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puyepuye/three-throws.ipynb

Created June 6, 2021 00:13
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Three Throws
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three-throws.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Three Throws ",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/puyepuye/0e07c7dacef2bd50a5a0d405d297737f/three-throws.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "vrLbiuJDwopm"
},
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-PTLiDb9yEEa",
"colab": {
"resources": {
"http://localhost:8080/nbextensions/google.colab/files.js": {
"data": 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",
"ok": true,
"headers": [
[
"content-type",
"application/javascript"
]
],
"status": 200,
"status_text": ""
}
},
"base_uri": "https://localhost:8080/",
"height": 346
},
"outputId": "76ff97f2-3cd3-4487-ad38-cf18233bcd03"
},
"source": [
"from google.colab import files\n",
"upload = files.upload()\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <input type=\"file\" id=\"files-2e491840-9f9b-4f5b-998c-a2c4a08420e1\" name=\"files[]\" multiple disabled\n",
" style=\"border:none\" />\n",
" <output id=\"result-2e491840-9f9b-4f5b-998c-a2c4a08420e1\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script src=\"/nbextensions/google.colab/files.js\"></script> "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "error",
"ename": "KeyboardInterrupt",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-8e1e89930b72>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mupload\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mupload\u001b[0;34m()\u001b[0m\n\u001b[1;32m 62\u001b[0m result = _output.eval_js(\n\u001b[1;32m 63\u001b[0m 'google.colab._files._uploadFiles(\"{input_id}\", \"{output_id}\")'.format(\n\u001b[0;32m---> 64\u001b[0;31m input_id=input_id, output_id=output_id))\n\u001b[0m\u001b[1;32m 65\u001b[0m \u001b[0mfiles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_collections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefaultdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_six\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbinary_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0;31m# Mapping from original filename to filename as saved locally.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/output/_js.py\u001b[0m in \u001b[0;36meval_js\u001b[0;34m(script, ignore_result, timeout_sec)\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mignore_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_message\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_next_input_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_NOT_READY\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.025\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 102\u001b[0m \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 103\u001b[0m if (reply.get('type') == 'colab_reply' and\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "9-p7GZcvyhN0"
},
"source": [
"data1 = pd.read_csv(\"Throw 1.csv\")\n",
"data2 = pd.read_csv(\"Throw 2.csv\")\n",
"data3 = pd.read_csv(\"Throw 3.csv\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "SUuNNcFr4a5F"
},
"source": [
"# Throw 1\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "KiUhVjTr4Kri",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 307
},
"outputId": "81bc9a98-d58a-42ed-ad54-046de544014e"
},
"source": [
"data1.drop(['Unnamed: 2'], axis=1, inplace=True)"
],
"execution_count": null,
"outputs": [
{
"output_type": "error",
"ename": "KeyError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-32-6386fe269999>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Unnamed: 2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m 4172\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4173\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4174\u001b[0;31m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4175\u001b[0m )\n\u001b[1;32m 4176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m 3887\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3888\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3889\u001b[0;31m \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3891\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m 3921\u001b[0m \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3922\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3923\u001b[0;31m \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3924\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3925\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m 5285\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5286\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5287\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5288\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5289\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: \"['Unnamed: 2'] not found in axis\""
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "dY-UfG0WyhPe",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "f2226912-1b0a-4a81-9a08-59527222d6d3"
},
"source": [
"data1.head(5)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Actual Time</th>\n",
" <th>Y (Real Height)</th>\n",
" <th>Actual Tme</th>\n",
" <th>Vy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4.395187</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.033333</td>\n",
" <td>0.141544</td>\n",
" <td>0.033333</td>\n",
" <td>4.211026</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.066667</td>\n",
" <td>0.288198</td>\n",
" <td>0.066667</td>\n",
" <td>3.834773</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.100000</td>\n",
" <td>0.401651</td>\n",
" <td>0.100000</td>\n",
" <td>3.342468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.133333</td>\n",
" <td>0.502862</td>\n",
" <td>0.133333</td>\n",
" <td>3.053107</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Actual Time Y (Real Height) Actual Tme Vy\n",
"0 0.000000 0.000000 0.000000 4.395187\n",
"1 0.033333 0.141544 0.033333 4.211026\n",
"2 0.066667 0.288198 0.066667 3.834773\n",
"3 0.100000 0.401651 0.100000 3.342468\n",
"4 0.133333 0.502862 0.133333 3.053107"
]
},
"metadata": {
"tags": []
},
"execution_count": 33
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "v8o0sGdy2VFS"
},
"source": [
"act1 = data1[\"Actual Time\"]\n",
"height1 = data1[\"Y (Real Height)\"]\n",
"vy1 = data1[\"Vy\"]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "lcjkML37zfzB",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 279
},
"outputId": "687c5b21-425c-460b-d7c1-9b6aab174d91"
},
"source": [
"data1.plot.scatter(\"Actual Time\", \"Y (Real Height)\", color=\"red\")\n",
"\n",
"model = np.poly1d(np.polyfit(act1, height1, 2))\n",
"polyline = np.linspace(0, 1, 50)\n",
"plt.plot(polyline, model(polyline), color=\"green\")\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "FwmOoDur3Zk3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 279
},
"outputId": "d679db6b-a121-4dc2-dc3b-d30e8c9edb55"
},
"source": [
"data1.plot.scatter(\"Actual Time\", \"Vy\", color=\"red\")\n",
"\n",
"model = np.poly1d(np.polyfit(act, vy, 2))\n",
"polyline = np.linspace(0, 1, 50)\n",
"plt.plot(polyline, model(polyline), color=\"green\")\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HJFqgoYfllqC"
},
"source": [
"#Throw 2\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "LGSGcXEhlndw"
},
"source": [
"data2.drop(['Unnamed: 2'], axis=1, inplace=True)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"id": "qz_lQchZlpPG",
"outputId": "51dfc672-a0cf-4e8e-d692-ad1596169740"
},
"source": [
"data2. head(5)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Actual Time</th>\n",
" <th>Y (Height)</th>\n",
" <th>Actual Time.1</th>\n",
" <th>Vy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>5.680702</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.033333</td>\n",
" <td>0.197570</td>\n",
" <td>0.033333</td>\n",
" <td>5.360013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.066667</td>\n",
" <td>0.366618</td>\n",
" <td>0.066667</td>\n",
" <td>4.994356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.100000</td>\n",
" <td>0.530436</td>\n",
" <td>0.100000</td>\n",
" <td>4.633961</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.133333</td>\n",
" <td>0.673810</td>\n",
" <td>0.133333</td>\n",
" <td>4.290146</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Actual Time Y (Height) Actual Time.1 Vy\n",
"0 0.000000 0.000000 0.000000 5.680702\n",
"1 0.033333 0.197570 0.033333 5.360013\n",
"2 0.066667 0.366618 0.066667 4.994356\n",
"3 0.100000 0.530436 0.100000 4.633961\n",
"4 0.133333 0.673810 0.133333 4.290146"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "TF3W9Nhklsd2"
},
"source": [
"act2 = data2[\"Actual Time\"]\n",
"height2 = data2[\"Y (Height)\"]\n",
"vy2 = data2[\"Vy\"]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 279
},
"id": "jSCewnn6mFM_",
"outputId": "c138bc8d-39f3-4f5c-8af4-8ab046b6eb82"
},
"source": [
"data2.plot.scatter(\"Actual Time\", \"Y (Height)\", color=\"red\")\n",
"\n",
"model = np.poly1d(np.polyfit(act2, height2, 2))\n",
"polyline = np.linspace(0, 1.3, 50)\n",
"plt.plot(polyline, model(polyline), color=\"green\")\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 279
},
"id": "x7ugpCrCmfzf",
"outputId": "7bb212ac-8ec1-47d1-99eb-d269625f4ee5"
},
"source": [
"data1.plot.scatter(\"Actual Time\", \"Vy\", color=\"red\")\n",
"\n",
"model = np.poly1d(np.polyfit(act, vy, 2))\n",
"polyline = np.linspace(0, 1, 50)\n",
"plt.plot(polyline, model(polyline), color=\"green\")\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3Gv4CepxmlZN"
},
"source": [
"#Throw 3\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "UqATRUMfmnnX"
},
"source": [
"data3.drop(['Unnamed: 2'], axis=1, inplace=True)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"id": "yN5vEO19mple",
"outputId": "53a72bf5-829e-4889-e78a-6f80637be42c"
},
"source": [
"data3.head(5)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Actual Time</th>\n",
" <th>Y (Height)</th>\n",
" <th>Unnamed: 2</th>\n",
" <th>Actual Time.1</th>\n",
" <th>Vy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>5.630220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.033333</td>\n",
" <td>0.183785</td>\n",
" <td>NaN</td>\n",
" <td>0.033333</td>\n",
" <td>5.692023</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.066667</td>\n",
" <td>0.385589</td>\n",
" <td>NaN</td>\n",
" <td>0.066667</td>\n",
" <td>5.583777</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.100000</td>\n",
" <td>0.567302</td>\n",
" <td>NaN</td>\n",
" <td>0.100000</td>\n",
" <td>5.199064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.133333</td>\n",
" <td>0.727279</td>\n",
" <td>NaN</td>\n",
" <td>0.133333</td>\n",
" <td>4.898190</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Actual Time Y (Height) Unnamed: 2 Actual Time.1 Vy\n",
"0 0.000000 0.000000 NaN 0.000000 5.630220\n",
"1 0.033333 0.183785 NaN 0.033333 5.692023\n",
"2 0.066667 0.385589 NaN 0.066667 5.583777\n",
"3 0.100000 0.567302 NaN 0.100000 5.199064\n",
"4 0.133333 0.727279 NaN 0.133333 4.898190"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7t1UWgdXmsFB"
},
"source": [
"act3 = data3[\"Actual Time\"]\n",
"height3 = data3[\"Y (Height)\"]\n",
"vy3 = data3[\"Vy\"]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 279
},
"id": "cE61dyhSmybv",
"outputId": "82fbf1d5-c86b-449c-8162-152541cbf7a2"
},
"source": [
"data3.plot.scatter(\"Actual Time\", \"Y (Height)\", color=\"red\")\n",
"\n",
"model = np.poly1d(np.polyfit(act3, height3, 2))\n",
"polyline = np.linspace(0, 1.3, 50)\n",
"plt.plot(polyline, model(polyline), color=\"green\")\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 279
},
"id": "ejN_jzmym4TR",
"outputId": "45d206f2-7797-48e6-ebd4-192674a7699b"
},
"source": [
"data1.plot.scatter(\"Actual Time\", \"Vy\", color=\"red\")\n",
"\n",
"model = np.poly1d(np.polyfit(act, vy, 2))\n",
"polyline = np.linspace(0, 1, 50)\n",
"plt.plot(polyline, model(polyline), color=\"green\")\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
}
]
}
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