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Update docs and examples
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@koenvo
koenvo committed Mar 5, 2023
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94 changes: 47 additions & 47 deletions docs/examples/broadcast_tracking_data.ipynb
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},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 1,
"metadata": {
"scrolled": true
},
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" raw_data=tracking_file,\n",
" limit=100)\n",
"\n",
"df = dataset.to_pandas()"
"df = dataset.to_df()"
]
},
{
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},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 2,
"metadata": {},
"outputs": [
{
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" 'Serge Gnabry (22)']"
]
},
"execution_count": 5,
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
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},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 3,
"metadata": {},
"outputs": [
{
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},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 4,
"metadata": {},
"outputs": [
{
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},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 5,
"metadata": {},
"outputs": [
{
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" <th></th>\n",
" <th>period_id</th>\n",
" <th>timestamp</th>\n",
" <th>frame_id</th>\n",
" <th>ball_state</th>\n",
" <th>ball_owning_team_id</th>\n",
" <th>ball_x</th>\n",
" <th>ball_y</th>\n",
" <th>ball_z</th>\n",
" <th>away_23_x</th>\n",
" <th>away_23_y</th>\n",
" <th>away_23_d</th>\n",
" <th>away_23_s</th>\n",
" <th>...</th>\n",
" <th>away_14_d</th>\n",
" <th>away_14_s</th>\n",
Expand All @@ -225,14 +225,14 @@
" <th>0</th>\n",
" <td>1</td>\n",
" <td>11.2</td>\n",
" <td>1523</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.747489</td>\n",
" <td>0.098509</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
Expand All @@ -249,14 +249,14 @@
" <th>1</th>\n",
" <td>1</td>\n",
" <td>11.3</td>\n",
" <td>1524</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>0.791347</td>\n",
" <td>-0.020033</td>\n",
" <td>2.243712</td>\n",
" <td>0.745323</td>\n",
" <td>0.099367</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <th>2</th>\n",
" <td>1</td>\n",
" <td>11.4</td>\n",
" <td>1525</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>0.772630</td>\n",
" <td>-0.009469</td>\n",
" <td>2.534799</td>\n",
" <td>0.742956</td>\n",
" <td>0.099743</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <th>3</th>\n",
" <td>1</td>\n",
" <td>11.5</td>\n",
" <td>1526</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>0.754625</td>\n",
" <td>0.001612</td>\n",
" <td>2.659813</td>\n",
" <td>0.740386</td>\n",
" <td>0.099638</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <th>4</th>\n",
" <td>1</td>\n",
" <td>11.6</td>\n",
" <td>1527</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>0.737330</td>\n",
" <td>0.013210</td>\n",
" <td>2.618755</td>\n",
" <td>0.737875</td>\n",
" <td>0.096646</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
Expand All @@ -343,42 +343,42 @@
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 90 columns</p>\n",
"<p>5 rows × 92 columns</p>\n",
"</div>"
],
"text/plain": [
" period_id timestamp ball_state ball_owning_team_id ball_x ball_y \\\n",
"0 1 11.2 None NaN NaN NaN \n",
"1 1 11.3 None NaN 0.791347 -0.020033 \n",
"2 1 11.4 None NaN 0.772630 -0.009469 \n",
"3 1 11.5 None NaN 0.754625 0.001612 \n",
"4 1 11.6 None NaN 0.737330 0.013210 \n",
" period_id timestamp frame_id ball_state ball_owning_team_id ball_x \\\n",
"0 1 11.2 1523 None NaN NaN \n",
"1 1 11.3 1524 None NaN 0.791347 \n",
"2 1 11.4 1525 None NaN 0.772630 \n",
"3 1 11.5 1526 None NaN 0.754625 \n",
"4 1 11.6 1527 None NaN 0.737330 \n",
"\n",
" away_23_x away_23_y away_23_d away_23_s ... away_14_d away_14_s \\\n",
"0 0.747489 0.098509 NaN NaN ... NaN NaN \n",
"1 0.745323 0.099367 NaN NaN ... NaN NaN \n",
"2 0.742956 0.099743 NaN NaN ... NaN NaN \n",
"3 0.740386 0.099638 NaN NaN ... NaN NaN \n",
"4 0.737875 0.096646 NaN NaN ... NaN NaN \n",
" ball_y ball_z away_23_x away_23_y ... away_14_d away_14_s \\\n",
"0 NaN NaN 0.747489 0.098509 ... NaN NaN \n",
"1 -0.020033 2.243712 0.745323 0.099367 ... NaN NaN \n",
"2 -0.009469 2.534799 0.742956 0.099743 ... NaN NaN \n",
"3 0.001612 2.659813 0.740386 0.099638 ... NaN NaN \n",
"4 0.013210 2.618755 0.737875 0.096646 ... NaN NaN \n",
"\n",
" home_9_x home_9_y home_9_d home_9_s home_anon_75_x home_anon_75_y \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 NaN NaN NaN NaN NaN NaN \n",
" home_9_x home_9_y home_9_d home_9_s home_anon_75_x home_anon_75_y \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 NaN NaN NaN NaN NaN NaN \n",
"\n",
" home_anon_75_d home_anon_75_s \n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
" home_anon_75_d home_anon_75_s \n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"\n",
"[5 rows x 90 columns]"
"[5 rows x 92 columns]"
]
},
"execution_count": 10,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.9"
"version": "3.10.6"
},
"toc": {
"base_numbering": 1,
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36 changes: 25 additions & 11 deletions docs/examples/code_data.ipynb
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}
],
"source": [
"code_dataset.to_pandas()"
"code_dataset.to_df()"
]
},
{
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],
"source": [
"passes = code_dataset.filter(lambda code: code.code == 'PASS')\n",
"passes.to_pandas()"
"passes.to_df()"
]
},
{
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},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 6,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/koen/Developer/Projects/PySport/kloppy/.venv/lib/python3.10/site-packages/kloppy-3.7.1-py3.10.egg/kloppy/_providers/statsbomb.py:67: UserWarning: \n",
"\n",
"You are about to use StatsBomb public data.\n",
"By using this data, you are agreeing to the user agreement. \n",
"The user agreement can be found here: https://github.com/statsbomb/open-data/blob/master/LICENSE.pdf\n",
"\n",
" warnings.warn(\n"
]
}
],
"source": [
"from kloppy import statsbomb\n",
"\n",
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},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 8,
"metadata": {},
"outputs": [
{
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"27 Lionel Andrés Messi Cuccittini Barcelona "
]
},
"execution_count": 14,
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"code_dataset.to_pandas()"
"code_dataset.to_df()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 9,
"metadata": {},
"outputs": [
{
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},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.9"
"version": "3.10.6"
}
},
"nbformat": 4,
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