Pivot a dataframe in pandas while creating additional new columns for values

I have a table that looks like this:

I'd like to pivot the data to look like this:

Effectively, this would turn the individually dated columns into a column called "Status" and rows with the individual dates.

I started with this basic code, but I'm getting mixed up in the syntax to create the new columns and assign the values to their respective columns.

import pandas as pd
import numpy as np

df = pd.read_csv(r'C:\Users\User\sample.csv')
df.pivot_table(columns=['Effective Date','First Name','Last Name','Date of Birth','Status','Start Date','End Date'],values=['1/1/2022','2/1/2022','3/1/2022','4/1/2022'], aggfunc=np.max)
df.head

Try melt:

df.melt(['ID', 'First Name', 'Last Name', 'Date of Birth', 'Start Date', 'End Date'], 
        var_name='Effective Date', value_name='Status')

Output:

     ID First Name Last Name Date of Birth Start Date   End Date Effective Date Status
0  MO12      Wanda    Sample      1/1/2020   1/1/2022  1/31/2022       1/1/2022   Good
1  MO12      Wanda    Sample      1/1/2020   1/1/2022  1/31/2022       2/1/2022   Good
2  MO12      Wanda    Sample      1/1/2020   1/1/2022  1/31/2022       3/1/2022   Good
3  MO12      Wanda    Sample      1/1/2020   1/1/2022  1/31/2022       4/1/2022   Good

You can try to .set_index() + .stack() + .reset_index(). Then just rename columns:

df = df.set_index(['ID', 'First Name', 'Last Name', 'Date of Birth', 'Start Date', 'End Date']).stack().reset_index(name='Status').rename(columns={'level_6': 'Effective Date'})
print(df)

Prints:

     ID First Name Last Name Date of Birth Start Date   End Date Effective Date Status
0  MO12      Wanda    Sample      1/1/2020   1/1/2022  1/31/2022       1/1/2022   Good
1  MO12      Wanda    Sample      1/1/2020   1/1/2022  1/31/2022       2/1/2022   Good
2  MO12      Wanda    Sample      1/1/2020   1/1/2022  1/31/2022       3/1/2022   Good
3  MO12      Wanda    Sample      1/1/2020   1/1/2022  1/31/2022       4/1/2022   Good