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@yunoooo111
Last active March 7, 2024 03:14
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# METADATA
# Metadata is data about data.Metadata are pieces of information that have some meaning in relation to another piece of information, that can be created, managed, stored, and preserved like any other data.
# Six types of metadata :
#• Structural metadata (Structural metadata provides valuable information that helps to establish the relationship between objects.)
#• Descriptive metadata (Descriptive metadata provides helpful information for discovering and identifying a data resource.)
#• Preservation metadata (Preservation metadata refers to the information related to the preservation management of collections and information resources.)
#• Administrative metadata (Administrative metadata provides information that is useful in managing resources.)
#• Provenance metadata (Provenance metadata provides helpful information on the origins of a data resource.)
#• Definitional metadata (Definitional metadata refers to the metadata that provides a common vocabulary that facilitates a shared understanding of the meaning of the data.)
# RESUME 2
# DATA SCIENCE
# Data Science is scientific process of transforming data into insight for making better decisions,
# that the goal is to turn data into actionable value
# Data :
# Data refers to raw facts, figures, and statistics that are collected
# and stored for analysis. It can be in various forms, such as numbers, text, images,
# or any other format that represents information.
# Data Science:
# Data science is a multidisciplinary field that uses scientific methods, processes, algorithms,
# and systems to extract insights and knowledge from structured and unstructured data.
# It involves a combination of skills from statistics, mathematics, computer science,
# and domain-specific knowledge to analyze and interpret complex data sets. The goal of data science is to uncover patterns,
# trends, and valuable insights from data that can be used to inform business decisions, solve problems, or gain a competitive advantage.
# Data Scientist:
# A data scientist is a professional who possesses a combination of skills in statistics, mathematics, programming, and domain expertise.
# Data scientists use their skills to analyze large and complex data sets, develop algorithms, and create predictive models to extract meaningful insights.
# They work on identifying patterns, trends, and correlations in data to help organizations make data-driven decisions.
# Data scientists also play a crucial role in designing and implementing machine learning models, building data pipelines, and communicating findings to non-technical stakeholders.
# Foundational aspects of data science
#• Mathematics :
#• It will cover foundational mathematical concepts, such as functions, relations, assumptions, conclusions, and abstraction, so that the concepts can be used to define and understand many aspects of data manipulation.
#• Other mathematics and statistics courses have also connections to data science, including graphs for social network analysis, matrices for finding themes in relations, and supervised machine learning.
#• Technology :
#Python knowledge will be extended from the prerequisite with more advanced table manipulation functions, extended practice with data cleaning and manipulation tasks, computational notebooks (such as Jupyter), and GitHub for version control and project publishing.
#• Visualization :
#• New types of plots will be learnt for a wide variety of data types and what you intend to communicate about them.
#• The general principles that govern when and how to use visualizations will be studied.
#• How to build and publish interactive online visualizations (dashboards) will also be learnt.
#• Communication :
#• How to write comments in code, documentation for code, motivations in computational notebooks, interpretation of results in computational notebooks, and technical reports about the results of analyses.
#• Clarity, brevity (concise), and knowing the target audience will be prioritized.
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "48d7df6e",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2d5aa01f",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 480x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(0,1,10)\n",
"y = np.linspace(0,1,10)\n",
"\n",
"X,Y = np.meshgrid(x,y)\n",
"Z = np.sqrt(X**2 + Y**2)\n",
"\n",
"plt.figure(figsize=(4.8,4.8))\n",
"plt.contourf(X,Y,Z)\n",
"plt.grid()\n",
"plt.xlabel('x')\n",
"plt.ylabel('y')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "27791e5a",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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J3v60RIYhjerVRZd1TjA7TsigoAAAYCKGd1pGQQEAwCRFx6q07bBb0VEW3TQo3ew4IaXVBWXt2rWaOnWqMjIaztNevnx5s8cNw9DcuXOVkZGhhIQEjR8/XoWFhc328Xq9uv/++9W1a1clJSVp2rRpOnTo0CX9IAAAhJu3Go+eXN2nq1I7WU1OE1paXVCqqqo0ZMgQLVy4sMXHFyxYoOeee04LFy7Uxo0b5XQ6NWnSJFVUVAT2ycvL07Jly7R06VKtW7dOlZWVmjJlinw+38X/JAAAhBHDMBje+QoWwzCMi36yxaJly5Zp+vTpkhre7IyMDOXl5enRRx+V1HC0xOFwaP78+Zo1a5bcbre6deumV199Vbfddpsk6ciRI8rMzNTKlSt14403nvf7ejwe2e12ud1uJSezHDAAIPzsOOLRTb/+UHExUdr804myxceaHanNtebzO6hzUIqKiuRyuZSbmxvYZrVaNW7cOK1fv16StHnzZtXV1TXbJyMjQzk5OYF9zuT1euXxeJrdAAAIZ01HT27om9YhyklrBbWguFwuSZLD4Wi23eFwBB5zuVyKi4tTly5dzrnPmfLz82W32wO3zMzMYMYGAKBdGYYRmH8ybSjDOy1pk7N4zrzIkWEY573w0VftM2fOHLnd7sCtuLg4aFkBAGhvWw6W6/DJanWyxuiGfmlmxwlJQS0oTqdTks46ElJWVhY4quJ0OlVbW6vy8vJz7nMmq9Wq5OTkZjcAAMLViq0NR09yBzgUHxttcprQFNSCkpWVJafTqYKCgsC22tparVmzRmPHjpUkjRgxQrGxsc32KSkp0fbt2wP7AAAQqep9fv1tW4kkaSrDO+cU09onVFZWat++fYH7RUVF2rp1q1JSUtSjRw/l5eVp3rx5ys7OVnZ2tubNm6fExETNmDFDkmS323XXXXfpwQcfVGpqqlJSUvTQQw9p0KBBmjhxYvB+MgAAQtBH+4/rWGWtuiTG6po+Xc2OE7JaXVA2bdqk66+/PnB/9uzZkqSZM2dq8eLFeuSRR1RdXa177rlH5eXlGj16tFavXi2bzRZ4zvPPP6+YmBjdeuutqq6u1oQJE7R48WJFR3OYCwAQ2ZqGd24alK7YaBZ0P5dLWgfFLKyDAgAIR956n0Y+854qaur1xr9fpdG9U82O1K5MWwcFAACc2we7j6qipl7p9niN6pVidpyQRkEBAKCdNC3ONmVwuqKivnr5jY6OggIAQDuo8tbr/Z2lkqRpQy4zOU3oo6AAANAOCnaUqqbOr6yuScq5jPmT50NBAQCgHTQN70wdknHe1dVBQQEAoM2VV9Vq7Z6jkqRpQ9JNThMeKCgAALSxVdtdqvcb6p+erD5ptvM/ARQUAADa2opPD0uSpg1hafsLRUEBAKANudw1+lfRCUnSVIZ3LhgFBQCANvS3bSUyDGlEzy7q3iXR7Dhhg4ICAEAbajp7h+Gd1qGgAADQRg4cr9KnxScVZWm4OCAuHAUFAIA28lbj0ZOr+3RVN5vV5DThhYICAEAbOX1xNrQOBQUAgDawy+XRntJKxUVH6caBTrPjhB0KCgAAbWDF1oajJ+P7dpM9IdbkNOGHggIAQJAZhqG3Pms8e2cowzsXg4ICAECQfVJ8UsUnqpUUF60J/RxmxwlLFBQAAIKsaXhn0gCHEuKiTU4TnigoAAAEkc9v6G/bSiQxvHMpKCgAAATRx/uP62iFV50TY3VNn25mxwlbFBQAAIKoaXhnck664mL4mL1YvHMAAASJt96nVdsbh3dYnO2SUFAAAAiStXuOyVNTL0eyVVdmpZgdJ6xRUAAACJKmpe2nDM5QdJTF5DThjYICAEAQnKqt13s7SiUxvBMMFBQAAILgfzcdUnWdTz1TEzW4u93sOGGPggIAwCU6WuHVf6zeLUn6wbW9ZbEwvHOpKCgAAFyi/FU7VVFTr0GX2TXjyh5mx4kIFBQAAC7BhqIT+uuWw7JYpKen5zA5NkgoKAAAXKQ6n19PLN8uSbp9VA8NzexsbqAIQkEBAOAiLVn/hXaXViglKU6P3NjX7DgRhYICAMBFcLlr9HzBHknSY1/rpy5JcSYniiwUFAAALsIzf9uhqlqfhvforG+M6G52nIgT9IJSX1+vn/70p8rKylJCQoJ69+6tp556Sn6/P7CPYRiaO3euMjIylJCQoPHjx6uwsDDYUQAAaBP/3HdMb39WoqjGibFRTIwNuqAXlPnz5+u//uu/tHDhQu3cuVMLFizQs88+qxdffDGwz4IFC/Tcc89p4cKF2rhxo5xOpyZNmqSKiopgxwEAIKi89T498WbDxNjvjOmlgRksytYWgl5QPvroI33961/XzTffrF69eukb3/iGcnNztWnTJkkNR09eeOEFPf7447rllluUk5OjJUuW6NSpU3r99deDHQcAgKD6/YdF2n+0St1sVs3OvcLsOBEr6AXlmmuu0fvvv689exomDn366adat26dbrrpJklSUVGRXC6XcnNzA8+xWq0aN26c1q9f3+Jrer1eeTyeZjcAANrbofJTevHveyVJj9/UX8nxsSYnilwxwX7BRx99VG63W/369VN0dLR8Pp9+8Ytf6Fvf+pYkyeVySZIcDkez5zkcDh04cKDF18zPz9eTTz4Z7KgAALTKU2/tUE2dX1f1TtHXh3JBwLYU9CMob7zxhl577TW9/vrr2rJli5YsWaL/+I//0JIlS5rtd+Z1CgzDOOe1C+bMmSO32x24FRcXBzs2AABf6e+7SrV6R6lioix6+us5XG+njQX9CMrDDz+sxx57TLfffrskadCgQTpw4IDy8/M1c+ZMOZ1OSQ1HUtLT0wPPKysrO+uoShOr1Sqr1RrsqAAAXJCaOp9+vqLhbNO7rslStsNmcqLIF/QjKKdOnVJUVPOXjY6ODpxmnJWVJafTqYKCgsDjtbW1WrNmjcaOHRvsOAAAXLKXPvhcxSeqlW6P1wMTss2O0yEE/QjK1KlT9Ytf/EI9evTQwIED9cknn+i5557T97//fUkNQzt5eXmaN2+esrOzlZ2drXnz5ikxMVEzZswIdhwAAC7JF8eq9F9rPpckPTFlgJKsQf/oRAuC/i6/+OKLeuKJJ3TPPfeorKxMGRkZmjVrln72s58F9nnkkUdUXV2te+65R+Xl5Ro9erRWr14tm41DZgCA0GEYhn6+olC19X5dm91Vk3OcZkfqMCyGYRhmh2gtj8cju90ut9ut5ORks+MAACLUO9tLdPdrWxQXHaV3f3ydsrommR0prLXm85tr8QAA0IJTtfV66q0dkqRZ43pTTtoZBQUAgBb8+v19OuKuUfcuCbpnfB+z43Q4FBQAAM6wr6xCv/9wvyRp7tSBSoiLNjlRx0NBAQDgNIZh6Inlhar3G5rYP00TB7S8RhfaFgUFAIDTrPj0iD7af1zWmCj9fOpAs+N0WBQUAAAaVdTU6Rd/2ylJuu/6PspMSTQ5UcdFQQEAoNHzBXtVVuFVVtck/fu43mbH6dAoKAAASNpZ4tGSj76QJD05baCsMUyMNRMFBQDQ4fn9hp5Yvl0+v6GbBjl13RXdzI7U4VFQAAAd3l+2HNKmA+VKjIvWE1MGmB0HoqAAADo496k6/XLVLknSjyZkK92eYHIiSBQUAEAH9+zqXTpeVavstE76/jVZZsdBIwoKAKDD+uzQSf33vw5Kkp76eo5io/lYDBX8lwAAdEi+xomxhiFNH5qhMZenmh0Jp6GgAAA6pKUbD+rTQ27ZrDH6yc39zY6DM1BQAAAdzvFKrxa8s1uSNDv3CqXZ4k1OhDNRUAAAHc78d3bJXV2nAenJuvOqnmbHQQsoKACADmXzgRP6n02HJElPT89RDBNjQxL/VQAAHUa9z6+fLi+UJN06srtG9OxiciKcCwUFANBhvPrxAe0s8cieEKtHv9bP7Dj4ChQUAECHUOap0XOr90iSHvlaX6V2spqcCF+FggIA6BDmrdypCm+9hnS36/ZRPcyOg/OgoAAAIt76z49p+dYjslgaJsZGR1nMjoTzoKAAACJabb1fP3uzYWLsHaN7aHD3zuYGwgWhoAAAItof/1mkfWWVSk2K08O5TIwNFxQUAEDEOnKyWr96b68k6bHJ/WRPjDU5ES4UBQUAELGefnuHqut8Gtmzi/7P8O5mx0ErUFAAABFpzZ6jWrXdpegoi56enqMoJsaGFQoKACDi1NT59PM3t0uSZo7ppf7pySYnQmtRUAAAEWfR2v364vgppdms+vGkbLPj4CJQUAAAEaX4xCn95h/7JEmP39xftngmxoYjCgoAIKLMXVEob71fY3qnatqQDLPj4CJRUAAAEaNgR6ne31Wm2GiLnp4+UBYLE2PDFQUFABARqmt9mruiYcXYH1zbW33SbCYnwqWgoAAAIsJv/rFPh09W67LOCbr/hj5mx8ElapOCcvjwYX37299WamqqEhMTNXToUG3evDnwuGEYmjt3rjIyMpSQkKDx48ersLCwLaIAADqA/UcrtWjtfknSE1MGKDEuxuREuFRBLyjl5eW6+uqrFRsbq1WrVmnHjh36z//8T3Xu3Dmwz4IFC/Tcc89p4cKF2rhxo5xOpyZNmqSKiopgxwEARDjDMPTzFYWq9fk1vm833TjQYXYkBEHQK+b8+fOVmZmpV155JbCtV69ega8Nw9ALL7ygxx9/XLfccoskacmSJXI4HHr99dc1a9asYEcCAESwldtc+nDvMcXFROnJaUyMjRRBP4KyYsUKjRw5Ut/85jeVlpamYcOG6Xe/+13g8aKiIrlcLuXm5ga2Wa1WjRs3TuvXr2/xNb1erzweT7MbAACV3no9/fYOSdL/HXe5eqYmmZwIwRL0grJ//369/PLLys7O1rvvvqu7775bDzzwgP70pz9JklwulyTJ4Wh+CM7hcAQeO1N+fr7sdnvglpmZGezYAIAw9Ov398rlqVGPlET93/GXmx0HQRT0guL3+zV8+HDNmzdPw4YN06xZs/TDH/5QL7/8crP9zjwEZxjGOQ/LzZkzR263O3ArLi4OdmwAQJjZU1qhP64rkiQ9OW2g4mOjTU6EYAp6QUlPT9eAAQOabevfv78OHjwoSXI6nZJ01tGSsrKys46qNLFarUpOTm52AwB0XIZh6KfLt6vebyh3gEPX90szOxKCLOgF5eqrr9bu3bubbduzZ4969uwpScrKypLT6VRBQUHg8draWq1Zs0Zjx44NdhwAQARavvWwNhSdUHxslH42dcD5n4CwE/SzeH784x9r7Nixmjdvnm699VZt2LBBixYt0qJFiyQ1DO3k5eVp3rx5ys7OVnZ2tubNm6fExETNmDEj2HEAABHGXV2nX/xtlyTp/huy1b1LosmJ0BaCXlBGjRqlZcuWac6cOXrqqaeUlZWlF154QXfccUdgn0ceeUTV1dW65557VF5ertGjR2v16tWy2ViWGADw1Z4v2KNjlV717pakH17b2+w4aCMWwzAMs0O0lsfjkd1ul9vtZj4KAHQg2w+7NW3hOvkN6b9/MFpX9+lqdiS0Qms+v7kWDwAgLPj9hp54c7v8hjRlcDrlJMJRUAAAYeF/Nxfrk4MnlRQXrZ/ezMTYSEdBAQCEvPKqWv1yVcPE2B9PukJOe7zJidDWKCgAgJC34N1dKj9Vp74Om2aO7WV2HLQDCgoAIKR9crBcSzc2rCD+9PQcxUbz0dUR8F8ZABCyfI0TYw1DumX4ZboyK8XsSGgnFBQAQMj6738d0PbDHtniYzRncn+z46AdUVAAACHpaIVXz77bcOmUh2/sq242q8mJ0J4oKACAkJS/aqcqauqVc1my7hjd0+w4aGcUFABAyNlQdEJ/3XJYFov09NdzFB1lMTsS2hkFBQAQUup8fj2xfLsk6fZRmRrWo4vJiWAGCgoAIKQsWf+FdpdWqEtirB65sZ/ZcWASCgoAIGS43DV6vmCPJOnRr/VTl6Q4kxPBLBQUAEDIeOZvO1RV69OwHp1168hMs+PARBQUAEBI+Oe+Y3r7sxJFNU6MjWJibIdGQQEAmM5b79MTbzZMjL3zqp7KucxuciKYjYICADDd7z8s0v6jVerayarZuX3NjoMQQEEBAJjqwPEqvfj3vZKkn9zUT/aEWJMTIRRQUAAApik6VqVvLfpYNXV+XZmVon8bdpnZkRAiYswOAADomHaWeHTnHzboWKVXl3dL0q9vHyaLhYmxaEBBAQC0u63FJzXzjxvkrq7TgPRk/emuK9W1ExcDxJcoKACAdvXx/uO6a/FGVdX6NLxHZ73yvSuZd4KzUFAAAO3mH7vLdPerm+Wt92vs5an63XdGKsnKRxHOxt8KAEC7WLmtRD9a+onqfIYm9k/TwhnDFR8bbXYshCgKCgCgzf3vpmI9+pfP5DekqUMy9NytQxQbzYmkODcKCgCgTS1Z/4V+vqJQknT7qEz94t8GKZpl7HEeFBQAQJv5zT/26dl3d0uS7romSz+9uT+nEuOCUFAAAEFnGIYWvLtbL3/wuSTpgQnZ+vHEbMoJLhgFBQAQVH6/oSffKtSSjw5Iali+/t+vu9zkVAg3FBQAQNDU+/x69C/b9Jcth2SxSM9Mz9Edo3uaHQthiIICAAiK2nq/frT0E63a7lJ0lEX/+c0hms61dXCRKCgAgEtWXevT3a9t1po9RxUXHaUXZwzTjQOdZsdCGKOgAAAuSUVNne5askkbik4oITZai74zQtdmdzM7FsIcBQUAcNFOnqrVzD9u0KeH3LJZY/TH743SqF4pZsdCBGjzZfzy8/NlsViUl5cX2GYYhubOnauMjAwlJCRo/PjxKiwsbOsoAIAgKquo0W2//VifHnKrS2Ks/t+/X0U5QdC0aUHZuHGjFi1apMGDBzfbvmDBAj333HNauHChNm7cKKfTqUmTJqmioqIt4wAAguTwyWrd9tuPtbu0Qmk2q/5n1hjlXGY3OxYiSJsVlMrKSt1xxx363e9+py5dugS2G4ahF154QY8//rhuueUW5eTkaMmSJTp16pRef/31tooDAAiSomNV+ubL61V0rErduyToz3ePVbbDZnYsRJg2Kyj33nuvbr75Zk2cOLHZ9qKiIrlcLuXm5ga2Wa1WjRs3TuvXr2+rOACAINjl8uib//WRjrhr1Ltbkv737jHqkZpodixEoDaZJLt06VJt2bJFGzduPOsxl8slSXI4HM22OxwOHThwoMXX83q98nq9gfsejyeIaQEAF2Jr8UnN/OMGuavr1D89Wa/edaW6drKaHQsRKuhHUIqLi/WjH/1Ir732muLj48+535nXYzAM45zXaMjPz5fdbg/cMjMzg5oZAPDVPt5/XHf87mO5q+s0vEdnLf3hVZQTtKmgF5TNmzerrKxMI0aMUExMjGJiYrRmzRr9+te/VkxMTODISdORlCZlZWVnHVVpMmfOHLnd7sCtuLg42LEBAOfwj91lmvnHDaqq9Wns5al69a7RsifGmh0LES7oQzwTJkzQtm3bmm373ve+p379+unRRx9V79695XQ6VVBQoGHDhkmSamtrtWbNGs2fP7/F17RarbJaaeoA0N5WbivRj5Z+ojqfoYn907RwxnDFx0abHQsdQNALis1mU05OTrNtSUlJSk1NDWzPy8vTvHnzlJ2drezsbM2bN0+JiYmaMWNGsOMAAC7Snzcf0iN//lR+Q5oyOF3P3zZUsdFtvnwWIMmklWQfeeQRVVdX65577lF5eblGjx6t1atXy2bjNDUACAV/+ugL/ezNhgU0bxuZqXm3DFJ0VMvzBIG2YDEMwzA7RGt5PB7Z7Xa53W4lJyebHQcAIspLH+zTgnd2S5K+f3WWnpjS/5wnMQCt0ZrPb67FAwCQ1HA25bPv7tZLH3wuSXpgQrZ+PDGbcgJTUFAAAPL7DT35VqGWfNSwHtVPbuqnf7/ucpNToSOjoABAB1fv8+uxv27TnzcfksUiPTM9R3eM7ml2LHRwFBQA6MBq6/3Ke+MTrdzmUnSURf/xzcH6t2HdzY4FUFAAoKOqqfPp7tc264PdRxUXHaUXZwzTjQOdZscCJFFQAKBDqqip0w+WbNK/ik4oITZai74zQtdmdzM7FhBAQQGADubkqVrN/OMGfXrILZs1Rn/83iiN6pVidiygGQoKAHQgZRU1uvP3G7S7tEJdEmP1p++P1qDudrNjAWehoABAB3H4ZLW+/ft/qehYldJsVr32g9G6wsEK3ghNFBQA6ACKjlXp27//lw6frNZlnRP0+g9Hq2dqktmxgHOioABAhNvl8ujbv9+gY5Ve9e6WpP/+wWil2xPMjgV8JQoKAESwrcUnNfOPG+SurlP/9GS9eteV6trJanYs4LwoKAAQoT7ef1x3Ld6oqlqfhvXorMXfvVL2xFizYwEXhIICABHog91lmvXqZnnr/Rp7eap+952RSrLyKx/hg7+tABBhVm0r0QNLP1Gdz9CEfmn6zR3DFR8bbXYsoFUoKAAQQf68+ZAe+fOn8hvSlMHpev62oYqNjjI7FtBqFBQAiBB/+ugL/ezNQknSbSMzNe+WQYqOspicCrg4FBQAiAAvfbBPC97ZLUn6/tVZemJKf1kslBOELwoKAIQxwzD07Lu79dIHn0uSHrihj3486QrKCcIeBQUAwpTfb+ipt3do8fovJElzJvfTrHGXmxsKCBIKCgCEoQ1FJ5S/aqc+OXhSFov09Ndz9O2repodCwgaCgoAhJG9pRWa/84uvbezTJKUEBut/FsGafqwy0xOBgQXBQUAwkCpp0bPF+zR/2wqlt+QoqMsum1UpvImZCstOd7seEDQUVAAIIR5aur02zWf6w/rilRT55ck3TjQoUe+1k+Xd+tkcjqg7VBQACAE1db79drHB/Ti3/eq/FSdJGlkzy6ac1M/jeiZYnI6oO1RUAAghPj9ht7eVqJn392l4hPVkqTLuyXp0a/106QBDk4fRodBQQGAELF+3zHlr9qlbYfdkqRuNqt+PPEK3Tqyu2JYrh4dDAUFAEy2s8SjX67apTV7jkqSOlljNOu63rrr2iwlxvFrGh0Tf/MBwCSHT1brP1fv1rJPDsswpJgoi759VU/df0MfpXaymh0PMBUFBQDamftUnV76YJ9eWf+FausbzsyZMjhdD9/YVz1Tk0xOB4QGCgoAtJOaOp/+9NEX+s0/Ppe7uuHMnKt6p2jO5P4aktnZ3HBAiKGgAEAb8/kNLf/ksJ4r2KPDJxvOzOnrsOmxyf00vm83zswBWkBBAYA2YhiG1uw5ql+u2qVdrgpJUro9XrMnXaFbhndXdBTFBDgXCgoAtIFth9zKX7VT6z8/Lkmyxcfo3uv76Ltjeyk+NtrkdEDoo6AAQBAdPH5Kz67erbc+PSJJiouO0nfG9NS91/dRl6Q4k9MB4SPoK//k5+dr1KhRstlsSktL0/Tp07V79+5m+xiGoblz5yojI0MJCQkaP368CgsLgx0FANrNiapaPflWoSY894He+vSILBbp34ZdpvcfHKefThlAOQFaKegFZc2aNbr33nv18ccfq6CgQPX19crNzVVVVVVgnwULFui5557TwoULtXHjRjmdTk2aNEkVFRXBjgMAbaq61qff/GOfxi34h1755xeq8xm6Nrur3r7/Gj1/21BlpiSaHREISxbDMIy2/AZHjx5VWlqa1qxZo+uuu06GYSgjI0N5eXl69NFHJUler1cOh0Pz58/XrFmzzvuaHo9HdrtdbrdbycnJbRkfAFpU7/Prz5sP6fn39qjU45UkDcxI1mOT++na7G4mpwNCU2s+v9t8Dorb3XBNiZSUhqtvFhUVyeVyKTc3N7CP1WrVuHHjtH79+hYLitfrldfrDdz3eDxtnBoAWmYYht7bWaYF7+zS3rJKSVL3Lgl6KLevpg3JUBRn5gBB0aYFxTAMzZ49W9dcc41ycnIkSS6XS5LkcDia7etwOHTgwIEWXyc/P19PPvlkW0YFgPPacrBcv1y5Sxu+OCFJ6pwYq/uu76M7x/SUNYYzc4BgatOCct999+mzzz7TunXrznrszIWJDMM452JFc+bM0ezZswP3PR6PMjMzgxsWAM5h/9FKPfvubq3a3vAPLGtMlL5/TZbuHne57AmxJqcDIlObFZT7779fK1as0Nq1a9W9e/fAdqfTKanhSEp6enpge1lZ2VlHVZpYrVZZrVw4C0D7Olrh1a/e36P/t6FYPr+hKIv0f4Z31+zcK5RuTzA7HhDRgl5QDMPQ/fffr2XLlumDDz5QVlZWs8ezsrLkdDpVUFCgYcOGSZJqa2u1Zs0azZ8/P9hxAKDVqrz1WrR2v3734X6dqvVJkm7ol6ZHv9ZPfZ02k9MBHUPQC8q9996r119/XW+++aZsNltgzondbldCQoIsFovy8vI0b948ZWdnKzs7W/PmzVNiYqJmzJgR7DgAcMHqfH4t3VisX723V8cqGybmD+lu15yb+uuq3qkmpwM6lqAXlJdfflmSNH78+GbbX3nlFX33u9+VJD3yyCOqrq7WPffco/Lyco0ePVqrV6+Wzca/TAC0P8Mw9M52l559d7f2H2tYs6lXaqIevrGfbhrk5GJ+gAnafB2UtsA6KACCZUPRCeWv2qlPDp6UJKUmxemBCdn61pU9FBcT9LUsgQ4tpNZBAYBQtLe0QvPf2aX3dpZJkhJio/XDa7P0w+t6yxbPmTmA2SgoADqMUk+NCnaUavWOUq3be1R+Q4qOsui2UZnKm5CttOR4syMCaERBARDR9pVVavUOl1YXlmpr8clmj9040KGHb+ynPmmdzAkH4JwoKAAiit9v6JPik1q9w6WCwtLApNcmQzM7K3egQ7kDnBQTIIRRUACEPW+9T+s/P67VhaV6b2epjlZ8ee2u2GiLxl7eVbkDHZrY3yEHwzhAWKCgAAhL7uo6fbC7TKsLS/XB7jJVNS6oJkk2a4zG90tT7gCHxvftxqRXIAxRUACEjRJ3td5rnOT60efHVe//cpUER7JVkwY0DN1c1TuVU4SBMEdBARCyDMPQ3rJKrS50qWBHqT495G72eHZap4ZSMtCpwZfZFRXFgmpApKCgAAgpPr+hTw6Wa/WOUq0udOmL46cCj1ks0vAeXZQ7wKFJAxzq3Y1JrkCkoqAAMF1NnU/rPz8WmOR6rLI28FhcdJSuye6qSQMcmtA/TWk2JrkCHQEFBYAp3Kfq9PfdpVpdWKo1e44GrhosSbb4GE3ol6bcgU5dd0U3dbLyqwroaPi/HkC7OXKyunElV5c+3n9CvtMmuTqT4wPrk4zunaLYaCa5Ah0ZBQVAmzEMQ7tLK7S6sKGUbD/safZ4X4ctUEpyLkvmqsEAAigoAILK5ze0+UC5Vhe6tHpHqQ6eaD7JdVTPFE1qnOTaq2uSiUkBhDIKCoBLVlPn04d7j2l1oUvv7yrTiarTJrnGROm67K7KHeDUDf3T1LWT1cSkAMIFBQXARSmvqtXfd5Vp9Q6X1u45puq6Lye52hNiNaF/w0qu12Z3UxKTXAG0Er81AFyQmjqf9pVVakPRCRXsKNWGL5pPcr2sc0LjomkOjerFJFcAl4aCAqAZwzB0+GS1drsqtMtVoZ0lHu1yVajoWFWzQiJJ/dOTA4umDcxgkiuA4KGgAB1YRU2d9pQ2FJFdJRXa5WooIxU19S3u3zkxVgMzknVDP4dyBziUmZLYzokBdBQUFKAD8PkNfXG8SrtKKrTb5dFOV0MZKT5R3eL+sdEWXd6tk/o5beqXnqx+Tpv6pycrzWblKAmAdkFBASLMiapa7SppKCG7G4+I7HZVyFvvb3F/Z3K8+jpt6pduU39nsvql29S7ayeuBgzAVBQUIEx56336vKwqMCyzs8Sj3a4KlVV4W9w/PjZKfZ3J6udoKCP9nA1HRrokxbVzcgA4PwoKEOIMw5DLU6NdJRXa6fIE5orsP1ql+jMmrTbpmZqofk6b+jqT1b9xmKZHSqKioxieARAeKChACKny1p82abVxrkiJR55zTFpNjo8JzBHp1zg8c4XDxsX1AIQ9fosBJvD7DR08cUq7XB7tPO3smQPHT7W4f3SURZd3S1I/Z7L6Om3q3zhEk26PZ9IqgIhEQQHaiN9v6MSpWpV6alTm8erA8aqGuSKuCu1xVTRbefV03WzWwFkzfRvni/RJ6yRrTHQ7/wQAYB4KCtBKhmGowluvMk+NSj1eudw1Kq1oKCGnf11WUaM6X8tzRCTJGhOlKxy2Zqfy9nXauFYNAIiCAjRTU+drKBqeGpU23soqGovHaV+f6+hHS7p2ilOaLV4ZnRPUP72hhPRzJqtXaqJiWA4eAFpEQUGHUO/z62ilV6Ueb+OQS01jCfEGhmBcnhq5q+su+DVt8TFyJsfLkRyvtGSrHMnxjfetSmvc3q2TlfVEAOAiUFAQ1gzD0Imq2oaiUVGjUndN4OvTS8ixSq+Mc4+2NGONiZLTHi+HraF4tFRC0pKtSozjfx8AaCv8hkXI8fkNVXrrVVFTp4qaepVX1TY72vHl7fzzPE4XHWVRmq3h6IazsWw4kuOVZrM2FJLkhlKSnBDDmTEAYDIKCoLK72+YQNpULhpudYE/Padt85zxWNP+ld6W1/z4KqlJcY2Fw9p4tOPL4ZamIpKSFMdCZQAQJigoCPD7DVXWNpQET/WZxaF5uTizVHgav76YcnEucTFRSo6PkT0hNjDk4rDHy2E7rYTYmecBAJGIghLGfH5D3nqfaur8gT9r6nzy1jf8eaqpbJyjcJx5hKOytv6C52mcT1x0lGzxMUpOiJUtPqbhZm36+sttyfHNt52+P+t+AEDHRUEJAsMwVOvzB4qC94yicK4CUdO0b+OfLe5z1uv45W187oXOvWitpnLRcpk4d7k4/ev4WMoFAODimVpQXnrpJT377LMqKSnRwIED9cILL+jaa681LU9ZRY1+u2Z/i0WhWYE4rVg07ROsIw8XKzbaoviYaFljo2Rt/DMpLqaFohGr5HMUDsoFACBUmFZQ3njjDeXl5emll17S1Vdfrd/+9reaPHmyduzYoR49epiSqaKmXn9YV3RJr2GxKFAU4mOiFd9YGOJjo2SNjZY1JkrxsdGKD3wddcb+X35tPe258S0+98vXZ/InACCSWAzDnH/7jx49WsOHD9fLL78c2Na/f39Nnz5d+fn5X/lcj8cju90ut9ut5OTkoGU6eapWL6/5/MuicEYJOLsofHm0oml7XHQUp6gCANCC1nx+m3IEpba2Vps3b9Zjjz3WbHtubq7Wr19/1v5er1derzdw3+PxtEmuzolxmjO5f5u8NgAAuHCmnJt57Ngx+Xw+ORyOZtsdDodcLtdZ++fn58tutwdumZmZ7RUVAACYwNTFI84cCjEMo8XhkTlz5sjtdgduxcXF7RURAACYwJQhnq5duyo6OvqsoyVlZWVnHVWRJKvVKquVS9ADANBRmHIEJS4uTiNGjFBBQUGz7QUFBRo7dqwZkQAAQAgx7TTj2bNn684779TIkSM1ZswYLVq0SAcPHtTdd99tViQAABAiTCsot912m44fP66nnnpKJSUlysnJ0cqVK9WzZ0+zIgEAgBBh2jool6Kt1kEBAABtpzWf31wCFgAAhBwKCgAACDkUFAAAEHIoKAAAIORQUAAAQMihoAAAgJBj2jool6LpzOi2uqoxAAAIvqbP7QtZ4SQsC0pFRYUkcVVjAADCUEVFhex2+1fuE5YLtfn9fh05ckQ2m63Fqx9fCo/Ho8zMTBUXF7MIXBvifW4fvM/tg/e5/fBet4+2ep8Nw1BFRYUyMjIUFfXVs0zC8ghKVFSUunfv3qbfIzk5mb/87YD3uX3wPrcP3uf2w3vdPtrifT7fkZMmTJIFAAAhh4ICAABCDgXlDFarVT//+c9ltVrNjhLReJ/bB+9z++B9bj+81+0jFN7nsJwkCwAAIhtHUAAAQMihoAAAgJBDQQEAACGHggIAAEIOBeU0L730krKyshQfH68RI0boww8/NDtSxFm7dq2mTp2qjIwMWSwWLV++3OxIESk/P1+jRo2SzWZTWlqapk+frt27d5sdK+K8/PLLGjx4cGAxqzFjxmjVqlVmx4p4+fn5slgsysvLMztKRJk7d64sFkuzm9PpNC0PBaXRG2+8oby8PD3++OP65JNPdO2112ry5Mk6ePCg2dEiSlVVlYYMGaKFCxeaHSWirVmzRvfee68+/vhjFRQUqL6+Xrm5uaqqqjI7WkTp3r27fvnLX2rTpk3atGmTbrjhBn39619XYWGh2dEi1saNG7Vo0SINHjzY7CgRaeDAgSopKQnctm3bZloWTjNuNHr0aA0fPlwvv/xyYFv//v01ffp05efnm5gsclksFi1btkzTp083O0rEO3r0qNLS0rRmzRpdd911ZseJaCkpKXr22Wd11113mR0l4lRWVmr48OF66aWX9Mwzz2jo0KF64YUXzI4VMebOnavly5dr69atZkeRxBEUSVJtba02b96s3NzcZttzc3O1fv16k1IBweN2uyU1fHiibfh8Pi1dulRVVVUaM2aM2XEi0r333qubb75ZEydONDtKxNq7d68yMjKUlZWl22+/Xfv37zctS1heLDDYjh07Jp/PJ4fD0Wy7w+GQy+UyKRUQHIZhaPbs2brmmmuUk5NjdpyIs23bNo0ZM0Y1NTXq1KmTli1bpgEDBpgdK+IsXbpUW7Zs0caNG82OErFGjx6tP/3pT7riiitUWlqqZ555RmPHjlVhYaFSU1PbPQ8F5TQWi6XZfcMwztoGhJv77rtPn332mdatW2d2lIjUt29fbd26VSdPntRf/vIXzZw5U2vWrKGkBFFxcbF+9KMfafXq1YqPjzc7TsSaPHly4OtBgwZpzJgxuvzyy7VkyRLNnj273fNQUCR17dpV0dHRZx0tKSsrO+uoChBO7r//fq1YsUJr165V9+7dzY4TkeLi4tSnTx9J0siRI7Vx40b96le/0m9/+1uTk0WOzZs3q6ysTCNGjAhs8/l8Wrt2rRYuXCiv16vo6GgTE0ampKQkDRo0SHv37jXl+zMHRQ2/YEaMGKGCgoJm2wsKCjR27FiTUgEXzzAM3XffffrrX/+qv//978rKyjI7UodhGIa8Xq/ZMSLKhAkTtG3bNm3dujVwGzlypO644w5t3bqVctJGvF6vdu7cqfT0dFO+P0dQGs2ePVt33nmnRo4cqTFjxmjRokU6ePCg7r77brOjRZTKykrt27cvcL+oqEhbt25VSkqKevToYWKyyHLvvffq9ddf15tvvimbzRY4Omi325WQkGByusjxk5/8RJMnT1ZmZqYqKiq0dOlSffDBB3rnnXfMjhZRbDbbWfOnkpKSlJqayryqIHrooYc0depU9ejRQ2VlZXrmmWfk8Xg0c+ZMU/JQUBrddtttOn78uJ566imVlJQoJydHK1euVM+ePc2OFlE2bdqk66+/PnC/aVxz5syZWrx4sUmpIk/T6fLjx49vtv2VV17Rd7/73fYPFKFKS0t15513qqSkRHa7XYMHD9Y777yjSZMmmR0NaLVDhw7pW9/6lo4dO6Zu3brpqquu0scff2za5yDroAAAgJDDHBQAABByKCgAACDkUFAAAEDIoaAAAICQQ0EBAAAhh4ICAABCDgUFAACEHAoKAAAIORQUAAAQcigoAAAg5FBQAABAyKGgAACAkPP/AUEq3ZmnLdoGAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"x = np.linspace(0, 5, 10) # [0, 0.55, 1.11, 1.66, 2.22, 2.77, 3.33, 3.88, 4.44, 5]\n",
"y = np.exp(x) # [1, 1.74, 3.03, 5.29, 9.22, 16.08, 28.03, 48.85, 85.15, 148.41]\n",
"\n",
"plt.plot(x, y)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "9a282419",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x =[5, 7, 8, 7, 2, 17, 2, 9,\n",
" 4, 11, 12, 9, 6] \n",
" \n",
"y =[99, 86, 87, 88, 100, 86, \n",
" 103, 87, 94, 78, 77, 85, 86]\n",
" \n",
"plt.scatter(x, y, c =\"blue\")\n",
" \n",
"# To show the plot\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "6b28d89d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x600 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib import cm\n",
"x = np.linspace(1, 4, 20)\n",
"y = np.linspace(1, 4, 20)\n",
"X, Y = np.meshgrid(x, y)\n",
"Z = np.sqrt((X - 2.5)**2 + (Y - 2.5)**2)\n",
"\n",
"plt.figure(figsize=(8, 6))\n",
"\n",
"plt.subplot(2, 3, 1)\n",
"plt.ylabel('y')\n",
"plt.grid()\n",
"plt.title('cm.jet')\n",
"plt.contourf(X, Y, Z, cmap=cm.jet)\n",
"\n",
"plt.subplot(2, 3, 2)\n",
"plt.grid()\n",
"plt.title('cm.gray')\n",
"plt.contourf(X, Y, Z, cmap=cm.gray)\n",
"\n",
"plt.subplot(2, 3, 3)\n",
"plt.grid()\n",
"plt.title('cm.viridis')\n",
"plt.contourf(X, Y, Z, cmap=cm.viridis)\n",
"\n",
"plt.subplot(2, 3, 4)\n",
"plt.xlabel('x')\n",
"plt.ylabel('y')\n",
"plt.grid()\n",
"plt.title('cm.winter')\n",
"plt.contourf(X, Y, Z, cmap=cm.winter)\n",
"\n",
"plt.subplot(2, 3, 5)\n",
"plt.xlabel('x')\n",
"plt.grid()\n",
"plt.title('cm.seismic')\n",
"plt.contourf(X, Y, Z, cmap=cm.seismic)\n",
"\n",
"plt.subplot(2, 3, 6)\n",
"plt.xlabel('x')\n",
"plt.grid()\n",
"plt.title('cm.Set3')\n",
"plt.contourf(X, Y, Z, cmap=cm.Set3)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "66c3aa5a",
"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": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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