diff --git a/docs/example-notebooks/Fast-f1.ipynb b/docs/example-notebooks/Fast-f1.ipynb deleted file mode 100644 index 597677e9..00000000 --- a/docs/example-notebooks/Fast-f1.ipynb +++ /dev/null @@ -1,563 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "88f555fa-aaa3-4053-bef6-125a4b1cb693", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "#import buckaroo" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "55ff0714-2059-4f6d-b085-de79a66a3362", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import fastf1" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d49ec2e5-a36a-4b06-a834-5515c9893fcc", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "req WARNING \tDEFAULT CACHE ENABLED! (181.74 MB) /Users/paddy/Library/Caches/fastf1\n" - ] - } - ], - "source": [ - "ev2020 = fastf1.get_event_schedule(2020)\n", - "small_df = ev2020[ev2020.columns[4:5]][:1]\n", - "#small_df\n", - "#ev2020" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8106b129-65bc-46e0-8686-deccab279c31", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['RoundNumber', 'Country', 'Location', 'OfficialEventName', 'EventDate',\n", - " 'EventName', 'EventFormat', 'Session1', 'Session1Date',\n", - " 'Session1DateUtc', 'Session2', 'Session2Date', 'Session2DateUtc',\n", - " 'Session3', 'Session3Date', 'Session3DateUtc', 'Session4',\n", - " 'Session4Date', 'Session4DateUtc', 'Session5', 'Session5Date',\n", - " 'Session5DateUtc', 'F1ApiSupport'],\n", - " dtype='object')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ev2020.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e70ad953-98f1-4b50-b6b0-b6bd0cfd9f88", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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02020-02-21
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" - ], - "text/plain": [ - " EventDate\n", - "0 2020-02-21" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "small_df" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "46aa6057-95e7-4c34-8b0b-235ae2ad2caf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.api.types.is_datetime64_dtype(small_df['EventDate'])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "3a2ab67e-3f40-4db4-9e95-96da9fdf22a5", - "metadata": {}, - "outputs": [], - "source": [ - "vc_ser = small_df['EventDate'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "7780d4d3-17c8-42f5-8ec3-d3a522ca15c3", - "metadata": {}, - "outputs": [], - "source": [ - "difficult_df = pd.DataFrame({'a': {'vc': vc_ser}})" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "91b9ff12-3ca4-4753-a56b-a34027866b77", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'{\\n \"schema\":{\\n \"fields\":[\\n {\\n \"name\":\"index\",\\n \"type\":\"string\"\\n },\\n {\\n \"name\":\"a\",\\n \"type\":\"string\"\\n }\\n ],\\n \"primaryKey\":[\\n \"index\"\\n ],\\n \"pandas_version\":\"1.4.0\"\\n },\\n \"data\":[\\n {\\n \"index\":\"vc\",\\n \"a\":[\\n 1\\n ]\\n }\\n ]\\n}'" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "difficult_df.to_json(orient='table', indent=2, default_handler=str)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "94ec6a61-e57b-4a20-8ae0-df6a63c68aba", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'2.0.2'" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "1748afd4-59ea-4d67-8629-f98c7bc563ad", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'a': 3, 'b': 9, 'c': 10, 'd': 11, 'e': 89}" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ab = dict(a=3, b=9, c=10, d=11, e=89)\n", - "ab" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "7b01e62b-b377-4778-b8d2-a3d1c57b0130", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(ab.keys())" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "59f5fa4f-48c6-4686-ba9d-85e015c12e12", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "5//2\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "1a7845a0-b196-4c7d-a0a8-50bc47a43338", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "f690e96c-7e67-4a2c-bcb1-cbdcfcc14192", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "numpy.datetime64" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.datetime64" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7214755b-f519-4dd1-822e-3031dff81d4d", - "metadata": {}, - "outputs": [], - "source": [ - "ev2020[['RoundNumber', 'EventName', 'EventFormat']]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e6920c43-8779-4f1a-bafa-c33960408eab", - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame({'a':[10,30,40]})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c84f170b-68a4-4efb-aeb2-73acf1504239", - "metadata": {}, - "outputs": [], - "source": [ - "pd.Series([]).value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "176141b7-db08-4462-900f-4455679d2afb", - "metadata": {}, - "outputs": [], - "source": [ - "small_df.dtypes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3524ea11-45f2-4a7c-beab-ae3fb1984ec5", - "metadata": {}, - "outputs": [], - "source": [ - "small_df['EventDate']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a1215255-7bab-4efa-a850-cbd77fa9e479", - "metadata": {}, - "outputs": [], - "source": [ - "pd.api.types.is_object_dtype(small_df['EventDate'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d199b69a-add4-4b55-8cfd-f805bbd71fd4", - "metadata": {}, - "outputs": [], - "source": [ - "pd.api.types.is_datetime64_dtype(small_df['EventDate'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7838698-d7fe-4695-890e-2204ad5e7db0", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "session = fastf1.get_session(2019, 'Monza', 'Q')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8fbc2433-c097-4422-b109-3f201ec891a0", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "session.load(telemetry=False, laps=False, weather=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e0aa12ba-d340-444b-b4e4-faef4e1798a3", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "vettel = session.get_driver('VET')\n", - "print(f\"Pronto {vettel['FirstName']}?\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a17b9eb-9198-475b-915a-375ed5f823ac", - "metadata": {}, - "outputs": [], - "source": [ - "vettel" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b4108f22-180d-4b4a-9e00-d7b470e1872f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from matplotlib import pyplot as plt\n", - "import fastf1\n", - "import fastf1.plotting" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e1d22212-1810-4afa-a5ed-b7687f68d30e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "\n", - "fastf1.plotting.setup_mpl()\n", - "\n", - "session = fastf1.get_session(2019, 'Monza', 'Q')\n", - "\n", - "session.load()\n", - "fast_leclerc = session.laps.pick_driver('LEC').pick_fastest()\n", - "lec_car_data = fast_leclerc.get_car_data()\n", - "t = lec_car_data['Time']\n", - "vCar = lec_car_data['Speed']\n", - "\n", - "# The rest is just plotting\n", - "fig, ax = plt.subplots()\n", - "ax.plot(t, vCar, label='Fast')\n", - "ax.set_xlabel('Time')\n", - "ax.set_ylabel('Speed [Km/h]')\n", - "ax.set_title('Leclerc is')\n", - "ax.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b6bf276-8054-458f-b10d-c5ab19afc756", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "session.results.iloc[0:7].loc[:, ['Abbreviation', 'Q3']]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a20a69cb-222f-4935-a96f-06f616f4ecfd", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "bw = buckaroo.BuckarooWidget(session.results.iloc[0:10].loc[:, ['Abbreviation', 'Q3']])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6fe1d915-6305-404e-bfa4-0237d8343d5c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "bw.df_data_dict['main']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dc8f7931-b26c-479a-9aa3-f28cf5efee6f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "fastf1.get_event_schedule(2007)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "513d2028-ad70-4183-9c74-309cecd5456d", - "metadata": {}, - "outputs": [], - "source": [ - "buckaroo.debug_packages()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8fb2f490-a792-4f82-bd2f-056b5006643c", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.8.18" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/example-notebooks/Histogram-debug.ipynb b/docs/example-notebooks/Histogram-debug.ipynb deleted file mode 100644 index 0694e49e..00000000 --- a/docs/example-notebooks/Histogram-debug.ipynb +++ /dev/null @@ -1,4243 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "1de00a49-4364-449f-87b2-1c61cded8384", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Buckaroo has been enabled as the default DataFrame viewer. To return to default dataframe visualization use `from buckaroo import disable; disable()`\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import buckaroo" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8187df3f-8025-423c-8c33-b5e8b86074e4", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "low_tail 4.04 high_tail 399.96 395\n", - "low_tail 1.0 high_tail 9.639999999999816 0\n", - "histogram_args {'meat_histogram': (array([40, 39, 40, 39, 39, 40, 39, 40, 39, 40]), array([ 5. , 44.4, 83.8, 123.2, 162.6, 202. , 241.4, 280.8, 320.2,\n", - " 359.6, 399. ])), 'normalized_populations': [0.10126582278481013, 0.09873417721518987, 0.10126582278481013, 0.09873417721518987, 0.09873417721518987, 0.10126582278481013, 0.09873417721518987, 0.10126582278481013, 0.09873417721518987, 0.10126582278481013], 'low_tail': 4.04, 'high_tail': 399.96}\n", - "histogram_args empty\n", - "histogram_args {}\n", - "Histogram.summary(placeholder['data'], pd.Series({'value_counts': pd.Series(), 'nan_per': 0.0, 'is_numeric': True, 'length': 405, 'min': 1, 'max': 50, }), placeholder['data']) # 'low_tail'\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "315a4fa58d954a5e96bde53226ddda86", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "BuckarooWidget(buckaroo_options={'sampled': ['random'], 'auto_clean': ['aggressive', 'conservative'], 'post_pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# one column that contains mostly 1s and a couple of larger values\n", - "pd.DataFrame({'data': [1] * 400 + [10, 20, 30, 40, 50]})" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f6c85cf1-3515-436f-bd0f-498c6b5a1323", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "low_tail 4.03 high_tail 398.96999999999997 394\n", - "low_tail 1.0 high_tail 1.0 0\n", - "histogram_args {'meat_histogram': (array([40, 39, 39, 40, 39, 39, 40, 39, 39, 40]), array([ 5. , 44.3, 83.6, 122.9, 162.2, 201.5, 240.8, 280.1, 319.4,\n", - " 358.7, 398. ])), 'normalized_populations': [0.10152284263959391, 0.09898477157360407, 0.09898477157360407, 0.10152284263959391, 0.09898477157360407, 0.09898477157360407, 0.10152284263959391, 0.09898477157360407, 0.09898477157360407, 0.10152284263959391], 'low_tail': 4.03, 'high_tail': 398.96999999999997}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "56cefa04b818435b9c63cecc796a84f0", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "BuckarooWidget(buckaroo_options={'sampled': ['random'], 'auto_clean': ['aggressive', 'conservative'], 'post_pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# one column that contains mostly 1s and a couple of larger values\n", - "pd.DataFrame({'data': [1] * 400 + [10, 20, 30, 50]})" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "79424051-75c7-45c1-83de-3f2afc3a87fe", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "low_tail 4.05 high_tail 400.95 396\n", - "low_tail 1.0 high_tail 19.499999999999886 1\n", - "histogram_args {'meat_histogram': (array([40, 39, 40, 39, 40, 39, 40, 39, 40, 40]), array([ 5. , 44.5, 84. , 123.5, 163. , 202.5, 242. , 281.5, 321. ,\n", - " 360.5, 400. ])), 'normalized_populations': [0.10101010101010101, 0.09848484848484848, 0.10101010101010101, 0.09848484848484848, 0.10101010101010101, 0.09848484848484848, 0.10101010101010101, 0.09848484848484848, 0.10101010101010101, 0.10101010101010101], 'low_tail': 4.05, 'high_tail': 400.95}\n", - "histogram_args {'meat_histogram': (array([0, 0, 0, 0, 0, 1, 0, 0, 0, 0]), array([ 9.5, 9.6, 9.7, 9.8, 9.9, 10. , 10.1, 10.2, 10.3, 10.4, 10.5])), 'normalized_populations': [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], 'low_tail': 1.0, 'high_tail': 19.499999999999886}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e7257f7135174c079d1b3f70bfd9f9e3", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "BuckarooWidget(buckaroo_options={'sampled': ['random'], 'auto_clean': ['aggressive', 'conservative'], 'post_pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# one column that contains mostly 1s and a couple of larger values\n", - "pd.DataFrame({'data': [1] * 400 + [10, 20, 30, 40, 50, 60]})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "034685ef-f5fe-4ac4-8f4f-5ad211f65ae8", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "buckaroo.BuckarooWidget(df, debug=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6a458c87-fb1e-4d49-854b-836bb418390c", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.11.7" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": { - "315a4fa58d954a5e96bde53226ddda86": { - "model_module": "buckaroo", - "model_module_version": "^0.6.4", - "model_name": "DCEFWidgetModel", - "state": { - "_model_module_version": "^0.6.4", - "_view_module_version": "^0.6.4", - "_view_name": "DCEFWidgetView", - "buckaroo_options": { - "auto_clean": [ - "aggressive", - "conservative" - ], - "df_display": [ - "main", - "summary" - 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"cells": [ - { - "cell_type": "markdown", - "id": "d69037a0-5b72-469c-8de7-d48a2788bede", - "metadata": {}, - "source": [ - "# Buckaroo works in Solara too" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "79fc4322-12b9-42bb-8bc6-2139e1a7d1f8", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "#import solara\n", - "#from buckaroo.solara_buckaroo import SolaraDFViewer, SolaraBuckarooWidget" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9176cd43-bad1-4073-a577-f7dbdeeca76d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "req WARNING \tDEFAULT CACHE ENABLED! (48.22 MB) /Users/paddy/Library/Caches/fastf1\n" - ] - } - ], - "source": [ - "#import buckaroo\n", - "import fastf1\n", - "ev2020 = fastf1.get_event_schedule(2020)\n", - "#buckaroo.BuckarooWidget(ev2020)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a912540d-05a7-4d24-9240-03640299d5a2", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "races = ev2020[ev2020['EventFormat'] == 'conventional']" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "36ab97fb-e72e-47fd-bcae-4dbc9904bf6b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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RoundNumberCountryLocationOfficialEventNameEventDateEventNameEventFormatSession1Session1DateSession1DateUtc...Session3Session3DateSession3DateUtcSession4Session4DateSession4DateUtcSession5Session5DateSession5DateUtcF1ApiSupport
21AustriaSpielbergFORMULA 1 ROLEX GROSSER PREIS VON ÖSTERREICH 20202020-07-05Austrian Grand PrixconventionalPractice 12020-07-03 11:00:00+02:002020-07-03 09:00:00...Practice 32020-07-04 12:00:00+02:002020-07-04 10:00:00Qualifying2020-07-04 15:00:00+02:002020-07-04 13:00:00Race2020-07-05 15:10:00+02:002020-07-05 13:10:00True
32AustriaSpielbergFORMULA 1 PIRELLI GROSSER PREIS DER STEIERMARK...2020-07-12Styrian Grand PrixconventionalPractice 12020-07-10 11:00:00+02:002020-07-10 09:00:00...Practice 32020-07-11 12:00:00+02:002020-07-11 10:00:00Qualifying2020-07-11 15:00:00+02:002020-07-11 13:00:00Race2020-07-12 15:10:00+02:002020-07-12 13:10:00True
43HungaryBudapestFORMULA 1 ARAMCO MAGYAR NAGYDÍJ 20202020-07-19Hungarian Grand PrixconventionalPractice 12020-07-17 11:00:00+02:002020-07-17 09:00:00...Practice 32020-07-18 12:00:00+02:002020-07-18 10:00:00Qualifying2020-07-18 15:00:00+02:002020-07-18 13:00:00Race2020-07-19 15:10:00+02:002020-07-19 13:10:00True
54Great BritainSilverstoneFORMULA 1 PIRELLI BRITISH GRAND PRIX 20202020-08-02British Grand PrixconventionalPractice 12020-07-31 11:00:00+01:002020-07-31 10:00:00...Practice 32020-08-01 11:00:00+01:002020-08-01 10:00:00Qualifying2020-08-01 14:00:00+01:002020-08-01 13:00:00Race2020-08-02 14:10:00+01:002020-08-02 13:10:00True
65Great BritainSilverstoneEMIRATES FORMULA 1 70TH ANNIVERSARY GRAND PRIX...2020-08-0970th Anniversary Grand PrixconventionalPractice 12020-08-07 11:00:00+01:002020-08-07 10:00:00...Practice 32020-08-08 11:00:00+01:002020-08-08 10:00:00Qualifying2020-08-08 14:00:00+01:002020-08-08 13:00:00Race2020-08-09 14:10:00+01:002020-08-09 13:10:00True
76SpainBarcelonaFORMULA 1 ARAMCO GRAN PREMIO DE ESPAÑA 20202020-08-16Spanish Grand PrixconventionalPractice 12020-08-14 11:00:00+02:002020-08-14 09:00:00...Practice 32020-08-15 12:00:00+02:002020-08-15 10:00:00Qualifying2020-08-15 15:00:00+02:002020-08-15 13:00:00Race2020-08-16 15:10:00+02:002020-08-16 13:10:00True
87BelgiumSpa-FrancorchampsFORMULA 1 ROLEX BELGIAN GRAND PRIX 20202020-08-30Belgian Grand PrixconventionalPractice 12020-08-28 11:00:00+02:002020-08-28 09:00:00...Practice 32020-08-29 12:00:00+02:002020-08-29 10:00:00Qualifying2020-08-29 15:00:00+02:002020-08-29 13:00:00Race2020-08-30 15:10:00+02:002020-08-30 13:10:00True
98ItalyMonzaFORMULA 1 GRAN PREMIO HEINEKEN D’ITALIA 20202020-09-06Italian Grand PrixconventionalPractice 12020-09-04 11:00:00+02:002020-09-04 09:00:00...Practice 32020-09-05 12:00:00+02:002020-09-05 10:00:00Qualifying2020-09-05 15:00:00+02:002020-09-05 13:00:00Race2020-09-06 15:10:00+02:002020-09-06 13:10:00True
109ItalyMugelloFORMULA 1 PIRELLI GRAN PREMIO DELLA TOSCANA FE...2020-09-13Tuscan Grand PrixconventionalPractice 12020-09-11 11:00:00+02:002020-09-11 09:00:00...Practice 32020-09-12 12:00:00+02:002020-09-12 10:00:00Qualifying2020-09-12 15:00:00+02:002020-09-12 13:00:00Race2020-09-13 15:10:00+02:002020-09-13 13:10:00True
1110RussiaSochiFORMULA 1 VTB RUSSIAN GRAND PRIX 20202020-09-27Russian Grand PrixconventionalPractice 12020-09-25 11:00:00+03:002020-09-25 08:00:00...Practice 32020-09-26 12:00:00+03:002020-09-26 09:00:00Qualifying2020-09-26 15:00:00+03:002020-09-26 12:00:00Race2020-09-27 14:10:00+03:002020-09-27 11:10:00True
1211GermanyNürburgringFORMULA 1 ARAMCO GROSSER PREIS DER EIFEL 20202020-10-11Eifel Grand PrixconventionalPractice 12020-10-09 11:00:00+02:002020-10-09 09:00:00...Practice 32020-10-10 12:00:00+02:002020-10-10 10:00:00Qualifying2020-10-10 15:00:00+02:002020-10-10 13:00:00Race2020-10-11 14:10:00+02:002020-10-11 12:10:00True
1312PortugalPortimãoFORMULA 1 HEINEKEN GRANDE PRÉMIO DE PORTUGAL 20202020-10-25Portuguese Grand PrixconventionalPractice 12020-10-23 11:00:00+00:002020-10-23 11:00:00...Practice 32020-10-24 11:00:00+00:002020-10-24 11:00:00Qualifying2020-10-24 14:00:00+00:002020-10-24 14:00:00Race2020-10-25 13:10:00+00:002020-10-25 13:10:00True
1413ItalyImolaFORMULA 1 EMIRATES GRAN PREMIO DELL'EMILIA ROM...2020-11-01Emilia Romagna Grand PrixconventionalPractice 12020-10-31 10:00:00+01:002020-10-31 09:00:00...NaTNaTQualifying2020-10-31 14:00:00+01:002020-10-31 13:00:00Race2020-11-01 13:10:00+01:002020-11-01 12:10:00True
1514TurkeyIstanbulFORMULA 1 DHL TURKISH GRAND PRIX 20202020-11-15Turkish Grand PrixconventionalPractice 12020-11-13 11:00:00+03:002020-11-13 08:00:00...Practice 32020-11-14 12:00:00+03:002020-11-14 09:00:00Qualifying2020-11-14 15:00:00+03:002020-11-14 12:00:00Race2020-11-15 13:10:00+03:002020-11-15 10:10:00True
1615BahrainSakhirFORMULA 1 GULF AIR BAHRAIN GRAND PRIX 20202020-11-29Bahrain Grand PrixconventionalPractice 12020-11-27 14:00:00+03:002020-11-27 11:00:00...Practice 32020-11-28 14:00:00+03:002020-11-28 11:00:00Qualifying2020-11-28 17:00:00+03:002020-11-28 14:00:00Race2020-11-29 17:10:00+03:002020-11-29 14:10:00True
1716BahrainSakhirFORMULA 1 ROLEX SAKHIR GRAND PRIX 20202020-12-06Sakhir Grand PrixconventionalPractice 12020-12-04 16:30:00+03:002020-12-04 13:30:00...Practice 32020-12-05 17:00:00+03:002020-12-05 14:00:00Qualifying2020-12-05 20:00:00+03:002020-12-05 17:00:00Race2020-12-06 20:10:00+03:002020-12-06 17:10:00True
1817Abu DhabiYas IslandFORMULA 1 ETIHAD AIRWAYS ABU DHABI GRAND PRIX ...2020-12-13Abu Dhabi Grand PrixconventionalPractice 12020-12-11 13:00:00+04:002020-12-11 09:00:00...Practice 32020-12-12 14:00:00+04:002020-12-12 10:00:00Qualifying2020-12-12 17:00:00+04:002020-12-12 13:00:00Race2020-12-13 17:10:00+04:002020-12-13 13:10:00True
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17 rows × 23 columns

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" - ], - "text/plain": [ - " RoundNumber Country Location \\\n", - "2 1 Austria Spielberg \n", - "3 2 Austria Spielberg \n", - "4 3 Hungary Budapest \n", - "5 4 Great Britain Silverstone \n", - "6 5 Great Britain Silverstone \n", - "7 6 Spain Barcelona \n", - "8 7 Belgium Spa-Francorchamps \n", - "9 8 Italy Monza \n", - "10 9 Italy Mugello \n", - "11 10 Russia Sochi \n", - "12 11 Germany Nürburgring \n", - "13 12 Portugal Portimão \n", - "14 13 Italy Imola \n", - "15 14 Turkey Istanbul \n", - "16 15 Bahrain Sakhir \n", - "17 16 Bahrain Sakhir \n", - "18 17 Abu Dhabi Yas Island \n", - "\n", - " OfficialEventName EventDate \\\n", - "2 FORMULA 1 ROLEX GROSSER PREIS VON ÖSTERREICH 2020 2020-07-05 \n", - "3 FORMULA 1 PIRELLI GROSSER PREIS DER STEIERMARK... 2020-07-12 \n", - "4 FORMULA 1 ARAMCO MAGYAR NAGYDÍJ 2020 2020-07-19 \n", - "5 FORMULA 1 PIRELLI BRITISH GRAND PRIX 2020 2020-08-02 \n", - "6 EMIRATES FORMULA 1 70TH ANNIVERSARY GRAND PRIX... 2020-08-09 \n", - "7 FORMULA 1 ARAMCO GRAN PREMIO DE ESPAÑA 2020 2020-08-16 \n", - "8 FORMULA 1 ROLEX BELGIAN GRAND PRIX 2020 2020-08-30 \n", - "9 FORMULA 1 GRAN PREMIO HEINEKEN D’ITALIA 2020 2020-09-06 \n", - "10 FORMULA 1 PIRELLI GRAN PREMIO DELLA TOSCANA FE... 2020-09-13 \n", - "11 FORMULA 1 VTB RUSSIAN GRAND PRIX 2020 2020-09-27 \n", - "12 FORMULA 1 ARAMCO GROSSER PREIS DER EIFEL 2020 2020-10-11 \n", - "13 FORMULA 1 HEINEKEN GRANDE PRÉMIO DE PORTUGAL 2020 2020-10-25 \n", - "14 FORMULA 1 EMIRATES GRAN PREMIO DELL'EMILIA ROM... 2020-11-01 \n", - "15 FORMULA 1 DHL TURKISH GRAND PRIX 2020 2020-11-15 \n", - "16 FORMULA 1 GULF AIR BAHRAIN GRAND PRIX 2020 2020-11-29 \n", - "17 FORMULA 1 ROLEX SAKHIR GRAND PRIX 2020 2020-12-06 \n", - "18 FORMULA 1 ETIHAD AIRWAYS ABU DHABI GRAND PRIX ... 2020-12-13 \n", - "\n", - " EventName EventFormat Session1 \\\n", - "2 Austrian Grand Prix conventional Practice 1 \n", - "3 Styrian Grand Prix conventional Practice 1 \n", - "4 Hungarian Grand Prix conventional Practice 1 \n", - "5 British Grand Prix conventional Practice 1 \n", - "6 70th Anniversary Grand Prix conventional Practice 1 \n", - "7 Spanish Grand Prix conventional Practice 1 \n", - "8 Belgian Grand Prix conventional Practice 1 \n", - "9 Italian Grand Prix conventional Practice 1 \n", - "10 Tuscan Grand Prix conventional Practice 1 \n", - "11 Russian Grand Prix conventional Practice 1 \n", - "12 Eifel Grand Prix conventional Practice 1 \n", - "13 Portuguese Grand Prix conventional Practice 1 \n", - "14 Emilia Romagna Grand Prix conventional Practice 1 \n", - "15 Turkish Grand Prix conventional Practice 1 \n", - "16 Bahrain Grand Prix conventional Practice 1 \n", - "17 Sakhir Grand Prix conventional Practice 1 \n", - "18 Abu Dhabi Grand Prix conventional Practice 1 \n", - "\n", - " Session1Date Session1DateUtc ... Session3 \\\n", - "2 2020-07-03 11:00:00+02:00 2020-07-03 09:00:00 ... Practice 3 \n", - "3 2020-07-10 11:00:00+02:00 2020-07-10 09:00:00 ... Practice 3 \n", - "4 2020-07-17 11:00:00+02:00 2020-07-17 09:00:00 ... Practice 3 \n", - "5 2020-07-31 11:00:00+01:00 2020-07-31 10:00:00 ... Practice 3 \n", - "6 2020-08-07 11:00:00+01:00 2020-08-07 10:00:00 ... Practice 3 \n", - "7 2020-08-14 11:00:00+02:00 2020-08-14 09:00:00 ... Practice 3 \n", - "8 2020-08-28 11:00:00+02:00 2020-08-28 09:00:00 ... Practice 3 \n", - "9 2020-09-04 11:00:00+02:00 2020-09-04 09:00:00 ... Practice 3 \n", - "10 2020-09-11 11:00:00+02:00 2020-09-11 09:00:00 ... Practice 3 \n", - "11 2020-09-25 11:00:00+03:00 2020-09-25 08:00:00 ... Practice 3 \n", - "12 2020-10-09 11:00:00+02:00 2020-10-09 09:00:00 ... Practice 3 \n", - "13 2020-10-23 11:00:00+00:00 2020-10-23 11:00:00 ... Practice 3 \n", - "14 2020-10-31 10:00:00+01:00 2020-10-31 09:00:00 ... \n", - "15 2020-11-13 11:00:00+03:00 2020-11-13 08:00:00 ... Practice 3 \n", - "16 2020-11-27 14:00:00+03:00 2020-11-27 11:00:00 ... Practice 3 \n", - "17 2020-12-04 16:30:00+03:00 2020-12-04 13:30:00 ... Practice 3 \n", - "18 2020-12-11 13:00:00+04:00 2020-12-11 09:00:00 ... Practice 3 \n", - "\n", - " Session3Date Session3DateUtc Session4 \\\n", - "2 2020-07-04 12:00:00+02:00 2020-07-04 10:00:00 Qualifying \n", - "3 2020-07-11 12:00:00+02:00 2020-07-11 10:00:00 Qualifying \n", - "4 2020-07-18 12:00:00+02:00 2020-07-18 10:00:00 Qualifying \n", - "5 2020-08-01 11:00:00+01:00 2020-08-01 10:00:00 Qualifying \n", - "6 2020-08-08 11:00:00+01:00 2020-08-08 10:00:00 Qualifying \n", - "7 2020-08-15 12:00:00+02:00 2020-08-15 10:00:00 Qualifying \n", - "8 2020-08-29 12:00:00+02:00 2020-08-29 10:00:00 Qualifying \n", - "9 2020-09-05 12:00:00+02:00 2020-09-05 10:00:00 Qualifying \n", - "10 2020-09-12 12:00:00+02:00 2020-09-12 10:00:00 Qualifying \n", - "11 2020-09-26 12:00:00+03:00 2020-09-26 09:00:00 Qualifying \n", - "12 2020-10-10 12:00:00+02:00 2020-10-10 10:00:00 Qualifying \n", - "13 2020-10-24 11:00:00+00:00 2020-10-24 11:00:00 Qualifying \n", - "14 NaT NaT Qualifying \n", - "15 2020-11-14 12:00:00+03:00 2020-11-14 09:00:00 Qualifying \n", - "16 2020-11-28 14:00:00+03:00 2020-11-28 11:00:00 Qualifying \n", - "17 2020-12-05 17:00:00+03:00 2020-12-05 14:00:00 Qualifying \n", - "18 2020-12-12 14:00:00+04:00 2020-12-12 10:00:00 Qualifying \n", - "\n", - " Session4Date Session4DateUtc Session5 \\\n", - "2 2020-07-04 15:00:00+02:00 2020-07-04 13:00:00 Race \n", - "3 2020-07-11 15:00:00+02:00 2020-07-11 13:00:00 Race \n", - "4 2020-07-18 15:00:00+02:00 2020-07-18 13:00:00 Race \n", - "5 2020-08-01 14:00:00+01:00 2020-08-01 13:00:00 Race \n", - "6 2020-08-08 14:00:00+01:00 2020-08-08 13:00:00 Race \n", - "7 2020-08-15 15:00:00+02:00 2020-08-15 13:00:00 Race \n", - "8 2020-08-29 15:00:00+02:00 2020-08-29 13:00:00 Race \n", - "9 2020-09-05 15:00:00+02:00 2020-09-05 13:00:00 Race \n", - "10 2020-09-12 15:00:00+02:00 2020-09-12 13:00:00 Race \n", - "11 2020-09-26 15:00:00+03:00 2020-09-26 12:00:00 Race \n", - "12 2020-10-10 15:00:00+02:00 2020-10-10 13:00:00 Race \n", - "13 2020-10-24 14:00:00+00:00 2020-10-24 14:00:00 Race \n", - "14 2020-10-31 14:00:00+01:00 2020-10-31 13:00:00 Race \n", - "15 2020-11-14 15:00:00+03:00 2020-11-14 12:00:00 Race \n", - "16 2020-11-28 17:00:00+03:00 2020-11-28 14:00:00 Race \n", - "17 2020-12-05 20:00:00+03:00 2020-12-05 17:00:00 Race \n", - "18 2020-12-12 17:00:00+04:00 2020-12-12 13:00:00 Race \n", - "\n", - " Session5Date Session5DateUtc F1ApiSupport \n", - "2 2020-07-05 15:10:00+02:00 2020-07-05 13:10:00 True \n", - "3 2020-07-12 15:10:00+02:00 2020-07-12 13:10:00 True \n", - "4 2020-07-19 15:10:00+02:00 2020-07-19 13:10:00 True \n", - "5 2020-08-02 14:10:00+01:00 2020-08-02 13:10:00 True \n", - "6 2020-08-09 14:10:00+01:00 2020-08-09 13:10:00 True \n", - "7 2020-08-16 15:10:00+02:00 2020-08-16 13:10:00 True \n", - "8 2020-08-30 15:10:00+02:00 2020-08-30 13:10:00 True \n", - "9 2020-09-06 15:10:00+02:00 2020-09-06 13:10:00 True \n", - "10 2020-09-13 15:10:00+02:00 2020-09-13 13:10:00 True \n", - "11 2020-09-27 14:10:00+03:00 2020-09-27 11:10:00 True \n", - "12 2020-10-11 14:10:00+02:00 2020-10-11 12:10:00 True \n", - "13 2020-10-25 13:10:00+00:00 2020-10-25 13:10:00 True \n", - "14 2020-11-01 13:10:00+01:00 2020-11-01 12:10:00 True \n", - "15 2020-11-15 13:10:00+03:00 2020-11-15 10:10:00 True \n", - "16 2020-11-29 17:10:00+03:00 2020-11-29 14:10:00 True \n", - "17 2020-12-06 20:10:00+03:00 2020-12-06 17:10:00 True \n", - "18 2020-12-13 17:10:00+04:00 2020-12-13 13:10:00 True \n", - "\n", - "[17 rows x 23 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "races" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "561d37b2-5a25-472d-8642-a152028df5eb", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "session = fastf1.get_session(2019, 'Monza', 'Race')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "dc319664-9bc3-46ef-be80-3aa4d57a52b4", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "core INFO \tLoading data for Italian Grand Prix - Race [v3.3.2]\n", - "req INFO \tNo cached data found for session_info. Loading data...\n", - "_api INFO \tFetching session info data...\n", - "req INFO \tData has been written to cache!\n", - "req INFO \tNo cached data found for driver_info. Loading data...\n", - "_api INFO \tFetching driver list...\n", - "req INFO \tData has been written to cache!\n", - "req INFO \tNo cached data found for session_status_data. Loading data...\n", - "_api INFO \tFetching session status data...\n", - "req INFO \tData has been written to cache!\n", - "req INFO \tNo cached data found for lap_count. Loading data...\n", - "_api INFO \tFetching lap count data...\n", - "req INFO \tData has been written to cache!\n", - "req INFO \tNo cached data found for track_status_data. Loading data...\n", - "_api INFO \tFetching track status data...\n", - "req INFO \tData has been written to cache!\n", - "req INFO \tNo cached data found for _extended_timing_data. Loading data...\n", - "_api INFO \tFetching timing data...\n", - "_api INFO \tParsing timing data...\n", - "req INFO \tData has been written to cache!\n", - "req INFO \tNo cached data found for timing_app_data. Loading data...\n", - "_api INFO \tFetching timing app data...\n", - "req INFO \tData has been written to cache!\n", - "core INFO \tProcessing timing data...\n", - "req INFO \tNo cached data found for car_data. Loading data...\n", - "_api INFO \tFetching car data...\n", - "_api INFO \tParsing car data...\n", - "req INFO \tData has been written to cache!\n", - "req INFO \tNo cached data found for position_data. Loading data...\n", - "_api INFO \tFetching position data...\n", - "_api INFO \tParsing position data...\n", - "req INFO \tData has been written to cache!\n", - "req INFO \tNo cached data found for weather_data. Loading data...\n", - "_api INFO \tFetching weather data...\n", - "req INFO \tData has been written to cache!\n", - "req INFO \tNo cached data found for race_control_messages. Loading data...\n", - "_api INFO \tFetching race control messages...\n", - "req INFO \tData has been written to cache!\n", - "core INFO \tFinished loading data for 20 drivers: ['16', '77', '44', '3', '27', '23', '11', '33', '99', '4', '10', '18', '5', '63', '7', '8', '88', '20', '26', '55']\n" - ] - } - ], - "source": [ - "session.load()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "d116c918-7cd4-40dd-b484-44f87e897eed", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "16 LEC\n", - "77 BOT\n", - "44 HAM\n", - "3 RIC\n", - "27 HUL\n", - "23 ALB\n", - "11 PER\n", - "33 VER\n", - "99 GIO\n", - "4 NOR\n", - "10 GAS\n", - "18 STR\n", - "5 VET\n", - "63 RUS\n", - "7 RAI\n", - "8 GRO\n", - "88 KUB\n", - "20 MAG\n", - "26 KVY\n", - "55 SAI\n", - "Name: Abbreviation, dtype: object" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "session.results['Abbreviation']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c074ce3d-f07b-453a-9115-3399572a9f0a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "ev2020" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b99ad895-e2c1-443c-8137-d1a4a670303c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "buckaroo.BuckarooWidget(pd.read_json(ev2020.to_json()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f9b893f0-f684-4738-996b-00c91b319c93", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "ev2020.__module__" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e47d8a27-f404-4ecf-8bdb-24a71bf2b00f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "buckaroo.BuckarooWidget(ev2020[ev2020.columns[:5]])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bd085457-3ecf-438d-bf03-be1c2f0fa8ef", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "df = pd.DataFrame({'a':[10,20]})\n", - "@solara.component\n", - "def Page():\n", - " bw = SolaraDFViewer(df=df)\n", - "display(Page())" - ] - }, - { - "cell_type": "markdown", - "id": "ce7aec8e-412a-46a4-af4e-7a026fd33fa1", - "metadata": {}, - "source": [ - "## You can turn off pinned_rows too" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b3558422-55f4-4f88-b85d-71e910d10bd5", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "df = pd.DataFrame({'a':[10,20]})\n", - "@solara.component\n", - "def Page():\n", - " bw = SolaraDFViewer(df=df, pinned_rows=[])\n", - "display(Page())" - ] - }, - { - "cell_type": "markdown", - "id": "1551cd59-2e52-4fd0-9d93-f136eed71e2d", - "metadata": {}, - "source": [ - "# And it works with Polars" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a7e2556c-b768-408b-8aa2-bb81bd91ee9c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import polars as pl\n", - "from buckaroo.solara_buckaroo import SolaraPolarsDFViewer\n", - "pldf = pl.DataFrame({'a':[10,20]})\n", - "@solara.component\n", - "def Page():\n", - " bw = SolaraPolarsDFViewer(df=pldf, pinned_rows=[])\n", - "display(Page())" - ] - }, - { - "cell_type": "markdown", - "id": "02db2eb2-0b6a-40c4-bcc8-b310a1d6e405", - "metadata": {}, - "source": [ - "# Let's look at more data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3f74b8fb-7ba3-4c33-ad2e-e17baad839bc", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "cb_trips_df = pd.read_csv(\"https://github.com/paddymul/buckaroo-data/raw/main/cb_data/2016-04.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "22d571e5-6bf2-4a5f-8bfe-2c69d679f8f8", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "@solara.component\n", - "def Page():\n", - " bw = SolaraDFViewer(df=cb_trips_df, pinned_rows=[])\n", - "display(Page())" - ] - }, - { - "cell_type": "markdown", - "id": "5e4c06cc-7e9c-4d77-afec-7c9e78d8ca3b", - "metadata": {}, - "source": [ - "# Building an app around SolaraDFViewer\n", - "The following app hardcodes two dataframes and lets you select between them" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4d1256ef-edf5-494a-8a2c-a5c1a6a38b29", - "metadata": {}, - "outputs": [], - "source": [ - "dataframe_names = [\"foo\", \"bar\"]\n", - "df_name = solara.reactive(\"foo\")\n", - "\n", - "dataframes = dict(foo=df,\n", - " bar=pd.DataFrame({'c':[20,50,90], 'd':[False, True, True]}))\n", - "\n", - "@solara.component\n", - "def Page():\n", - " solara.Select(label=\"df_name\", value=df_name, values=dataframe_names)\n", - " active_df = dataframes[df_name.value]\n", - " bw = SolaraDFViewer(active_df)\n", - "\n", - " solara.Markdown(f\"**Selected**: {df_name.value}\")\n", - "display(Page())" - ] - }, - { - "cell_type": "markdown", - "id": "9ca33684-b15f-48f9-a19d-f3a5221343ba", - "metadata": {}, - "source": [ - "# Reading large dataframes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "48f11839-2fe0-454b-b616-d82d5fcabf38", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "url_base = \"https://github.com/paddymul/buckaroo-data/raw/main/cb_data/\"\n", - "\n", - "dataframe_names = [\n", - " \"2016-01.parq\", \"2016-02.parq\",\n", - " \"2016-03.parq\", \"2016-04.parq\"]\n", - "\n", - "df_name = solara.reactive(\"2016-01.parq\")\n", - "\n", - "@solara.component\n", - "def Page():\n", - " solara.Select(label=\"df_name\", value=df_name, values=dataframe_names)\n", - "\n", - " @solara.lab.use_task(dependencies=[df_name.value])\n", - " def read_df():\n", - " return pl.read_parquet(url_base+df_name.value)\n", - "\n", - " solara.ProgressLinear(read_df.pending)\n", - " if read_df.latest is not None:\n", - " with solara.Div(style={\"opacity\": 0.3 if read_df.pending else 1.0}):\n", - " SolaraPolarsDFViewer(read_df.latest)\n", - "\n", - " solara.Markdown(f\"**Selected**: {df_name.value}\")\n", - "#display(Page())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "301b53c4-32b2-40af-825b-81827f017fe3", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import fastf1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ef6bdd21-0a53-457a-8ba1-4aebd947070a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "ev2020 = fastf1.get_event_schedule(2020)\n", - "ev2020.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f00283e1-ab6a-4b7f-a49f-334fe94fb973", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import buckaroo" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "005032ff-4c43-4511-a4a4-6772948325f0", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "buckaroo.BuckarooWidget(ev2020)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a66a636c-3790-498a-997d-11c0834b18ae", - "metadata": {}, - "outputs": [], - "source": [ - "session = fastf1.get_session(2019, 'Monza', 'Q')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dc5c9afa-7f25-45bc-bd71-a5d6557c6b90", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "url_base = \"https://github.com/paddymul/buckaroo-data/raw/main/cb_data/\"\n", - "\n", - "dataframe_names = [\n", - " \"2016-01.parq\", \"2016-02.parq\",\n", - " \"2016-03.parq\", \"2016-04.parq\"]\n", - "\n", - "df_name = solara.reactive(\"2016-01.parq\")\n", - "\n", - "@solara.component\n", - "def Page():\n", - " solara.Select(label=\"df_name\", value=df_name, values=dataframe_names)\n", - "\n", - " @solara.lab.use_task(dependencies=[df_name.value])\n", - " def read_df():\n", - " return pl.read_parquet(url_base+df_name.value)\n", - "\n", - " solara.ProgressLinear(read_df.pending)\n", - " if read_df.latest is not None:\n", - " with solara.Div(style={\"opacity\": 0.3 if read_df.pending else 1.0}):\n", - " SolaraPolarsDFViewer(read_df.latest)\n", - "\n", - " solara.Markdown(f\"**Selected**: {df_name.value}\")\n", - "display(Page())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "28917b3a-45d1-47cd-ae71-c0794fbf2644", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.11.7" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/example-notebooks/Untitled.ipynb b/docs/example-notebooks/Untitled.ipynb deleted file mode 100644 index 2faf9f13..00000000 --- a/docs/example-notebooks/Untitled.ipynb +++ /dev/null @@ -1,1136 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "cd0c5b27-346e-493d-955f-a59c7b2ad1e1", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "20525cce-7d58-49ea-af06-9cfb4c90eb2b", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "891b3d19-f8c3-49ae-8969-c9b839dcf4d1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Buckaroo has been enabled as the default DataFrame viewer. To return to default dataframe visualization use `from buckaroo import disable; disable()`\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "eec10251f063493d93bcdeb7f2ba9a6f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "BuckarooWidget(buckaroo_options={'sampled': ['random'], 'auto_clean': ['aggressive', 'conservative'], 'post_pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "#import polars as pl\n", - "import buckaroo\n", - "df2 = pd.DataFrame({'a':[10,20,30,40], 'b': ['a', 'aa', 'ab', 'bb']})\n", - "df2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "cd7f7dca-835e-4223-a437-9a4fb70266ce", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ec08568affff4599832a94cf144902e8", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "PolarsBuckarooWidget(buckaroo_options={'sampled': ['random'], 'auto_clean': ['aggressive', 'conservative'], 'p…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df = pl.DataFrame({'a':[10,20,30,40, 50], 'b': ['a', 'aa', 'ab', 'bb', 'cc',], 'c': ['8', '9', 'cc', 'ff', 'aa']})\n", - "\n", - "df.filter(pl.col('b').str.contains('cc'))\n", - "df.filter(pl.any_horizontal(pl.col(pl.String).str.contains('aa')))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "55db9868-06a1-4622-ba81-7eaa2e62cc70", - "metadata": {}, - "outputs": [], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b181849b-e33a-4f21-bd06-07092edd03ee", - "metadata": {}, - "outputs": [], - "source": [ - "ab = pl.col('a').str\n", - "dir(ab)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0d9d9a77-d2e6-494f-b3a9-24c98c971f00", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import buckaroo\n", - "\n", - "\n", - "\n", - "df.filter(pl.col('col1').str.contains_any(a_list))\n", - "# Create a sample DataFrame with a string index\n", - "data = {'value': [10, 20, 30]}\n", - "index = ['2024-06-24 09:32:00-04:00', '2024-06-24 09:33:00-04:00', '2024-06-24 09:34:00-04:00']\n", - "\n", - "# Build the data frame\n", - "df = pd.DataFrame(data, index=index)\n", - "\n", - "# Convert string index to datetime\n", - "df.index = pd.to_datetime(df.index)\n", - "\n", - "# remove localize\n", - "#df.index = df.index.tz_localize(None)\n", - "\n", - "# Display df\n", - "buckaroo.BuckarooWidget(df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4df94745-739c-4b18-99a6-59dc9cb6967a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import json\n", - "import fastf1\n", - "ev2020 = fastf1.get_event_schedule(2020)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0d27a1d2-904e-4bea-b01d-8b0ff3f8b0f9", - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_parquet(\"/Users/paddy/code/girlfriend-clock/full_df.parquet\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6613f9a8-18eb-4d91-94b9-b572307d8ce3", - "metadata": {}, - "outputs": [], - "source": [ - "df = pl.read_parquet(\"/Users/paddy/code/girlfriend-clock/full_df.parquet\")\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e572c170-6fb3-4532-8c78-a896b270a6f6", - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_parquet(\"/Users/paddy/code/girlfriend-clock/full_df.parquet\")\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "36a3b444-cd49-43ca-a5dd-70db862d5d62", - "metadata": {}, - "outputs": [], - "source": [ - "bw = buckaroo.BuckarooWidget(df2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5c8e94ac-aeb2-4274-80a6-5685c7f67ec4", - "metadata": {}, - "outputs": [], - "source": [ - "bw.df_data_dict" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4a78e4dc-8ce8-4a43-9d48-8fe908801d5c", - "metadata": {}, - "outputs": [], - "source": [ - "pip install -ve ." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e37425c9-78e9-4233-966e-3b3ce49aa55d", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "ts = pd.Timestamp('2020-02-21T00:00:00.000000000')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7926b4b3-5112-4111-9da9-d3a2db893f39", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "type(ts)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "79ed81f5-76ea-4f94-8121-74bc5cad9366", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "val2 = pd.Series([ts]).mode().values[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ee35779f-e599-46f8-abc8-6bcf3b28343d", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "sdf = pd.DataFrame({'a':[ts], 'b':[val2]}, index=['mode'])\n", - "\n", - "sdf.to_json(orient='table', indent=2, default_handler=str)" - ] - }, - { - "cell_type": "code", - 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