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Replace usage of deprecated DataFrame.append method #495

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Jun 2, 2023
Merged

Replace usage of deprecated DataFrame.append method #495

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98cad17
Replace deprecated Pandas append method
michaelnchin May 31, 2023
06e575f
update changelog
michaelnchin May 31, 2023
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1 change: 1 addition & 0 deletions ChangeLog.md
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@ Starting with v1.31.6, this file will contain a record of major features and upd
- Added support for setting `%graph_notebook_vis_options` from a variable ([Link to PR](https://github.com/aws/graph-notebook/pull/487))
- Pinned JupyterLab<4.x to fix Python 3.8/3.10 builds ([Link to PR](https://github.com/aws/graph-notebook/pull/490))
- Changed datatype of "amount" from String to numeric for "Transaction" vertices in Fraud Graph sample notebook ([Link to PR](https://github.com/aws/graph-notebook/pull/489))
- Replaced usages of deprecated DataFrame.append method in ML samples([Link to PR](https://github.com/aws/graph-notebook/pull/495))

## Release 3.8.1 (April 17, 2023)
- Reinstate Python 3.7 support for compatibility with legacy AL1 Neptune Notebooks ([Link to PR](https://github.com/aws/graph-notebook/pull/479))
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60 changes: 44 additions & 16 deletions ...-Learning/Sample-Applications/01-People-Analytics/People-Analytics-using-Neptune-ML.ipynb
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Expand Up @@ -332,6 +332,12 @@
"role_dept_list = []\n",
"role_field_list = []\n",
"\n",
"edge_emp_dept_rows_list = [edge_emp_dept]\n",
"edge_emp_role_rows_list = [edge_emp_role]\n",
"edge_emp_field_rows_list = [edge_emp_field]\n",
"edge_role_dept_rows_list = [edge_role_dept]\n",
"edge_role_field_rows_list = [edge_role_field]\n",
"\n",
"for index, row in df.iterrows():\n",
" emp = row['EmployeeNumber']\n",
" emp_id = emp_map[emp]\n",
Expand All @@ -341,30 +347,52 @@
" field_id = field_map[field]\n",
" dept = row['Department']\n",
" dept_id = dept_map[dept]\n",
" \n",
" edge_emp_dept = edge_emp_dept.append({'~id': uuid.uuid4(), '~from': emp_id, \n",
" '~to': dept_id, \n",
" '~label': 'works_in'}, ignore_index=True)\n",
" edge_emp_role = edge_emp_role.append({'~id': uuid.uuid4(), '~from': emp_id, \n",
" '~to': role_id, \n",
" '~label': 'works_as'}, ignore_index=True)\n",
" edge_emp_field = edge_emp_field.append({'~id': uuid.uuid4(), '~from': emp_id, \n",
" '~to': field_id, \n",
" '~label': 'has_education_level'}, ignore_index=True)\n",
"\n",
" edge_emp_dept_row_df = pd.DataFrame.from_dict({'~id': uuid.uuid4(),\n",
" '~from': emp_id,\n",
" '~to': dept_id,\n",
" '~label': 'works_in'},\n",
" orient='index').T\n",
" edge_emp_dept_rows_list.append(edge_emp_dept_row_df)\n",
" edge_emp_role_row_df = pd.DataFrame.from_dict({'~id': uuid.uuid4(),\n",
" '~from': emp_id,\n",
" '~to': role_id,\n",
" '~label': 'works_as'},\n",
" orient='index').T\n",
" edge_emp_role_rows_list.append(edge_emp_role_row_df)\n",
" edge_emp_field_row_df = pd.DataFrame.from_dict({'~id': uuid.uuid4(),\n",
" '~from': emp_id,\n",
" '~to': field_id,\n",
" '~label': 'has_education_level'},\n",
" orient='index').T\n",
" edge_emp_field_rows_list.append(edge_emp_field_row_df)\n",
" \n",
" role_dept = f\"{role_id}-{dept_id}\"\n",
" role_field = f\"{role_id}-{field_id}\"\n",
" if role_dept not in role_dept_list:\n",
" edge_role_dept = edge_role_dept.append({'~id': uuid.uuid4(), '~from': role_id, \n",
" '~to': dept_id, \n",
" '~label': 'part_of'}, ignore_index=True)\n",
" edge_role_dept_row_df = pd.DataFrame.from_dict({'~id': uuid.uuid4(),\n",
" '~from': role_id,\n",
" '~to': dept_id,\n",
" '~label': 'part_of'},\n",
" orient='index').T\n",
" edge_role_dept_rows_list.append(edge_role_dept_row_df)\n",
" #edge_role_dept = pd.concat([edge_role_dept, edge_role_dept_row_df], ignore_index=True)\n",
" role_dept_list.append(role_dept)\n",
" if role_field not in role_field_list:\n",
" edge_role_field = edge_role_field.append({'~id': uuid.uuid4(), '~from': role_id, \n",
" '~to': field_id, \n",
" '~label': 'requires'}, ignore_index=True)\n",
" edge_role_field_row_df = pd.DataFrame.from_dict({'~id': uuid.uuid4(), '~from': role_id,\n",
" '~to': field_id,\n",
" '~label': 'requires'},\n",
" orient='index').T\n",
" edge_role_field_rows_list.append(edge_role_field_row_df)\n",
" role_field_list.append(role_field)\n",
" edge_cnt = edge_cnt + 1\n",
"\n",
"edge_emp_dept = pd.concat(edge_emp_dept_rows_list, ignore_index=True)\n",
"edge_emp_role = pd.concat(edge_emp_role_rows_list, ignore_index=True)\n",
"edge_emp_field = pd.concat(edge_emp_field_rows_list, ignore_index=True)\n",
"edge_role_dept = pd.concat(edge_role_dept_rows_list, ignore_index=True)\n",
"edge_role_field = pd.concat(edge_role_field_rows_list, ignore_index=True)\n",
"\n",
"edge_df = pd.concat([edge_emp_dept, edge_emp_role, edge_emp_field, edge_role_dept, edge_role_field])\n",
"edge_df.to_csv(os.path.join(output_folder, 'edge.csv'), index=False)\n",
"\n",
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11 changes: 8 additions & 3 deletions src/graph_notebook/notebooks/04-Machine-Learning/neptune_ml_utils.py
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Expand Up @@ -360,18 +360,23 @@ def __process_movies_genres(self):
genre_df['name'] = genre_df['~id']
genre_df.to_csv(os.path.join(self.formatted_directory,
'genre_vertex.csv'), index=False)
genres_edge_df_rows_list = [genres_edges_df]

# Loop through all the movies and pull out the genres
for index, row in movie_genre_df.iterrows():
genre_lst = []
for g in genres:
if row[g] == 1:
genres_edges_df = genres_edges_df.append(
{'~id': f"{row['~id']}-included_in-{g}", '~label': 'included_in',
'~from': row['~id'], '~to': g}, ignore_index=True)
row_as_df = pd.DataFrame.from_dict({'~id': f"{row['~id']}-included_in-{g}",
'~label': 'included_in',
'~from': row['~id'],
'~to': g},
orient='index').T
genres_edge_df_rows_list.append(row_as_df)
genre_lst.append(g)
movies_df.loc[index, 'genre:String[]'] = ';'.join(genre_lst)

genres_edges_df = pd.concat(genres_edge_df_rows_list, ignore_index=True)
# rename the release data column to specify the data type
movies_df['release_date:Date'] = movies_df['release_date']
# Drop the genre columns as well as the uneeded release date columns
Expand Down