Create extra rows using date column pandas dataframe

You can use:

# ensure datetime and fill NA with default date
df[['Start Date', 'End Date']] = df[['Start Date', 'End Date']].apply(pd.to_datetime)
df['End Date'] = df['End Date'].fillna('2023-03-30')

# repeat index and create output
idx = df.index.repeat(df['End Date'].sub(df['Start Date']).dt.days.add(1))
out = df.loc[idx]

# increment days
out['Start Date'] += pd.TimedeltaIndex(out.groupby(level=0).cumcount(), unit='D')
out['End Date'] = out['Start Date']

Output:

   ID Leave Type Start Date   End Date
0   1       Sick 2022-01-01 2022-01-01
1   1    Holiday 2023-03-28 2023-03-28
1   1    Holiday 2023-03-29 2023-03-29
1   1    Holiday 2023-03-30 2023-03-30
2   2    Holiday 2023-01-01 2023-01-01
2   2    Holiday 2023-01-02 2023-01-02
3   3       Work 2023-01-01 2023-01-01

Reproducible input:

df = pd.DataFrame({'ID': [1, 1, 2, 3],
                   'Leave Type': ['Sick', 'Holiday', 'Holiday', 'Work'],
                   'Start Date': ['2022-01-01', '2023-03-28', '2023-01-01', '2023-01-01'],
                   'End Date': ['2022-01-01', None, '2023-01-02', '2023-01-01']})

Assuming that you incorrectly pasted an extra row(5th row) in the output. You can try this as well:

import pandas as pd
from datetime import timedelta, datetime

# create the dataframe
df = pd.DataFrame({'ID': [1, 1, 2, 3], 
                   'Leave Type': ['Sick', 'Holiday', 'Holiday', 'Work'], 
                   'Start Date': ['2022-01-01', '2023-03-28', '2023-01-01', '2023-01-01'], 
                   'End Date': ['2022-01-01', '', '2023-01-02', '2023-01-01']})

# convert date columns to datetime format
df['Start Date'] = pd.to_datetime(df['Start Date'])
df['End Date'] = pd.to_datetime(df['End Date'])

# fill in blank end dates with a maximum date value
df['End Date'] = df['End Date'].fillna(datetime(2023, 3, 30))

# create an empty list to store new rows
create_rows = []

# loop through each row in the dataframe
for index, row in df.iterrows():
    
    # if the start and end dates are not the same, add rows for each day in between
    if row['Start Date'] != row['End Date']:
        delta = row['End Date'] - row['Start Date']
        for i in range(delta.days + 1):
            date = row['Start Date'] + timedelta(days=i)
            create_rows.append({'ID': row['ID'], 'Leave Type': row['Leave Type'], 'Start Date': date, 'End Date': date})
    
    # if the start and end dates are the same, append the original row
    else:
        create_rows.append({'ID': row['ID'], 'Leave Type': row['Leave Type'], 'Start Date': row['Start Date'], 'End Date': row['End Date']})
    
# create a new dataframe with the original rows and the new rows
output_df = pd.DataFrame(create_rows)

# sort the dataframe by ID and Start Date
output_df = output_df.sort_values(['ID', 'Start Date'])

# reset the index
output_df = output_df.reset_index(drop=True)

print(output_df)