generated from mwc/project_argument
4b4b74d964
I have just started the project so there is nothing to reflect on yet. I am excited for this proejct as NBA data is very interesting to me.
577 lines
16 KiB
Plaintext
577 lines
16 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "worldwide-blood",
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"metadata": {},
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"source": [
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"# Introduction"
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]
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},
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{
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"cell_type": "markdown",
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"id": "understanding-numbers",
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"metadata": {},
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"source": [
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"*✏️ Write 2-3 sentences describing your research.*"
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]
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},
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{
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"cell_type": "markdown",
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"id": "greater-circular",
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"metadata": {},
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"source": [
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"## Overarching Question: [✏️ PUT YOUR QUESTION HERE ✏️]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "appreciated-testimony",
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"metadata": {},
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"source": [
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"*✏️ Write 2-3 sentences explaining why this question.*"
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]
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},
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{
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"cell_type": "markdown",
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"id": "permanent-pollution",
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"metadata": {},
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"source": [
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"# Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "technical-evans",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Include any import statements you will need\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "overhead-sigma",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
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"\n",
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"file_name = \"modern_RAPTOR_by_player.csv\"\n",
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"dataset_path = \"data/\" + file_name\n",
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"\n",
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"df = pd.read_csv(dataset_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "heated-blade",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>player_name</th>\n",
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" <th>player_id</th>\n",
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" <th>season</th>\n",
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" <th>poss</th>\n",
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" <th>mp</th>\n",
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" <th>raptor_box_offense</th>\n",
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" <th>raptor_box_defense</th>\n",
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" <th>raptor_box_total</th>\n",
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" <th>raptor_onoff_offense</th>\n",
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" <th>raptor_onoff_defense</th>\n",
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" <th>...</th>\n",
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" <th>raptor_offense</th>\n",
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" <th>raptor_defense</th>\n",
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" <th>raptor_total</th>\n",
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" <th>war_total</th>\n",
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" <th>war_reg_season</th>\n",
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" <th>war_playoffs</th>\n",
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" <th>predator_offense</th>\n",
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" <th>predator_defense</th>\n",
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" <th>predator_total</th>\n",
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" <th>pace_impact</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Alex Abrines</td>\n",
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" <td>abrinal01</td>\n",
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" <td>2017</td>\n",
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" <td>2387</td>\n",
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" <td>1135</td>\n",
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" <td>0.745505</td>\n",
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" <td>-0.372938</td>\n",
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" <td>0.372567</td>\n",
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" <td>-0.418553</td>\n",
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" <td>-3.857011</td>\n",
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" <td>...</td>\n",
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" <td>0.543421</td>\n",
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" <td>-1.144832</td>\n",
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" <td>-0.601411</td>\n",
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" <td>1.249008</td>\n",
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" <td>1.447708</td>\n",
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" <td>-0.198700</td>\n",
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" <td>0.077102</td>\n",
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" <td>-1.038677</td>\n",
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" <td>-0.961575</td>\n",
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" <td>0.326413</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Alex Abrines</td>\n",
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" <td>abrinal01</td>\n",
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" <td>2018</td>\n",
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" <td>2546</td>\n",
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" <td>1244</td>\n",
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" <td>0.317549</td>\n",
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" <td>-1.725325</td>\n",
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" <td>-1.407776</td>\n",
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" <td>-1.291727</td>\n",
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" <td>-0.049694</td>\n",
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" <td>...</td>\n",
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" <td>-0.020826</td>\n",
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" <td>-1.502642</td>\n",
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" <td>-1.523468</td>\n",
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" <td>0.777304</td>\n",
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" <td>0.465912</td>\n",
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" <td>0.311392</td>\n",
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" <td>-0.174621</td>\n",
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" <td>-1.112625</td>\n",
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" <td>-1.287247</td>\n",
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" <td>-0.456141</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Alex Abrines</td>\n",
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" <td>abrinal01</td>\n",
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" <td>2019</td>\n",
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" <td>1279</td>\n",
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" <td>588</td>\n",
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" <td>-3.215683</td>\n",
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" <td>1.078399</td>\n",
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" <td>-2.137285</td>\n",
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" <td>-6.158856</td>\n",
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" <td>4.901168</td>\n",
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" <td>...</td>\n",
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" <td>-4.040157</td>\n",
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" <td>1.885618</td>\n",
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" <td>-2.154538</td>\n",
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" <td>0.178167</td>\n",
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" <td>0.178167</td>\n",
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" <td>0.000000</td>\n",
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" <td>-4.577678</td>\n",
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" <td>1.543282</td>\n",
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" <td>-3.034396</td>\n",
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" <td>-0.268013</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>Precious Achiuwa</td>\n",
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" <td>achiupr01</td>\n",
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" <td>2021</td>\n",
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" <td>1581</td>\n",
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" <td>749</td>\n",
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" <td>-4.122966</td>\n",
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" <td>1.359278</td>\n",
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" <td>-2.763688</td>\n",
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" <td>-4.050779</td>\n",
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" <td>-0.919712</td>\n",
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" <td>...</td>\n",
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" <td>-4.347596</td>\n",
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" <td>0.954821</td>\n",
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" <td>-3.392775</td>\n",
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" <td>-0.246055</td>\n",
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" <td>-0.246776</td>\n",
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" <td>0.000721</td>\n",
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" <td>-3.817713</td>\n",
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" <td>0.474828</td>\n",
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" <td>-3.342885</td>\n",
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" <td>0.329157</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Precious Achiuwa</td>\n",
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" <td>achiupr01</td>\n",
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" <td>2022</td>\n",
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" <td>3802</td>\n",
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" <td>1892</td>\n",
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" <td>-2.521510</td>\n",
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" <td>1.763502</td>\n",
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" <td>-0.758008</td>\n",
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" <td>-1.687893</td>\n",
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" <td>3.103441</td>\n",
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" <td>...</td>\n",
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" <td>-2.517372</td>\n",
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" <td>2.144151</td>\n",
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" <td>-0.373221</td>\n",
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" <td>2.262658</td>\n",
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" <td>2.309611</td>\n",
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" <td>-0.046953</td>\n",
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" <td>-2.483956</td>\n",
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" <td>2.024360</td>\n",
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" <td>-0.459596</td>\n",
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" <td>-0.728609</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>5 rows × 21 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" player_name player_id season poss mp raptor_box_offense \\\n",
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"0 Alex Abrines abrinal01 2017 2387 1135 0.745505 \n",
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"1 Alex Abrines abrinal01 2018 2546 1244 0.317549 \n",
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"2 Alex Abrines abrinal01 2019 1279 588 -3.215683 \n",
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"3 Precious Achiuwa achiupr01 2021 1581 749 -4.122966 \n",
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"4 Precious Achiuwa achiupr01 2022 3802 1892 -2.521510 \n",
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"\n",
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" raptor_box_defense raptor_box_total raptor_onoff_offense \\\n",
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"0 -0.372938 0.372567 -0.418553 \n",
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"1 -1.725325 -1.407776 -1.291727 \n",
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"2 1.078399 -2.137285 -6.158856 \n",
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"3 1.359278 -2.763688 -4.050779 \n",
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"4 1.763502 -0.758008 -1.687893 \n",
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"\n",
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" raptor_onoff_defense ... raptor_offense raptor_defense raptor_total \\\n",
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"0 -3.857011 ... 0.543421 -1.144832 -0.601411 \n",
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"1 -0.049694 ... -0.020826 -1.502642 -1.523468 \n",
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"2 4.901168 ... -4.040157 1.885618 -2.154538 \n",
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"3 -0.919712 ... -4.347596 0.954821 -3.392775 \n",
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"4 3.103441 ... -2.517372 2.144151 -0.373221 \n",
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"\n",
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" war_total war_reg_season war_playoffs predator_offense \\\n",
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"0 1.249008 1.447708 -0.198700 0.077102 \n",
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"1 0.777304 0.465912 0.311392 -0.174621 \n",
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"2 0.178167 0.178167 0.000000 -4.577678 \n",
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"3 -0.246055 -0.246776 0.000721 -3.817713 \n",
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"4 2.262658 2.309611 -0.046953 -2.483956 \n",
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"\n",
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" predator_defense predator_total pace_impact \n",
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"0 -1.038677 -0.961575 0.326413 \n",
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"1 -1.112625 -1.287247 -0.456141 \n",
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"2 1.543282 -3.034396 -0.268013 \n",
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"3 0.474828 -3.342885 0.329157 \n",
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"4 2.024360 -0.459596 -0.728609 \n",
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"\n",
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"[5 rows x 21 columns]"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "continental-franklin",
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"metadata": {},
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"source": [
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"**Data Overview**\n",
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"\n",
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"*✏️ Write 2-3 sentences describing this dataset. Be sure to include where the data comes from and what it contains.*"
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]
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},
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{
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"cell_type": "markdown",
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"id": "infinite-instrument",
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"metadata": {},
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"source": [
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"# Methods and Results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "basic-canadian",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Import any helper files you need here"
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]
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},
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{
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"cell_type": "markdown",
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"id": "recognized-positive",
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"metadata": {},
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"source": [
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"## First Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "graduate-palmer",
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"metadata": {},
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"source": [
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"### Methods"
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]
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},
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{
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"cell_type": "markdown",
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"id": "endless-variation",
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"metadata": {},
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"source": [
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"*Explain how you will approach this research question below. Consider the following:* \n",
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" - *Which aspects of the dataset will you use?* \n",
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" - *How will you reorganize/store the data?* \n",
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" - *What data science tools/functions will you use and why?* \n",
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" \n",
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"✏️ *Write your answer below:*\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "portuguese-japan",
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"metadata": {},
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"source": [
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"### Results "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "negative-highlight",
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"metadata": {},
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"outputs": [],
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"source": [
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"#######################################################################\n",
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"### 💻 YOUR WORK GOES HERE TO ANSWER THE FIRST RESEARCH QUESTION 💻 \n",
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"### \n",
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"### Your data analysis may include a statistic and/or a data visualization\n",
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"#######################################################################"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "victorian-burning",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
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]
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},
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{
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"cell_type": "markdown",
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"id": "collectible-puppy",
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"metadata": {},
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"source": [
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"## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "demographic-future",
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"metadata": {},
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"source": [
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"### Methods"
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]
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},
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{
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"cell_type": "markdown",
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"id": "incorporate-roller",
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"metadata": {},
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"source": [
|
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"*Explain how you will approach this research question below. Consider the following:* \n",
|
||
" - *Which aspects of the dataset will you use?* \n",
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" - *How will you reorganize/store the data?* \n",
|
||
" - *What data science tools/functions will you use and why?* \n",
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"\n",
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"✏️ *Write your answer below:*\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "juvenile-creation",
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"metadata": {},
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"source": [
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"### Results "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "pursuant-surrey",
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"metadata": {},
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"outputs": [],
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"source": [
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"#######################################################################\n",
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"### 💻 YOUR WORK GOES HERE TO ANSWER THE SECOND RESEARCH QUESTION 💻 \n",
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"###\n",
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"### Your data analysis may include a statistic and/or a data visualization\n",
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"#######################################################################"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "located-night",
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"metadata": {},
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||
"outputs": [],
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"source": [
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"# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
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||
]
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||
},
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{
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"cell_type": "markdown",
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"id": "infectious-symbol",
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"metadata": {},
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"source": [
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"# Discussion"
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]
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},
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{
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"cell_type": "markdown",
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"id": "furnished-camping",
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"metadata": {
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"code_folding": []
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},
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"source": [
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"## Considerations"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bearing-stadium",
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"metadata": {},
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"source": [
|
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"*It's important to recognize the limitations of our research.\n",
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||
"Consider the following:*\n",
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"\n",
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"- *Do the results give an accurate depiction of your research question? Why or why not?*\n",
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"- *What were limitations of your datset?*\n",
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"- *Are there any known biases in the data?*\n",
|
||
"\n",
|
||
"✏️ *Write your answer below:*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "beneficial-invasion",
|
||
"metadata": {},
|
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"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": {
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"cell_metadata_json": true,
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"format_name": "rmarkdown",
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"format_version": "1.2",
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}
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},
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
|
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": false,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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},
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"varInspector": {
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"cols": {
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"lenName": 16,
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"lenType": 16,
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"lenVar": 40
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},
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
|
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"r": {
|
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"delete_cmd_postfix": ") ",
|
||
"delete_cmd_prefix": "rm(",
|
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"library": "var_list.r",
|
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"varRefreshCmd": "cat(var_dic_list()) "
|
||
}
|
||
},
|
||
"types_to_exclude": [
|
||
"module",
|
||
"function",
|
||
"builtin_function_or_method",
|
||
"instance",
|
||
"_Feature"
|
||
],
|
||
"window_display": false
|
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}
|
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},
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"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|