset by month that does not already have a "month" column. AFAICT, the existing transformations we have do not support adding the "month" column based on an existing column of pd.Timestamp values. This small PR adds that ability.

[walljcg]

Thanks Charles!

Have you seen the temporal and period indexers we have here?

My assumption has been that we use these functions to create the indexes and then use DataFrame.GroupBy([groupers]) to create a Grouped object which is then passed into the split-apply_combine?

[cisaacstern]

A-ha, I had not been aware of those utils.

So using the linked indexers in ecoscope core, how would I group this dataframe by month?

import pandas as pd

df = pd.DataFrame(
    {
        "recorded_at": [
            pd.Timestamp("2021-01"),
            pd.Timestamp("2021-01"),
            pd.Timestamp("2021-02"),
            pd.Timestamp("2021-02"),
            pd.Timestamp("2021-03"),
        ],
        "value": [5, 6, 7, 8, 9],
    }
)

with this PR, it would be:

from ecoscope_workflows.tasks.transformation import assign_from_column_attribute

df_new = assign_from_column_attribute(
    df, column_name="month", dotted_attribute_name="recorded_at.dt.month"
)
df_new.groupby("month")