Pandas: Add column using Numpy vectorization?

I am trying to replicate the following logic from Pandas, but using Numpy vectorization.

Also, I feel there might be a more Pythonic way of adding the Actual Available column without creating two separate variables series_1 and series_2 first, and also that is not verbose.

The logic behind [Actual Available] is,

• if [Is First?] column is True then [Actual Available] = [Stock] + [Requirements] + [Receipts],
• if [Is First?] column is False then [Actual Available] = [Prev row of Actual Available] + [Requirements] + [Receipts]

Any ideas?

import pandas as pd
import numpy as np

df = pd.DataFrame({
    "Material": ["ABC", "ABC", "ABC", "ABC", "XYZ", "XYZ", "XYZ"],
    "Plant": [2685, 2685, 2685, 2685, 2685, 2685, 2685],
    "Year": ["2020", "2020", "2020", "2020", "2020", "2020", "2020"],
    "Week": [1, 2, 3, 4, 1, 2, 3],
    "Stock": [30, 30, 30, 30, 70, 70, 70],
    "Requirements": [10, 15, 20, 25, 20, 30, 40],
    "Receipts": [1, 2, 3, 4, 11, 12, 13]
})

print(df)

# Add [Is First?] column
df["Is First?"] = np.where(
    (df["Material"] == df["Material"].shift(1)) &
    (df["Plant"] == df["Plant"].shift(1)),
    False,
    True,
)

# Add [Actual Available] column
df["Actual Available"] = (df["Stock"] + df["Requirements"] +
                          df["Receipts"]).where(df["Is First?"].eq(True))

series_1 = df["Is First?"].eq(True).cumsum()
series_2 = (df["Actual Available"].ffill() +
            (df["Receipts"] +
             df["Requirements"]).shift(-1).groupby(series_1).cumsum().shift())

df["Actual Available"] = df["Actual Available"].fillna(series_2)

print(df)