How list in pandas dataframe can be converted into multiple cells with no duplicate values?

You can "explode" the list values to rows using the explode function. You'll find this and other ways to reshape dataframes in the Reshaping and pivot tables page

By default, to_excel generates merged cells for multi-index rows, so you need to use a MultiIndex with this dataframe.

The resulting code is rather short :

import pandas as pd

data=[{"id": 1, "name": "Alex", "projects": ["A", "B", "C"]}, {"id": 2, "name": "Bob", "projects": None}]

df=pd.DataFrame(data)
df=df.explode('projects')
df=df.set_index(['id','name','projects'])
df.to_excel(r'c:\spikes\exploded.xlsx')

Which generates

Loading from a database

A comment suggests the data is loaded from a database. In that case it's probably easier to use read_sql to load the flat data and avoid explode. The index columns can be specified in read_sql directly. The code could look like this :

df=pd.read_sql(sql, conn,index_col=['id','name','projects'])
df.to_excel(r'c:\spikes\exploded.xlsx')

The projects can be grouped afterwards if needed using eg

df_grouped=df.groupby(['id','name']).agg({'projects':list})

You can iterate over the rows in Pandas DF and append each row.

import pandas as pd
from openpyxl import Workbook
from openpyxl.utils import get_column_letter

# Sample data
data = [
    {"id": 1, "name": "Alex", "projects": ["A", "B", "C"]},
    {"id": 2, "name": "Bob", "projects": None},
    {"id": 3, "name": "Robby", "projects": ["Z"]}
]

# Prepare a list to hold the rows for the DataFrame
rows = []

# Process the data
for entry in data:
    id_value = entry["id"]
    name_value = entry["name"]
    projects = entry["projects"]
    
    if projects is None:
        rows.append({"id": id_value, "name": name_value, "project": None})
    else:
        for project in projects:
            rows.append({"id": id_value, "name": name_value, "project": project})

# Create a DataFrame
df = pd.DataFrame(rows)

# Create a new Excel workbook and select the active worksheet
wb = Workbook()
ws = wb.active

# Write the header
header = df.columns.tolist()
ws.append(header)

# Write the data
for index, row in df.iterrows():
    ws.append(row.tolist())

# Merge cells for 'id' and 'name' where applicable
for index in range(len(df)):
    if index == 0 or df.iloc[index]['id'] != df.iloc[index - 1]['id']:
        start_row = index + 2  # +2 because openpyxl is 1-indexed and we have a header
        end_row = start_row
        while end_row < len(df) + 1 and df.iloc[end_row - 2]['id'] == df.iloc[start_row - 2]['id']:
            end_row += 1
        if end_row - start_row > 1:
            ws.merge_cells(start_row=start_row, start_column=1, end_row=end_row - 1, end_column=1)  # Merge 'id'
            ws.merge_cells(start_row=start_row, start_column=2, end_row=end_row - 1, end_column=2)  # Merge 'name'

# Save the workbook
wb.save("output.xlsx")

print("Done!")

An answer similar to Mario's but with a single pass through the data (and keeps the "project" column as the last one):

data = [{"id": 1, "name": "Alex", "projects": ["A", "B", "C"]},
        {"id": 2, "name": "Bob", "projects": ["D", "E"]},
        {"id": 3, "name": "Charlie", "projects": None},
        {"id": 4, "name": "Devin", "projects": []},
        {"id": 5, "name": "Ellen", "projects": ["F", "G"]},]

# create a new excel file
import openpyxl
wb = openpyxl.Workbook()

# fill the first row with the keys of the first dictionary
for key in data[0].keys():
    wb.active.cell(row=1, column=list(data[0].keys()).index(key) + 1).value = key

current_row = 2
for item in data:

    item_without_projects = item.copy()
    item_without_projects.pop("projects")

    # handle the case where projects is None or an empty list
    if item["projects"] is None or len(item["projects"]) == 0:
        for col, key in enumerate(item_without_projects.keys()):
            wb.active.cell(row=current_row, column=col + 1).value = str(item[key])
            
        current_row += 1
        continue

    # fill a row with data for each project
    for project in item["projects"]:
        max_col = 0
        for col, key in enumerate(item_without_projects.keys()):
            wb.active.cell(row=current_row, column=col + 1).value = str(item[key])
            max_col = max(max_col, col + 1)
        wb.active.cell(row=current_row, column=max_col + 1).value = project
        current_row += 1

    # merge the newly created cells
    for col, key in enumerate(item_without_projects.keys()):
        print(current_row - len(item["projects"]), current_row, col + 1)
        wb.active.merge_cells(start_row=current_row - len(item["projects"]), start_column=col + 1, end_row=current_row - 1, end_column=col + 1)
        # style: set text alignment to center
        cell = wb.active.cell(row=current_row - len(item["projects"]), column=col + 1)
        cell.alignment = openpyxl.styles.Alignment(horizontal='center', vertical='center')

# write the workbook
wb.save("data.xlsx")

This clearly doesn't use pandas, but from the other comments you posted I understand .explode won't solve your problem. I'd be happy to answer if you can better formulate the various issues for the data loading