{ "cells": [ { "cell_type": "markdown", "id": "worldwide-blood", "metadata": {}, "source": [ "# Introduction" ] }, { "cell_type": "markdown", "id": "understanding-numbers", "metadata": {}, "source": [ "*✏️ Write 2-3 sentences describing your research.*" ] }, { "cell_type": "markdown", "id": "greater-circular", "metadata": {}, "source": [ "## Overarching Question: [✏️ PUT YOUR QUESTION HERE ✏️]" ] }, { "cell_type": "markdown", "id": "appreciated-testimony", "metadata": {}, "source": [ "*✏️ Write 2-3 sentences explaining why this question.*" ] }, { "cell_type": "markdown", "id": "permanent-pollution", "metadata": {}, "source": [ "# Data" ] }, { "cell_type": "code", "execution_count": 10, "id": "technical-evans", "metadata": {}, "outputs": [], "source": [ "#Include any import statements you will need\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 11, "id": "overhead-sigma", "metadata": { "scrolled": true }, "outputs": [], "source": [ "### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n", "\n", "file_name = \"modern_RAPTOR_by_player.csv\"\n", "dataset_path = \"data/\" + file_name\n", "\n", "df = pd.read_csv(dataset_path)" ] }, { "cell_type": "code", "execution_count": 12, "id": "heated-blade", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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player_nameplayer_idseasonpossmpraptor_box_offenseraptor_box_defenseraptor_box_totalraptor_onoff_offenseraptor_onoff_defense...raptor_offenseraptor_defenseraptor_totalwar_totalwar_reg_seasonwar_playoffspredator_offensepredator_defensepredator_totalpace_impact
0Alex Abrinesabrinal012017238711350.745505-0.3729380.372567-0.418553-3.857011...0.543421-1.144832-0.6014111.2490081.447708-0.1987000.077102-1.038677-0.9615750.326413
1Alex Abrinesabrinal012018254612440.317549-1.725325-1.407776-1.291727-0.049694...-0.020826-1.502642-1.5234680.7773040.4659120.311392-0.174621-1.112625-1.287247-0.456141
2Alex Abrinesabrinal0120191279588-3.2156831.078399-2.137285-6.1588564.901168...-4.0401571.885618-2.1545380.1781670.1781670.000000-4.5776781.543282-3.034396-0.268013
3Precious Achiuwaachiupr0120211581749-4.1229661.359278-2.763688-4.050779-0.919712...-4.3475960.954821-3.392775-0.246055-0.2467760.000721-3.8177130.474828-3.3428850.329157
4Precious Achiuwaachiupr01202238021892-2.5215101.763502-0.758008-1.6878933.103441...-2.5173722.144151-0.3732212.2626582.309611-0.046953-2.4839562.024360-0.459596-0.728609
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5 rows × 21 columns

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" ], "text/plain": [ " player_name player_id season poss mp raptor_box_offense \\\n", "0 Alex Abrines abrinal01 2017 2387 1135 0.745505 \n", "1 Alex Abrines abrinal01 2018 2546 1244 0.317549 \n", "2 Alex Abrines abrinal01 2019 1279 588 -3.215683 \n", "3 Precious Achiuwa achiupr01 2021 1581 749 -4.122966 \n", "4 Precious Achiuwa achiupr01 2022 3802 1892 -2.521510 \n", "\n", " raptor_box_defense raptor_box_total raptor_onoff_offense \\\n", "0 -0.372938 0.372567 -0.418553 \n", "1 -1.725325 -1.407776 -1.291727 \n", "2 1.078399 -2.137285 -6.158856 \n", "3 1.359278 -2.763688 -4.050779 \n", "4 1.763502 -0.758008 -1.687893 \n", "\n", " raptor_onoff_defense ... raptor_offense raptor_defense raptor_total \\\n", "0 -3.857011 ... 0.543421 -1.144832 -0.601411 \n", "1 -0.049694 ... -0.020826 -1.502642 -1.523468 \n", "2 4.901168 ... -4.040157 1.885618 -2.154538 \n", "3 -0.919712 ... -4.347596 0.954821 -3.392775 \n", "4 3.103441 ... -2.517372 2.144151 -0.373221 \n", "\n", " war_total war_reg_season war_playoffs predator_offense \\\n", "0 1.249008 1.447708 -0.198700 0.077102 \n", "1 0.777304 0.465912 0.311392 -0.174621 \n", "2 0.178167 0.178167 0.000000 -4.577678 \n", "3 -0.246055 -0.246776 0.000721 -3.817713 \n", "4 2.262658 2.309611 -0.046953 -2.483956 \n", "\n", " predator_defense predator_total pace_impact \n", "0 -1.038677 -0.961575 0.326413 \n", "1 -1.112625 -1.287247 -0.456141 \n", "2 1.543282 -3.034396 -0.268013 \n", "3 0.474828 -3.342885 0.329157 \n", "4 2.024360 -0.459596 -0.728609 \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "id": "continental-franklin", "metadata": {}, "source": [ "**Data Overview**\n", "\n", "*✏️ Write 2-3 sentences describing this dataset. Be sure to include where the data comes from and what it contains.*" ] }, { "cell_type": "markdown", "id": "infinite-instrument", "metadata": {}, "source": [ "# Methods and Results" ] }, { "cell_type": "code", "execution_count": null, "id": "basic-canadian", "metadata": {}, "outputs": [], "source": [ "#Import any helper files you need here" ] }, { "cell_type": "markdown", "id": "recognized-positive", "metadata": {}, "source": [ "## First Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n" ] }, { "cell_type": "markdown", "id": "graduate-palmer", "metadata": {}, "source": [ "### Methods" ] }, { "cell_type": "markdown", "id": "endless-variation", "metadata": {}, "source": [ "*Explain how you will approach this research question below. Consider the following:* \n", " - *Which aspects of the dataset will you use?* \n", " - *How will you reorganize/store the data?* \n", " - *What data science tools/functions will you use and why?* \n", " \n", "✏️ *Write your answer below:*\n", "\n" ] }, { "cell_type": "markdown", "id": "portuguese-japan", "metadata": {}, "source": [ "### Results " ] }, { "cell_type": "code", "execution_count": 17, "id": "negative-highlight", "metadata": {}, "outputs": [], "source": [ "#######################################################################\n", "### 💻 YOUR WORK GOES HERE TO ANSWER THE FIRST RESEARCH QUESTION 💻 \n", "### \n", "### Your data analysis may include a statistic and/or a data visualization\n", "#######################################################################" ] }, { "cell_type": "code", "execution_count": 16, "id": "victorian-burning", "metadata": {}, "outputs": [], "source": [ "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON " ] }, { "cell_type": "markdown", "id": "collectible-puppy", "metadata": {}, "source": [ "## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n" ] }, { "cell_type": "markdown", "id": "demographic-future", "metadata": {}, "source": [ "### Methods" ] }, { "cell_type": "markdown", "id": "incorporate-roller", "metadata": {}, "source": [ "*Explain how you will approach this research question below. Consider the following:* \n", " - *Which aspects of the dataset will you use?* \n", " - *How will you reorganize/store the data?* \n", " - *What data science tools/functions will you use and why?* \n", "\n", "✏️ *Write your answer below:*\n" ] }, { "cell_type": "markdown", "id": "juvenile-creation", "metadata": {}, "source": [ "### Results " ] }, { "cell_type": "code", "execution_count": 14, "id": "pursuant-surrey", "metadata": {}, "outputs": [], "source": [ "#######################################################################\n", "### 💻 YOUR WORK GOES HERE TO ANSWER THE SECOND RESEARCH QUESTION 💻 \n", "###\n", "### Your data analysis may include a statistic and/or a data visualization\n", "#######################################################################" ] }, { "cell_type": "code", "execution_count": 15, "id": "located-night", "metadata": {}, "outputs": [], "source": [ "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON " ] }, { "cell_type": "markdown", "id": "infectious-symbol", "metadata": {}, "source": [ "# Discussion" ] }, { "cell_type": "markdown", "id": "furnished-camping", "metadata": { "code_folding": [] }, "source": [ "## Considerations" ] }, { "cell_type": "markdown", "id": "bearing-stadium", "metadata": {}, "source": [ "*It's important to recognize the limitations of our research.\n", "Consider the following:*\n", "\n", "- *Do the results give an accurate depiction of your research question? Why or why not?*\n", "- *What were limitations of your datset?*\n", "- *Are there any known biases in the data?*\n", "\n", "✏️ *Write your answer below:*" ] }, { "cell_type": "markdown", "id": "beneficial-invasion", "metadata": {}, "source": [ "## Summary" ] }, { "cell_type": "markdown", "id": "about-raise", "metadata": {}, "source": [ "*Summarize what you discovered through the research. Consider the following:*\n", "\n", "- *What did you learn about your media consumption/digital habits?*\n", "- *Did the results make sense?*\n", "- *What was most surprising?*\n", "- *How will this project impact you going forward?*\n", "\n", "✏️ *Write your answer below:*" ] } ], "metadata": { "jupytext": { "cell_metadata_json": true, "text_representation": { "extension": ".Rmd", "format_name": "rmarkdown", "format_version": "1.2", "jupytext_version": "1.9.1" } }, "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": "3.12.3" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 5 }