{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":81933,"databundleVersionId":9643020,"sourceType":"competition"}],"dockerImageVersionId":30761,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nimport seaborn as sns\nimport warnings","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:47:05.947406Z","iopub.execute_input":"2024-12-21T03:47:05.947847Z","iopub.status.idle":"2024-12-21T03:47:05.954064Z","shell.execute_reply.started":"2024-12-21T03:47:05.947810Z","shell.execute_reply":"2024-12-21T03:47:05.952388Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"warnings.filterwarnings('ignore', category=FutureWarning)\n\nsns.set(style=\"whitegrid\")\n%matplotlib inline","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:47:05.956385Z","iopub.execute_input":"2024-12-21T03:47:05.956763Z","iopub.status.idle":"2024-12-21T03:47:05.970791Z","shell.execute_reply.started":"2024-12-21T03:47:05.956727Z","shell.execute_reply":"2024-12-21T03:47:05.969459Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Data Preview","metadata":{}},{"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv')\ntest = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/test.csv')\ndata_dict = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/data_dictionary.csv')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:47:05.972527Z","iopub.execute_input":"2024-12-21T03:47:05.972960Z","iopub.status.idle":"2024-12-21T03:47:06.068626Z","shell.execute_reply.started":"2024-12-21T03:47:05.972924Z","shell.execute_reply":"2024-12-21T03:47:06.067144Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Train data","metadata":{}},{"cell_type":"code","source":"display(train.head())\nprint(f\"Train shape: {train.shape}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:47:06.071029Z","iopub.execute_input":"2024-12-21T03:47:06.071562Z","iopub.status.idle":"2024-12-21T03:47:06.121033Z","shell.execute_reply.started":"2024-12-21T03:47:06.071475Z","shell.execute_reply":"2024-12-21T03:47:06.119746Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Test data","metadata":{}},{"cell_type":"code","source":"display(test.head())\nprint(f\"Test shape: {test.shape}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:47:06.194143Z","iopub.execute_input":"2024-12-21T03:47:06.194658Z","iopub.status.idle":"2024-12-21T03:47:06.223171Z","shell.execute_reply.started":"2024-12-21T03:47:06.194581Z","shell.execute_reply":"2024-12-21T03:47:06.221880Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Data dictionary","metadata":{}},{"cell_type":"code","source":"data_dict.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:47:06.226321Z","iopub.execute_input":"2024-12-21T03:47:06.226848Z","iopub.status.idle":"2024-12-21T03:47:06.249795Z","shell.execute_reply.started":"2024-12-21T03:47:06.226798Z","shell.execute_reply":"2024-12-21T03:47:06.248392Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Helper functions","metadata":{}},{"cell_type":"code","source":"def calculate_stats(data, columns):\n if isinstance(columns, str):\n columns = [columns]\n\n stats = []\n for col in columns:\n if data[col].dtype in ['object', 'category']:\n counts = data[col].value_counts(dropna=False, sort=False)\n percents = data[col].value_counts(normalize=True, dropna=False, sort=False) * 100\n formatted = counts.astype(str) + ' (' + percents.round(2).astype(str) + '%)'\n stats_col = pd.DataFrame({'count (%)': formatted})\n stats.append(stats_col)\n else:\n stats_col = data[col].describe().to_frame().transpose()\n stats_col['missing'] = data[col].isnull().sum()\n stats_col.index.name = col\n stats.append(stats_col)\n\n return pd.concat(stats, axis=0)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:47:06.251651Z","iopub.execute_input":"2024-12-21T03:47:06.251999Z","iopub.status.idle":"2024-12-21T03:47:06.262149Z","shell.execute_reply.started":"2024-12-21T03:47:06.251966Z","shell.execute_reply":"2024-12-21T03:47:06.260970Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#

Missing values

","metadata":{}},{"cell_type":"code","source":"import os\nimport missingno as msno","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:47:06.264010Z","iopub.execute_input":"2024-12-21T03:47:06.264488Z","iopub.status.idle":"2024-12-21T03:47:06.274963Z","shell.execute_reply.started":"2024-12-21T03:47:06.264438Z","shell.execute_reply":"2024-12-21T03:47:06.273366Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def read_ids_from_csv(csv_file_path):\n df = pd.read_csv(csv_file_path)\n ids = set(df['id'])\n return ids\n\ndef get_folders_in_directory(directory_path):\n folders = set(name[3:] for name in os.listdir(directory_path) if os.path.isdir(os.path.join(directory_path, name)))\n return folders\n\ntrain_ids=read_ids_from_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv')\nfolders=get_folders_in_directory('/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet/')\nprint (len(set(train_ids)),len(set(folders)))\nmissing_folders = train_ids - folders\nmissing_percentage = (len(missing_folders) / len(train_ids)) * 100\nprint(f\"Percentage of train IDs missing as folders: {missing_percentage:.2f}%\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:47:06.277721Z","iopub.execute_input":"2024-12-21T03:47:06.278200Z","iopub.status.idle":"2024-12-21T03:47:07.281038Z","shell.execute_reply.started":"2024-12-21T03:47:06.278158Z","shell.execute_reply":"2024-12-21T03:47:07.279816Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"## test\nspecific_id='00115b9f'\nif specific_id in folders:\n print(f\"The folder '{specific_id}' is present in the directory.\")\nelse:\n print(f\"The folder '{specific_id}' is NOT present in the directory.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:47:07.282426Z","iopub.execute_input":"2024-12-21T03:47:07.282807Z","iopub.status.idle":"2024-12-21T03:47:07.289318Z","shell.execute_reply.started":"2024-12-21T03:47:07.282773Z","shell.execute_reply":"2024-12-21T03:47:07.288096Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df=pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv')\nprint(df.shape)\nprint(df.info())\nprint(df.isnull().sum())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:47:07.290853Z","iopub.execute_input":"2024-12-21T03:47:07.291173Z","iopub.status.idle":"2024-12-21T03:47:07.373271Z","shell.execute_reply.started":"2024-12-21T03:47:07.291141Z","shell.execute_reply":"2024-12-21T03:47:07.371980Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"msno.matrix(df)\nplt.title('Missing Data Matrix')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:47:07.375131Z","iopub.execute_input":"2024-12-21T03:47:07.375484Z","iopub.status.idle":"2024-12-21T03:47:08.021286Z","shell.execute_reply.started":"2024-12-21T03:47:07.375450Z","shell.execute_reply":"2024-12-21T03:47:08.020109Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"msno.bar(df)\nplt.title('Missing Data Bar Chart')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:47:08.022715Z","iopub.execute_input":"2024-12-21T03:47:08.023061Z","iopub.status.idle":"2024-12-21T03:47:10.740774Z","shell.execute_reply.started":"2024-12-21T03:47:08.023026Z","shell.execute_reply":"2024-12-21T03:47:10.739709Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(25, 12)) # Adjust the figsize as needed\n\n# Generate the missing data heatmap\nmsno.heatmap(df, fontsize=12, ax=ax)\n\n# Rotate the x-axis labels\nax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha='center')\n\n# Rotate the y-axis labels if needed\nax.set_yticklabels(ax.get_yticklabels(), rotation=0, ha='right')\n\n# Adjust layout\nplt.tight_layout()\n\n# Show the plot\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:47:10.742465Z","iopub.execute_input":"2024-12-21T03:47:10.742828Z","iopub.status.idle":"2024-12-21T03:47:18.290202Z","shell.execute_reply.started":"2024-12-21T03:47:10.742792Z","shell.execute_reply":"2024-12-21T03:47:18.289140Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"msno.dendrogram(df)\nplt.title('Missing Data Dendrogram')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:47:18.293952Z","iopub.execute_input":"2024-12-21T03:47:18.294410Z","iopub.status.idle":"2024-12-21T03:47:19.362677Z","shell.execute_reply.started":"2024-12-21T03:47:18.294367Z","shell.execute_reply":"2024-12-21T03:47:19.361680Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"From the above analysis what we learn is that \n1) Demographics insturment does not have any missing values\n\n2) Missing values are clustered to the instruments (PCIAT, FGC ,BIA, Physical)\n\n3) In the correlation plot we also learn that the instruments missing values are highly correlated and is less correlated to other instruments. So we could assume that if a instrument values are missing it does not mean that other instruments would also be empty. ","metadata":{}},{"cell_type":"code","source":"import dask.dataframe as dd\n# dask used as pandas causes OOM error\nfull_antigraphy=dd.read_parquet('/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:47:19.364290Z","iopub.execute_input":"2024-12-21T03:47:19.364764Z","iopub.status.idle":"2024-12-21T03:47:22.974579Z","shell.execute_reply.started":"2024-12-21T03:47:19.364716Z","shell.execute_reply":"2024-12-21T03:47:22.973403Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"null_percentage = (full_antigraphy.isnull().sum() / len(full_antigraphy)) * 100\n\n# Compute the result and display it\nnull_percentage_computed = null_percentage.compute()\n\nprint(null_percentage_computed)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:47:22.976007Z","iopub.execute_input":"2024-12-21T03:47:22.976527Z","iopub.status.idle":"2024-12-21T03:48:49.685736Z","shell.execute_reply.started":"2024-12-21T03:47:22.976492Z","shell.execute_reply":"2024-12-21T03:48:49.684341Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"1) No null present in antigraphy files. These would be really helpful in EDA and some feature engineering. \n\n2) We should go with semi supervised learning approach to fill in the null.","metadata":{}},{"cell_type":"markdown","source":"#

Target Variables and Internet use

","metadata":{}},{"cell_type":"markdown","source":"Let's identify the features that are related to the target variable and that are not present in the test set.","metadata":{}},{"cell_type":"code","source":"train_cols = set(train.columns)\ntest_cols = set(test.columns)\ncolumns_not_in_test = sorted(list(train_cols - test_cols))\ndata_dict[data_dict['Field'].isin(columns_not_in_test)]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:49.687303Z","iopub.execute_input":"2024-12-21T03:48:49.687742Z","iopub.status.idle":"2024-12-21T03:48:49.727792Z","shell.execute_reply.started":"2024-12-21T03:48:49.687708Z","shell.execute_reply":"2024-12-21T03:48:49.724928Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"pciat_min_max = train.groupby('sii')['PCIAT-PCIAT_Total'].agg(['min', 'max'])\npciat_min_max = pciat_min_max.rename(\n columns={'min': 'Minimum PCIAT total Score', 'max': 'Maximum total PCIAT Score'}\n)\npciat_min_max","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:49.731254Z","iopub.execute_input":"2024-12-21T03:48:49.733847Z","iopub.status.idle":"2024-12-21T03:48:49.778737Z","shell.execute_reply.started":"2024-12-21T03:48:49.733806Z","shell.execute_reply":"2024-12-21T03:48:49.775084Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"data_dict[data_dict['Field'] == 'PCIAT-PCIAT_Total']['Value Labels'].iloc[0]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:49.784522Z","iopub.execute_input":"2024-12-21T03:48:49.785723Z","iopub.status.idle":"2024-12-21T03:48:49.799961Z","shell.execute_reply.started":"2024-12-21T03:48:49.785592Z","shell.execute_reply":"2024-12-21T03:48:49.798078Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Check missing answers","metadata":{}},{"cell_type":"code","source":"train_with_sii = train[train['sii'].notna()][columns_not_in_test]\ntrain_with_sii[train_with_sii.isna().any(axis=1)].head().style.applymap(\n lambda x: 'background-color: #FFC0CB' if pd.isna(x) else ''\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:49.801727Z","iopub.execute_input":"2024-12-21T03:48:49.802127Z","iopub.status.idle":"2024-12-21T03:48:49.973955Z","shell.execute_reply.started":"2024-12-21T03:48:49.802092Z","shell.execute_reply":"2024-12-21T03:48:49.970931Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"PCIAT_cols = [f'PCIAT-PCIAT_{i+1:02d}' for i in range(20)]\nrecalc_total_score = train_with_sii[PCIAT_cols].sum(\n axis=1, skipna=True\n)\n(recalc_total_score == train_with_sii['PCIAT-PCIAT_Total']).all()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:49.977579Z","iopub.execute_input":"2024-12-21T03:48:49.977950Z","iopub.status.idle":"2024-12-21T03:48:50.006291Z","shell.execute_reply.started":"2024-12-21T03:48:49.977917Z","shell.execute_reply":"2024-12-21T03:48:50.002511Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Recalculate the SII based on `PCIAT_Total` and the maximum possible score if missing values were answered (5 points), ensuring that the recalculated SII meets the intended thresholds even with some missing answers.","metadata":{}},{"cell_type":"code","source":"def recalculate_sii(row):\n if pd.isna(row['PCIAT-PCIAT_Total']):\n return np.nan\n max_possible = row['PCIAT-PCIAT_Total'] + row[PCIAT_cols].isna().sum() * 5\n if row['PCIAT-PCIAT_Total'] <= 30 and max_possible <= 30:\n return 0\n elif 31 <= row['PCIAT-PCIAT_Total'] <= 49 and max_possible <= 49:\n return 1\n elif 50 <= row['PCIAT-PCIAT_Total'] <= 79 and max_possible <= 79:\n return 2\n elif row['PCIAT-PCIAT_Total'] >= 80 and max_possible >= 80:\n return 3\n return np.nan\n\ntrain['recalc_sii'] = train.apply(recalculate_sii, axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:50.010721Z","iopub.execute_input":"2024-12-21T03:48:50.011866Z","iopub.status.idle":"2024-12-21T03:48:52.013526Z","shell.execute_reply.started":"2024-12-21T03:48:50.011817Z","shell.execute_reply":"2024-12-21T03:48:52.011931Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Verification of rows with different original and recalculated SII:","metadata":{}},{"cell_type":"code","source":"mismatch_rows = train[\n (train['recalc_sii'] != train['sii']) & train['sii'].notna()\n]\n\nmismatch_rows[PCIAT_cols + [\n 'PCIAT-PCIAT_Total', 'sii', 'recalc_sii'\n]].style.applymap(\n lambda x: 'background-color: #FFC0CB' if pd.isna(x) else ''\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:52.015482Z","iopub.execute_input":"2024-12-21T03:48:52.015951Z","iopub.status.idle":"2024-12-21T03:48:52.049958Z","shell.execute_reply.started":"2024-12-21T03:48:52.015917Z","shell.execute_reply":"2024-12-21T03:48:52.048333Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"In the following analyses I'll only use the corrected SII. I will only use total scores if all PCIAT_cols have non-NA values (all questions of the Parent-Child Internet Addiction Test have been answered).","metadata":{}},{"cell_type":"code","source":"train['sii'] = train['recalc_sii']\ntrain['complete_resp_total'] = train['PCIAT-PCIAT_Total'].where(\n train[PCIAT_cols].notna().all(axis=1), np.nan\n)\n\nsii_map = {0: '0 (None)', 1: '1 (Mild)', 2: '2 (Moderate)', 3: '3 (Severe)'}\ntrain['sii'] = train['sii'].map(sii_map).fillna('Missing')\n\nsii_order = ['Missing', '0 (None)', '1 (Mild)', '2 (Moderate)', '3 (Severe)']\ntrain['sii'] = pd.Categorical(train['sii'], categories=sii_order, ordered=True)\n\ntrain.drop(columns='recalc_sii', inplace=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:52.052399Z","iopub.execute_input":"2024-12-21T03:48:52.052923Z","iopub.status.idle":"2024-12-21T03:48:52.073903Z","shell.execute_reply.started":"2024-12-21T03:48:52.052865Z","shell.execute_reply":"2024-12-21T03:48:52.072089Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Plot distribution of the target variable","metadata":{}},{"cell_type":"code","source":"sii_counts = train['sii'].value_counts().reset_index()\nsii_counts.columns = ['sii', 'count']\ntotal = sii_counts['count'].sum()\nsii_counts['percentage'] = (sii_counts['count'] / total) * 100\n\n# Create the plot\nplt.figure(figsize=(8, 5))\nsns.barplot(x='sii', y='count', data=sii_counts, palette='Blues_d')\n\n# Add title\n# plt.title('Distribution of Severity Impairment Index (SII)', fontsize=14)\n\n# Add count and percentage annotations on bars\nfor p in plt.gca().patches:\n height = p.get_height()\n percentage = sii_counts.loc[sii_counts['count'] == height, 'percentage'].values[0]\n plt.text(\n p.get_x() + p.get_width() / 2,\n height + 5, f'{int(height)} ({percentage:.1f}%)',\n ha=\"center\", fontsize=12\n )\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:52.075726Z","iopub.execute_input":"2024-12-21T03:48:52.076151Z","iopub.status.idle":"2024-12-21T03:48:52.417039Z","shell.execute_reply.started":"2024-12-21T03:48:52.076116Z","shell.execute_reply":"2024-12-21T03:48:52.415709Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"len(train[train['complete_resp_total'] == 0])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:52.418687Z","iopub.execute_input":"2024-12-21T03:48:52.419177Z","iopub.status.idle":"2024-12-21T03:48:52.428504Z","shell.execute_reply.started":"2024-12-21T03:48:52.419130Z","shell.execute_reply":"2024-12-21T03:48:52.427441Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"
\n 💡 Note: Apparently, 40% of the participants were not affected by Internet use, 31% were not assessed, and only the minority (~10%) are moderately to severely impaired. There are 307 participants who scored 0 on all PCIAT questions.\n
","metadata":{}},{"cell_type":"markdown","source":"### SII by age and sex","metadata":{}},{"cell_type":"code","source":"assert train['Basic_Demos-Age'].isna().sum() == 0\nassert train['Basic_Demos-Sex'].isna().sum() == 0","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:52.430194Z","iopub.execute_input":"2024-12-21T03:48:52.430622Z","iopub.status.idle":"2024-12-21T03:48:52.443285Z","shell.execute_reply.started":"2024-12-21T03:48:52.430566Z","shell.execute_reply":"2024-12-21T03:48:52.442116Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train['Age Group'] = pd.cut(\n train['Basic_Demos-Age'],\n bins=[4, 12, 18, 22],\n labels=['Children (5-12)', 'Adolescents (13-18)', 'Adults (19-22)']\n)\ncalculate_stats(train, 'Age Group')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:52.444992Z","iopub.execute_input":"2024-12-21T03:48:52.445343Z","iopub.status.idle":"2024-12-21T03:48:52.472208Z","shell.execute_reply.started":"2024-12-21T03:48:52.445303Z","shell.execute_reply":"2024-12-21T03:48:52.471143Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sex_map = {0: 'Male', 1: 'Female'}\ntrain['Basic_Demos-Sex'] = train['Basic_Demos-Sex'].map(sex_map)\ncalculate_stats(train, 'Basic_Demos-Sex')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:52.473894Z","iopub.execute_input":"2024-12-21T03:48:52.474233Z","iopub.status.idle":"2024-12-21T03:48:52.488706Z","shell.execute_reply.started":"2024-12-21T03:48:52.474202Z","shell.execute_reply":"2024-12-21T03:48:52.487410Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 3, figsize=(20, 8))\n\n# SII by Age\nsns.boxplot(y=train['Basic_Demos-Age'], x=train['sii'], ax=axes[0], palette=\"Set3\")\naxes[0].set_title('SII by Age')\naxes[0].set_ylabel('Age')\naxes[0].set_xlabel('SII')\n\n# Complete PCIAT Responses by Age Group\nsns.boxplot(\n x='Age Group', y='complete_resp_total',\n data=train, palette=\"Set3\", ax=axes[1]\n)\naxes[1].set_title('Complete PCIAT Responses by Age Group')\naxes[1].set_ylabel('PCIAT_Total for Complete Responses')\naxes[1].set_xlabel('Age Group')\n\n# PCIAT_Total by Sex\nsns.histplot(\n data=train, x='complete_resp_total',\n hue='Basic_Demos-Sex', multiple='stack',\n palette=\"Set3\", bins=20, ax=axes[2]\n)\naxes[2].set_title('PCIAT_Total Distribution by Sex')\naxes[2].set_xlabel('PCIAT_Total for Complete Responses')\naxes[2].set_ylabel('Frequency')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:52.490822Z","iopub.execute_input":"2024-12-21T03:48:52.491197Z","iopub.status.idle":"2024-12-21T03:48:53.575135Z","shell.execute_reply.started":"2024-12-21T03:48:52.491158Z","shell.execute_reply":"2024-12-21T03:48:53.573664Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"stats = train.groupby(['Age Group', 'sii']).size().unstack(fill_value=0)\nfig, axes = plt.subplots(1, len(stats), figsize=(18, 8))\n\nfor i, age_group in enumerate(stats.index):\n group_counts = stats.loc[age_group] / stats.loc[age_group].sum()\n axes[i].pie(\n group_counts, labels=group_counts.index, autopct='%1.1f%%',\n startangle=90, colors=sns.color_palette(\"Set3\"),\n labeldistance=1.05, pctdistance=0.80\n )\n axes[i].set_title(f'SII Distribution for {age_group}')\n axes[i].axis('equal')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:53.583881Z","iopub.execute_input":"2024-12-21T03:48:53.584261Z","iopub.status.idle":"2024-12-21T03:48:54.174829Z","shell.execute_reply.started":"2024-12-21T03:48:53.584229Z","shell.execute_reply":"2024-12-21T03:48:54.173141Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"The distribution of sii across different age groups:","metadata":{}},{"cell_type":"code","source":"stats = train.groupby(['Age Group', 'sii']).size().unstack(fill_value=0)\nstats_prop = stats.div(stats.sum(axis=1), axis=0) * 100\n\nstats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)'\nstats","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:54.176482Z","iopub.execute_input":"2024-12-21T03:48:54.176915Z","iopub.status.idle":"2024-12-21T03:48:54.200304Z","shell.execute_reply.started":"2024-12-21T03:48:54.176880Z","shell.execute_reply":"2024-12-21T03:48:54.199185Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Calculate percentages for participants with non-missing SII only:","metadata":{}},{"cell_type":"code","source":"stats = train[train['sii'] != 'Missing'].groupby(\n ['Age Group', 'sii']\n).size().unstack(fill_value=0)\nstats_prop = stats.div(stats.sum(axis=1), axis=0) * 100\n\nstats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)'\nstats","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:54.201803Z","iopub.execute_input":"2024-12-21T03:48:54.202258Z","iopub.status.idle":"2024-12-21T03:48:54.228211Z","shell.execute_reply.started":"2024-12-21T03:48:54.202211Z","shell.execute_reply":"2024-12-21T03:48:54.226936Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Internet Use","metadata":{}},{"cell_type":"code","source":"data = train[train['PreInt_EduHx-computerinternet_hoursday'].notna()]\nage_range = data['Basic_Demos-Age']\nprint(\n f\"Age range for participants with measured PreInt_EduHx-computerinternet_hoursday data:\"\n f\" {age_range.min()} - {age_range.max()} years\"\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:54.229908Z","iopub.execute_input":"2024-12-21T03:48:54.230253Z","iopub.status.idle":"2024-12-21T03:48:54.239993Z","shell.execute_reply.started":"2024-12-21T03:48:54.230221Z","shell.execute_reply":"2024-12-21T03:48:54.238711Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train['PreInt_EduHx-computerinternet_hoursday'].unique()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:54.242031Z","iopub.execute_input":"2024-12-21T03:48:54.242395Z","iopub.status.idle":"2024-12-21T03:48:54.259319Z","shell.execute_reply.started":"2024-12-21T03:48:54.242360Z","shell.execute_reply":"2024-12-21T03:48:54.258186Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"param_map = {0: '< 1h/day', 1: '~ 1h/day', 2: '~ 2hs/day', 3: '> 3hs/day'}\ntrain['internet_use_encoded'] = train[\n 'PreInt_EduHx-computerinternet_hoursday'\n].map(param_map).fillna('Missing')\n\nparam_ord = ['Missing', '< 1h/day', '~ 1h/day', '~ 2hs/day', '> 3hs/day']\ntrain['internet_use_encoded'] = pd.Categorical(\n train['internet_use_encoded'], categories=param_ord,\n ordered=True\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:54.261010Z","iopub.execute_input":"2024-12-21T03:48:54.261811Z","iopub.status.idle":"2024-12-21T03:48:54.275320Z","shell.execute_reply.started":"2024-12-21T03:48:54.261764Z","shell.execute_reply":"2024-12-21T03:48:54.274182Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"calculate_stats(train, 'PreInt_EduHx-Season')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:54.277032Z","iopub.execute_input":"2024-12-21T03:48:54.277461Z","iopub.status.idle":"2024-12-21T03:48:54.298349Z","shell.execute_reply.started":"2024-12-21T03:48:54.277404Z","shell.execute_reply":"2024-12-21T03:48:54.296738Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 3, figsize=(21, 8))\n\n# Hours of Internet Use\nax1 = sns.countplot(x='internet_use_encoded', data=train, palette=\"Set3\", ax=axes[0])\naxes[0].set_title('Distribution of Hours of Internet Use')\naxes[0].set_xlabel('Hours per Day Group')\naxes[0].set_ylabel('Count')\n\ntotal = len(train['internet_use_encoded'])\nfor p in ax1.patches:\n count = int(p.get_height())\n percentage = '{:.1f}%'.format(100 * count / total)\n ax1.annotate(f'{count} ({percentage})', (p.get_x() + p.get_width() / 2., p.get_height()), \n ha='center', va='baseline', fontsize=10, color='black', xytext=(0, 5), \n textcoords='offset points')\n\n# Hours of Internet Use by Age\nsns.boxplot(y=train['Basic_Demos-Age'], x=train['internet_use_encoded'], ax=axes[1], palette=\"Set3\")\naxes[1].set_title('Hours of Internet Use by Age')\naxes[1].set_ylabel('Age')\naxes[1].set_xlabel('Hours per Day Group')\n\n# Hours of Internet Use (numeric) by Age Group\nsns.boxplot(y='PreInt_EduHx-computerinternet_hoursday', x='Age Group', data=train, ax=axes[2], palette=\"Set3\")\naxes[2].set_title('Internet Hours by Age Group')\naxes[2].set_ylabel('Hours per Day (Numeric)')\naxes[2].set_xlabel('Age Group')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:54.300072Z","iopub.execute_input":"2024-12-21T03:48:54.300429Z","iopub.status.idle":"2024-12-21T03:48:55.410430Z","shell.execute_reply.started":"2024-12-21T03:48:54.300396Z","shell.execute_reply":"2024-12-21T03:48:55.409274Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"stats = train.groupby(\n ['Age Group', 'internet_use_encoded']\n).size().unstack(fill_value=0)\nfig, axes = plt.subplots(1, len(stats), figsize=(18, 8))\n\nfor i, age_group in enumerate(stats.index):\n group_counts = stats.loc[age_group] / stats.loc[age_group].sum()\n axes[i].pie(group_counts, labels=group_counts.index, autopct='%1.1f%%',\n startangle=90, colors=sns.color_palette(\"Set3\"), labeldistance=1.1)\n axes[i].set_title(f'Distribution of Hours of Internet Use\\n{age_group}')\n axes[i].axis('equal')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:55.412116Z","iopub.execute_input":"2024-12-21T03:48:55.412495Z","iopub.status.idle":"2024-12-21T03:48:55.981779Z","shell.execute_reply.started":"2024-12-21T03:48:55.412461Z","shell.execute_reply":"2024-12-21T03:48:55.980558Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_non_na = train.dropna(subset=['PreInt_EduHx-computerinternet_hoursday'])\nrows = (train_non_na['PreInt_EduHx-computerinternet_hoursday'] == 3).sum()\nprint(f\"Non-NA Rows - Internet use 3h or more: {(rows / len(train_non_na)) * 100:.2f}%\")\n\nrows = (train_non_na['PreInt_EduHx-computerinternet_hoursday'] == 0).sum()\nprint(f\"Non-NA Rows - Internet use 1h or less: {(rows / len(train_non_na)) * 100:.2f}%\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:55.983382Z","iopub.execute_input":"2024-12-21T03:48:55.983847Z","iopub.status.idle":"2024-12-21T03:48:55.997815Z","shell.execute_reply.started":"2024-12-21T03:48:55.983805Z","shell.execute_reply":"2024-12-21T03:48:55.996295Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"stats = train.groupby(['Basic_Demos-Sex', 'internet_use_encoded']\n).size().unstack(fill_value=0)\nstats_prop = stats.div(stats.sum(axis=1), axis=0) * 100\n\nstats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)'\nstats","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:55.999022Z","iopub.execute_input":"2024-12-21T03:48:55.999438Z","iopub.status.idle":"2024-12-21T03:48:56.028668Z","shell.execute_reply.started":"2024-12-21T03:48:55.999406Z","shell.execute_reply":"2024-12-21T03:48:56.027540Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Internet usage vs SII (target)","metadata":{}},{"cell_type":"code","source":"sii_reported = train[train['sii'] != \"Missing\"]\nsii_reported.loc[:, 'sii'] = sii_reported['sii'].cat.remove_unused_categories()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:56.030115Z","iopub.execute_input":"2024-12-21T03:48:56.030455Z","iopub.status.idle":"2024-12-21T03:48:56.042505Z","shell.execute_reply.started":"2024-12-21T03:48:56.030424Z","shell.execute_reply":"2024-12-21T03:48:56.041176Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"stats = sii_reported.groupby(\n ['internet_use_encoded', 'sii']\n).size().unstack(fill_value=0)\nstats_prop = stats.div(stats.sum(axis=1), axis=0) * 100\n\nstats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)'\nstats","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:56.043932Z","iopub.execute_input":"2024-12-21T03:48:56.044262Z","iopub.status.idle":"2024-12-21T03:48:56.072424Z","shell.execute_reply.started":"2024-12-21T03:48:56.044219Z","shell.execute_reply":"2024-12-21T03:48:56.071091Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig = plt.figure(figsize=(15, 13))\ngs = fig.add_gridspec(2, 2, height_ratios=[1, 1.5])\n\n# SII vs Hours of Internet Use\nax1 = fig.add_subplot(gs[0, 0])\nsns.boxplot(\n x='sii', y='PreInt_EduHx-computerinternet_hoursday',\n data=sii_reported,\n ax=ax1, palette=\"Set3\"\n)\nax1.set_title('SII vs Hours of Internet Use')\nax1.set_ylabel('Hours per Day')\nax1.set_xlabel('SII')\n\n# PCIAT_Total for Complete PCIAT Responses by Hours of Internet Use\nax2 = fig.add_subplot(gs[0, 1])\nsns.boxplot(\n x='internet_use_encoded', y='complete_resp_total',\n data=sii_reported,\n palette=\"Set3\", ax=ax2\n)\nax2.set_title('PCIAT_Total by Hours of Internet Use')\nax2.set_ylabel('PCIAT_Total for Complete PCIAT Responses')\nax2.set_xlabel('Hours per Day Group')\n\n# SII vs Hours of Internet Use by Age Group (Full width)\nax3 = fig.add_subplot(gs[1, :])\nsns.boxplot(\n x='internet_use_encoded', y='complete_resp_total',\n data=sii_reported,\n hue='Age Group', ax=ax3, palette=\"Set3\"\n)\nax3.set_title('PCIAT_Total vs Hours of Internet Use by Age Group')\nax3.set_ylabel('PCIAT_Total for Complete PCIAT Responses')\nax3.set_xlabel('Hours per Day Group')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:56.073949Z","iopub.execute_input":"2024-12-21T03:48:56.074280Z","iopub.status.idle":"2024-12-21T03:48:57.885452Z","shell.execute_reply.started":"2024-12-21T03:48:56.074249Z","shell.execute_reply":"2024-12-21T03:48:57.884301Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"stats = sii_reported.groupby(\n ['sii', 'internet_use_encoded']\n).size().unstack(fill_value=0)\nfig, axes = plt.subplots(1, len(stats), figsize=(18, 8))\n\nfor i, sii_group in enumerate(stats.index):\n group_counts = stats.loc[sii_group] / stats.loc[sii_group].sum()\n axes[i].pie(\n group_counts, labels=group_counts.index, autopct='%1.1f%%',\n startangle=90, colors=sns.color_palette(\"Set3\"), labeldistance=1.1\n )\n axes[i].set_title(f'Hours of using computer/internet\\n for SII = {sii_group}')\n axes[i].axis('equal')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:57.887532Z","iopub.execute_input":"2024-12-21T03:48:57.887977Z","iopub.status.idle":"2024-12-21T03:48:58.555986Z","shell.execute_reply.started":"2024-12-21T03:48:57.887933Z","shell.execute_reply":"2024-12-21T03:48:58.554683Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"stats = sii_reported.groupby(\n ['sii', 'internet_use_encoded']\n).size().unstack(fill_value=0)\nstats_prop = stats.div(stats.sum(axis=1), axis=0) * 100\n\nstats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)'\nstats","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:58.557731Z","iopub.execute_input":"2024-12-21T03:48:58.558176Z","iopub.status.idle":"2024-12-21T03:48:58.583129Z","shell.execute_reply.started":"2024-12-21T03:48:58.558128Z","shell.execute_reply":"2024-12-21T03:48:58.581863Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train[\n (train['internet_use_encoded'] == '< 1h/day') & \n (train['sii'].isin(['2 (Moderate)', '3 (Severe)']))\n]['Basic_Demos-Age'].describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:58.584427Z","iopub.execute_input":"2024-12-21T03:48:58.584849Z","iopub.status.idle":"2024-12-21T03:48:58.602067Z","shell.execute_reply.started":"2024-12-21T03:48:58.584817Z","shell.execute_reply":"2024-12-21T03:48:58.600694Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"
\n

Summary of Findings

\n
    \n
  1. The SII scores tend to increase with age but show a U-shaped relationship, with adolescents having the highest median PCIAT scores.
    2. \n
  2. The higher the age, the more hours participants spent online (clear linear trend).
    3. \n
  3. People with higher SII scores generally spend more time online, but adolescents stand out as the most affected age group across all categories of internet use.
    4. \n
  4. There are participants of almost all ages (5 to 21) who spend less than an hour a day online and have high SII scores.
    5. \n
\n

Note: These results should be interpreted with caution, as there is considerable overlap between the different SII and internet use categories, and severe cases and adults are under-represented in the data.

\n
","metadata":{}},{"cell_type":"markdown","source":"# Features EDA by Groups","metadata":{}},{"cell_type":"code","source":"groups = data_dict.groupby('Instrument')['Field'].apply(list).to_dict()\n\nfor instrument, features in groups.items():\n print(f\"{instrument}: {features}\\n\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:58.603866Z","iopub.execute_input":"2024-12-21T03:48:58.604213Z","iopub.status.idle":"2024-12-21T03:48:58.619114Z","shell.execute_reply.started":"2024-12-21T03:48:58.604179Z","shell.execute_reply":"2024-12-21T03:48:58.617658Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Season-related columns","metadata":{}},{"cell_type":"code","source":"season_columns = [col for col in train.columns if 'Season' in col]\nseason_df = train[season_columns]\nseason_df","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:58.621657Z","iopub.execute_input":"2024-12-21T03:48:58.622169Z","iopub.status.idle":"2024-12-21T03:48:58.651707Z","shell.execute_reply.started":"2024-12-21T03:48:58.622121Z","shell.execute_reply":"2024-12-21T03:48:58.650450Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train[season_columns] = train[season_columns].fillna(\"Missing\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:58.653546Z","iopub.execute_input":"2024-12-21T03:48:58.653963Z","iopub.status.idle":"2024-12-21T03:48:58.674744Z","shell.execute_reply.started":"2024-12-21T03:48:58.653929Z","shell.execute_reply":"2024-12-21T03:48:58.673333Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Grouping of features by type and measurement method\nHaving examined the contents of data_dict in detail, I believe that the characteristics can also be grouped according to their type and method of measurement (the diagram was made with [napkin](https://app.napkin.ai/)):\n","metadata":{}},{"cell_type":"markdown","source":"![napkin-selection 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"}}},{"cell_type":"markdown","source":"### Potential connection to problematic internet use (PIU)\n- Behavioral (subjective reported) \n- Physical Health and Fitness (objective measurements)\n- Demographic features","metadata":{}},{"cell_type":"markdown","source":"Remove target-related columns and continue EDA by feature groups.","metadata":{}},{"cell_type":"code","source":"data_dict = data_dict[data_dict['Instrument'] != 'Parent-Child Internet Addiction Test']\ncontinuous_cols = data_dict[data_dict['Type'].str.contains(\n 'float|int', case=False\n)]['Field'].tolist()\n\n# target = train[['sii']]\n# train = train.drop(columns = columns_not_in_test)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:58.676726Z","iopub.execute_input":"2024-12-21T03:48:58.677178Z","iopub.status.idle":"2024-12-21T03:48:58.691232Z","shell.execute_reply.started":"2024-12-21T03:48:58.677131Z","shell.execute_reply":"2024-12-21T03:48:58.689927Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#

- Demographics

","metadata":{}},{"cell_type":"code","source":"groups.get('Demographics', [])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:58.692914Z","iopub.execute_input":"2024-12-21T03:48:58.693356Z","iopub.status.idle":"2024-12-21T03:48:58.708507Z","shell.execute_reply.started":"2024-12-21T03:48:58.693307Z","shell.execute_reply":"2024-12-21T03:48:58.706926Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n\n# Season of Enrollment\nseason_counts = train['Basic_Demos-Enroll_Season'].value_counts(dropna=False)\n\naxes[0].pie(\n season_counts, labels=season_counts.index,\n autopct='%1.1f%%', startangle=90,\n colors=sns.color_palette(\"Set3\")\n)\naxes[0].set_title('Season of Enrollment')\naxes[0].axis('equal')\n\n# Age Distribution by Sex\nsns.histplot(\n data=train, x='Basic_Demos-Age',\n hue='Basic_Demos-Sex', multiple='dodge',\n palette=\"Set2\", bins=20, ax=axes[1]\n)\naxes[1].set_title('Age Distribution by Sex')\naxes[1].set_xlabel('Age')\naxes[1].set_ylabel('Count')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:58.710508Z","iopub.execute_input":"2024-12-21T03:48:58.711060Z","iopub.status.idle":"2024-12-21T03:48:59.483624Z","shell.execute_reply.started":"2024-12-21T03:48:58.711014Z","shell.execute_reply":"2024-12-21T03:48:59.482491Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"0=Male, 1=Female","metadata":{}},{"cell_type":"code","source":"calculate_stats(train, 'Basic_Demos-Age')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:59.485337Z","iopub.execute_input":"2024-12-21T03:48:59.485716Z","iopub.status.idle":"2024-12-21T03:48:59.504411Z","shell.execute_reply.started":"2024-12-21T03:48:59.485683Z","shell.execute_reply":"2024-12-21T03:48:59.503204Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#

Physical Health and Fitness (objective measurements)

","metadata":{}},{"cell_type":"markdown","source":"# - Children's Global Assessment Scale","metadata":{}},{"cell_type":"code","source":"groups.get(\"Children's Global Assessment Scale\", [])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:59.506662Z","iopub.execute_input":"2024-12-21T03:48:59.507063Z","iopub.status.idle":"2024-12-21T03:48:59.519743Z","shell.execute_reply.started":"2024-12-21T03:48:59.507029Z","shell.execute_reply":"2024-12-21T03:48:59.518373Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"data = train[train['CGAS-CGAS_Score'].notnull()]\nage_range = data['Basic_Demos-Age']\nprint(\n f\"Age range for participants with CGAS-CGAS_Score data:\"\n f\" {age_range.min()} - {age_range.max()} years\"\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:59.521345Z","iopub.execute_input":"2024-12-21T03:48:59.521766Z","iopub.status.idle":"2024-12-21T03:48:59.537371Z","shell.execute_reply.started":"2024-12-21T03:48:59.521713Z","shell.execute_reply":"2024-12-21T03:48:59.536174Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"calculate_stats(train, 'CGAS-CGAS_Score')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:59.539071Z","iopub.execute_input":"2024-12-21T03:48:59.539415Z","iopub.status.idle":"2024-12-21T03:48:59.561346Z","shell.execute_reply.started":"2024-12-21T03:48:59.539383Z","shell.execute_reply":"2024-12-21T03:48:59.560163Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train[train['CGAS-CGAS_Score'] > 100]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:59.562776Z","iopub.execute_input":"2024-12-21T03:48:59.563110Z","iopub.status.idle":"2024-12-21T03:48:59.591970Z","shell.execute_reply.started":"2024-12-21T03:48:59.563080Z","shell.execute_reply":"2024-12-21T03:48:59.590545Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"
\n 💡 Note: \n \n
","metadata":{}},{"cell_type":"code","source":"train.loc[train['CGAS-CGAS_Score'] == 999, 'CGAS-CGAS_Score'] = np.nan","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:59.593337Z","iopub.execute_input":"2024-12-21T03:48:59.593807Z","iopub.status.idle":"2024-12-21T03:48:59.604209Z","shell.execute_reply.started":"2024-12-21T03:48:59.593750Z","shell.execute_reply":"2024-12-21T03:48:59.602987Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(12, 5))\n\n# CGAS-Season\nplt.subplot(1, 2, 1)\ncgas_season_counts = train['CGAS-Season'].value_counts(normalize=True)\nplt.pie(\n cgas_season_counts, \n labels=cgas_season_counts.index, \n autopct='%1.1f%%', \n startangle=90, \n colors=sns.color_palette(\"Set3\")\n)\nplt.title('CGAS-Season')\nplt.axis('equal')\n\n# CGAS-CGAS_Score without outliers (score == 999)\nplt.subplot(1, 2, 2)\nsns.histplot(\n train['CGAS-CGAS_Score'].dropna(),\n bins=20, kde=True\n)\nplt.title('CGAS-CGAS_Score (Without Outlier)')\nplt.xlabel('CGAS Score')\nplt.ylabel('Count')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:48:59.605641Z","iopub.execute_input":"2024-12-21T03:48:59.606047Z","iopub.status.idle":"2024-12-21T03:49:00.280197Z","shell.execute_reply.started":"2024-12-21T03:48:59.606014Z","shell.execute_reply":"2024-12-21T03:49:00.279002Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Stats without outlier:","metadata":{}},{"cell_type":"code","source":"calculate_stats(train, 'CGAS-CGAS_Score')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:00.281736Z","iopub.execute_input":"2024-12-21T03:49:00.282085Z","iopub.status.idle":"2024-12-21T03:49:00.302771Z","shell.execute_reply.started":"2024-12-21T03:49:00.282052Z","shell.execute_reply":"2024-12-21T03:49:00.301442Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"bins = np.arange(0, 101, 10)\nlabels = [\n \"1-10: Needs constant supervision (24 hour care)\",\n \"11-20: Needs considerable supervision\",\n \"21-30: Unable to function in almost all areas\",\n \"31-40: Major impairment in functioning in several areas\",\n \"41-50: Moderate degree of interference in functioning\",\n \"51-60: Variable functioning with sporadic difficulties\",\n \"61-70: Some difficulty in a single area\",\n \"71-80: No more than slight impairment in functioning\",\n \"81-90: Good functioning in all areas\",\n \"91-100: Superior functioning\"\n]\n\ntrain['CGAS_Score_Bin'] = pd.cut(\n train['CGAS-CGAS_Score'], bins=bins, labels=labels\n)\n\ncounts = train['CGAS_Score_Bin'].value_counts().reindex(labels)\nprop = (counts / counts.sum() * 100).round(1)\ncount_prop_labels = counts.astype(str) + \" (\" + prop.astype(str) + \"%)\"\n\nplt.figure(figsize=(18, 6))\nbars = plt.barh(labels, counts)\nplt.xlabel('Count')\nplt.title('CGAS Score Distribution')\n\nfor bar, label in zip(bars, count_prop_labels):\n plt.text(\n bar.get_width(), bar.get_y() + bar.get_height() / 2, label, va='center'\n )\n\nplt.gca().invert_yaxis()\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:00.305078Z","iopub.execute_input":"2024-12-21T03:49:00.305538Z","iopub.status.idle":"2024-12-21T03:49:00.894431Z","shell.execute_reply.started":"2024-12-21T03:49:00.305489Z","shell.execute_reply":"2024-12-21T03:49:00.893295Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"
\n 💡 Note: \n \n
","metadata":{}},{"cell_type":"markdown","source":"Examine relationships with the target variable:","metadata":{}},{"cell_type":"code","source":"train_filt = train.dropna(subset=['CGAS_Score_Bin', 'complete_resp_total'])\ntrain_filt.loc[:, 'CGAS_Score_Bin'] = train_filt['CGAS_Score_Bin'].cat.remove_unused_categories()\ntrain_filt.loc[:, 'sii'] = train_filt['sii'].cat.remove_unused_categories()\nlen(train_filt)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:00.896940Z","iopub.execute_input":"2024-12-21T03:49:00.897284Z","iopub.status.idle":"2024-12-21T03:49:00.914696Z","shell.execute_reply.started":"2024-12-21T03:49:00.897250Z","shell.execute_reply":"2024-12-21T03:49:00.913347Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 2, figsize=(18, 8))\n\n# CGAS-CGAS_Score vs sii\nsns.boxplot(\n data=train_filt,\n x='sii', y='CGAS-CGAS_Score',\n palette='Set3', ax=axes[0]\n)\naxes[0].set_xlabel('SII Score')\naxes[0].set_ylabel('CGAS Score')\naxes[0].set_title('Distribution of CGAS Scores by SII')\n\n# complete_resp_total vs CGAS_Score_Bin\nsns.boxplot(\n data=train_filt,\n x='CGAS_Score_Bin', y='complete_resp_total',\n ax=axes[1], palette='Set3'\n)\n\n# Get the tick positions and match the labels\nrange_labels = [label.split(\":\")[0] for label in train_filt['CGAS_Score_Bin'].cat.categories]\naxes[1].set_xticklabels(range_labels)\n\naxes[1].set_xlabel('CGAS Score category')\naxes[1].set_ylabel('PCIAT_Total for Complete PCIAT Responses')\naxes[1].set_title('Distribution of PCIAT_Total by CGAS Score categories')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:00.916463Z","iopub.execute_input":"2024-12-21T03:49:00.916871Z","iopub.status.idle":"2024-12-21T03:49:01.888007Z","shell.execute_reply.started":"2024-12-21T03:49:00.916813Z","shell.execute_reply":"2024-12-21T03:49:01.886751Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"score_min_max = train.groupby('sii')['CGAS-CGAS_Score'].agg(['min', 'max'])\nscore_min_max = score_min_max.rename(\n columns={'min': 'Minimum CGAS Score', 'max': 'Maximum CGAS Score'}\n)\nscore_min_max","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:01.889348Z","iopub.execute_input":"2024-12-21T03:49:01.889724Z","iopub.status.idle":"2024-12-21T03:49:01.905211Z","shell.execute_reply.started":"2024-12-21T03:49:01.889690Z","shell.execute_reply":"2024-12-21T03:49:01.903898Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Let's check the SII and Internet usage data for the participants with the worst global functioning:","metadata":{}},{"cell_type":"code","source":"train_filt[train_filt['CGAS-CGAS_Score'] < 35][\n ['Basic_Demos-Age', 'Basic_Demos-Sex', 'sii',\n 'CGAS-CGAS_Score',\n 'PreInt_EduHx-computerinternet_hoursday']\n]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:01.907061Z","iopub.execute_input":"2024-12-21T03:49:01.907453Z","iopub.status.idle":"2024-12-21T03:49:01.923899Z","shell.execute_reply.started":"2024-12-21T03:49:01.907419Z","shell.execute_reply":"2024-12-21T03:49:01.922584Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"And the same for the participants with the best global functioning:","metadata":{}},{"cell_type":"code","source":"train[train['CGAS-CGAS_Score'] > 90][\n ['Basic_Demos-Age', 'Basic_Demos-Sex', 'sii',\n 'CGAS-CGAS_Score',\n 'PreInt_EduHx-computerinternet_hoursday']\n]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:01.925688Z","iopub.execute_input":"2024-12-21T03:49:01.926153Z","iopub.status.idle":"2024-12-21T03:49:01.952883Z","shell.execute_reply.started":"2024-12-21T03:49:01.926105Z","shell.execute_reply":"2024-12-21T03:49:01.951492Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# - Physical Measures","metadata":{}},{"cell_type":"code","source":"groups.get('Physical Measures', [])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:01.954767Z","iopub.execute_input":"2024-12-21T03:49:01.955265Z","iopub.status.idle":"2024-12-21T03:49:01.968966Z","shell.execute_reply.started":"2024-12-21T03:49:01.955215Z","shell.execute_reply":"2024-12-21T03:49:01.967962Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"features_physical = groups.get('Physical Measures', [])\ncols = [col for col in features_physical if col in continuous_cols]\n\nplt.figure(figsize=(24, 10))\nn_cols = 4\nn_rows = len(cols) // n_cols + 1\n\nfor i, col in enumerate(cols):\n plt.subplot(n_rows, n_cols, i + 1)\n train[col].hist(bins=20)\n plt.title(col)\n\nplt.subplot(n_rows, n_cols, len(cols) + 1)\nseason_counts = train['Physical-Season'].value_counts(dropna=False)\nplt.pie(\n season_counts,\n labels=season_counts.index,\n autopct='%1.1f%%',\n startangle=90,\n colors=sns.color_palette(\"Set3\")\n)\nplt.title('Physical-Season')\n\nplt.suptitle('Histograms for Physical Measures and Physical-Season Pie Chart', y=1.05)\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:01.970830Z","iopub.execute_input":"2024-12-21T03:49:01.971208Z","iopub.status.idle":"2024-12-21T03:49:04.632036Z","shell.execute_reply.started":"2024-12-21T03:49:01.971175Z","shell.execute_reply":"2024-12-21T03:49:04.630393Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"calculate_stats(train, cols)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:04.634241Z","iopub.execute_input":"2024-12-21T03:49:04.635019Z","iopub.status.idle":"2024-12-21T03:49:04.686407Z","shell.execute_reply.started":"2024-12-21T03:49:04.634979Z","shell.execute_reply":"2024-12-21T03:49:04.685143Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Weight and Height","metadata":{}},{"cell_type":"code","source":"wh_cols = [\n 'Physical-BMI', 'Physical-Height',\n 'Physical-Weight', 'Physical-Waist_Circumference'\n]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:04.688205Z","iopub.execute_input":"2024-12-21T03:49:04.688677Z","iopub.status.idle":"2024-12-21T03:49:04.693998Z","shell.execute_reply.started":"2024-12-21T03:49:04.688636Z","shell.execute_reply":"2024-12-21T03:49:04.692631Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"The minimum values of 0 for measures like BMI, weight, and blood pressure are biologically unrealistic, and likely indicate missing or erroneous data. Let's check number of zeros in these columns:","metadata":{}},{"cell_type":"code","source":"(train[wh_cols] == 0).sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:04.695571Z","iopub.execute_input":"2024-12-21T03:49:04.696044Z","iopub.status.idle":"2024-12-21T03:49:04.724051Z","shell.execute_reply.started":"2024-12-21T03:49:04.696010Z","shell.execute_reply":"2024-12-21T03:49:04.722524Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Replace the 0 values by NaN and check the stats again:","metadata":{}},{"cell_type":"code","source":"train[wh_cols] = train[wh_cols].replace(0, np.nan)\ncalculate_stats(train, wh_cols)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:04.727866Z","iopub.execute_input":"2024-12-21T03:49:04.728325Z","iopub.status.idle":"2024-12-21T03:49:04.778268Z","shell.execute_reply.started":"2024-12-21T03:49:04.728290Z","shell.execute_reply":"2024-12-21T03:49:04.777140Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Convert weight to kilograms, and height to centimeters and recalculate BMI:","metadata":{}},{"cell_type":"code","source":"lbs_to_kg = 0.453592\ninches_to_cm = 2.54\n\ntrain['Physical-Weight'] = train['Physical-Weight'] * lbs_to_kg\ntrain['Physical-Height'] = train['Physical-Height'] * inches_to_cm\ntrain['Physical-Waist_Circumference'] = train['Physical-Waist_Circumference'] * inches_to_cm\n\n# Recalculate BMI: BMI = weight (kg) / (height (m)^2)\ntrain['Physical-BMI'] = np.where(\n train['Physical-Weight'].notna() & train['Physical-Height'].notna(),\n train['Physical-Weight'] / ((train['Physical-Height'] / 100) ** 2),\n np.nan # If either is NaN, set BMI to NaN\n)\n\ncalculate_stats(train, wh_cols)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:04.781050Z","iopub.execute_input":"2024-12-21T03:49:04.781511Z","iopub.status.idle":"2024-12-21T03:49:04.831443Z","shell.execute_reply.started":"2024-12-21T03:49:04.781449Z","shell.execute_reply":"2024-12-21T03:49:04.829483Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"A lot of values seem to be out of normal ranges... especially max values of weight (142kg) and waist circumference (127cm).","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(18, 5))\n\n# Physical-Weight by Age\nplt.subplot(1, 3, 1)\nsns.scatterplot(x='Basic_Demos-Age', y='Physical-Weight', data=train)\nplt.title('Physical-Weight by Age')\nplt.xlabel('Age')\nplt.ylabel('Weight (kg)')\n\n# Physical-Height by Age\nplt.subplot(1, 3, 2)\nsns.scatterplot(x='Basic_Demos-Age', y='Physical-Height', data=train)\nplt.title('Physical-Height by Age')\nplt.xlabel('Age')\nplt.ylabel('Height (cm)')\n\n# Physical-Waist_Circumference vs Physical-Weight\nplt.subplot(1, 3, 3)\nsns.scatterplot(x='Physical-Weight', y='Physical-Waist_Circumference', data=train)\nplt.title('Waist Circumference vs Weight')\nplt.xlabel('Weight (kg)')\nplt.ylabel('Waist Circumference (cm)')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:04.833845Z","iopub.execute_input":"2024-12-21T03:49:04.834286Z","iopub.status.idle":"2024-12-21T03:49:06.061889Z","shell.execute_reply.started":"2024-12-21T03:49:04.834242Z","shell.execute_reply":"2024-12-21T03:49:06.060541Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Blood Pressure & Heart Rate","metadata":{}},{"cell_type":"markdown","source":"There is 1000% incorrect data in the BP/HR columns as the minimum values are lethal to humans. We can clean up these kinds of mistakes.","metadata":{}},{"cell_type":"code","source":"bp_hr_cols = [\n 'Physical-Diastolic_BP', 'Physical-Systolic_BP',\n 'Physical-HeartRate'\n]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:06.063945Z","iopub.execute_input":"2024-12-21T03:49:06.064383Z","iopub.status.idle":"2024-12-21T03:49:06.070257Z","shell.execute_reply.started":"2024-12-21T03:49:06.064337Z","shell.execute_reply":"2024-12-21T03:49:06.068946Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"(train[bp_hr_cols] < 50).sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:06.072181Z","iopub.execute_input":"2024-12-21T03:49:06.072592Z","iopub.status.idle":"2024-12-21T03:49:06.088662Z","shell.execute_reply.started":"2024-12-21T03:49:06.072540Z","shell.execute_reply":"2024-12-21T03:49:06.086947Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"We also know that systolic BP cannot be lower than diastolic BP:","metadata":{}},{"cell_type":"code","source":"train[train['Physical-Systolic_BP'] <= train['Physical-Diastolic_BP']][bp_hr_cols]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:06.090356Z","iopub.execute_input":"2024-12-21T03:49:06.091484Z","iopub.status.idle":"2024-12-21T03:49:06.112138Z","shell.execute_reply.started":"2024-12-21T03:49:06.091446Z","shell.execute_reply":"2024-12-21T03:49:06.110747Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train[cols] = train[cols].replace(0, np.nan)\ntrain.loc[train['Physical-Systolic_BP'] <= train['Physical-Diastolic_BP'], bp_hr_cols] = np.nan","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:06.114021Z","iopub.execute_input":"2024-12-21T03:49:06.114358Z","iopub.status.idle":"2024-12-21T03:49:06.130370Z","shell.execute_reply.started":"2024-12-21T03:49:06.114325Z","shell.execute_reply":"2024-12-21T03:49:06.129038Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Blood Pressure vs Heart Rate","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(12, 5))\n\n# Diastolic BP vs Heart Rate\nplt.subplot(1, 2, 1)\nsns.scatterplot(x='Physical-Diastolic_BP', y='Physical-HeartRate', data=train)\nplt.title('Diastolic BP vs Heart Rate')\nplt.xlabel('Diastolic Blood Pressure (mmHg)')\nplt.ylabel('Heart rate (beats/min)')\n\n# Systolic BP vs Heart Rate\nplt.subplot(1, 2, 2)\nsns.scatterplot(x='Physical-Systolic_BP', y='Physical-HeartRate', data=train)\nplt.title('Systolic BP vs Heart Rate')\nplt.xlabel('Systolic Blood Pressure (mmHg)')\nplt.ylabel('Heart rate (beats/min)')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:06.132571Z","iopub.execute_input":"2024-12-21T03:49:06.133364Z","iopub.status.idle":"2024-12-21T03:49:07.191273Z","shell.execute_reply.started":"2024-12-21T03:49:06.133309Z","shell.execute_reply":"2024-12-21T03:49:07.189928Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Blood pressure vs Body Mass Index (BMI)","metadata":{}},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n\n# BMI vs Systolic Blood Pressure\nsns.scatterplot(x='Physical-BMI', y='Physical-Systolic_BP', data=train, ax=axes[0], color='b')\naxes[0].set_title('BMI vs Systolic Blood Pressure')\naxes[0].set_xlabel('Body Mass Index (BMI) (kg/m^2)')\naxes[0].set_ylabel('Systolic Blood Pressure (mmHg)')\n\n# Systolic Blood Pressure vs Diastolic Blood Pressure\nsns.scatterplot(\n x='Physical-Systolic_BP', y='Physical-Diastolic_BP',\n data=train, ax=axes[1], color='g'\n)\naxes[1].set_title('Systolic Blood Pressure vs Diastolic Blood Pressure')\naxes[1].set_xlabel('Systolic Blood Pressure (mmHg)')\naxes[1].set_ylabel('Diastolic Blood Pressure (mmHg)')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:07.193176Z","iopub.execute_input":"2024-12-21T03:49:07.193645Z","iopub.status.idle":"2024-12-21T03:49:08.572027Z","shell.execute_reply.started":"2024-12-21T03:49:07.193578Z","shell.execute_reply":"2024-12-21T03:49:08.571024Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Compare to normal rages ","metadata":{}},{"cell_type":"code","source":"normal_ranges = {\n 'Physical-BMI': (18.5, 24.9),\n 'Physical-Height': (100, 193),\n 'Physical-Weight': (20, 120),\n 'Physical-Waist_Circumference': (50, 90),\n 'Physical-Diastolic_BP': (60, 80),\n 'Physical-HeartRate': (60, 100),\n 'Physical-Systolic_BP': (90, 120)\n}\n\ndef count_out_of_range(data, column, low, high):\n return ((data[column] < low) | (data[column] > high)).sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:08.587627Z","iopub.execute_input":"2024-12-21T03:49:08.588018Z","iopub.status.idle":"2024-12-21T03:49:08.594709Z","shell.execute_reply.started":"2024-12-21T03:49:08.587985Z","shell.execute_reply":"2024-12-21T03:49:08.593622Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"out_of_range_counts = {\n col: count_out_of_range(train, col, *normal_ranges[col])\n for col in normal_ranges\n}\nprint(\"Number of rows with values outside normal ranges:\")\n\nfor col, count in out_of_range_counts.items():\n total_valid = train[col].notna().sum()\n percentage = (count / total_valid) * 100\n print(f\"{col}: {count} ({percentage:.2f}%)\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:08.596083Z","iopub.execute_input":"2024-12-21T03:49:08.596516Z","iopub.status.idle":"2024-12-21T03:49:08.615389Z","shell.execute_reply.started":"2024-12-21T03:49:08.596467Z","shell.execute_reply":"2024-12-21T03:49:08.614216Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Group BMI by obesity level according to [WHO BMI-for-age (5-19 years)](https://www.who.int/tools/growth-reference-data-for-5to19-years/indicators/bmi-for-age)","metadata":{}},{"cell_type":"code","source":"bmi_categories = [\n ('Underweight', train['Physical-BMI'] < 18.5),\n ('Normal weight', (train['Physical-BMI'] >= 18.5) & (train['Physical-BMI'] <= 24.9)),\n ('Overweight', (train['Physical-BMI'] >= 25) & (train['Physical-BMI'] <= 29.9)),\n ('Obesity', train['Physical-BMI'] >= 30)\n]\nbmi_category_counts = {label: condition.sum() for label, condition in bmi_categories}\n\nplt.figure(figsize=(5, 6))\nplt.pie(bmi_category_counts.values(),\n labels=bmi_category_counts.keys(),\n autopct='%1.1f%%', startangle=90,\n colors=plt.cm.Set3.colors)\nplt.title('BMI Distribution by Category')\nplt.axis('equal')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:08.617450Z","iopub.execute_input":"2024-12-21T03:49:08.618453Z","iopub.status.idle":"2024-12-21T03:49:08.807313Z","shell.execute_reply.started":"2024-12-21T03:49:08.618403Z","shell.execute_reply":"2024-12-21T03:49:08.804567Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Check extreme deviations cases","metadata":{}},{"cell_type":"code","source":"train[train['Physical-BMI'] < 12][cols + ['Basic_Demos-Age']].sort_values(by = 'Physical-BMI')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:08.809537Z","iopub.execute_input":"2024-12-21T03:49:08.810289Z","iopub.status.idle":"2024-12-21T03:49:08.849254Z","shell.execute_reply.started":"2024-12-21T03:49:08.810229Z","shell.execute_reply":"2024-12-21T03:49:08.848048Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train[train['Physical-Systolic_BP'] > 160][cols + ['Basic_Demos-Age']].sort_values(by = 'Physical-Systolic_BP')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:08.850562Z","iopub.execute_input":"2024-12-21T03:49:08.851090Z","iopub.status.idle":"2024-12-21T03:49:08.877372Z","shell.execute_reply.started":"2024-12-21T03:49:08.851015Z","shell.execute_reply":"2024-12-21T03:49:08.875715Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Relationships with the target variable (PCIAT_Total for complete PCIAT responses)","metadata":{}},{"cell_type":"code","source":"data_subset = train[cols + ['complete_resp_total']]\n\ncorr_matrix = data_subset.corr()\n\nplt.figure(figsize=(10, 8))\nsns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f', vmin=-1, vmax=1)\nplt.title('Correlation Heatmap')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:08.879218Z","iopub.execute_input":"2024-12-21T03:49:08.879759Z","iopub.status.idle":"2024-12-21T03:49:09.518915Z","shell.execute_reply.started":"2024-12-21T03:49:08.879698Z","shell.execute_reply":"2024-12-21T03:49:09.517703Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# - Bio-electric Impedance Analysis","metadata":{}},{"cell_type":"code","source":"data_dict[data_dict['Instrument'] == 'Bio-electric Impedance Analysis']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:09.520404Z","iopub.execute_input":"2024-12-21T03:49:09.520784Z","iopub.status.idle":"2024-12-21T03:49:09.536339Z","shell.execute_reply.started":"2024-12-21T03:49:09.520748Z","shell.execute_reply":"2024-12-21T03:49:09.534936Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"bia_data_dict = data_dict[data_dict['Instrument'] == 'Bio-electric Impedance Analysis']\ncategorical_columns = bia_data_dict[bia_data_dict['Type'] == 'categorical int']['Field'].tolist()\ncontinuous_columns = bia_data_dict[bia_data_dict['Type'] == 'float']['Field'].tolist()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:09.538186Z","iopub.execute_input":"2024-12-21T03:49:09.538700Z","iopub.status.idle":"2024-12-21T03:49:09.554242Z","shell.execute_reply.started":"2024-12-21T03:49:09.538649Z","shell.execute_reply":"2024-12-21T03:49:09.552387Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n\n# Season\nseason_counts = train['BIA-Season'].value_counts(normalize=True)\naxes[0].pie(\n season_counts, \n labels=season_counts.index, \n autopct='%1.1f%%', \n startangle=90, \n colors=sns.color_palette(\"Set3\")\n)\naxes[0].set_title(\n f\"{bia_data_dict[bia_data_dict['Field'] == 'BIA-Season']['Description'].values[0]}\"\n)\naxes[0].axis('equal')\n\n# Other categorical columns\nfor idx, col in enumerate(categorical_columns):\n sns.countplot(x=col, data=train, palette=\"Set3\", ax=axes[idx+1])\n axes[idx+1].set_title(data_dict[data_dict['Field'] == col]['Description'].values[0])\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:09.556324Z","iopub.execute_input":"2024-12-21T03:49:09.556734Z","iopub.status.idle":"2024-12-21T03:49:10.219058Z","shell.execute_reply.started":"2024-12-21T03:49:09.556699Z","shell.execute_reply":"2024-12-21T03:49:10.217746Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(24, 20))\n\nfor idx, col in enumerate(continuous_columns):\n plt.subplot(4, 4, idx + 1)\n sns.histplot(train[col].dropna(), bins=20, kde=True)\n plt.title(data_dict[data_dict['Field'] == col]['Description'].values[0])\n plt.xlabel('Value')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:10.220990Z","iopub.execute_input":"2024-12-21T03:49:10.221480Z","iopub.status.idle":"2024-12-21T03:49:15.694913Z","shell.execute_reply.started":"2024-12-21T03:49:10.221422Z","shell.execute_reply":"2024-12-21T03:49:15.693449Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"calculate_stats(train, continuous_columns)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:15.696788Z","iopub.execute_input":"2024-12-21T03:49:15.697254Z","iopub.status.idle":"2024-12-21T03:49:15.759572Z","shell.execute_reply.started":"2024-12-21T03:49:15.697206Z","shell.execute_reply":"2024-12-21T03:49:15.758378Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Compare the two measured BMI","metadata":{}},{"cell_type":"code","source":"bmi_data = train[['BIA-BIA_BMI', 'Physical-BMI']].dropna()\n\nplt.figure(figsize=(8, 6))\nsns.scatterplot(\n x='BIA-BIA_BMI', y='Physical-BMI',\n data=bmi_data,\n color='b'\n)\nplt.title('Comparison of BIA-BMI vs Physical-BMI')\nplt.xlabel('BIA-BMI')\nplt.ylabel('Physical-BMI')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:15.761046Z","iopub.execute_input":"2024-12-21T03:49:15.761380Z","iopub.status.idle":"2024-12-21T03:49:16.220379Z","shell.execute_reply.started":"2024-12-21T03:49:15.761347Z","shell.execute_reply":"2024-12-21T03:49:16.219179Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"bmi_measures = train[['BIA-Season', 'Physical-Season']].dropna()\nbmi_measures.groupby(['BIA-Season', 'Physical-Season']).size().reset_index(name='Count')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:16.221747Z","iopub.execute_input":"2024-12-21T03:49:16.222096Z","iopub.status.idle":"2024-12-21T03:49:16.242533Z","shell.execute_reply.started":"2024-12-21T03:49:16.222064Z","shell.execute_reply":"2024-12-21T03:49:16.240960Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# - FitnessGram","metadata":{}},{"cell_type":"markdown","source":"## FitnessGram Vitals and Treadmill","metadata":{}},{"cell_type":"code","source":"groups.get('FitnessGram Vitals and Treadmill', [])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:16.244525Z","iopub.execute_input":"2024-12-21T03:49:16.245040Z","iopub.status.idle":"2024-12-21T03:49:16.253136Z","shell.execute_reply.started":"2024-12-21T03:49:16.244994Z","shell.execute_reply":"2024-12-21T03:49:16.251802Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"data = train[train['Fitness_Endurance-Max_Stage'].notnull()]\nage_range = data['Basic_Demos-Age']\nprint(\n f\"Age range for participants with Fitness_Endurance-Max_Stage data:\"\n f\" {age_range.min()} - {age_range.max()} years\"\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:16.255318Z","iopub.execute_input":"2024-12-21T03:49:16.256040Z","iopub.status.idle":"2024-12-21T03:49:16.270348Z","shell.execute_reply.started":"2024-12-21T03:49:16.255986Z","shell.execute_reply":"2024-12-21T03:49:16.269060Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 4, figsize=(24, 5))\n\n# Fitness Endurance Season\ntrain['Fitness_Endurance-Season'].value_counts(normalize=True).plot.pie(\n autopct='%1.1f%%', colors=plt.cm.Set3.colors, ax=axes[0]\n)\naxes[0].set_title('Fitness Endurance Season')\naxes[0].axis('equal') # Equal aspect ratio ensures the pie is drawn as a circle.\n\n# Box plot for Max Stage by Season\nsns.violinplot(\n x='Fitness_Endurance-Season',\n y='Fitness_Endurance-Max_Stage',\n data=train, palette=\"Set3\",\n ax=axes[1]\n)\naxes[1].set_title('Max Stage by Season')\naxes[1].set_xlabel('Season')\naxes[1].set_ylabel('Max Stage')\n\n# Fitness Endurance Time (Minutes)\nsns.histplot(train['Fitness_Endurance-Time_Mins'], bins=20, kde=True, ax=axes[2])\naxes[2].set_title('Fitness Endurance Time (Minutes)')\naxes[2].set_xlabel('Time (Minutes)')\n\n# Fitness Endurance Time (Seconds)\nsns.histplot(train['Fitness_Endurance-Time_Sec'], bins=20, kde=True, ax=axes[3])\naxes[3].set_title('Fitness Endurance Time (Seconds)')\naxes[3].set_xlabel('Time (Seconds)')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:16.272236Z","iopub.execute_input":"2024-12-21T03:49:16.272616Z","iopub.status.idle":"2024-12-21T03:49:17.746152Z","shell.execute_reply.started":"2024-12-21T03:49:16.272561Z","shell.execute_reply":"2024-12-21T03:49:17.744996Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Endurance by age:","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(12, 5))\n\nsns.violinplot(x='Basic_Demos-Age', y='Fitness_Endurance-Max_Stage', data=train, palette=\"Set3\")\nplt.title('Fitness Endurance Max Stage by Age')\nplt.xlabel('Age')\nplt.ylabel('Max Stage')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:17.748295Z","iopub.execute_input":"2024-12-21T03:49:17.748748Z","iopub.status.idle":"2024-12-21T03:49:18.438405Z","shell.execute_reply.started":"2024-12-21T03:49:17.748700Z","shell.execute_reply":"2024-12-21T03:49:18.436875Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"cols = [\n 'Fitness_Endurance-Max_Stage',\n 'Fitness_Endurance-Time_Mins',\n 'Fitness_Endurance-Time_Sec'\n]\ncalculate_stats(train, cols)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:18.440352Z","iopub.execute_input":"2024-12-21T03:49:18.440822Z","iopub.status.idle":"2024-12-21T03:49:18.467243Z","shell.execute_reply.started":"2024-12-21T03:49:18.440774Z","shell.execute_reply":"2024-12-21T03:49:18.465943Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Check the combinations of missing values","metadata":{}},{"cell_type":"markdown","source":"Max_Stage present, time (mins or secs) missing:","metadata":{}},{"cell_type":"code","source":"train[\n (train['Fitness_Endurance-Max_Stage'].notna()) & \n (train['Fitness_Endurance-Time_Mins'].isna() | \n train['Fitness_Endurance-Time_Sec'].isna())\n][cols]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:18.468820Z","iopub.execute_input":"2024-12-21T03:49:18.469276Z","iopub.status.idle":"2024-12-21T03:49:18.484421Z","shell.execute_reply.started":"2024-12-21T03:49:18.469228Z","shell.execute_reply":"2024-12-21T03:49:18.483192Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"It's possible that during data entry minutes or seconds were left blank (entered as NaN) when they should have been recorded as 0 minutes/seconds. While the missing seconds are not as important, the missing minutes may actually be missing and treating them as 0 would give an incorrect test result. I think it's better to just remove these suspicious cases.","metadata":{}},{"cell_type":"code","source":"train.loc[\n (train['Fitness_Endurance-Max_Stage'].notna()) & \n (train['Fitness_Endurance-Time_Mins'].isna() | \n train['Fitness_Endurance-Time_Sec'].isna()), cols\n] = np.nan","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:18.485996Z","iopub.execute_input":"2024-12-21T03:49:18.486519Z","iopub.status.idle":"2024-12-21T03:49:18.497923Z","shell.execute_reply.started":"2024-12-21T03:49:18.486470Z","shell.execute_reply":"2024-12-21T03:49:18.496716Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Get one time column (mins + sec)","metadata":{}},{"cell_type":"code","source":"train['Fitness_Endurance-Total_Time_Sec'] = train[\n 'Fitness_Endurance-Time_Mins'\n] * 60 + train['Fitness_Endurance-Time_Sec']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:18.499563Z","iopub.execute_input":"2024-12-21T03:49:18.500441Z","iopub.status.idle":"2024-12-21T03:49:18.513246Z","shell.execute_reply.started":"2024-12-21T03:49:18.500402Z","shell.execute_reply":"2024-12-21T03:49:18.512125Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Recalculate stats:","metadata":{}},{"cell_type":"code","source":"calculate_stats(train, ['Fitness_Endurance-Max_Stage', 'Fitness_Endurance-Total_Time_Sec'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:18.515016Z","iopub.execute_input":"2024-12-21T03:49:18.515369Z","iopub.status.idle":"2024-12-21T03:49:18.545112Z","shell.execute_reply.started":"2024-12-21T03:49:18.515335Z","shell.execute_reply":"2024-12-21T03:49:18.543857Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## FitnessGram Child","metadata":{}},{"cell_type":"code","source":"data_dict[data_dict['Instrument'] == 'FitnessGram Child']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:18.547064Z","iopub.execute_input":"2024-12-21T03:49:18.547520Z","iopub.status.idle":"2024-12-21T03:49:18.563389Z","shell.execute_reply.started":"2024-12-21T03:49:18.547472Z","shell.execute_reply":"2024-12-21T03:49:18.562101Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fgc_data_dict = data_dict[data_dict['Instrument'] == 'FitnessGram Child']\n\nfgc_columns = []\n\nfor index, row in fgc_data_dict.iterrows():\n if '_Zone' not in row['Field']:\n measure_field = row['Field']\n measure_desc = row['Description']\n \n zone_field = measure_field + '_Zone'\n zone_row = fgc_data_dict[fgc_data_dict['Field'] == zone_field]\n \n if not zone_row.empty:\n zone_desc = zone_row['Description'].values[0]\n fgc_columns.append((measure_field, zone_field, measure_desc, zone_desc))\n \nfig, axes = plt.subplots(2, 4, figsize=(24, 10))\n\nfor idx, (measure, zone, measure_desc, zone_desc) in enumerate(fgc_columns):\n row = idx // 4\n col = idx % 4\n \n sns.histplot(\n data=train, x=measure,\n hue=zone, bins=20, palette='Set2',\n ax=axes[row, col], kde=True\n )\n axes[row, col].set_title(f'{measure_desc}')\n\nseason_counts = train['FGC-Season'].value_counts(normalize=True)\naxes[1, 3].pie(\n season_counts, labels=season_counts.index,\n autopct='%1.1f%%', startangle=90,\n colors=sns.color_palette(\"Set3\")\n)\naxes[1, 3].set_title('Season of participation')\naxes[1, 3].axis('equal') \n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:18.565162Z","iopub.execute_input":"2024-12-21T03:49:18.566072Z","iopub.status.idle":"2024-12-21T03:49:22.586671Z","shell.execute_reply.started":"2024-12-21T03:49:18.566019Z","shell.execute_reply":"2024-12-21T03:49:22.585320Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"measurement_columns = [measure for measure, _, _, _ in fgc_columns]\ncalculate_stats(train, measurement_columns)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:22.588341Z","iopub.execute_input":"2024-12-21T03:49:22.588820Z","iopub.status.idle":"2024-12-21T03:49:22.629774Z","shell.execute_reply.started":"2024-12-21T03:49:22.588764Z","shell.execute_reply":"2024-12-21T03:49:22.628573Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Overlap between fitness zones","metadata":{}},{"cell_type":"code","source":"def compute_min_max_by_sex(train, sex, fgc_columns):\n results = []\n \n for measure, zone, _, _ in fgc_columns:\n sorted_zones = sorted(train[zone].dropna().unique())\n \n for zone_value in sorted_zones:\n data = train[(train[zone] == zone_value) & \n (train['Basic_Demos-Sex'] == sex)][measure]\n \n if not data.empty:\n min_val, max_val = data.min(), data.max()\n results.append({\n 'Zone': int(zone_value),\n 'Measure': measure,\n 'Min-Max': f'{min_val} - {max_val}'\n })\n \n df = pd.DataFrame(results).pivot_table(\n index='Zone', columns='Measure', values='Min-Max', aggfunc='first'\n )\n \n return df","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:22.631084Z","iopub.execute_input":"2024-12-21T03:49:22.631410Z","iopub.status.idle":"2024-12-21T03:49:22.640168Z","shell.execute_reply.started":"2024-12-21T03:49:22.631377Z","shell.execute_reply":"2024-12-21T03:49:22.638804Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Output ranges for each measure and zone for males:","metadata":{}},{"cell_type":"code","source":"compute_min_max_by_sex(train, 'Male', fgc_columns)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:22.642171Z","iopub.execute_input":"2024-12-21T03:49:22.642654Z","iopub.status.idle":"2024-12-21T03:49:22.711958Z","shell.execute_reply.started":"2024-12-21T03:49:22.642579Z","shell.execute_reply":"2024-12-21T03:49:22.710787Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Same for females;","metadata":{}},{"cell_type":"code","source":"compute_min_max_by_sex(train, 'Female', fgc_columns)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:22.713058Z","iopub.execute_input":"2024-12-21T03:49:22.713375Z","iopub.status.idle":"2024-12-21T03:49:22.768627Z","shell.execute_reply.started":"2024-12-21T03:49:22.713344Z","shell.execute_reply":"2024-12-21T03:49:22.767484Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"The ranges for each measure and zone by age (only for males, just to check if the overlap still exists):","metadata":{}},{"cell_type":"code","source":"results_male = []\n\nfor measure, zone, _, _ in fgc_columns:\n sorted_zones = sorted(train[zone].dropna().unique())\n for zone_value in sorted_zones:\n age_sex_data_by_zone = train[train[zone] == zone_value][\n ['Basic_Demos-Age', 'Basic_Demos-Sex', measure]\n ]\n unique_ages = age_sex_data_by_zone['Basic_Demos-Age'].dropna().unique()\n\n for age in sorted(unique_ages):\n age_sex_data = age_sex_data_by_zone[\n (age_sex_data_by_zone['Basic_Demos-Age'] == age) &\n (age_sex_data_by_zone['Basic_Demos-Sex'] == 'Male')\n ][measure]\n \n if not age_sex_data.empty:\n min_val, max_val = age_sex_data.min(), age_sex_data.max()\n results_male.append({\n 'Age': age,\n 'Sex': 'Male',\n 'Zone': zone_value,\n 'Measure': measure,\n 'Min-Max': f'{min_val} - {max_val}'\n })\n\ndf_male = pd.DataFrame(results_male).pivot_table(\n index=['Age', 'Sex', 'Zone'], columns='Measure', values='Min-Max', aggfunc='first'\n)\n\ndf_male","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:22.769981Z","iopub.execute_input":"2024-12-21T03:49:22.770302Z","iopub.status.idle":"2024-12-21T03:49:23.013102Z","shell.execute_reply.started":"2024-12-21T03:49:22.770272Z","shell.execute_reply":"2024-12-21T03:49:23.011856Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Age Ranges for each measurement column","metadata":{}},{"cell_type":"code","source":"age_ranges = []\n\nfor measure in measurement_columns:\n valid_rows = train[~train[measure].isna()]\n \n min_age = valid_rows['Basic_Demos-Age'].min()\n max_age = valid_rows['Basic_Demos-Age'].max()\n \n age_ranges.append({\n 'Measurement': measure,\n 'Min Age': min_age,\n 'Max Age': max_age\n })\n\nage_ranges_df = pd.DataFrame(age_ranges)\nage_ranges_df","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:23.014621Z","iopub.execute_input":"2024-12-21T03:49:23.014974Z","iopub.status.idle":"2024-12-21T03:49:23.041404Z","shell.execute_reply.started":"2024-12-21T03:49:23.014940Z","shell.execute_reply":"2024-12-21T03:49:23.040150Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Relationships with the target variable (PCIAT_Total for complete PCIAT responses)","metadata":{}},{"cell_type":"code","source":"cols = [col for col in train.columns if col.startswith('FGC-') \n and 'Zone' not in col and 'Season' not in col]\ncols.extend(['Fitness_Endurance-Max_Stage', 'Fitness_Endurance-Total_Time_Sec'])\n\ndata_subset = train[cols + ['complete_resp_total']]\n\ncorr_matrix = data_subset.corr()\n\nplt.figure(figsize=(10, 8))\nsns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f', vmin=-1, vmax=1)\nplt.title('Correlation Heatmap')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:23.043772Z","iopub.execute_input":"2024-12-21T03:49:23.044147Z","iopub.status.idle":"2024-12-21T03:49:23.760247Z","shell.execute_reply.started":"2024-12-21T03:49:23.044107Z","shell.execute_reply":"2024-12-21T03:49:23.758841Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"age_groups = train['Age Group'].unique()\n\nfig, axes = plt.subplots(1, 3, figsize=(18, 6), sharey=True)\n\nfor i, age_group in enumerate(age_groups):\n group_data = train[train['Age Group'] == age_group]\n corr_matrix = group_data[cols + ['complete_resp_total', 'Basic_Demos-Age']].corr()\n sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.1f',\n vmin=-1, vmax=1, ax=axes[i], cbar=i == 0)\n axes[i].set_title(f'{age_group}')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:23.761907Z","iopub.execute_input":"2024-12-21T03:49:23.762346Z","iopub.status.idle":"2024-12-21T03:49:25.890159Z","shell.execute_reply.started":"2024-12-21T03:49:23.762297Z","shell.execute_reply":"2024-12-21T03:49:25.888941Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train[\n (train['Age Group'] == 'Adults (19-22)') &\n (train['complete_resp_total'].notna()) &\n (train[cols].notna().any(axis=1))\n][cols + ['complete_resp_total', 'Basic_Demos-Age']]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:25.892153Z","iopub.execute_input":"2024-12-21T03:49:25.892622Z","iopub.status.idle":"2024-12-21T03:49:25.917753Z","shell.execute_reply.started":"2024-12-21T03:49:25.892547Z","shell.execute_reply":"2024-12-21T03:49:25.916673Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# - Sleep Disturbance Scale","metadata":{}},{"cell_type":"code","source":"groups.get('Sleep Disturbance Scale', [])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:25.918884Z","iopub.execute_input":"2024-12-21T03:49:25.919208Z","iopub.status.idle":"2024-12-21T03:49:25.926418Z","shell.execute_reply.started":"2024-12-21T03:49:25.919176Z","shell.execute_reply":"2024-12-21T03:49:25.925116Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"data = train[train['SDS-SDS_Total_Raw'].notnull()]\nage_range = data['Basic_Demos-Age']\nprint(\n f\"Age range for participants with SDS-SDS_Total_Raw data:\"\n f\" {age_range.min()} - {age_range.max()} years\"\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:25.927881Z","iopub.execute_input":"2024-12-21T03:49:25.928237Z","iopub.status.idle":"2024-12-21T03:49:25.942261Z","shell.execute_reply.started":"2024-12-21T03:49:25.928204Z","shell.execute_reply":"2024-12-21T03:49:25.940978Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(18, 5))\n\n# SDS-Season (Pie Chart)\nplt.subplot(1, 3, 1)\nsds_season_counts = train['SDS-Season'].value_counts(normalize=True)\nplt.pie(\n sds_season_counts, \n labels=sds_season_counts.index, \n autopct='%1.1f%%', \n startangle=90, \n colors=sns.color_palette(\"Set3\")\n)\nplt.title('SDS-Season')\n\n# SDS-SDS_Total_Raw\nplt.subplot(1, 3, 2)\nsns.histplot(train['SDS-SDS_Total_Raw'].dropna(), bins=20, kde=True)\nplt.title('SDS-SDS_Total_Raw')\nplt.xlabel('Value')\n\n# SDS-SDS_Total_T\nplt.subplot(1, 3, 3)\nsns.histplot(train['SDS-SDS_Total_T'].dropna(), bins=20, kde=True)\nplt.title('SDS-SDS_Total_T')\nplt.xlabel('Value')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:25.943763Z","iopub.execute_input":"2024-12-21T03:49:25.944198Z","iopub.status.idle":"2024-12-21T03:49:27.062297Z","shell.execute_reply.started":"2024-12-21T03:49:25.944151Z","shell.execute_reply":"2024-12-21T03:49:27.061142Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"calculate_stats(train, ['SDS-SDS_Total_Raw', 'SDS-SDS_Total_T'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:27.063964Z","iopub.execute_input":"2024-12-21T03:49:27.064395Z","iopub.status.idle":"2024-12-21T03:49:27.088673Z","shell.execute_reply.started":"2024-12-21T03:49:27.064349Z","shell.execute_reply":"2024-12-21T03:49:27.087460Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"
\n 💡 Note: \n \n
","metadata":{}},{"cell_type":"markdown","source":"#

Behavioral (subjective reported)

","metadata":{}},{"cell_type":"markdown","source":"# - Physical Activity Questionnaire","metadata":{}},{"cell_type":"markdown","source":"### Adolescents","metadata":{}},{"cell_type":"code","source":"groups.get('Physical Activity Questionnaire (Adolescents)', [])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:27.090321Z","iopub.execute_input":"2024-12-21T03:49:27.090808Z","iopub.status.idle":"2024-12-21T03:49:27.098447Z","shell.execute_reply.started":"2024-12-21T03:49:27.090752Z","shell.execute_reply":"2024-12-21T03:49:27.097036Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"data = train[train['PAQ_A-PAQ_A_Total'].notnull()]\nage_range = data['Basic_Demos-Age']\nprint(\n f\"Age range for Adolescents (with PAQ_A_Total data):\"\n f\" {age_range.min()} - {age_range.max()} years\"\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:27.100273Z","iopub.execute_input":"2024-12-21T03:49:27.100704Z","iopub.status.idle":"2024-12-21T03:49:27.115226Z","shell.execute_reply.started":"2024-12-21T03:49:27.100667Z","shell.execute_reply":"2024-12-21T03:49:27.113971Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(18, 5))\n\n# PAQ_A-Season\nplt.subplot(1, 3, 1)\ntrain['PAQ_A-Season'].value_counts(normalize=True).plot.pie(\n autopct='%1.1f%%', colors=plt.cm.Set3.colors\n)\nplt.title('PAQ_A-Season (Adolescents)')\n\n# PAQ_A-PAQ_A_Total\nplt.subplot(1, 3, 2)\nsns.histplot(train['PAQ_A-PAQ_A_Total'], bins=20, kde=True)\nplt.title('PAQ_A-PAQ_A_Total (Adolescents)')\n\n# PAQ_A_Total by Season\nplt.subplot(1, 3, 3)\nsns.violinplot(x='PAQ_A-Season', y='PAQ_A-PAQ_A_Total', data=train, palette=\"Set3\")\nplt.title('PAQ_A_Total by Season (Adolescents)')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:27.117236Z","iopub.execute_input":"2024-12-21T03:49:27.117623Z","iopub.status.idle":"2024-12-21T03:49:28.108950Z","shell.execute_reply.started":"2024-12-21T03:49:27.117516Z","shell.execute_reply":"2024-12-21T03:49:28.107767Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"calculate_stats(train, ['PAQ_A-PAQ_A_Total'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:28.110484Z","iopub.execute_input":"2024-12-21T03:49:28.110824Z","iopub.status.idle":"2024-12-21T03:49:28.131778Z","shell.execute_reply.started":"2024-12-21T03:49:28.110793Z","shell.execute_reply":"2024-12-21T03:49:28.130117Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Children","metadata":{}},{"cell_type":"code","source":"groups.get('Physical Activity Questionnaire (Children)', [])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:28.133366Z","iopub.execute_input":"2024-12-21T03:49:28.133766Z","iopub.status.idle":"2024-12-21T03:49:28.140520Z","shell.execute_reply.started":"2024-12-21T03:49:28.133731Z","shell.execute_reply":"2024-12-21T03:49:28.139440Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"data = train[train['PAQ_C-PAQ_C_Total'].notnull()]\nage_range = data['Basic_Demos-Age']\nprint(\n f\"Age range for Children (with PAQ_C_Total data):\"\n f\" {age_range.min()} - {age_range.max()} years\"\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:28.142694Z","iopub.execute_input":"2024-12-21T03:49:28.143161Z","iopub.status.idle":"2024-12-21T03:49:28.161082Z","shell.execute_reply.started":"2024-12-21T03:49:28.143113Z","shell.execute_reply":"2024-12-21T03:49:28.159854Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(18, 5))\n\n# PAQ_C-Season\nplt.subplot(1, 3, 1)\ntrain['PAQ_C-Season'].value_counts(normalize=True).plot.pie(\n autopct='%1.1f%%', colors=plt.cm.Set3.colors\n)\nplt.title('PAQ_C-Season (Children)')\n\n# PAQ_C-PAQ_C_Total\nplt.subplot(1, 3, 2)\nsns.histplot(train['PAQ_C-PAQ_C_Total'], bins=20, kde=True)\nplt.title('PAQ_C-PAQ_C_Total (Children)')\n\n# PAQ_C_Total by Season\nplt.subplot(1, 3, 3)\nsns.violinplot(x='PAQ_C-Season', y='PAQ_C-PAQ_C_Total', data=train, palette=\"Set3\")\nplt.title('PAQ_C_Total by Season (Children)')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:28.162744Z","iopub.execute_input":"2024-12-21T03:49:28.163089Z","iopub.status.idle":"2024-12-21T03:49:29.168420Z","shell.execute_reply.started":"2024-12-21T03:49:28.163057Z","shell.execute_reply":"2024-12-21T03:49:29.167299Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"calculate_stats(train, ['PAQ_C-PAQ_C_Total'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:29.170403Z","iopub.execute_input":"2024-12-21T03:49:29.170872Z","iopub.status.idle":"2024-12-21T03:49:29.191341Z","shell.execute_reply.started":"2024-12-21T03:49:29.170823Z","shell.execute_reply":"2024-12-21T03:49:29.190220Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"paq_columns = [col for col in train.columns if 'PAQ' in col]\ntrain[(train['PAQ_A-PAQ_A_Total'].notnull()) &\n (train['PAQ_C-PAQ_C_Total'].notnull())][\n paq_columns + ['Basic_Demos-Age']\n]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-21T03:49:29.192696Z","iopub.execute_input":"2024-12-21T03:49:29.193018Z","iopub.status.idle":"2024-12-21T03:49:29.210350Z","shell.execute_reply.started":"2024-12-21T03:49:29.192987Z","shell.execute_reply":"2024-12-21T03:49:29.209359Z"}},"outputs":[],"execution_count":null}]}