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{
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{
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"text": [
"Collecting datasets\n",
" Using cached datasets-3.6.0-py3-none-any.whl.metadata (19 kB)\n",
"Collecting huggingface_hub\n",
" Using cached huggingface_hub-0.33.0-py3-none-any.whl.metadata (14 kB)\n",
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" Downloading fsspec-2025.5.1-py3-none-any.whl.metadata (11 kB)\n",
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" Using cached filelock-3.18.0-py3-none-any.whl.metadata (2.9 kB)\n",
"Collecting numpy>=1.17 (from datasets)\n",
" Using cached numpy-2.3.0-cp313-cp313-manylinux_2_28_x86_64.whl.metadata (62 kB)\n",
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"Collecting requests>=2.32.2 (from datasets)\n",
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"Requirement already satisfied: packaging in ./.venv/lib/python3.13/site-packages (from datasets) (25.0)\n",
"Collecting pyyaml>=5.1 (from datasets)\n",
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"Collecting aiohttp!=4.0.0a0,!=4.0.0a1 (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets)\n",
" Using cached aiohttp-3.12.12-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (7.6 kB)\n",
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"Collecting aiohappyeyeballs>=2.5.0 (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets)\n",
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"Using cached xxhash-3.5.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
"Installing collected packages: pytz, xxhash, urllib3, tzdata, typing-extensions, tqdm, pyyaml, pyarrow, propcache, numpy, multidict, idna, hf-xet, fsspec, frozenlist, filelock, dill, charset_normalizer, certifi, attrs, aiohappyeyeballs, yarl, requests, pandas, multiprocess, aiosignal, huggingface_hub, aiohttp, datasets\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29/29\u001b[0m [datasets]/29\u001b[0m [datasets]ce_hub]]\n",
"\u001b[1A\u001b[2KSuccessfully installed aiohappyeyeballs-2.6.1 aiohttp-3.12.12 aiosignal-1.3.2 attrs-25.3.0 certifi-2025.4.26 charset_normalizer-3.4.2 datasets-3.6.0 dill-0.3.8 filelock-3.18.0 frozenlist-1.7.0 fsspec-2025.3.0 hf-xet-1.1.3 huggingface_hub-0.33.0 idna-3.10 multidict-6.4.4 multiprocess-0.70.16 numpy-2.3.0 pandas-2.3.0 propcache-0.3.2 pyarrow-20.0.0 pytz-2025.2 pyyaml-6.0.2 requests-2.32.4 tqdm-4.67.1 typing-extensions-4.14.0 tzdata-2025.2 urllib3-2.4.0 xxhash-3.5.0 yarl-1.20.1\n"
]
}
],
"source": [
"!pip install -U datasets huggingface_hub fsspec"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "edF1DuNE6nSg"
},
"source": [
"### 📥 Step 1: Preprocess the Data\n",
"\n",
"Since the dataset is hosted on Hugging Face, you can load it directly using the `load_dataset` function."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "HmHDx6cU7WRG"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/escher/Projects/OpenPsychometrics/2018-11-08-IPIP-FFM-data/2018-11-08-OpenPsychometrics-IPIP-FFM/.venv/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n",
"Generating train split: 100%|██████████| 609204/609204 [00:00<00:00, 781185.05 examples/s]\n",
"Generating validation split: 100%|██████████| 203068/203068 [00:00<00:00, 842451.01 examples/s]\n",
"Generating test split: 100%|██████████| 203069/203069 [00:00<00:00, 769756.37 examples/s]\n"
]
},
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" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>...</td>\n",
" <td>360.0</td>\n",
" <td>640.0</td>\n",
" <td>15.0</td>\n",
" <td>180.0</td>\n",
" <td>17</td>\n",
" <td>1</td>\n",
" <td>CR</td>\n",
" <td>9.8533</td>\n",
" <td>-83.9023</td>\n",
" <td>365838</td>\n",
" </tr>\n",
" <tr>\n",
" <th>609201</th>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>...</td>\n",
" <td>400.0</td>\n",
" <td>640.0</td>\n",
" <td>24.0</td>\n",
" <td>519.0</td>\n",
" <td>392</td>\n",
" <td>6</td>\n",
" <td>US</td>\n",
" <td>38.0</td>\n",
" <td>-97.0</td>\n",
" <td>131932</td>\n",
" </tr>\n",
" <tr>\n",
" <th>609202</th>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>1368.0</td>\n",
" <td>912.0</td>\n",
" <td>217.0</td>\n",
" <td>209.0</td>\n",
" <td>17</td>\n",
" <td>2</td>\n",
" <td>US</td>\n",
" <td>38.0</td>\n",
" <td>-97.0</td>\n",
" <td>671155</td>\n",
" </tr>\n",
" <tr>\n",
" <th>609203</th>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
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" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>1366.0</td>\n",
" <td>768.0</td>\n",
" <td>11.0</td>\n",
" <td>203.0</td>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>CA</td>\n",
" <td>45.9784</td>\n",
" <td>-66.6905</td>\n",
" <td>121958</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>609204 rows × 111 columns</p>\n",
"</div>"
],
"text/plain": [
" EXT1 EXT2 EXT3 EXT4 EXT5 EXT6 EXT7 EXT8 EXT9 EXT10 ... \\\n",
"0 2.0 2.0 5.0 4.0 4.0 1.0 2.0 4.0 4.0 2.0 ... \n",
"1 2.0 5.0 5.0 5.0 2.0 5.0 2.0 5.0 3.0 5.0 ... \n",
"2 3.0 3.0 5.0 1.0 5.0 1.0 5.0 5.0 3.0 4.0 ... \n",
"3 1.0 5.0 3.0 5.0 2.0 4.0 3.0 5.0 1.0 5.0 ... \n",
"4 4.0 3.0 3.0 3.0 3.0 2.0 4.0 3.0 2.0 3.0 ... \n",
"... ... ... ... ... ... ... ... ... ... ... ... \n",
"609199 1.0 5.0 1.0 5.0 1.0 5.0 1.0 5.0 1.0 5.0 ... \n",
"609200 4.0 1.0 4.0 3.0 4.0 0.0 5.0 2.0 4.0 2.0 ... \n",
"609201 4.0 2.0 3.0 2.0 5.0 1.0 4.0 2.0 5.0 2.0 ... \n",
"609202 1.0 4.0 3.0 3.0 2.0 4.0 2.0 4.0 2.0 5.0 ... \n",
"609203 4.0 2.0 2.0 3.0 2.0 1.0 2.0 5.0 3.0 5.0 ... \n",
"\n",
" screenw screenh introelapse testelapse endelapse IPC country \\\n",
"0 1324.0 745.0 7.0 2872.0 17 1 US \n",
"1 1366.0 768.0 70.0 223.0 18 47 US \n",
"2 1920.0 1080.0 30.0 207.0 10 1 US \n",
"3 1440.0 960.0 4.0 166.0 10 1 AU \n",
"4 1920.0 1200.0 12.0 194.0 4 2 US \n",
"... ... ... ... ... ... ... ... \n",
"609199 360.0 640.0 6.0 364.0 12 1 US \n",
"609200 360.0 640.0 15.0 180.0 17 1 CR \n",
"609201 400.0 640.0 24.0 519.0 392 6 US \n",
"609202 1368.0 912.0 217.0 209.0 17 2 US \n",
"609203 1366.0 768.0 11.0 203.0 12 1 CA \n",
"\n",
" lat_appx_lots_of_err long_appx_lots_of_err __index_level_0__ \n",
"0 38.0 -97.0 422981 \n",
"1 38.0 -97.0 325363 \n",
"2 42.8886 -88.0384 105821 \n",
"3 -37.7919 145.084 169213 \n",
"4 37.5402 -122.3041 839500 \n",
"... ... ... ... \n",
"609199 38.0 -97.0 259178 \n",
"609200 9.8533 -83.9023 365838 \n",
"609201 38.0 -97.0 131932 \n",
"609202 38.0 -97.0 671155 \n",
"609203 45.9784 -66.6905 121958 \n",
"\n",
"[609204 rows x 111 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"from datasets import load_dataset\n",
"\n",
"# Load the dataset\n",
"dataset = load_dataset(\"Tetratics/2018-11-08-OpenPsychometrics-IPIP-FFM\")\n",
"\n",
"# Convert to a pandas DataFrame for easier manipulation\n",
"df = pd.DataFrame(dataset[\"train\"])\n",
"df"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|