├── .gitignore ├── Data Analysis With Python ├── House Sales_in_King_Count_USA.ipynb ├── IMG │ ├── Q1.PNG │ ├── Q10.PNG │ ├── Q2.PNG │ ├── Q3.PNG │ ├── Q4.PNG │ ├── Q5.PNG │ ├── Q6.PNG │ ├── Q7.PNG │ ├── Q8.PNG │ └── Q9.PNG └── README.md ├── Data Analyst Capstone Project ├── Accessing_APIs_review_lab.ipynb ├── Collecting_job_data_using_APIs-Lab.ipynb ├── M1ExploreDataSet-lab.ipynb ├── M2DataWrangling-lab.ipynb ├── M3ExploratoryDataAnalysis-lab.ipynb ├── M4DataVisualization-lab.ipynb ├── README.md └── Web-Scraping-Review-Lab.ipynb ├── Data Visualization Dashboards Excel Cognos ├── 5_Peer_Graded_Assignment_Questions.py ├── CarSalesByModelEnd.xlsx ├── Car_Sales_Kaggle_DV0130EN_Lab1_END.xlsx ├── Car_Sales_Kaggle_DV0130EN_Lab2_END.xlsx ├── Cognos_Advanced_Dashboard.pdf ├── Cognos_Basic_Dashboard.pdf ├── Cognos_Final Assignment.pdf ├── IMG │ ├── Cognos_Advanced.PNG │ ├── Cognos_Basic_1.PNG │ ├── Cognos_Basic_2.PNG │ ├── Cognos_FA_1.PNG │ ├── Cognos_FA_2.PNG │ ├── Excel_FA.PNG │ └── W2Dashboard.PNG └── README.md ├── Data Visualization with Python ├── Exercise-Area-Plots-Histograms-and-Bar-Charts.ipynb ├── Exercise-Waffle-Charts-Word-Clouds-and-Regression-Plots.ipynb ├── Final_Assignment.py ├── IMG │ ├── Basic_Plotly │ │ ├── newplot (1).png │ │ ├── newplot (2).png │ │ ├── newplot (3).png │ │ ├── newplot (4).png │ │ ├── newplot (5).png │ │ ├── newplot (6).png │ │ └── newplot.png │ ├── Exercise-Area-Plots-Histograms-and-Bar-Charts │ │ ├── output_41_0.png │ │ ├── output_43_0.png │ │ ├── output_46_1.png │ │ ├── output_51_1.png │ │ ├── output_62_0.png │ │ ├── output_77_0.png │ │ ├── output_86_0.png │ │ └── output_92_0.png │ ├── Exercise-Waffle-Charts-Word-Clouds-and-Regression-Plots │ │ ├── output_46_2.png │ │ ├── output_69_0.png │ │ ├── output_80_0.png │ │ ├── output_89_1.png │ │ ├── output_91_0.png │ │ └── output_93_0.png │ ├── Introduction-to-Matplotlib-and-Line-Plots │ │ ├── output_103_0.png │ │ └── output_91_0.png │ └── Pie-Charts-Box-Plots-Scatter-Plots-and-Bubble-Plots │ │ ├── output_105_1.png │ │ ├── output_26_0.png │ │ ├── output_28_0.png │ │ ├── output_36_0.png │ │ ├── output_47_0.png │ │ ├── output_51_0.png │ │ ├── output_54_0.png │ │ ├── output_67_0.png │ │ ├── output_78_0.png │ │ ├── output_83_0.png │ │ ├── output_90_0.png │ │ └── output_98_1.png ├── Introduction-to-Matplotlib-and-Line-Plots -checkpoint.ipynb ├── Introduction-to-Matplotlib-and-Line-Plots .ipynb ├── Pie-Charts-Box-Plots-Scatter-Plots-and-Bubble-Plots.ipynb ├── Plotly_Basics.ipynb ├── README.md └── dash_interactivity.py ├── Databases and SQL for Data Science with Python ├── Chicago_Datasets_PeerAssign-v5.ipynb ├── IMG │ ├── Q1.PNG │ ├── Q10.PNG │ ├── Q2.PNG │ ├── Q3.PNG │ ├── Q4.PNG │ ├── Q5.PNG │ ├── Q6.PNG │ ├── Q7.PNG │ ├── Q8.PNG │ └── Q9.PNG ├── README.md └── dataset │ ├── ChicagoCensusData.csv │ ├── ChicagoCrimeData.csv │ └── ChicagoPublicSchools.csv ├── Excel Basics for Data Analysis ├── Montgomery_Fleet_Equipment_Inventory_FA_PART_1_END.xlsx ├── Montgomery_Fleet_Equipment_Inventory_FA_PART_2_END.xlsx └── README.md ├── Python Project for Data Science ├── Analyzing_Historical_Stock.ipynb ├── Extracting_Stock_Data_Using_Webscraping.ipynb ├── Extracting_Stock_Data_Using_a_Python_Library.ipynb ├── IMG │ ├── Q1.PNG │ ├── Q2.PNG │ ├── Q3.PNG │ ├── Q4.PNG │ ├── Q5.PNG │ └── Q6.PNG ├── README.md └── WebScraping_Review_Lab.ipynb ├── Python for Data Science, AI & Development ├── PY0101EN-1-1-Types.ipynb ├── PY0101EN-1-2-Strings.ipynb ├── PY0101EN-2-1-Tuples.ipynb ├── PY0101EN-2-2-Lists.ipynb ├── PY0101EN-2-3-Dictionaries.ipynb ├── PY0101EN-2-4-Sets.ipynb ├── PY0101EN-3-1-Conditions.ipynb ├── PY0101EN-3-2-Loops.ipynb ├── PY0101EN-3-3-Functions.ipynb ├── PY0101EN-3-4-Classes.ipynb ├── PY0101EN-4-1-ReadFile.ipynb ├── PY0101EN-4-2-WriteFile.ipynb ├── PY0101EN-5-1-Numpy1D.ipynb ├── PY0101EN-5-2-Numpy2D.ipynb ├── PY0101EN-5.1_Intro_API.ipynb ├── PY0101EN-5.2_API_2.ipynb ├── Python Basics using Pandas.ipynb └── README.md ├── README.md └── certificate ├── 8H6UT8SHWYPD-1.png ├── Data Analysis with Python-1.png ├── Data Visualization and Dashboards with Excel and Cognos-1.png ├── Data Visualization with Python-1.png ├── Database SQL Data Science-1.png ├── Excel Basic for Data Analysis-1.png ├── IBM Data Analyst Capstone Project-1.png ├── Intro to Data Analysis-1.png ├── Python Project for Data Science AI-1.png └── Python for AI, Data Science Development-1.png /.gitignore: -------------------------------------------------------------------------------- 1 | env.py 2 | .ipynb_checkpoints/ 3 | __pycache__ 4 | .DS_Store 5 | Thumbs.db 6 | .vscode/ 7 | *.csv 8 | *.json 9 | *.xlsx -------------------------------------------------------------------------------- /Data Analysis With Python/IMG/Q1.PNG: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/Data Analysis With Python/IMG/Q8.PNG -------------------------------------------------------------------------------- /Data Analysis With Python/IMG/Q9.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/Data Analysis With Python/IMG/Q9.PNG -------------------------------------------------------------------------------- /Data Analysis With Python/README.md: -------------------------------------------------------------------------------- 1 | [![View Repositories](https://img.shields.io/badge/View-My_Repositories-blue?logo=GitHub)](https://github.com/ndleah?tab=repositories) 2 | [![View My Profile](https://img.shields.io/badge/View-My_Profile-green?logo=GitHub)](https://github.com/ndleah) 3 | 4 | # Data Analysis With Python 5 | 6 | ### Libraries Used : Pandas, Matplotlib, Seaborn, NumPy, sklearn 7 | 8 | ### Tasks Performed on the dataset: 9 | * **Importing Data** 10 | * Import of Data (csv file) using Pandas 11 | * Understanding the data 12 | * **Data Wrangling** 13 | * Analyzing Dataset values 14 | * Finding an replacing NaN values 15 | * **Exploratory Data Analysis** 16 | * Using functions to obtain value count of houses that fall under certain conditions 17 | * Plotting data to check for the presence of outliers 18 | * Understanding correlation in the data 19 | * **Model Development** 20 | * Scaling the data 21 | * Fitting a Regression Model 22 | * Predictions using a list of features 23 | * Obtaining R^2 value and analysing this score 24 | * **Model Refinement and Evaluation** 25 | * Splitting the data into training and testing data 26 | * Using Ridge regression to introduce a regularization parameter 27 | * Fitting a regression model 28 | * Making predictions using data 29 | * Obtaining a score to check the performance of model 30 | * Refining the model by performing a polynomial transform on the data 31 | * Analyzing the model performance 32 | 33 | ### Solutions: 34 |

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-------------------------------------------------------------------------------- /Data Analyst Capstone Project/Collecting_job_data_using_APIs-Lab.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "
\n", 8 | " \"cognitiveclass.ai\n", 9 | "
\n" 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "# **Collecting Job Data Using APIs**\n" 17 | ] 18 | }, 19 | { 20 | "cell_type": "markdown", 21 | "metadata": {}, 22 | "source": [ 23 | "Estimated time needed: **45 to 60** minutes\n" 24 | ] 25 | }, 26 | { 27 | "cell_type": "markdown", 28 | "metadata": {}, 29 | "source": [ 30 | "## Objectives\n" 31 | ] 32 | }, 33 | { 34 | "cell_type": "markdown", 35 | "metadata": {}, 36 | "source": [ 37 | "After completing this lab, you will be able to:\n" 38 | ] 39 | }, 40 | { 41 | "cell_type": "markdown", 42 | "metadata": {}, 43 | "source": [ 44 | "* Collect job data from GitHub Jobs API\n", 45 | "* Store the collected data into an excel spreadsheet.\n" 46 | ] 47 | }, 48 | { 49 | "cell_type": "markdown", 50 | "metadata": {}, 51 | "source": [ 52 | "## Warm-Up Exercise\n" 53 | ] 54 | }, 55 | { 56 | "cell_type": "markdown", 57 | "metadata": {}, 58 | "source": [ 59 | "Before you attempt the actual lab, here is a fully solved warmup exercise that will help you to learn how to access an API.\n" 60 | ] 61 | }, 62 | { 63 | "cell_type": "markdown", 64 | "metadata": {}, 65 | "source": [ 66 | "Using an API, let us find out who currently are on the International Space Station (ISS).
The API at [http://api.open-notify.org/astros.json](http://api.open-notify.org/astros.json?utm_medium=Exinfluencer\\&utm_source=Exinfluencer\\&utm_content=000026UJ\\&utm_term=10006555\\&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2021-01-01\\&cm_mmc=Email_Newsletter-\\_-Developer_Ed%2BTech-\\_-WW_WW-\\_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264\\&cm_mmca1=000026UJ\\&cm_mmca2=10006555\\&cm_mmca3=M12345678\\&cvosrc=email.Newsletter.M12345678\\&cvo_campaign=000026UJ) gives us the information of astronauts currently on ISS in json format.
\n", 67 | "You can read more about this API at [http://open-notify.org/Open-Notify-API/People-In-Space/](http://open-notify.org/Open-Notify-API/People-In-Space?utm_medium=Exinfluencer\\&utm_source=Exinfluencer\\&utm_content=000026UJ\\&utm_term=10006555\\&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2021-01-01\\&cm_mmc=Email_Newsletter-\\_-Developer_Ed%2BTech-\\_-WW_WW-\\_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264\\&cm_mmca1=000026UJ\\&cm_mmca2=10006555\\&cm_mmca3=M12345678\\&cvosrc=email.Newsletter.M12345678\\&cvo_campaign=000026UJ)\n" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": 1, 73 | "metadata": {}, 74 | "outputs": [], 75 | "source": [ 76 | "import requests # you need this module to make an API call" 77 | ] 78 | }, 79 | { 80 | "cell_type": "code", 81 | "execution_count": 2, 82 | "metadata": {}, 83 | "outputs": [], 84 | "source": [ 85 | "api_url = \"http://api.open-notify.org/astros.json\" # this url gives use the astronaut data" 86 | ] 87 | }, 88 | { 89 | "cell_type": "code", 90 | "execution_count": 3, 91 | "metadata": {}, 92 | "outputs": [], 93 | "source": [ 94 | "response = requests.get(api_url) # Call the API using the get method and store the\n", 95 | " # output of the API call in a variable called response." 96 | ] 97 | }, 98 | { 99 | "cell_type": "code", 100 | "execution_count": 4, 101 | "metadata": {}, 102 | "outputs": [], 103 | "source": [ 104 | "if response.ok: # if all is well() no errors, no network timeouts)\n", 105 | " data = response.json() # store the result in json format in a variable called data\n", 106 | " # the variable data is of type dictionary." 107 | ] 108 | }, 109 | { 110 | "cell_type": "code", 111 | "execution_count": 5, 112 | "metadata": {}, 113 | "outputs": [ 114 | { 115 | "name": "stdout", 116 | "output_type": "stream", 117 | "text": [ 118 | "{'people': [{'name': 'Mark Vande Hei', 'craft': 'ISS'}, {'name': 'Oleg Novitskiy', 'craft': 'ISS'}, {'name': 'Pyotr Dubrov', 'craft': 'ISS'}, {'name': 'Thomas Pesquet', 'craft': 'ISS'}, {'name': 'Megan McArthur', 'craft': 'ISS'}, {'name': 'Shane Kimbrough', 'craft': 'ISS'}, {'name': 'Akihiko Hoshide', 'craft': 'ISS'}, {'name': 'Nie Haisheng', 'craft': 'Tiangong'}, {'name': 'Liu Boming', 'craft': 'Tiangong'}, {'name': 'Tang Hongbo', 'craft': 'Tiangong'}], 'number': 10, 'message': 'success'}\n" 119 | ] 120 | } 121 | ], 122 | "source": [ 123 | "print(data) # print the data just to check the output or for debugging" 124 | ] 125 | }, 126 | { 127 | "cell_type": "markdown", 128 | "metadata": {}, 129 | "source": [ 130 | "Print the number of astronauts currently on ISS.\n" 131 | ] 132 | }, 133 | { 134 | "cell_type": "code", 135 | "execution_count": 6, 136 | "metadata": {}, 137 | "outputs": [ 138 | { 139 | "name": "stdout", 140 | "output_type": "stream", 141 | "text": [ 142 | "10\n" 143 | ] 144 | } 145 | ], 146 | "source": [ 147 | "print(data.get('number'))" 148 | ] 149 | }, 150 | { 151 | "cell_type": "markdown", 152 | "metadata": {}, 153 | "source": [ 154 | "Print the names of the astronauts currently on ISS.\n" 155 | ] 156 | }, 157 | { 158 | "cell_type": "code", 159 | "execution_count": 7, 160 | "metadata": {}, 161 | "outputs": [ 162 | { 163 | "name": "stdout", 164 | "output_type": "stream", 165 | "text": [ 166 | "There are 10 astronauts on ISS\n", 167 | "And their names are :\n", 168 | "Mark Vande Hei\n", 169 | "Oleg Novitskiy\n", 170 | "Pyotr Dubrov\n", 171 | "Thomas Pesquet\n", 172 | "Megan McArthur\n", 173 | "Shane Kimbrough\n", 174 | "Akihiko Hoshide\n", 175 | "Nie Haisheng\n", 176 | "Liu Boming\n", 177 | "Tang Hongbo\n" 178 | ] 179 | } 180 | ], 181 | "source": [ 182 | "astronauts = data.get('people')\n", 183 | "print(\"There are {} astronauts on ISS\".format(len(astronauts)))\n", 184 | "print(\"And their names are :\")\n", 185 | "for astronaut in astronauts:\n", 186 | " print(astronaut.get('name'))" 187 | ] 188 | }, 189 | { 190 | "cell_type": "markdown", 191 | "metadata": {}, 192 | "source": [ 193 | "Hope the warmup was helpful. Good luck with your next lab!\n" 194 | ] 195 | }, 196 | { 197 | "cell_type": "markdown", 198 | "metadata": {}, 199 | "source": [ 200 | "## Lab: Collect Jobs Data using GitHub Jobs API\n" 201 | ] 202 | }, 203 | { 204 | "cell_type": "markdown", 205 | "metadata": {}, 206 | "source": [ 207 | "### Objective: Determine the number of jobs currently open for various technologies\n" 208 | ] 209 | }, 210 | { 211 | "cell_type": "markdown", 212 | "metadata": {}, 213 | "source": [ 214 | "Collect the number of job postings for the following languages using the API:\n", 215 | "\n", 216 | "* C\n", 217 | "* C#\n", 218 | "* C++\n", 219 | "* Java\n", 220 | "* JavaScript\n", 221 | "* Python\n", 222 | "* Scala\n", 223 | "* Oracle\n", 224 | "* SQL Server\n", 225 | "* MySQL Server\n", 226 | "* PostgreSQL\n", 227 | "* MongoDB\n" 228 | ] 229 | }, 230 | { 231 | "cell_type": "code", 232 | "execution_count": 8, 233 | "metadata": {}, 234 | "outputs": [], 235 | "source": [ 236 | "#Import required libraries\n", 237 | "import requests\n", 238 | "import pandas as pd" 239 | ] 240 | }, 241 | { 242 | "cell_type": "code", 243 | "execution_count": 9, 244 | "metadata": {}, 245 | "outputs": [], 246 | "source": [ 247 | "baseurl = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/labs/module%201/datasets/githubposting.json\"" 248 | ] 249 | }, 250 | { 251 | "cell_type": "code", 252 | "execution_count": 10, 253 | "metadata": {}, 254 | "outputs": [], 255 | "source": [ 256 | " response=requests.get(baseurl)" 257 | ] 258 | }, 259 | { 260 | "cell_type": "code", 261 | "execution_count": 11, 262 | "metadata": {}, 263 | "outputs": [ 264 | { 265 | "data": { 266 | "text/plain": [ 267 | "[{'A': 'technology', 'B': 'number of job posting'},\n", 268 | " {'A': 'java', 'B': '92'},\n", 269 | " {'A': 'C', 'B': '184'},\n", 270 | " {'A': 'C#', 'B': '14'},\n", 271 | " {'A': 'C++', 'B': '24'},\n", 272 | " {'A': 'Java', 'B': '92'},\n", 273 | " {'A': 'JavaScript', 'B': '65'},\n", 274 | " {'A': 'Python', 'B': '51'},\n", 275 | " {'A': 'Scala', 'B': '47'},\n", 276 | " {'A': 'Oracle', 'B': '6'},\n", 277 | " {'A': 'SQL Server', 'B': '16'},\n", 278 | " {'A': 'MySQL Server', 'B': '5'},\n", 279 | " {'A': 'PostgreSQL', 'B': '17'},\n", 280 | " {'A': 'MongoDB', 'B': '4'}]" 281 | ] 282 | }, 283 | "execution_count": 11, 284 | "metadata": {}, 285 | "output_type": "execute_result" 286 | } 287 | ], 288 | "source": [ 289 | "if response.ok: \n", 290 | " data = response.json()\n", 291 | "data" 292 | ] 293 | }, 294 | { 295 | "cell_type": "markdown", 296 | "metadata": {}, 297 | "source": [ 298 | "Write a function to get the number of jobs for the given technology.
\n", 299 | "*Note:* The API gives a maximum of 50 jobs per page.
\n", 300 | "If you get 50 jobs per page, it means there could be some more job listings available.
\n", 301 | "So if you get 50 jobs per page you should make another API call for next page to check for more jobs.
\n", 302 | "If you get less than 50 jobs per page, you can take it as the final count.
\n" 303 | ] 304 | }, 305 | { 306 | "cell_type": "code", 307 | "execution_count": 12, 308 | "metadata": {}, 309 | "outputs": [], 310 | "source": [ 311 | "def get_number_of_jobs(technology):\n", 312 | " number_of_jobs = 0\n", 313 | " payload={\"description\":technology,\"page\":1}\n", 314 | " r=requests.get(baseurl,params=payload)\n", 315 | " if r.ok: # if all is well() no errors, no network timeouts)\n", 316 | " data = r.json()\n", 317 | " while len(data) == 50:\n", 318 | " payload['page'] = payload['page'] + 1\n", 319 | " r=requests.get(baseurl,params=payload)\n", 320 | " if response.ok:\n", 321 | " data = r.json()\n", 322 | " number_of_jobs += len(data)\n", 323 | " else:\n", 324 | " number_of_jobs += len(data) \n", 325 | " \n", 326 | " return technology,number_of_jobs" 327 | ] 328 | }, 329 | { 330 | "cell_type": "markdown", 331 | "metadata": {}, 332 | "source": [ 333 | "Call the function for Python and check if it is working.\n" 334 | ] 335 | }, 336 | { 337 | "cell_type": "code", 338 | "execution_count": 13, 339 | "metadata": {}, 340 | "outputs": [ 341 | { 342 | "name": "stdout", 343 | "output_type": "stream", 344 | "text": [ 345 | "('java', 14)\n" 346 | ] 347 | } 348 | ], 349 | "source": [ 350 | "print(get_number_of_jobs('java'))" 351 | ] 352 | }, 353 | { 354 | "cell_type": "markdown", 355 | "metadata": {}, 356 | "source": [ 357 | "### Store the results in an excel file\n" 358 | ] 359 | }, 360 | { 361 | "cell_type": "markdown", 362 | "metadata": {}, 363 | "source": [ 364 | "Call the API for all the given technologies above and write the results in an excel spreadsheet.\n" 365 | ] 366 | }, 367 | { 368 | "cell_type": "markdown", 369 | "metadata": {}, 370 | "source": [ 371 | "If you do not know how create excel file using python, double click here for **hints**.\n", 372 | "\n", 373 | "\n" 386 | ] 387 | }, 388 | { 389 | "cell_type": "markdown", 390 | "metadata": {}, 391 | "source": [ 392 | "Create a python list of all technologies for which you need to find the number of jobs postings.\n" 393 | ] 394 | }, 395 | { 396 | "cell_type": "code", 397 | "execution_count": 14, 398 | "metadata": {}, 399 | "outputs": [ 400 | { 401 | "data": { 402 | "text/plain": [ 403 | "['C',\n", 404 | " 'C#',\n", 405 | " 'C++',\n", 406 | " 'Java',\n", 407 | " 'JavaScript',\n", 408 | " 'Python',\n", 409 | " 'Scala',\n", 410 | " 'Oracle',\n", 411 | " 'SQL Server',\n", 412 | " 'MySQL Server',\n", 413 | " 'PostgreSQL',\n", 414 | " 'MongoDB']" 415 | ] 416 | }, 417 | "execution_count": 14, 418 | "metadata": {}, 419 | "output_type": "execute_result" 420 | } 421 | ], 422 | "source": [ 423 | "tech_list=['C','C#','C++','Java','JavaScript','Python','Scala','Oracle','SQL Server','MySQL Server','PostgreSQL','MongoDB']\n", 424 | "tech_list" 425 | ] 426 | }, 427 | { 428 | "cell_type": "markdown", 429 | "metadata": {}, 430 | "source": [ 431 | "Import libraries required to create excel spreadsheet\n" 432 | ] 433 | }, 434 | { 435 | "cell_type": "code", 436 | "execution_count": 15, 437 | "metadata": {}, 438 | "outputs": [ 439 | { 440 | "name": "stdout", 441 | "output_type": "stream", 442 | "text": [ 443 | "Requirement already satisfied: openpyxl in c:\\users\\leah nguyen\\anaconda3\\lib\\site-packages (3.0.7)\n", 444 | "Requirement already satisfied: et-xmlfile in c:\\users\\leah nguyen\\anaconda3\\lib\\site-packages (from openpyxl) (1.1.0)\n" 445 | ] 446 | } 447 | ], 448 | "source": [ 449 | "!pip install openpyxl" 450 | ] 451 | }, 452 | { 453 | "cell_type": "code", 454 | "execution_count": 16, 455 | "metadata": {}, 456 | "outputs": [], 457 | "source": [ 458 | "from openpyxl import Workbook" 459 | ] 460 | }, 461 | { 462 | "cell_type": "markdown", 463 | "metadata": {}, 464 | "source": [ 465 | "Create a workbook and select the active worksheet\n" 466 | ] 467 | }, 468 | { 469 | "cell_type": "code", 470 | "execution_count": 17, 471 | "metadata": {}, 472 | "outputs": [], 473 | "source": [ 474 | "wb=Workbook() \n", 475 | "ws=wb.active " 476 | ] 477 | }, 478 | { 479 | "cell_type": "markdown", 480 | "metadata": {}, 481 | "source": [ 482 | "Find the number of jobs postings for each of the technology in the above list.\n", 483 | "Write the technology name and the number of jobs postings into the excel spreadsheet.\n" 484 | ] 485 | }, 486 | { 487 | "cell_type": "code", 488 | "execution_count": 18, 489 | "metadata": {}, 490 | "outputs": [], 491 | "source": [ 492 | "ws.append(['Technology','Number_of_Jobs'])\n", 493 | "\n", 494 | "for i in tech_list:\n", 495 | " ws.append(get_number_of_jobs(i))" 496 | ] 497 | }, 498 | { 499 | "cell_type": "markdown", 500 | "metadata": {}, 501 | "source": [ 502 | "Save into an excel spreadsheet named 'github-job-postings.xlsx'.\n" 503 | ] 504 | }, 505 | { 506 | "cell_type": "code", 507 | "execution_count": 19, 508 | "metadata": {}, 509 | "outputs": [], 510 | "source": [ 511 | "wb.save(\"github-job-postings.xlsx\")" 512 | ] 513 | }, 514 | { 515 | "cell_type": "code", 516 | "execution_count": 20, 517 | "metadata": {}, 518 | "outputs": [ 519 | { 520 | "data": { 521 | "text/html": [ 522 | "
\n", 523 | "\n", 536 | "\n", 537 | " \n", 538 | " \n", 539 | " \n", 540 | " \n", 541 | " \n", 542 | " \n", 543 | " \n", 544 | " \n", 545 | " \n", 546 | " \n", 547 | " \n", 548 | " \n", 549 | " \n", 550 | " \n", 551 | " \n", 552 | " \n", 553 | " \n", 554 | " \n", 555 | " \n", 556 | " \n", 557 | " \n", 558 | " \n", 559 | " \n", 560 | " \n", 561 | " \n", 562 | " \n", 563 | " \n", 564 | " \n", 565 | " \n", 566 | " \n", 567 | " \n", 568 | " \n", 569 | " \n", 570 | " \n", 571 | " \n", 572 | " \n", 573 | " \n", 574 | " \n", 575 | " \n", 576 | " \n", 577 | " \n", 578 | " \n", 579 | " \n", 580 | " \n", 581 | " \n", 582 | " \n", 583 | " \n", 584 | " \n", 585 | " \n", 586 | " \n", 587 | " \n", 588 | " \n", 589 | " \n", 590 | " \n", 591 | " \n", 592 | " \n", 593 | " \n", 594 | " \n", 595 | " \n", 596 | " \n", 597 | " \n", 598 | " \n", 599 | " \n", 600 | " \n", 601 | " \n", 602 | " \n", 603 | " \n", 604 | " \n", 605 | " \n", 606 | "
TechnologyNumber_of_Jobs
0C14
1C#14
2C++14
3Java14
4JavaScript14
5Python14
6Scala14
7Oracle14
8SQL Server14
9MySQL Server14
10PostgreSQL14
11MongoDB14
\n", 607 | "
" 608 | ], 609 | "text/plain": [ 610 | " Technology Number_of_Jobs\n", 611 | "0 C 14\n", 612 | "1 C# 14\n", 613 | "2 C++ 14\n", 614 | "3 Java 14\n", 615 | "4 JavaScript 14\n", 616 | "5 Python 14\n", 617 | "6 Scala 14\n", 618 | "7 Oracle 14\n", 619 | "8 SQL Server 14\n", 620 | "9 MySQL Server 14\n", 621 | "10 PostgreSQL 14\n", 622 | "11 MongoDB 14" 623 | ] 624 | }, 625 | "execution_count": 20, 626 | "metadata": {}, 627 | "output_type": "execute_result" 628 | } 629 | ], 630 | "source": [ 631 | "job_data = pd.read_excel(\"github-job-postings.xlsx\")\n", 632 | "job_data" 633 | ] 634 | }, 635 | { 636 | "cell_type": "markdown", 637 | "metadata": {}, 638 | "source": [ 639 | "## Authors\n" 640 | ] 641 | }, 642 | { 643 | "cell_type": "markdown", 644 | "metadata": {}, 645 | "source": [ 646 | "Ramesh Sannareddy\n" 647 | ] 648 | }, 649 | { 650 | "cell_type": "markdown", 651 | "metadata": {}, 652 | "source": [ 653 | "### Other Contributors\n" 654 | ] 655 | }, 656 | { 657 | "cell_type": "markdown", 658 | "metadata": {}, 659 | "source": [ 660 | "Rav Ahuja\n" 661 | ] 662 | }, 663 | { 664 | "cell_type": "markdown", 665 | "metadata": {}, 666 | "source": [ 667 | "## Change Log\n" 668 | ] 669 | }, 670 | { 671 | "cell_type": "markdown", 672 | "metadata": {}, 673 | "source": [ 674 | "| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n", 675 | "| ----------------- | ------- | ----------------- | ---------------------------------- |\n", 676 | "| 2021-6-25 | 0.2 | Malika | Updated GitHub job json link |\n", 677 | "| 2020-10-17 | 0.1 | Ramesh Sannareddy | Created initial version of the lab |\n" 678 | ] 679 | }, 680 | { 681 | "cell_type": "markdown", 682 | "metadata": {}, 683 | "source": [ 684 | "Copyright © 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license?utm_medium=Exinfluencer\\&utm_source=Exinfluencer\\&utm_content=000026UJ\\&utm_term=10006555\\&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2021-01-01\\&cm_mmc=Email_Newsletter-\\_-Developer_Ed%2BTech-\\_-WW_WW-\\_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264\\&cm_mmca1=000026UJ\\&cm_mmca2=10006555\\&cm_mmca3=M12345678\\&cvosrc=email.Newsletter.M12345678\\&cvo_campaign=000026UJ).\n" 685 | ] 686 | } 687 | ], 688 | "metadata": { 689 | "kernelspec": { 690 | "display_name": "Python 3", 691 | "language": "python", 692 | "name": "python3" 693 | }, 694 | "language_info": { 695 | "codemirror_mode": { 696 | "name": "ipython", 697 | "version": 3 698 | }, 699 | "file_extension": ".py", 700 | "mimetype": "text/x-python", 701 | "name": "python", 702 | "nbconvert_exporter": "python", 703 | "pygments_lexer": "ipython3", 704 | "version": "3.8.10" 705 | } 706 | }, 707 | "nbformat": 4, 708 | "nbformat_minor": 4 709 | } 710 | -------------------------------------------------------------------------------- /Data Analyst Capstone Project/M1ExploreDataSet-lab.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "
\n", 8 | " \"cognitiveclass.ai\n", 9 | "
\n" 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "# **Survey Dataset Exploration Lab**\n" 17 | ] 18 | }, 19 | { 20 | "cell_type": "markdown", 21 | "metadata": {}, 22 | "source": [ 23 | "Estimated time needed: **30** minutes\n" 24 | ] 25 | }, 26 | { 27 | "cell_type": "markdown", 28 | "metadata": {}, 29 | "source": [ 30 | "## Objectives\n" 31 | ] 32 | }, 33 | { 34 | "cell_type": "markdown", 35 | "metadata": {}, 36 | "source": [ 37 | "After completing this lab you will be able to:\n" 38 | ] 39 | }, 40 | { 41 | "cell_type": "markdown", 42 | "metadata": {}, 43 | "source": [ 44 | "* Load the dataset that will used thru the capstone project.\n", 45 | "* Explore the dataset.\n", 46 | "* Get familier with the data types.\n" 47 | ] 48 | }, 49 | { 50 | "cell_type": "markdown", 51 | "metadata": {}, 52 | "source": [ 53 | "## Load the dataset\n" 54 | ] 55 | }, 56 | { 57 | "cell_type": "markdown", 58 | "metadata": {}, 59 | "source": [ 60 | "Import the required libraries.\n" 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": 1, 66 | "metadata": {}, 67 | "outputs": [], 68 | "source": [ 69 | "import pandas as pd" 70 | ] 71 | }, 72 | { 73 | "cell_type": "markdown", 74 | "metadata": {}, 75 | "source": [ 76 | "The dataset is available on the IBM Cloud at the below url.\n" 77 | ] 78 | }, 79 | { 80 | "cell_type": "code", 81 | "execution_count": 2, 82 | "metadata": {}, 83 | "outputs": [], 84 | "source": [ 85 | "dataset_url = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/LargeData/m1_survey_data.csv\"" 86 | ] 87 | }, 88 | { 89 | "cell_type": "markdown", 90 | "metadata": {}, 91 | "source": [ 92 | "Load the data available at dataset_url into a dataframe.\n" 93 | ] 94 | }, 95 | { 96 | "cell_type": "code", 97 | "execution_count": null, 98 | "metadata": {}, 99 | "outputs": [], 100 | "source": [ 101 | "df=pd.read_csv(dataset_url)" 102 | ] 103 | }, 104 | { 105 | "cell_type": "markdown", 106 | "metadata": {}, 107 | "source": [ 108 | "## Explore the data set\n" 109 | ] 110 | }, 111 | { 112 | "cell_type": "markdown", 113 | "metadata": {}, 114 | "source": [ 115 | "It is a good idea to print the top 5 rows of the dataset to get a feel of how the dataset will look.\n" 116 | ] 117 | }, 118 | { 119 | "cell_type": "markdown", 120 | "metadata": {}, 121 | "source": [ 122 | "Display the top 5 rows and columns from your dataset.\n" 123 | ] 124 | }, 125 | { 126 | "cell_type": "code", 127 | "execution_count": null, 128 | "metadata": {}, 129 | "outputs": [], 130 | "source": [ 131 | "df.head()" 132 | ] 133 | }, 134 | { 135 | "cell_type": "markdown", 136 | "metadata": {}, 137 | "source": [ 138 | "## Find out the number of rows and columns\n" 139 | ] 140 | }, 141 | { 142 | "cell_type": "markdown", 143 | "metadata": {}, 144 | "source": [ 145 | "Start by exploring the numbers of rows and columns of data in the dataset.\n" 146 | ] 147 | }, 148 | { 149 | "cell_type": "markdown", 150 | "metadata": {}, 151 | "source": [ 152 | "Print the number of rows in the dataset.\n" 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": null, 158 | "metadata": {}, 159 | "outputs": [], 160 | "source": [ 161 | "df.shape[0]" 162 | ] 163 | }, 164 | { 165 | "cell_type": "markdown", 166 | "metadata": {}, 167 | "source": [ 168 | "Print the number of columns in the dataset.\n" 169 | ] 170 | }, 171 | { 172 | "cell_type": "code", 173 | "execution_count": null, 174 | "metadata": {}, 175 | "outputs": [], 176 | "source": [ 177 | "df.shape[1]" 178 | ] 179 | }, 180 | { 181 | "cell_type": "markdown", 182 | "metadata": {}, 183 | "source": [ 184 | "## Identify the data types of each column\n" 185 | ] 186 | }, 187 | { 188 | "cell_type": "markdown", 189 | "metadata": {}, 190 | "source": [ 191 | "Explore the dataset and identify the data types of each column.\n" 192 | ] 193 | }, 194 | { 195 | "cell_type": "markdown", 196 | "metadata": {}, 197 | "source": [ 198 | "Print the datatype of all columns.\n" 199 | ] 200 | }, 201 | { 202 | "cell_type": "code", 203 | "execution_count": null, 204 | "metadata": {}, 205 | "outputs": [], 206 | "source": [ 207 | "df.dtype" 208 | ] 209 | }, 210 | { 211 | "cell_type": "markdown", 212 | "metadata": {}, 213 | "source": [ 214 | "Print the mean age of the survey participants.\n" 215 | ] 216 | }, 217 | { 218 | "cell_type": "code", 219 | "execution_count": null, 220 | "metadata": {}, 221 | "outputs": [], 222 | "source": [ 223 | "df[\"Age\"].mean()" 224 | ] 225 | }, 226 | { 227 | "cell_type": "markdown", 228 | "metadata": {}, 229 | "source": [ 230 | "The dataset is the result of a world wide survey. Print how many unique countries are there in the Country column.\n" 231 | ] 232 | }, 233 | { 234 | "cell_type": "code", 235 | "execution_count": null, 236 | "metadata": {}, 237 | "outputs": [], 238 | "source": [ 239 | "df[\"Country\"].nunique()" 240 | ] 241 | }, 242 | { 243 | "cell_type": "markdown", 244 | "metadata": {}, 245 | "source": [ 246 | "## Authors\n" 247 | ] 248 | }, 249 | { 250 | "cell_type": "markdown", 251 | "metadata": {}, 252 | "source": [ 253 | "Ramesh Sannareddy\n" 254 | ] 255 | }, 256 | { 257 | "cell_type": "markdown", 258 | "metadata": {}, 259 | "source": [ 260 | "### Other Contributors\n" 261 | ] 262 | }, 263 | { 264 | "cell_type": "markdown", 265 | "metadata": {}, 266 | "source": [ 267 | "Rav Ahuja\n" 268 | ] 269 | }, 270 | { 271 | "cell_type": "markdown", 272 | "metadata": {}, 273 | "source": [ 274 | "## Change Log\n" 275 | ] 276 | }, 277 | { 278 | "cell_type": "markdown", 279 | "metadata": {}, 280 | "source": [ 281 | "| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n", 282 | "| ----------------- | ------- | ----------------- | ---------------------------------- |\n", 283 | "| 2020-10-17 | 0.1 | Ramesh Sannareddy | Created initial version of the lab |\n" 284 | ] 285 | }, 286 | { 287 | "cell_type": "markdown", 288 | "metadata": {}, 289 | "source": [ 290 | "Copyright © 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license?utm_medium=Exinfluencer\\&utm_source=Exinfluencer\\&utm_content=000026UJ\\&utm_term=10006555\\&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2021-01-01\\&cm_mmc=Email_Newsletter-\\_-Developer_Ed%2BTech-\\_-WW_WW-\\_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264\\&cm_mmca1=000026UJ\\&cm_mmca2=10006555\\&cm_mmca3=M12345678\\&cvosrc=email.Newsletter.M12345678\\&cvo_campaign=000026UJ).\n" 291 | ] 292 | } 293 | ], 294 | "metadata": { 295 | "kernelspec": { 296 | "display_name": "Python 3", 297 | "language": "python", 298 | "name": "python3" 299 | }, 300 | "language_info": { 301 | "codemirror_mode": { 302 | "name": "ipython", 303 | "version": 3 304 | }, 305 | "file_extension": ".py", 306 | "mimetype": "text/x-python", 307 | "name": "python", 308 | "nbconvert_exporter": "python", 309 | "pygments_lexer": "ipython3", 310 | "version": "3.8.10" 311 | } 312 | }, 313 | "nbformat": 4, 314 | "nbformat_minor": 4 315 | } 316 | -------------------------------------------------------------------------------- /Data Analyst Capstone Project/README.md: -------------------------------------------------------------------------------- 1 | 2 | [![View Repositories](https://img.shields.io/badge/View-My_Repositories-blue?logo=GitHub)](https://github.com/ndleah?tab=repositories) 3 | [![View My Profile](https://img.shields.io/badge/View-My_Profile-green?logo=GitHub)](https://github.com/ndleah) 4 | 5 | # Data Visualization Dashboards Excel Cognos 6 | 7 | ## About this Project 8 | In this course you will apply various Data Analytics skills and techniques that you have learned as part of the previous courses in the IBM Data Analyst Professional Certificate. You will assume the role of an Associate Data Analyst who has recently joined the organization and be presented with a business challenge that requires data analysis to be performed on real-world datasets. 9 | 10 | ## Modules 11 | 12 | ### **Module 1:** Data Collection 13 | - Collecting Data Using APIs 14 | - Collecting Data Using Webscraping 15 | - Exploring Data 16 | 17 | ### **Module 2:** Data Wrangling 18 | - Finding Missing Values 19 | - Determine Missing Values 20 | - Finding Duplicates 21 | - Removing Duplicates 22 | - Normalizing Data 23 | 24 | ### **Module 3:** Exploratory Data Analysis 25 | - Distribution 26 | - Outliers 27 | - Correlation 28 | 29 | ### **Module 4:** Data Visualization 30 | - Visualizing Distribution of Data 31 | - Relationship 32 | - Composition 33 | - Comparison 34 | 35 | ### **Module 5:** Dashboard 36 | - Creation 37 | - Dashboards 38 | 39 | ### **Module 6:** Presentation of Findings 40 | - Final Presentation 41 | 42 | -------------------------------------------------------------------------------- /Data Analyst Capstone Project/Web-Scraping-Review-Lab.ipynb: -------------------------------------------------------------------------------- 1 | {"cells":[{"cell_type":"markdown","metadata":{},"source":["
\n"," \"cognitiveclass.ai\n","
\n"]},{"cell_type":"markdown","metadata":{},"source":["# **Web Scraping Lab**\n"]},{"cell_type":"markdown","metadata":{},"source":["Estimated time needed: **30** minutes\n"]},{"cell_type":"markdown","metadata":{},"source":["## Objectives\n"]},{"cell_type":"markdown","metadata":{},"source":["After completing this lab you will be able to:\n"]},{"cell_type":"markdown","metadata":{},"source":["* Download a webpage using requests module\n","* Scrape all links from a web page\n","* Scrape all image urls from a web page\n","* Scrape data from html tables\n"]},{"cell_type":"markdown","metadata":{},"source":["## Scrape [www.ibm.com](http://www.ibm.com/?utm_medium=Exinfluencer\\&utm_source=Exinfluencer\\&utm_content=000026UJ\\&utm_term=10006555\\&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2021-01-01)\n"]},{"cell_type":"markdown","metadata":{},"source":["Import the required modules and functions\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from bs4 import BeautifulSoup # this module helps in web scrapping.\n","import requests # this module helps us to download a web page"]},{"cell_type":"markdown","metadata":{},"source":["Download the contents of the web page\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["url = \"http://www.ibm.com\""]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# get the contents of the webpage in text format and store in a variable called data\n","data = requests.get(url).text "]},{"cell_type":"markdown","metadata":{},"source":["Create a soup object using the class BeautifulSoup\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["soup = BeautifulSoup(data,\"html5lib\") # create a soup object using the variable 'data'"]},{"cell_type":"markdown","metadata":{},"source":["Scrape all links\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["for link in soup.find_all('a'): # in html anchor/link is represented by the tag \n"," print(link.get('href'))"]},{"cell_type":"markdown","metadata":{},"source":["Scrape all images\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["for link in soup.find_all('img'):# in html image is represented by the tag \n"," print(link.get('src'))"]},{"cell_type":"markdown","metadata":{},"source":["## Scrape data from html tables\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["#The below url contains a html table with data about colors and color codes."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["url = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/labs/datasets/HTMLColorCodes.html\""]},{"cell_type":"markdown","metadata":{},"source":["Before proceeding to scrape a web site, you need to examine the contents, and the way data is organized on the website. Open the above url in your browser and check how many rows and columns are there in the color table.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# get the contents of the webpage in text format and store in a variable called data\n","data = requests.get(url).text"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["soup = BeautifulSoup(data,\"html5lib\")"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["#find a html table in the web page\n","table = soup.find('table') # in html table is represented by the tag "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["#Get all rows from the table\n","for row in table.find_all('tr'): # in html table row is represented by the tag \n"," # Get all columns in each row.\n"," cols = row.find_all('td') # in html a column is represented by the tag
\n"," color_name = cols[2].getText() # store the value in column 3 as color_name\n"," color_code = cols[3].getText() # store the value in column 4 as color_code\n"," print(\"{}--->{}\".format(color_name,color_code))"]},{"cell_type":"markdown","metadata":{},"source":["## Authors\n"]},{"cell_type":"markdown","metadata":{},"source":["Ramesh Sannareddy\n"]},{"cell_type":"markdown","metadata":{},"source":["### Other Contributors\n"]},{"cell_type":"markdown","metadata":{},"source":["Rav Ahuja\n"]},{"cell_type":"markdown","metadata":{},"source":["## Change Log\n"]},{"cell_type":"markdown","metadata":{},"source":["| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n","| ----------------- | ------- | ----------------- | ---------------------------------- |\n","| 2020-10-17 | 0.1 | Ramesh Sannareddy | Created initial version of the lab |\n"]},{"cell_type":"markdown","metadata":{},"source":["Copyright © 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license/?utm_medium=Exinfluencer\\&utm_source=Exinfluencer\\&utm_content=000026UJ\\&utm_term=10006555\\&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2021-01-01).\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","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":"3.7.6"}},"nbformat":4,"nbformat_minor":4} -------------------------------------------------------------------------------- /Data Visualization Dashboards Excel Cognos/5_Peer_Graded_Assignment_Questions.py: -------------------------------------------------------------------------------- 1 | # Import required libraries 2 | import pandas as pd 3 | import dash 4 | import dash_html_components as html 5 | import dash_core_components as dcc 6 | from dash.dependencies import Input, Output, State 7 | import plotly.graph_objects as go 8 | import plotly.express as px 9 | from dash import no_update 10 | 11 | 12 | # Create a dash application 13 | app = dash.Dash(__name__) 14 | 15 | # REVIEW1: Clear the layout and do not display exception till callback gets executed 16 | app.config.suppress_callback_exceptions = True 17 | 18 | # Read the airline data into pandas dataframe 19 | airline_data = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/airline_data.csv', 20 | encoding = "ISO-8859-1", 21 | dtype={'Div1Airport': str, 'Div1TailNum': str, 22 | 'Div2Airport': str, 'Div2TailNum': str}) 23 | 24 | 25 | # List of years 26 | year_list = [i for i in range(2005, 2021, 1)] 27 | 28 | """Compute graph data for creating yearly airline performance report 29 | 30 | Function that takes airline data as input and create 5 dataframes based on the grouping condition to be used for plottling charts and grphs. 31 | 32 | Argument: 33 | 34 | df: Filtered dataframe 35 | 36 | Returns: 37 | Dataframes to create graph. 38 | """ 39 | def compute_data_choice_1(df): 40 | # Cancellation Category Count 41 | bar_data = df.groupby(['Month','CancellationCode'])['Flights'].sum().reset_index() 42 | # Average flight time by reporting airline 43 | line_data = df.groupby(['Month','Reporting_Airline'])['AirTime'].mean().reset_index() 44 | # Diverted Airport Landings 45 | div_data = df[df['DivAirportLandings'] != 0.0] 46 | # Source state count 47 | map_data = df.groupby(['OriginState'])['Flights'].sum().reset_index() 48 | # Destination state count 49 | tree_data = df.groupby(['DestState', 'Reporting_Airline'])['Flights'].sum().reset_index() 50 | return bar_data, line_data, div_data, map_data, tree_data 51 | 52 | 53 | """Compute graph data for creating yearly airline delay report 54 | 55 | This function takes in airline data and selected year as an input and performs computation for creating charts and plots. 56 | 57 | Arguments: 58 | df: Input airline data. 59 | 60 | Returns: 61 | Computed average dataframes for carrier delay, weather delay, NAS delay, security delay, and late aircraft delay. 62 | """ 63 | def compute_data_choice_2(df): 64 | # Compute delay averages 65 | avg_car = df.groupby(['Month','Reporting_Airline'])['CarrierDelay'].mean().reset_index() 66 | avg_weather = df.groupby(['Month','Reporting_Airline'])['WeatherDelay'].mean().reset_index() 67 | avg_NAS = df.groupby(['Month','Reporting_Airline'])['NASDelay'].mean().reset_index() 68 | avg_sec = df.groupby(['Month','Reporting_Airline'])['SecurityDelay'].mean().reset_index() 69 | avg_late = df.groupby(['Month','Reporting_Airline'])['LateAircraftDelay'].mean().reset_index() 70 | return avg_car, avg_weather, avg_NAS, avg_sec, avg_late 71 | 72 | 73 | # Application layout 74 | app.layout = html.Div(children=[ 75 | # TASK1: Add title to the dashboard 76 | html.H1('US Domestic Airline Flights Performance', 77 | style={'text-align-last':'centre','color':'#503D36','font-size':24} 78 | ), 79 | # REVIEW2: Dropdown creation 80 | html.Div([ 81 | # Add an division 82 | html.Div([ 83 | # Create an division for adding dropdown helper text for report type 84 | html.Div( 85 | [ 86 | html.H2('Report Type:', style={'margin-right': '2em'}), 87 | ] 88 | ), 89 | # TASK2: Add a dropdown 90 | # Enter your code below. Make sure you have correct formatting. 91 | dcc.Dropdown(id='input-type', 92 | options=[ 93 | {'label': 'Yearly Airline Performance Report', 'value': 'OPT1'}, 94 | {'label': 'Yearly Airline Delay Report', 'value': 'OPT2'} 95 | ], 96 | placeholder='Select a report type', 97 | style={'width':'80%', 'padding':'3px', 'font-size':'20px', 'text-align-last':'center'} 98 | ) 99 | # Place them next to each other using the division style 100 | ], style={'display':'flex'}), 101 | 102 | # Add next division 103 | html.Div([ 104 | # Create an division for adding dropdown helper text for choosing year 105 | html.Div( 106 | [ 107 | html.H2('Choose Year:', style={'margin-right': '2em'}) 108 | ] 109 | ), 110 | dcc.Dropdown(id='input-year', 111 | # Update dropdown values using list comphrehension 112 | options=[{'label': i, 'value': i} for i in year_list], 113 | placeholder="Select a year", 114 | style={'width':'80%', 'padding':'3px', 'font-size': '20px', 'text-align-last' : 'center'}), 115 | # Place them next to each other using the division style 116 | ], style={'display': 'flex'}), 117 | ]), 118 | 119 | # Add Computed graphs 120 | # REVIEW3: Observe how we add an empty division and providing an id that will be updated during callback 121 | html.Div([ ], id='plot1'), 122 | 123 | html.Div([ 124 | html.Div([ ], id='plot2'), 125 | html.Div([ ], id='plot3') 126 | ], style={'display': 'flex'}), 127 | 128 | # TASK3: Add a division with two empty divisions inside. See above disvision for example. 129 | html.Div([ 130 | html.Div([ ], id='plot4'), 131 | html.Div([ ], id='plot5') 132 | ], 133 | style={'display': 'flex'}) 134 | 135 | ]) 136 | 137 | # Callback function definition 138 | # TASK4: Add 5 ouput components 139 | # Enter your code below. Make sure you have correct formatting. 140 | @app.callback( [Output(component_id='plot1', component_property='children'), 141 | Output(component_id='plot2', component_property='children'), 142 | Output(component_id='plot3', component_property='children'), 143 | Output(component_id='plot4', component_property='children'), 144 | Output(component_id='plot5', component_property='children') 145 | ], 146 | [Input(component_id='input-type', component_property='value'), 147 | Input(component_id='input-year', component_property='value') 148 | ], 149 | # REVIEW4: Holding output state till user enters all the form information. In this case, it will be chart type and year 150 | [State("plot1", 'children'), 151 | State("plot2", "children"), 152 | State("plot3", "children"), 153 | State("plot4", "children"), 154 | State("plot5", "children") 155 | ]) 156 | # Add computation to callback function and return graph 157 | def get_graph(chart, year, children1, children2, c3, c4, c5): 158 | 159 | # Select data 160 | df = airline_data[airline_data['Year']==int(year)] 161 | 162 | if chart == 'OPT1': 163 | # Compute required information for creating graph from the data 164 | bar_data, line_data, div_data, map_data, tree_data = compute_data_choice_1(df) 165 | 166 | # Number of flights under different cancellation categories 167 | bar_fig = px.bar(bar_data, x='Month', y='Flights', color='CancellationCode', title='Monthly Flight Cancellation') 168 | 169 | # TASK5: Average flight time by reporting airline 170 | line_fig = px.line(line_data, x='Month', y='AirTime', color='Reporting_Airline', title='Average monthly flight time (minutes) by airline') 171 | 172 | # Percentage of diverted airport landings per reporting airline 173 | pie_fig = px.pie(div_data, values='Flights', names='Reporting_Airline', title='% of flights by reporting airline') 174 | 175 | # REVIEW5: Number of flights flying from each state using choropleth 176 | map_fig = px.choropleth(map_data, # Input data 177 | locations='OriginState', 178 | color='Flights', 179 | hover_data=['OriginState', 'Flights'], 180 | locationmode = 'USA-states', # Set to plot as US States 181 | color_continuous_scale='GnBu', 182 | range_color=[0, map_data['Flights'].max()]) 183 | map_fig.update_layout( 184 | title_text = 'Number of flights from origin state', 185 | geo_scope='usa') # Plot only the USA instead of globe 186 | 187 | # TASK6: Number of flights flying to each state from each reporting airline 188 | tree_fig = px.treemap(tree_data, path=['DestState', 'Reporting_Airline'], 189 | values='Flights', 190 | color='Flights', 191 | color_continuous_scale='RdBu', 192 | title='Flight count by airline to destination state' 193 | ) 194 | 195 | 196 | # REVIEW6: Return dcc.Graph component to the empty division 197 | return [dcc.Graph(figure=tree_fig), 198 | dcc.Graph(figure=pie_fig), 199 | dcc.Graph(figure=map_fig), 200 | dcc.Graph(figure=bar_fig), 201 | dcc.Graph(figure=line_fig) 202 | ] 203 | else: 204 | # REVIEW7: This covers chart type 2 and we have completed this exercise under Flight Delay Time Statistics Dashboard section 205 | # Compute required information for creating graph from the data 206 | avg_car, avg_weather, avg_NAS, avg_sec, avg_late = compute_data_choice_2(df) 207 | 208 | # Create graph 209 | carrier_fig = px.line(avg_car, x='Month', y='CarrierDelay', color='Reporting_Airline', title='Average carrrier delay time (minutes) by airline') 210 | weather_fig = px.line(avg_weather, x='Month', y='WeatherDelay', color='Reporting_Airline', title='Average weather delay time (minutes) by airline') 211 | nas_fig = px.line(avg_NAS, x='Month', y='NASDelay', color='Reporting_Airline', title='Average NAS delay time (minutes) by airline') 212 | sec_fig = px.line(avg_sec, x='Month', y='SecurityDelay', color='Reporting_Airline', title='Average security delay time (minutes) by airline') 213 | late_fig = px.line(avg_late, x='Month', y='LateAircraftDelay', color='Reporting_Airline', title='Average late aircraft delay time (minutes) by airline') 214 | 215 | return[dcc.Graph(figure=carrier_fig), 216 | dcc.Graph(figure=weather_fig), 217 | dcc.Graph(figure=nas_fig), 218 | dcc.Graph(figure=sec_fig), 219 | dcc.Graph(figure=late_fig)] 220 | 221 | 222 | # Run the app 223 | if __name__ == '__main__': 224 | app.run_server() -------------------------------------------------------------------------------- /Data Visualization Dashboards Excel Cognos/CarSalesByModelEnd.xlsx: -------------------------------------------------------------------------------- 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Begin the process of telling a story with your data by creating the many types of charts that are available in spreadsheets like Excel. Explore the different tools of a spreadsheet, such as the important pivot function and the ability to create dashboards and learn how each one has its own unique property to transform your data. Continue to gain valuable experience by becoming familiar with the popular analytics tool - IBM Cognos Analytics - to create interactive dashboards. 8 | 9 | ## Assets 10 | 11 | * ### **Excel Dashboard** 12 | 13 |

14 | 15 | 16 |

17 | 18 | 19 | * ### **IBM Cognos Dashboard** 20 | 21 |

22 | 23 | 24 |

25 | 26 | 27 |

28 | 29 | 30 |

31 | 32 | 33 |

34 | -------------------------------------------------------------------------------- /Data Visualization with Python/Final_Assignment.py: -------------------------------------------------------------------------------- 1 | # Import required libraries 2 | import pandas as pd 3 | import dash 4 | import dash_html_components as html 5 | import dash_core_components as dcc 6 | from dash.dependencies import Input, Output, State 7 | import plotly.graph_objects as go 8 | import plotly.express as px 9 | from dash import no_update 10 | 11 | 12 | # Create a dash application 13 | app = dash.Dash(__name__) 14 | 15 | # REVIEW1: Clear the layout and do not display exception till callback gets executed 16 | app.config.suppress_callback_exceptions = True 17 | 18 | # Read the airline data into pandas dataframe 19 | airline_data = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/airline_data.csv', 20 | encoding = "ISO-8859-1", 21 | dtype={'Div1Airport': str, 'Div1TailNum': str, 22 | 'Div2Airport': str, 'Div2TailNum': str}) 23 | 24 | 25 | # List of years 26 | year_list = [i for i in range(2005, 2021, 1)] 27 | 28 | """Compute graph data for creating yearly airline performance report 29 | 30 | Function that takes airline data as input and create 5 dataframes based on the grouping condition to be used for plottling charts and grphs. 31 | 32 | Argument: 33 | 34 | df: Filtered dataframe 35 | 36 | Returns: 37 | Dataframes to create graph. 38 | """ 39 | def compute_data_choice_1(df): 40 | # Cancellation Category Count 41 | bar_data = df.groupby(['Month','CancellationCode'])['Flights'].sum().reset_index() 42 | # Average flight time by reporting airline 43 | line_data = df.groupby(['Month','Reporting_Airline'])['AirTime'].mean().reset_index() 44 | # Diverted Airport Landings 45 | div_data = df[df['DivAirportLandings'] != 0.0] 46 | # Source state count 47 | map_data = df.groupby(['OriginState'])['Flights'].sum().reset_index() 48 | # Destination state count 49 | tree_data = df.groupby(['DestState', 'Reporting_Airline'])['Flights'].sum().reset_index() 50 | return bar_data, line_data, div_data, map_data, tree_data 51 | 52 | 53 | """Compute graph data for creating yearly airline delay report 54 | 55 | This function takes in airline data and selected year as an input and performs computation for creating charts and plots. 56 | 57 | Arguments: 58 | df: Input airline data. 59 | 60 | Returns: 61 | Computed average dataframes for carrier delay, weather delay, NAS delay, security delay, and late aircraft delay. 62 | """ 63 | def compute_data_choice_2(df): 64 | # Compute delay averages 65 | avg_car = df.groupby(['Month','Reporting_Airline'])['CarrierDelay'].mean().reset_index() 66 | avg_weather = df.groupby(['Month','Reporting_Airline'])['WeatherDelay'].mean().reset_index() 67 | avg_NAS = df.groupby(['Month','Reporting_Airline'])['NASDelay'].mean().reset_index() 68 | avg_sec = df.groupby(['Month','Reporting_Airline'])['SecurityDelay'].mean().reset_index() 69 | avg_late = df.groupby(['Month','Reporting_Airline'])['LateAircraftDelay'].mean().reset_index() 70 | return avg_car, avg_weather, avg_NAS, avg_sec, avg_late 71 | 72 | 73 | # Application layout 74 | app.layout = html.Div(children=[ 75 | # TASK1: Add title to the dashboard 76 | html.H1('US Domestic Airline Flights Performance', 77 | style={'text-align-last':'centre','color':'#503D36','font-size':24} 78 | 79 | # REVIEW2: Dropdown creation 80 | # Create an outer division 81 | html.Div([ 82 | # Add an division 83 | html.Div([ 84 | # Create an division for adding dropdown helper text for report type 85 | html.Div( 86 | [ 87 | html.H2('Report Type:', style={'margin-right': '2em'}), 88 | ] 89 | ), 90 | # TASK2: Add a dropdown 91 | # Enter your code below. Make sure you have correct formatting. 92 | dcc.Dropdown(id='input-type', 93 | options=[ 94 | {'label': 'Yearly Airline Performance Report', 'value': 'OPT1'}, 95 | {'label': 'Yearly Airline Delay Report', 'value': 'OPT2'} 96 | ], 97 | placeholder='Select a report type', 98 | style={'width':'80%', 'padding':'3px', 'font-size':'20px', 'text-align-last':'center'} 99 | # Place them next to each other using the division style 100 | ], style={'display':'flex'}), 101 | 102 | # Add next division 103 | html.Div([ 104 | # Create an division for adding dropdown helper text for choosing year 105 | html.Div( 106 | [ 107 | html.H2('Choose Year:', style={'margin-right': '2em'}) 108 | ] 109 | ), 110 | dcc.Dropdown(id='input-year', 111 | # Update dropdown values using list comphrehension 112 | options=[{'label': i, 'value': i} for i in year_list], 113 | placeholder="Select a year", 114 | style={'width':'80%', 'padding':'3px', 'font-size': '20px', 'text-align-last' : 'center'}), 115 | # Place them next to each other using the division style 116 | ], style={'display': 'flex'}), 117 | ]), 118 | 119 | # Add Computed graphs 120 | # REVIEW3: Observe how we add an empty division and providing an id that will be updated during callback 121 | html.Div([ ], id='plot1'), 122 | 123 | html.Div([ 124 | html.Div([ ], id='plot2'), 125 | html.Div([ ], id='plot3') 126 | ], style={'display': 'flex'}), 127 | 128 | # TASK3: Add a division with two empty divisions inside. See above disvision for example. 129 | html.Div([ 130 | html.Div([ ], id='plot4'), 131 | html.Div([ ], id='plot5') 132 | ], 133 | style={'display': 'flex'}) 134 | 135 | ]) 136 | 137 | # Callback function definition 138 | # TASK4: Add 5 ouput components 139 | # Enter your code below. Make sure you have correct formatting. 140 | @app.callback( [Output(component_id='plot1', component_property='children'), 141 | Output(component_id='plot2', component_property='children'), 142 | Output(component_id='plot3', component_property='children'), 143 | Output(component_id='plot4', component_property='children'), 144 | Output(component_id='plot5', component_property='children') 145 | ], 146 | [Input(component_id='input-type', component_property='value'), 147 | Input(component_id='input-year', component_property='value') 148 | ], 149 | # REVIEW4: Holding output state till user enters all the form information. In this case, it will be chart type and year 150 | [State("plot1", 'children'), 151 | State("plot2", "children"), 152 | State("plot3", "children"), 153 | State("plot4", "children"), 154 | State("plot5", "children") 155 | ]) 156 | # Add computation to callback function and return graph 157 | def get_graph(chart, year, children1, children2, c3, c4, c5): 158 | 159 | # Select data 160 | df = airline_data[airline_data['Year']==int(year)] 161 | 162 | if chart == 'OPT1': 163 | # Compute required information for creating graph from the data 164 | bar_data, line_data, div_data, map_data, tree_data = compute_data_choice_1(df) 165 | 166 | # Number of flights under different cancellation categories 167 | bar_fig = px.bar(bar_data, x='Month', y='Flights', color='CancellationCode', title='Monthly Flight Cancellation') 168 | 169 | # TASK5: Average flight time by reporting airline 170 | line_fig = px.line(line_data, x='Month', y='AirTime', color='Reporting_Airline', title='Average monthly flight time (minutes) by airline') 171 | 172 | # Percentage of diverted airport landings per reporting airline 173 | pie_fig = px.pie(div_data, values='Flights', names='Reporting_Airline', title='% of flights by reporting airline') 174 | 175 | # REVIEW5: Number of flights flying from each state using choropleth 176 | map_fig = px.choropleth(map_data, # Input data 177 | locations='OriginState', 178 | color='Flights', 179 | hover_data=['OriginState', 'Flights'], 180 | locationmode = 'USA-states', # Set to plot as US States 181 | color_continuous_scale='GnBu', 182 | range_color=[0, map_data['Flights'].max()]) 183 | map_fig.update_layout( 184 | title_text = 'Number of flights from origin state', 185 | geo_scope='usa') # Plot only the USA instead of globe 186 | 187 | # TASK6: Number of flights flying to each state from each reporting airline 188 | tree_fig = px.treemap(tree_data, path=['DestState', 'Reporting_Airline'], 189 | values='Flights', 190 | color='Flights', 191 | color_continuous_scale='RdBu', 192 | title='Flight count by airline to destination state' 193 | ) 194 | 195 | 196 | # REVIEW6: Return dcc.Graph component to the empty division 197 | return [dcc.Graph(figure=tree_fig), 198 | dcc.Graph(figure=pie_fig), 199 | dcc.Graph(figure=map_fig), 200 | dcc.Graph(figure=bar_fig), 201 | dcc.Graph(figure=line_fig) 202 | ] 203 | else: 204 | # REVIEW7: This covers chart type 2 and we have completed this exercise under Flight Delay Time Statistics Dashboard section 205 | # Compute required information for creating graph from the data 206 | avg_car, avg_weather, avg_NAS, avg_sec, avg_late = compute_data_choice_2(df) 207 | 208 | # Create graph 209 | carrier_fig = px.line(avg_car, x='Month', y='CarrierDelay', color='Reporting_Airline', title='Average carrrier delay time (minutes) by airline') 210 | weather_fig = px.line(avg_weather, x='Month', y='WeatherDelay', color='Reporting_Airline', title='Average weather delay time (minutes) by airline') 211 | nas_fig = px.line(avg_NAS, x='Month', y='NASDelay', color='Reporting_Airline', title='Average NAS delay time (minutes) by airline') 212 | sec_fig = px.line(avg_sec, x='Month', y='SecurityDelay', color='Reporting_Airline', title='Average security delay time (minutes) by airline') 213 | late_fig = px.line(avg_late, x='Month', y='LateAircraftDelay', color='Reporting_Airline', title='Average late aircraft delay time (minutes) by airline') 214 | 215 | return[dcc.Graph(figure=carrier_fig), 216 | dcc.Graph(figure=weather_fig), 217 | dcc.Graph(figure=nas_fig), 218 | dcc.Graph(figure=sec_fig), 219 | dcc.Graph(figure=late_fig)] 220 | 221 | 222 | # Run the app 223 | if __name__ == '__main__': 224 | app.run_server() -------------------------------------------------------------------------------- /Data Visualization with Python/IMG/Basic_Plotly/newplot (1).png: -------------------------------------------------------------------------------- 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Repositories](https://img.shields.io/badge/View-My_Repositories-blue?logo=GitHub)](https://github.com/ndleah?tab=repositories) 2 | [![View My Profile](https://img.shields.io/badge/View-My_Profile-green?logo=GitHub)](https://github.com/ndleah) 3 | 4 | # Data Visualization with Python 5 | 6 | ## About this Course 7 | One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions. 8 | 9 | The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium. 10 | 11 | ## Assets 12 | 13 | * ### **Introduction to Matplotlib and Line Plots** 14 |

15 | 16 | 17 |

18 | 19 | 20 | --- 21 | 22 | * ### **Exercise Area Plots Histograms and Bar Charts** 23 |

24 | 25 | 26 |

27 | 28 | 29 |

30 | 31 | 32 |

33 | 34 | 35 |

36 | 37 | 38 |

39 | 40 | 41 |

42 | 43 | 44 |

45 | 46 | 47 | --- 48 | 49 | * ### **Pie Charts, Box Plots, Scatter Plots and Bubble-Plots** 50 |

51 | 52 | 53 |

54 | 55 | 56 |

57 | 58 | 59 |

60 | 61 | 62 |

63 | 64 | 65 |

66 | 67 | 68 |

69 | 70 | 71 |

72 | 73 | 74 |

75 | 76 | 77 |

78 | 79 | 80 |

81 | 82 | 83 |

84 | 85 | 86 | --- 87 | 88 | * ### **Basic Plotly Charts** 89 |

90 | 91 | 92 |

93 | 94 | 95 |

96 | 97 | 98 |

99 | 100 | 101 |

102 | 103 | 104 |

105 | 106 | 107 |

108 | -------------------------------------------------------------------------------- /Data Visualization with Python/dash_interactivity.py: -------------------------------------------------------------------------------- 1 | # Import required libraries 2 | import pandas as pd 3 | import plotly.graph_objects as go 4 | import dash 5 | import dash_html_components as html 6 | import dash_core_components as dcc 7 | from dash.dependencies import Input, Output 8 | 9 | # Read the airline data into pandas dataframe 10 | airline_data = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/airline_data.csv', 11 | encoding = "ISO-8859-1", 12 | dtype={'Div1Airport': str, 'Div1TailNum': str, 13 | 'Div2Airport': str, 'Div2TailNum': str}) 14 | # Create a dash application 15 | app = dash.Dash(__name__) 16 | 17 | app.layout = html.Div(children=[ html.H1('Airline Performance Dashboard', 18 | style={'textAlign': 'center', 'color': '#503D36', 19 | 'font-size': 40}), 20 | html.Div(["Input Year: ", dcc.Input(id='input-year', value='2010', 21 | type='number', style={'height':'50px', 'font-size': 35}),], 22 | style={'font-size': 40}), 23 | html.Br(), 24 | html.Br(), 25 | html.Div(dcc.Graph(id='line-plot')), 26 | ]) 27 | 28 | # add callback decorator 29 | @app.callback( Output(component_id='line-plot', component_property='figure'), 30 | Input(component_id='input-year', component_property='value')) 31 | 32 | # Add computation to callback function and return graph 33 | def get_graph(entered_year): 34 | # Select 2019 data 35 | df = airline_data[airline_data['Year']==int(entered_year)] 36 | 37 | # Group the data by Month and compute average over arrival delay time. 38 | line_data = df.groupby('Month')['ArrDelay'].mean().reset_index() 39 | 40 | fig = go.Figure(data=go.Scatter(x=line_data['Month'], y=line_data['ArrDelay'], mode='lines', marker=dict(color='green'))) 41 | fig.update_layout(title='Month vs Average Flight Delay Time', xaxis_title='Month', yaxis_title='ArrDelay') 42 | return fig 43 | 44 | # Run the app 45 | if __name__ == '__main__': 46 | app.run_server() -------------------------------------------------------------------------------- /Databases and SQL for Data Science with Python/IMG/Q1.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/Databases and SQL for Data Science with Python/IMG/Q1.PNG -------------------------------------------------------------------------------- /Databases and SQL for Data Science with Python/IMG/Q10.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/Databases and SQL for Data Science with Python/IMG/Q10.PNG 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/Databases and SQL for Data Science with Python/IMG/Q9.PNG -------------------------------------------------------------------------------- /Databases and SQL for Data Science with Python/README.md: -------------------------------------------------------------------------------- 1 | [![View Repositories](https://img.shields.io/badge/View-My_Repositories-blue?logo=GitHub)](https://github.com/ndleah?tab=repositories) 2 | [![View My Profile](https://img.shields.io/badge/View-My_Profile-green?logo=GitHub)](https://github.com/ndleah) 3 | 4 | # Databases and SQL for Data Science 5 | 6 | ## About this Course 7 | 8 | **The purpose of this course** is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment. 9 | 10 | **The emphasis in this course** is on hands-on and practical learning . As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and Python. 11 | 12 | ## Final Course Assignment 13 | This folder contains data files and a Jupyter Notebook script that demonstrates use of a variety of SQL commands to query the three datasets. 14 | * _Census_Data.csv_ contains six socioeconomic indicators of public health significance and a “hardship index,” by Chicago community area, for the years 2008 – 2012. For more detailed information, see the [data description](https://data.cityofchicago.org/Health-Human-Services/Census-Data-Selected-socioeconomic-indicators-in-C/kn9c-c2s2). 15 | * _Chicago_Crime_Data.csv_ reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. For more detailed information, see the [data description](https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2). 16 | * _Chicago_Public_Schools.csv_ shows all school level performance data used to create CPS School Report Cards for the 2011-2012 school year. For more detailed information, see the [data description](https://data.cityofchicago.org/Education/Chicago-Public-Schools-Progress-Report-Cards-2011-/9xs2-f89t). 17 | * _Chicago_Datasets_PeerAssign-v5.ipynb_ contains the script with ipython-sql code, which is used to query the datasets after they were loaded into a Db2 database on IBM Cloud. 18 | 19 | ## Solutions 20 |

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-------------------------------------------------------------------------------- /Databases and SQL for Data Science with Python/dataset/ChicagoCensusData.csv: -------------------------------------------------------------------------------- 1 | COMMUNITY_AREA_NUMBER,COMMUNITY_AREA_NAME,PERCENT_OF_HOUSING_CROWDED,PERCENT_HOUSEHOLDS_BELOW_POVERTY,PERCENT_AGED_16__UNEMPLOYED,PERCENT_AGED_25__WITHOUT_HIGH_SCHOOL_DIPLOMA,PERCENT_AGED_UNDER_18_OR_OVER_64,PER_CAPITA_INCOME,HARDSHIP_INDEX 2 | 1,Rogers Park,7.7,23.6,8.7,18.2,27.5,23939,39 3 | 2,West Ridge,7.8,17.2,8.8,20.8,38.5,23040,46 4 | 3,Uptown,3.8,24.0,8.9,11.8,22.2,35787,20 5 | 4,Lincoln Square,3.4,10.9,8.2,13.4,25.5,37524,17 6 | 5,North Center,0.3,7.5,5.2,4.5,26.2,57123,6 7 | 6,Lake View,1.1,11.4,4.7,2.6,17.0,60058,5 8 | 7,Lincoln Park,0.8,12.3,5.1,3.6,21.5,71551,2 9 | 8,Near North Side,1.9,12.9,7.0,2.5,22.6,88669,1 10 | 9,Edison Park,1.1,3.3,6.5,7.4,35.3,40959,8 11 | 10,Norwood Park,2.0,5.4,9.0,11.5,39.5,32875,21 12 | 11,Jefferson Park,2.7,8.6,12.4,13.4,35.5,27751,25 13 | 12,Forest Glen,1.1,7.5,6.8,4.9,40.5,44164,11 14 | 13,North Park,3.9,13.2,9.9,14.4,39.0,26576,33 15 | 14,Albany Park,11.3,19.2,10.0,32.9,32.0,21323,53 16 | 15,Portage Park,4.1,11.6,12.6,19.3,34.0,24336,35 17 | 16,Irving Park,6.3,13.1,10.0,22.4,31.6,27249,34 18 | 17,Dunning,5.2,10.6,10.0,16.2,33.6,26282,28 19 | 18,Montclaire,8.1,15.3,13.8,23.5,38.6,22014,50 20 | 19,Belmont Cragin,10.8,18.7,14.6,37.3,37.3,15461,70 21 | 20,Hermosa,6.9,20.5,13.1,41.6,36.4,15089,71 22 | 21,Avondale,6.0,15.3,9.2,24.7,31.0,20039,42 23 | 22,Logan Square,3.2,16.8,8.2,14.8,26.2,31908,23 24 | 23,Humboldt park,14.8,33.9,17.3,35.4,38.0,13781,85 25 | 24,West Town,2.3,14.7,6.6,12.9,21.7,43198,10 26 | 25,Austin,6.3,28.6,22.6,24.4,37.9,15957,73 27 | 26,West Garfield Park,9.4,41.7,25.8,24.5,43.6,10934,92 28 | 27,East Garfield Park,8.2,42.4,19.6,21.3,43.2,12961,83 29 | 28,Near West Side,3.8,20.6,10.7,9.6,22.2,44689,15 30 | 29,North Lawndale,7.4,43.1,21.2,27.6,42.7,12034,87 31 | 30,South Lawndale,15.2,30.7,15.8,54.8,33.8,10402,96 32 | 31,Lower West Side,9.6,25.8,15.8,40.7,32.6,16444,76 33 | 32,Loop,1.5,14.7,5.7,3.1,13.5,65526,3 34 | 33,Near South Side,1.3,13.8,4.9,7.4,21.8,59077,7 35 | 34,Armour Square,5.7,40.1,16.7,34.5,38.3,16148,82 36 | 35,Douglas,1.8,29.6,18.2,14.3,30.7,23791,47 37 | 36,Oakland,1.3,39.7,28.7,18.4,40.4,19252,78 38 | 37,Fuller Park,3.2,51.2,33.9,26.6,44.9,10432,97 39 | 38,Grand Boulevard,3.3,29.3,24.3,15.9,39.5,23472,57 40 | 39,Kenwood,2.4,21.7,15.7,11.3,35.4,35911,26 41 | 40,Washington Park,5.6,42.1,28.6,25.4,42.8,13785,88 42 | 41,Hyde Park,1.5,18.4,8.4,4.3,26.2,39056,14 43 | 42,Woodlawn,2.9,30.7,23.4,16.5,36.1,18672,58 44 | 43,South Shore,2.8,31.1,20.0,14.0,35.7,19398,55 45 | 44,Chatham,3.3,27.8,24.0,14.5,40.3,18881,60 46 | 45,Avalon Park,1.4,17.2,21.1,10.6,39.3,24454,41 47 | 46,South Chicago,4.7,29.8,19.7,26.6,41.1,16579,75 48 | 47,Burnside,6.8,33.0,18.6,19.3,42.7,12515,79 49 | 48,Calumet Heights,2.1,11.5,20.0,11.0,44.0,28887,38 50 | 49,Roseland,2.5,19.8,20.3,16.9,41.2,17949,52 51 | 50,Pullman,1.5,21.6,22.8,13.1,38.6,20588,51 52 | 51,South Deering,4.0,29.2,16.3,21.0,39.5,14685,65 53 | 52,East Side,6.8,19.2,12.1,31.9,42.8,17104,64 54 | 53,West Pullman,3.3,25.9,19.4,20.5,42.1,16563,62 55 | 54,Riverdale,5.8,56.5,34.6,27.5,51.5,8201,98 56 | 55,Hegewisch,3.3,17.1,9.6,19.2,42.9,22677,44 57 | 56,Garfield Ridge,2.6,8.8,11.3,19.3,38.1,26353,32 58 | 57,Archer Heights,8.5,14.1,16.5,35.9,39.2,16134,67 59 | 58,Brighton Park,14.4,23.6,13.9,45.1,39.3,13089,84 60 | 59,McKinley Park,7.2,18.7,13.4,32.9,35.6,16954,61 61 | 60,Bridgeport,4.5,18.9,13.7,22.2,31.3,22694,43 62 | 61,New City,11.9,29.0,23.0,41.5,38.9,12765,91 63 | 62,West Elsdon,11.1,15.6,16.7,37.0,37.7,15754,69 64 | 63,Gage Park,15.8,23.4,18.2,51.5,38.8,12171,93 65 | 64,Clearing,2.7,8.9,9.5,18.8,37.6,25113,29 66 | 65,West Lawn,5.8,14.9,9.6,33.6,39.6,16907,56 67 | 66,Chicago Lawn,7.6,27.9,17.1,31.2,40.6,13231,80 68 | 67,West Englewood,4.8,34.4,35.9,26.3,40.7,11317,89 69 | 68,Englewood,3.8,46.6,28.0,28.5,42.5,11888,94 70 | 69,Greater Grand Crossing,3.6,29.6,23.0,16.5,41.0,17285,66 71 | 70,Ashburn,4.0,10.4,11.7,17.7,36.9,23482,37 72 | 71,Auburn Gresham,4.0,27.6,28.3,18.5,41.9,15528,74 73 | 72,Beverly,0.9,5.1,8.0,3.7,40.5,39523,12 74 | 73,Washington Height,1.1,16.9,20.8,13.7,42.6,19713,48 75 | 74,Mount Greenwood,1.0,3.4,8.7,4.3,36.8,34381,16 76 | 75,Morgan Park,0.8,13.2,15.0,10.8,40.3,27149,30 77 | 76,O'Hare,3.6,15.4,7.1,10.9,30.3,25828,24 78 | 77,Edgewater,4.1,18.2,9.2,9.7,23.8,33385,19 79 | ,CHICAGO,4.7,19.7,12.9,19.5,33.5,28202, 80 | -------------------------------------------------------------------------------- /Excel Basics for Data Analysis/Montgomery_Fleet_Equipment_Inventory_FA_PART_1_END.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/Excel Basics for Data Analysis/Montgomery_Fleet_Equipment_Inventory_FA_PART_1_END.xlsx -------------------------------------------------------------------------------- /Excel Basics for Data Analysis/Montgomery_Fleet_Equipment_Inventory_FA_PART_2_END.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/Excel Basics for Data Analysis/Montgomery_Fleet_Equipment_Inventory_FA_PART_2_END.xlsx -------------------------------------------------------------------------------- /Excel Basics for Data Analysis/README.md: -------------------------------------------------------------------------------- 1 | [![View Repositories](https://img.shields.io/badge/View-My_Repositories-blue?logo=GitHub)](https://github.com/ndleah?tab=repositories) 2 | [![View My Profile](https://img.shields.io/badge/View-My_Profile-green?logo=GitHub)](https://github.com/ndleah) 3 | 4 | # Excel Basics for Data Analysis 5 | 6 | ## About this Course 7 | This course is designed to provide you with basic working knowledge for using Excel spreadsheets for Data Analysis. It covers some of the first steps for working with spreadsheets and their usage in the process of analyzing data. It includes plenty of videos, demos, and examples for you to learn, followed by step-by-step instructions for you to apply and practice on a live spreadsheet. -------------------------------------------------------------------------------- /Python Project for Data Science/Extracting_Stock_Data_Using_Webscraping.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "
\n", 8 | " \"cognitiveclass.ai\n", 9 | "
\n" 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "

Extracting Stock Data Using a Web Scraping

\n" 17 | ] 18 | }, 19 | { 20 | "cell_type": "markdown", 21 | "metadata": {}, 22 | "source": [ 23 | "Not all stock data is available via API in this assignment; you will use web-scraping to obtain financial data. You will be quizzed on your results. \n", 24 | " Using beautiful soup we will extract historical share data from a web-page.\n" 25 | ] 26 | }, 27 | { 28 | "cell_type": "markdown", 29 | "metadata": {}, 30 | "source": [ 31 | "

Table of Contents

\n", 32 | "
\n", 33 | "
    \n", 34 | "
  • Downloading the Webpage Using Requests Library
    • \n", 35 | "
  • Parsing Webpage HTML Using BeautifulSoup
    • \n", 36 | "
  • Extracting Data and Building DataFrame
    • \n", 37 | "
\n", 38 | "

\n", 39 | " Estimated Time Needed: 30 min

\n", 40 | "
\n", 41 | "\n", 42 | "
\n" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": 11, 48 | "metadata": {}, 49 | "outputs": [ 50 | { 51 | "name": "stdout", 52 | "output_type": "stream", 53 | "text": [ 54 | "Collecting bs4\n", 55 | " Downloading https://files.pythonhosted.org/packages/10/ed/7e8b97591f6f456174139ec089c769f89a94a1a4025fe967691de971f314/bs4-0.0.1.tar.gz\n", 56 | "Collecting beautifulsoup4 (from bs4)\n", 57 | "\u001b[?25l Downloading https://files.pythonhosted.org/packages/d1/41/e6495bd7d3781cee623ce23ea6ac73282a373088fcd0ddc809a047b18eae/beautifulsoup4-4.9.3-py3-none-any.whl (115kB)\n", 58 | "\u001b[K |████████████████████████████████| 122kB 2.4MB/s eta 0:00:01\n", 59 | "\u001b[?25hCollecting soupsieve>1.2; python_version >= \"3.0\" (from beautifulsoup4->bs4)\n", 60 | " Downloading https://files.pythonhosted.org/packages/36/69/d82d04022f02733bf9a72bc3b96332d360c0c5307096d76f6bb7489f7e57/soupsieve-2.2.1-py3-none-any.whl\n", 61 | "Building wheels for collected packages: bs4\n", 62 | " Building wheel for bs4 (setup.py) ... \u001b[?25ldone\n", 63 | "\u001b[?25h Stored in directory: /home/jupyterlab/.cache/pip/wheels/a0/b0/b2/4f80b9456b87abedbc0bf2d52235414c3467d8889be38dd472\n", 64 | "Successfully built bs4\n", 65 | "Installing collected packages: soupsieve, beautifulsoup4, bs4\n", 66 | "Successfully installed beautifulsoup4-4.9.3 bs4-0.0.1 soupsieve-2.2.1\n" 67 | ] 68 | } 69 | ], 70 | "source": [ 71 | "#!pip install pandas\n", 72 | "#!pip install requests\n", 73 | "!pip install bs4\n", 74 | "#!pip install plotly" 75 | ] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "execution_count": 12, 80 | "metadata": {}, 81 | "outputs": [], 82 | "source": [ 83 | "import pandas as pd\n", 84 | "import requests\n", 85 | "from bs4 import BeautifulSoup" 86 | ] 87 | }, 88 | { 89 | "cell_type": "markdown", 90 | "metadata": {}, 91 | "source": [ 92 | "## Using Webscraping to Extract Stock Data\n" 93 | ] 94 | }, 95 | { 96 | "cell_type": "markdown", 97 | "metadata": {}, 98 | "source": [ 99 | "Use the `requests` library to download the webpage [https://finance.yahoo.com/quote/AMZN/history?period1=1451606400&period2=1612137600&interval=1mo&filter=history&frequency=1mo&includeAdjustedClose=true](https://finance.yahoo.com/quote/AMZN/history?period1=1451606400&period2=1612137600&interval=1mo&filter=history&frequency=1mo&includeAdjustedClose=true&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork-23455606&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork-23455606&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork-23455606&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork-23455606&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ). Save the text of the response as a variable named `html_data`.\n" 100 | ] 101 | }, 102 | { 103 | "cell_type": "code", 104 | "execution_count": 13, 105 | "metadata": {}, 106 | "outputs": [], 107 | "source": [ 108 | "url = \"https://finance.yahoo.com/quote/AMZN/history?period1=1451606400&period2=1612137600&interval=1mo&filter=history&frequency=1mo&includeAdjustedClose=true\"\n", 109 | "html_data = requests.get(url).text" 110 | ] 111 | }, 112 | { 113 | "cell_type": "markdown", 114 | "metadata": {}, 115 | "source": [ 116 | "Parse the html data using `beautiful_soup`.\n" 117 | ] 118 | }, 119 | { 120 | "cell_type": "code", 121 | "execution_count": 14, 122 | "metadata": {}, 123 | "outputs": [], 124 | "source": [ 125 | "soup = BeautifulSoup(html_data,\"html5lib\")" 126 | ] 127 | }, 128 | { 129 | "cell_type": "markdown", 130 | "metadata": {}, 131 | "source": [ 132 | "Question 1 what is the content of the title attribute:\n" 133 | ] 134 | }, 135 | { 136 | "cell_type": "code", 137 | "execution_count": 15, 138 | "metadata": {}, 139 | "outputs": [ 140 | { 141 | "data": { 142 | "text/plain": [ 143 | "Amazon.com, Inc. (AMZN) Stock Historical Prices & Data - Yahoo Finance" 144 | ] 145 | }, 146 | "execution_count": 15, 147 | "metadata": {}, 148 | "output_type": "execute_result" 149 | } 150 | ], 151 | "source": [ 152 | "soup.title" 153 | ] 154 | }, 155 | { 156 | "cell_type": "markdown", 157 | "metadata": {}, 158 | "source": [ 159 | "Using beautiful soup extract the table with historical share prices and store it into a dataframe named `amazon_data`. The dataframe should have columns Date, Open, High, Low, Close, Adj Close, and Volume. Fill in each variable with the correct data from the list `col`. \n", 160 | "\n", 161 | "Hint: Print the `col` list to see what data to use\n" 162 | ] 163 | }, 164 | { 165 | "cell_type": "code", 166 | "execution_count": 16, 167 | "metadata": {}, 168 | "outputs": [], 169 | "source": [ 170 | "amazon_data = pd.DataFrame(columns=[\"Date\", \"Open\", \"High\", \"Low\", \"Close\", \"Volume\"])\n", 171 | "\n", 172 | "for row in soup.find(\"tbody\").find_all(\"tr\"):\n", 173 | " col = row.find_all(\"td\")\n", 174 | " date =col[0].text\n", 175 | " Open =col[1].text\n", 176 | " high =col[2].text\n", 177 | " low =col[3].text\n", 178 | " close =col[4].text\n", 179 | " adj_close =col[5].text\n", 180 | " volume =col[6].text\n", 181 | " \n", 182 | " amazon_data = amazon_data.append({\"Date\":date, \"Open\":Open, \"High\":high, \"Low\":low, \"Close\":close, \"Adj Close\":adj_close, \"Volume\":volume}, ignore_index=True)" 183 | ] 184 | }, 185 | { 186 | "cell_type": "markdown", 187 | "metadata": {}, 188 | "source": [ 189 | "Print out the first five rows of the `amazon_data` dataframe you created.\n" 190 | ] 191 | }, 192 | { 193 | "cell_type": "code", 194 | "execution_count": 17, 195 | "metadata": {}, 196 | "outputs": [ 197 | { 198 | "data": { 199 | "text/html": [ 200 | "
\n", 201 | "\n", 214 | "\n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | "
DateOpenHighLowCloseVolumeAdj Close
0Jan 01, 20213,270.003,363.893,086.003,206.2071,529,9003,206.20
1Dec 01, 20203,188.503,350.653,072.823,256.9377,567,8003,256.93
2Nov 01, 20203,061.743,366.802,950.123,168.0490,810,5003,168.04
3Oct 01, 20203,208.003,496.243,019.003,036.15116,242,3003,036.15
4Sep 01, 20203,489.583,552.252,871.003,148.73115,943,5003,148.73
\n", 280 | "
" 281 | ], 282 | "text/plain": [ 283 | " Date Open High Low Close Volume Adj Close\n", 284 | "0 Jan 01, 2021 3,270.00 3,363.89 3,086.00 3,206.20 71,529,900 3,206.20\n", 285 | "1 Dec 01, 2020 3,188.50 3,350.65 3,072.82 3,256.93 77,567,800 3,256.93\n", 286 | "2 Nov 01, 2020 3,061.74 3,366.80 2,950.12 3,168.04 90,810,500 3,168.04\n", 287 | "3 Oct 01, 2020 3,208.00 3,496.24 3,019.00 3,036.15 116,242,300 3,036.15\n", 288 | "4 Sep 01, 2020 3,489.58 3,552.25 2,871.00 3,148.73 115,943,500 3,148.73" 289 | ] 290 | }, 291 | "execution_count": 17, 292 | "metadata": {}, 293 | "output_type": "execute_result" 294 | } 295 | ], 296 | "source": [ 297 | "amazon_data.head()" 298 | ] 299 | }, 300 | { 301 | "cell_type": "markdown", 302 | "metadata": {}, 303 | "source": [ 304 | "Question 2 What is the name of the columns of the dataframe \n" 305 | ] 306 | }, 307 | { 308 | "cell_type": "code", 309 | "execution_count": 18, 310 | "metadata": {}, 311 | "outputs": [ 312 | { 313 | "data": { 314 | "text/plain": [ 315 | "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close'], dtype='object')" 316 | ] 317 | }, 318 | "execution_count": 18, 319 | "metadata": {}, 320 | "output_type": "execute_result" 321 | } 322 | ], 323 | "source": [ 324 | "amazon_data.columns" 325 | ] 326 | }, 327 | { 328 | "cell_type": "markdown", 329 | "metadata": {}, 330 | "source": [ 331 | "Question 3 What is the `Open` of `Jun 01, 2019` of the dataframe?\n" 332 | ] 333 | }, 334 | { 335 | "cell_type": "code", 336 | "execution_count": 62, 337 | "metadata": {}, 338 | "outputs": [ 339 | { 340 | "data": { 341 | "text/html": [ 342 | "
\n", 343 | "\n", 356 | "\n", 357 | " \n", 358 | " \n", 359 | " \n", 360 | " \n", 361 | " \n", 362 | " \n", 363 | " \n", 364 | " \n", 365 | " \n", 366 | " \n", 367 | " \n", 368 | " \n", 369 | " \n", 370 | " \n", 371 | " \n", 372 | " \n", 373 | " \n", 374 | " \n", 375 | " \n", 376 | " \n", 377 | " \n", 378 | " \n", 379 | " \n", 380 | " \n", 381 | "
DateOpenHighLowCloseVolumeAdj Close
19Jun 01, 20191,760.011,935.201,672.001,893.6374,746,5001,893.63
\n", 382 | "
" 383 | ], 384 | "text/plain": [ 385 | " Date Open High Low Close Volume Adj Close\n", 386 | "19 Jun 01, 2019 1,760.01 1,935.20 1,672.00 1,893.63 74,746,500 1,893.63" 387 | ] 388 | }, 389 | "execution_count": 62, 390 | "metadata": {}, 391 | "output_type": "execute_result" 392 | } 393 | ], 394 | "source": [ 395 | "amazon_data.loc[amazon_data[\"Date\"]==\"Jun 01, 2019\"]" 396 | ] 397 | }, 398 | { 399 | "cell_type": "markdown", 400 | "metadata": {}, 401 | "source": [ 402 | "

About the Authors:

\n", 403 | "\n", 404 | "Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n", 405 | "\n", 406 | "Azim Hirjani\n" 407 | ] 408 | }, 409 | { 410 | "cell_type": "markdown", 411 | "metadata": {}, 412 | "source": [ 413 | "## Change Log\n", 414 | "\n", 415 | "| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n", 416 | "| ----------------- | ------- | ------------- | ------------------------- |\n", 417 | "| 2020-11-10 | 1.1 | Malika Singla | Deleted the Optional part |\n", 418 | "| 2020-08-27 | 1.0 | Malika Singla | Added lab to GitLab |\n", 419 | "\n", 420 | "
\n", 421 | "\n", 422 | "##

© IBM Corporation 2020. All rights reserved.

\n", 423 | "\n", 424 | "

\n" 425 | ] 426 | } 427 | ], 428 | "metadata": { 429 | "kernelspec": { 430 | "display_name": "Python", 431 | "language": "python", 432 | "name": "conda-env-python-py" 433 | }, 434 | "language_info": { 435 | "codemirror_mode": { 436 | "name": "ipython", 437 | "version": 3 438 | }, 439 | "file_extension": ".py", 440 | "mimetype": "text/x-python", 441 | "name": "python", 442 | "nbconvert_exporter": "python", 443 | "pygments_lexer": "ipython3", 444 | "version": "3.6.12" 445 | } 446 | }, 447 | "nbformat": 4, 448 | "nbformat_minor": 4 449 | } 450 | -------------------------------------------------------------------------------- /Python Project for Data Science/IMG/Q1.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/Python Project for Data Science/IMG/Q1.PNG -------------------------------------------------------------------------------- /Python Project for Data Science/IMG/Q2.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/Python Project for Data Science/IMG/Q2.PNG -------------------------------------------------------------------------------- /Python Project for Data Science/IMG/Q3.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/Python Project for Data Science/IMG/Q3.PNG -------------------------------------------------------------------------------- /Python Project for Data Science/IMG/Q4.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/Python Project for Data Science/IMG/Q4.PNG -------------------------------------------------------------------------------- /Python Project for Data Science/IMG/Q5.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/Python Project for Data Science/IMG/Q5.PNG -------------------------------------------------------------------------------- /Python Project for Data Science/IMG/Q6.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/Python Project for Data Science/IMG/Q6.PNG -------------------------------------------------------------------------------- /Python Project for Data Science/README.md: -------------------------------------------------------------------------------- 1 | [![View Repositories](https://img.shields.io/badge/View-My_Repositories-blue?logo=GitHub)](https://github.com/nduongthucanh?tab=repositories) 2 | [![View My Profile](https://img.shields.io/badge/View-My_Profile-green?logo=GitHub)](https://github.com/nduongthucanh) 3 | 4 | # Excel Basics for Data Analysis 5 | 6 | 7 | ## About this Project 8 | For this project, you will assume the role of a Data Scientist / Data Analyst working for a new startup investment firm that helps customers invest their money in stocks. Your job is to extract financial data like historical share price and quarterly revenue reportings from various sources using Python libraries and webscraping on popular stocks. After collecting this data you will visualize it in a dashboard to identify patterns or trends. The stocks we will work with are Tesla, Amazon, AMD, and GameStop. 9 | 10 |
11 | 12 | ## Course Structure 13 | 14 | - [x] **Intro to Web Scraping Using BeautifulSoup** 15 | 16 | In this lab you will learn how to use BeautifulSoup and specifically how to extract data in HTML tables to a DataFrame. 17 | * **Environment Setup** 18 | ```python 19 | !pip install bs4 20 | !pip install requests 21 | ``` 22 | 23 | --- 24 | 25 | - [x] **Extracting Stock Data Using a Python Library** 26 | 27 | In this lab, you will use a Python library to obtain financial data. You will extract historical stock data using yfinance. A graded quiz will follow to test you on the results in the lab. 28 | 29 | * **Environment Setup** 30 | ```python 31 | !pip install yfinance 32 | !pip install pandas 33 | ``` 34 | 35 | --- 36 | 37 | - [x] **Extracting Stock Data Using Web Scraping** 38 | 39 | In this lab you will use web scraping to obtain financial data. You will extract historical stock data from a web-page using beautiful soup. A graded quiz follows to test you on the results in this lab. 40 | 41 | * **Environment Setup** 42 | ```python 43 | !pip install pandas 44 | !pip install requests 45 | !pip install bs4 46 | !pip install plotly 47 | ``` 48 | 49 | --- 50 | 51 | - [x] **Analyzing Historical Stock/Revenue Data and Building a Dashboard** 52 | 53 | Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph. 54 | 55 | * **Environment Setup** 56 | ```python 57 | !pip install yfinance 58 | !pip install pandas 59 | !pip install requests 60 | !pip install bs4 61 | !pip install plotly 62 | ``` 63 | -------------------------------------------------------------------------------- /Python for Data Science, AI & Development/PY0101EN-3-2-Loops.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [] 7 | }, 8 | { 9 | "cell_type": "markdown", 10 | "metadata": {}, 11 | "source": [ 12 | "\n", 13 | " \n", 14 | "" 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "

Loops in Python

" 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "

Welcome! This notebook will teach you about the loops in the Python Programming Language. By the end of this lab, you'll know how to use the loop statements in Python, including for loop, and while loop.

" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "
\n", 36 | " \n", 37 | " \n", 38 | " \n", 39 | "
" 40 | ] 41 | }, 42 | { 43 | "cell_type": "markdown", 44 | "metadata": {}, 45 | "source": [ 46 | "

Table of Contents

\n", 47 | "
\n", 48 | " \n", 61 | "

\n", 62 | " Estimated time needed: 20 min\n", 63 | "

\n", 64 | "
\n", 65 | "\n", 66 | "
" 67 | ] 68 | }, 69 | { 70 | "cell_type": "markdown", 71 | "metadata": {}, 72 | "source": [ 73 | "

Loops

" 74 | ] 75 | }, 76 | { 77 | "cell_type": "markdown", 78 | "metadata": {}, 79 | "source": [ 80 | "

Range

" 81 | ] 82 | }, 83 | { 84 | "cell_type": "markdown", 85 | "metadata": {}, 86 | "source": [ 87 | "Sometimes, you might want to repeat a given operation many times. Repeated executions like this are performed by loops. We will look at two types of loops, for loops and while loops.\n", 88 | "\n", 89 | "Before we discuss loops lets discuss the range object. It is helpful to think of the range object as an ordered list. For now, let's look at the simplest case. If we would like to generate a sequence that contains three elements ordered from 0 to 2 we simply use the following command:" 90 | ] 91 | }, 92 | { 93 | "cell_type": "code", 94 | "execution_count": 1, 95 | "metadata": { 96 | "collapsed": false, 97 | "jupyter": { 98 | "outputs_hidden": false 99 | } 100 | }, 101 | "outputs": [ 102 | { 103 | "data": { 104 | "text/plain": [ 105 | "range(0, 3)" 106 | ] 107 | }, 108 | "execution_count": 1, 109 | "metadata": {}, 110 | "output_type": "execute_result" 111 | } 112 | ], 113 | "source": [ 114 | "# Use the range\n", 115 | "\n", 116 | "range(3)" 117 | ] 118 | }, 119 | { 120 | "cell_type": "markdown", 121 | "metadata": {}, 122 | "source": [ 123 | "" 124 | ] 125 | }, 126 | { 127 | "cell_type": "markdown", 128 | "metadata": {}, 129 | "source": [ 130 | "

What is for loop?

" 131 | ] 132 | }, 133 | { 134 | "cell_type": "markdown", 135 | "metadata": {}, 136 | "source": [ 137 | "The for loop enables you to execute a code block multiple times. For example, you would use this if you would like to print out every element in a list. \n", 138 | "Let's try to use a for loop to print all the years presented in the list dates:" 139 | ] 140 | }, 141 | { 142 | "cell_type": "markdown", 143 | "metadata": {}, 144 | "source": [ 145 | "This can be done as follows:" 146 | ] 147 | }, 148 | { 149 | "cell_type": "code", 150 | "execution_count": 2, 151 | "metadata": { 152 | "collapsed": false, 153 | "jupyter": { 154 | "outputs_hidden": false 155 | } 156 | }, 157 | "outputs": [ 158 | { 159 | "name": "stdout", 160 | "output_type": "stream", 161 | "text": [ 162 | "1982\n", 163 | "1980\n", 164 | "1973\n" 165 | ] 166 | } 167 | ], 168 | "source": [ 169 | "# For loop example\n", 170 | "\n", 171 | "dates = [1982,1980,1973]\n", 172 | "N = len(dates)\n", 173 | "\n", 174 | "for i in range(N):\n", 175 | " print(dates[i]) " 176 | ] 177 | }, 178 | { 179 | "cell_type": "markdown", 180 | "metadata": {}, 181 | "source": [ 182 | "The code in the indent is executed N times, each time the value of i is increased by 1 for every execution. The statement executed is to print out the value in the list at index i as shown here:" 183 | ] 184 | }, 185 | { 186 | "cell_type": "markdown", 187 | "metadata": {}, 188 | "source": [ 189 | "" 190 | ] 191 | }, 192 | { 193 | "cell_type": "markdown", 194 | "metadata": {}, 195 | "source": [ 196 | "In this example we can print out a sequence of numbers from 0 to 7:" 197 | ] 198 | }, 199 | { 200 | "cell_type": "code", 201 | "execution_count": 3, 202 | "metadata": { 203 | "collapsed": false, 204 | "jupyter": { 205 | "outputs_hidden": false 206 | } 207 | }, 208 | "outputs": [ 209 | { 210 | "name": "stdout", 211 | "output_type": "stream", 212 | "text": [ 213 | "0\n", 214 | "1\n", 215 | "2\n", 216 | "3\n", 217 | "4\n", 218 | "5\n", 219 | "6\n", 220 | "7\n" 221 | ] 222 | } 223 | ], 224 | "source": [ 225 | "# Example of for loop\n", 226 | "\n", 227 | "for i in range(0, 8):\n", 228 | " print(i)" 229 | ] 230 | }, 231 | { 232 | "cell_type": "markdown", 233 | "metadata": {}, 234 | "source": [ 235 | "In Python we can directly access the elements in the list as follows: " 236 | ] 237 | }, 238 | { 239 | "cell_type": "code", 240 | "execution_count": 4, 241 | "metadata": { 242 | "collapsed": false, 243 | "jupyter": { 244 | "outputs_hidden": false 245 | } 246 | }, 247 | "outputs": [ 248 | { 249 | "name": "stdout", 250 | "output_type": "stream", 251 | "text": [ 252 | "1982\n", 253 | "1980\n", 254 | "1973\n" 255 | ] 256 | } 257 | ], 258 | "source": [ 259 | "# Exmaple of for loop, loop through list\n", 260 | "\n", 261 | "for year in dates: \n", 262 | " print(year) " 263 | ] 264 | }, 265 | { 266 | "cell_type": "markdown", 267 | "metadata": {}, 268 | "source": [ 269 | "For each iteration, the value of the variable years behaves like the value of dates[i] in the first example:" 270 | ] 271 | }, 272 | { 273 | "cell_type": "markdown", 274 | "metadata": {}, 275 | "source": [ 276 | "" 277 | ] 278 | }, 279 | { 280 | "cell_type": "markdown", 281 | "metadata": {}, 282 | "source": [ 283 | "We can change the elements in a list:" 284 | ] 285 | }, 286 | { 287 | "cell_type": "code", 288 | "execution_count": 5, 289 | "metadata": { 290 | "collapsed": false, 291 | "jupyter": { 292 | "outputs_hidden": false 293 | } 294 | }, 295 | "outputs": [ 296 | { 297 | "name": "stdout", 298 | "output_type": "stream", 299 | "text": [ 300 | "Before square 0 is red\n", 301 | "After square 0 is weight\n", 302 | "Before square 1 is yellow\n", 303 | "After square 1 is weight\n", 304 | "Before square 2 is green\n", 305 | "After square 2 is weight\n", 306 | "Before square 3 is purple\n", 307 | "After square 3 is weight\n", 308 | "Before square 4 is blue\n", 309 | "After square 4 is weight\n" 310 | ] 311 | } 312 | ], 313 | "source": [ 314 | "# Use for loop to change the elements in list\n", 315 | "\n", 316 | "squares = ['red', 'yellow', 'green', 'purple', 'blue']\n", 317 | "\n", 318 | "for i in range(0, 5):\n", 319 | " print(\"Before square \", i, 'is', squares[i])\n", 320 | " squares[i] = 'weight'\n", 321 | " print(\"After square \", i, 'is', squares[i])" 322 | ] 323 | }, 324 | { 325 | "cell_type": "markdown", 326 | "metadata": {}, 327 | "source": [ 328 | " We can access the index and the elements of a list as follows: " 329 | ] 330 | }, 331 | { 332 | "cell_type": "code", 333 | "execution_count": 8, 334 | "metadata": { 335 | "collapsed": false, 336 | "jupyter": { 337 | "outputs_hidden": false 338 | } 339 | }, 340 | "outputs": [ 341 | { 342 | "name": "stdout", 343 | "output_type": "stream", 344 | "text": [ 345 | "0 red\n", 346 | "1 yellow\n", 347 | "2 green\n", 348 | "3 purple\n", 349 | "4 blue\n" 350 | ] 351 | } 352 | ], 353 | "source": [ 354 | "# Loop through the list and iterate on both index and element value\n", 355 | "\n", 356 | "squares=['red', 'yellow', 'green', 'purple', 'blue']\n", 357 | "\n", 358 | "for i, square in enumerate(squares):\n", 359 | " print(i, square)" 360 | ] 361 | }, 362 | { 363 | "cell_type": "markdown", 364 | "metadata": {}, 365 | "source": [ 366 | "

What is while loop?

" 367 | ] 368 | }, 369 | { 370 | "cell_type": "markdown", 371 | "metadata": {}, 372 | "source": [ 373 | "As you can see, the for loop is used for a controlled flow of repetition. However, what if we don't know when we want to stop the loop? What if we want to keep executing a code block until a certain condition is met? The while loop exists as a tool for repeated execution based on a condition. The code block will keep being executed until the given logical condition returns a **False** boolean value.\n" 374 | ] 375 | }, 376 | { 377 | "cell_type": "markdown", 378 | "metadata": {}, 379 | "source": [ 380 | "Let’s say we would like to iterate through list dates and stop at the year 1973, then print out the number of iterations. This can be done with the following block of code:" 381 | ] 382 | }, 383 | { 384 | "cell_type": "code", 385 | "execution_count": 9, 386 | "metadata": { 387 | "collapsed": false, 388 | "jupyter": { 389 | "outputs_hidden": false 390 | } 391 | }, 392 | "outputs": [ 393 | { 394 | "name": "stdout", 395 | "output_type": "stream", 396 | "text": [ 397 | "1982\n", 398 | "1980\n", 399 | "1973\n", 400 | "It took 3 repetitions to get out of loop.\n" 401 | ] 402 | } 403 | ], 404 | "source": [ 405 | "# While Loop Example\n", 406 | "\n", 407 | "dates = [1982, 1980, 1973, 2000]\n", 408 | "\n", 409 | "i = 0\n", 410 | "year = 0\n", 411 | "\n", 412 | "while(year != 1973):\n", 413 | " year = dates[i]\n", 414 | " i = i + 1\n", 415 | " print(year)\n", 416 | "\n", 417 | "print(\"It took \", i ,\"repetitions to get out of loop.\")" 418 | ] 419 | }, 420 | { 421 | "cell_type": "markdown", 422 | "metadata": {}, 423 | "source": [ 424 | "A while loop iterates merely until the condition in the argument is not met, as shown in the following figure:" 425 | ] 426 | }, 427 | { 428 | "cell_type": "markdown", 429 | "metadata": {}, 430 | "source": [ 431 | "" 432 | ] 433 | }, 434 | { 435 | "cell_type": "markdown", 436 | "metadata": {}, 437 | "source": [ 438 | "
" 439 | ] 440 | }, 441 | { 442 | "cell_type": "markdown", 443 | "metadata": {}, 444 | "source": [ 445 | "

Quiz on Loops

" 446 | ] 447 | }, 448 | { 449 | "cell_type": "markdown", 450 | "metadata": {}, 451 | "source": [ 452 | "Write a for loop the prints out all the element between -5 and 5 using the range function." 453 | ] 454 | }, 455 | { 456 | "cell_type": "code", 457 | "execution_count": 10, 458 | "metadata": {}, 459 | "outputs": [ 460 | { 461 | "name": "stdout", 462 | "output_type": "stream", 463 | "text": [ 464 | "-5\n", 465 | "-4\n", 466 | "-3\n", 467 | "-2\n", 468 | "-1\n", 469 | "0\n", 470 | "1\n", 471 | "2\n", 472 | "3\n", 473 | "4\n", 474 | "5\n" 475 | ] 476 | } 477 | ], 478 | "source": [ 479 | "# Write your code below and press Shift+Enter to execute\n", 480 | "\n", 481 | "for i in range(-5,6):\n", 482 | " print(i)" 483 | ] 484 | }, 485 | { 486 | "cell_type": "markdown", 487 | "metadata": {}, 488 | "source": [ 489 | "Double-click __here__ for the solution.\n", 490 | "" 494 | ] 495 | }, 496 | { 497 | "cell_type": "markdown", 498 | "metadata": {}, 499 | "source": [ 500 | "Print the elements of the following list:\n", 501 | "Genres=[ 'rock', 'R&B', 'Soundtrack', 'R&B', 'soul', 'pop']\n", 502 | "Make sure you follow Python conventions." 503 | ] 504 | }, 505 | { 506 | "cell_type": "code", 507 | "execution_count": 11, 508 | "metadata": {}, 509 | "outputs": [ 510 | { 511 | "name": "stdout", 512 | "output_type": "stream", 513 | "text": [ 514 | "rock\n", 515 | "R&B\n", 516 | "Soundtrack\n", 517 | "R&B\n", 518 | "soul\n", 519 | "pop\n" 520 | ] 521 | } 522 | ], 523 | "source": [ 524 | "# Write your code below and press Shift+Enter to execute\n", 525 | "Genres=[ 'rock', 'R&B', 'Soundtrack', 'R&B', 'soul', 'pop']\n", 526 | "\n", 527 | "for genre in Genres:\n", 528 | " print(genre)" 529 | ] 530 | }, 531 | { 532 | "cell_type": "markdown", 533 | "metadata": {}, 534 | "source": [ 535 | "Double-click __here__ for the solution.\n", 536 | "" 541 | ] 542 | }, 543 | { 544 | "cell_type": "markdown", 545 | "metadata": {}, 546 | "source": [ 547 | "
" 548 | ] 549 | }, 550 | { 551 | "cell_type": "markdown", 552 | "metadata": {}, 553 | "source": [ 554 | "Write a for loop that prints out the following list: squares=['red', 'yellow', 'green', 'purple', 'blue']" 555 | ] 556 | }, 557 | { 558 | "cell_type": "code", 559 | "execution_count": 12, 560 | "metadata": {}, 561 | "outputs": [ 562 | { 563 | "name": "stdout", 564 | "output_type": "stream", 565 | "text": [ 566 | "red\n", 567 | "yellow\n", 568 | "green\n", 569 | "purple\n", 570 | "blue\n" 571 | ] 572 | } 573 | ], 574 | "source": [ 575 | "# Write your code below and press Shift+Enter to execute\n", 576 | "squares=['red', 'yellow', 'green', 'purple', 'blue']\n", 577 | "for square in squares:\n", 578 | " print(square)" 579 | ] 580 | }, 581 | { 582 | "cell_type": "markdown", 583 | "metadata": {}, 584 | "source": [ 585 | "Double-click __here__ for the solution.\n", 586 | "" 591 | ] 592 | }, 593 | { 594 | "cell_type": "markdown", 595 | "metadata": {}, 596 | "source": [ 597 | "
" 598 | ] 599 | }, 600 | { 601 | "cell_type": "markdown", 602 | "metadata": {}, 603 | "source": [ 604 | "Write a while loop to display the values of the Rating of an album playlist stored in the list PlayListRatings. If the score is less than 6, exit the loop. The list PlayListRatings is given by: PlayListRatings = [10, 9.5, 10, 8, 7.5, 5, 10, 10]" 605 | ] 606 | }, 607 | { 608 | "cell_type": "code", 609 | "execution_count": 7, 610 | "metadata": {}, 611 | "outputs": [ 612 | { 613 | "name": "stdout", 614 | "output_type": "stream", 615 | "text": [ 616 | "10\n", 617 | "9.5\n", 618 | "10\n", 619 | "8\n", 620 | "7.5\n" 621 | ] 622 | } 623 | ], 624 | "source": [ 625 | "# Write your code below and press Shift+Enter to execute\n", 626 | "PlayListRatings = [10, 9.5, 10, 8, 7.5, 5, 10, 10]\n", 627 | "i=1\n", 628 | "R = PlayListRatings[0]\n", 629 | "while(R >= 6):\n", 630 | " print(R)\n", 631 | " R = PlayListRatings[i]\n", 632 | " i=i+1 " 633 | ] 634 | }, 635 | { 636 | "cell_type": "markdown", 637 | "metadata": {}, 638 | "source": [ 639 | "Double-click __here__ for the solution.\n", 640 | "" 649 | ] 650 | }, 651 | { 652 | "cell_type": "markdown", 653 | "metadata": {}, 654 | "source": [ 655 | "
" 656 | ] 657 | }, 658 | { 659 | "cell_type": "markdown", 660 | "metadata": {}, 661 | "source": [ 662 | "Write a while loop to copy the strings 'orange' of the list squares to the list new_squares. Stop and exit the loop if the value on the list is not 'orange':" 663 | ] 664 | }, 665 | { 666 | "cell_type": "code", 667 | "execution_count": null, 668 | "metadata": { 669 | "collapsed": false, 670 | "jupyter": { 671 | "outputs_hidden": false 672 | } 673 | }, 674 | "outputs": [], 675 | "source": [ 676 | "# Write your code below and press Shift+Enter to execute\n", 677 | "\n", 678 | "squares = ['orange', 'orange', 'purple', 'blue ', 'orange']\n", 679 | "new_squares = []\n", 680 | "\n", 681 | "i = 0\n", 682 | "while(squares[i] == 'orange'):\n", 683 | " new_squares.append(squares[i])\n", 684 | " i = i + 1 \n", 685 | "print (new_squares)\n" 686 | ] 687 | }, 688 | { 689 | "cell_type": "markdown", 690 | "metadata": {}, 691 | "source": [ 692 | "Double-click __here__ for the solution.\n", 693 | "" 702 | ] 703 | }, 704 | { 705 | "cell_type": "markdown", 706 | "metadata": {}, 707 | "source": [ 708 | "
\n", 709 | "

The last exercise!

\n", 710 | "

Congratulations, you have completed your first lesson and hands-on lab in Python. However, there is one more thing you need to do. The Data Science community encourages sharing work. The best way to share and showcase your work is to share it on GitHub. By sharing your notebook on GitHub you are not only building your reputation with fellow data scientists, but you can also show it off when applying for a job. Even though this was your first piece of work, it is never too early to start building good habits. So, please read and follow this article to learn how to share your work.\n", 711 | "

" 712 | ] 713 | }, 714 | { 715 | "cell_type": "markdown", 716 | "metadata": {}, 717 | "source": [ 718 | "
\n", 719 | "

Get IBM Watson Studio free of charge!

\n", 720 | "

\n", 721 | "
" 722 | ] 723 | }, 724 | { 725 | "cell_type": "markdown", 726 | "metadata": {}, 727 | "source": [ 728 | "

About the Authors:

\n", 729 | "

Joseph Santarcangelo is a Data Scientist at IBM, and holds a PhD in Electrical Engineering. His research focused on using Machine Learning, Signal Processing, and Computer Vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

" 730 | ] 731 | }, 732 | { 733 | "cell_type": "markdown", 734 | "metadata": {}, 735 | "source": [ 736 | "Other contributors: Mavis Zhou, James Reeve" 737 | ] 738 | }, 739 | { 740 | "cell_type": "markdown", 741 | "metadata": {}, 742 | "source": [ 743 | "
" 744 | ] 745 | }, 746 | { 747 | "cell_type": "markdown", 748 | "metadata": {}, 749 | "source": [ 750 | "

Copyright © 2018 IBM Developer Skills Network. This notebook and its source code are released under the terms of the MIT License.

" 751 | ] 752 | } 753 | ], 754 | "metadata": { 755 | "kernelspec": { 756 | "display_name": "Python", 757 | "language": "python", 758 | "name": "conda-env-python-py" 759 | }, 760 | "language_info": { 761 | "codemirror_mode": { 762 | "name": "ipython", 763 | "version": 3 764 | }, 765 | "file_extension": ".py", 766 | "mimetype": "text/x-python", 767 | "name": "python", 768 | "nbconvert_exporter": "python", 769 | "pygments_lexer": "ipython3", 770 | "version": "3.6.10" 771 | } 772 | }, 773 | "nbformat": 4, 774 | "nbformat_minor": 4 775 | } 776 | -------------------------------------------------------------------------------- /Python for Data Science, AI & Development/PY0101EN-4-1-ReadFile.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "
\n", 8 | " \n", 9 | " \n", 10 | " \n", 11 | "
" 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "metadata": {}, 17 | "source": [ 18 | "\n", 19 | " \n", 20 | "" 21 | ] 22 | }, 23 | { 24 | "cell_type": "markdown", 25 | "metadata": {}, 26 | "source": [ 27 | "

Reading Files Python

" 28 | ] 29 | }, 30 | { 31 | "cell_type": "markdown", 32 | "metadata": {}, 33 | "source": [ 34 | "

Welcome! This notebook will teach you about reading the text file in the Python Programming Language. By the end of this lab, you'll know how to read text files.

" 35 | ] 36 | }, 37 | { 38 | "cell_type": "markdown", 39 | "metadata": {}, 40 | "source": [ 41 | "

Table of Contents

\n", 42 | "
\n", 43 | " \n", 48 | "

\n", 49 | " Estimated time needed: 40 min\n", 50 | "

\n", 51 | "
\n", 52 | "\n", 53 | "
" 54 | ] 55 | }, 56 | { 57 | "cell_type": "markdown", 58 | "metadata": {}, 59 | "source": [ 60 | "

Download Data

" 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": 5, 66 | "metadata": {}, 67 | "outputs": [ 68 | { 69 | "name": "stdout", 70 | "output_type": "stream", 71 | "text": [ 72 | "--2020-05-05 00:29:11-- https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/labs/example1.txt\n", 73 | "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n", 74 | "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n", 75 | "HTTP request sent, awaiting response... 200 OK\n", 76 | "Length: 45 [text/plain]\n", 77 | "Saving to: ‘/resources/data/Example1.txt’\n", 78 | "\n", 79 | "/resources/data/Exa 100%[===================>] 45 --.-KB/s in 0s \n", 80 | "\n", 81 | "2020-05-05 00:29:12 (30.6 MB/s) - ‘/resources/data/Example1.txt’ saved [45/45]\n", 82 | "\n" 83 | ] 84 | } 85 | ], 86 | "source": [ 87 | "# Download Example file\n", 88 | "\n", 89 | "!wget -O /resources/data/Example1.txt https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/labs/example1.txt" 90 | ] 91 | }, 92 | { 93 | "cell_type": "markdown", 94 | "metadata": {}, 95 | "source": [ 96 | "
" 97 | ] 98 | }, 99 | { 100 | "cell_type": "markdown", 101 | "metadata": {}, 102 | "source": [ 103 | "

Reading Text Files

" 104 | ] 105 | }, 106 | { 107 | "cell_type": "markdown", 108 | "metadata": {}, 109 | "source": [ 110 | "One way to read or write a file in Python is to use the built-in open function. The open function provides a File object that contains the methods and attributes you need in order to read, save, and manipulate the file. In this notebook, we will only cover .txt files. The first parameter you need is the file path and the file name. An example is shown as follow:" 111 | ] 112 | }, 113 | { 114 | "cell_type": "markdown", 115 | "metadata": {}, 116 | "source": [ 117 | "" 118 | ] 119 | }, 120 | { 121 | "cell_type": "markdown", 122 | "metadata": {}, 123 | "source": [ 124 | " The mode argument is optional and the default value is r. In this notebook we only cover two modes: \n", 125 | "
    \n", 126 | "
  • r Read mode for reading files
    • \n", 127 | "
  • w Write mode for writing files
    • \n", 128 | "
" 129 | ] 130 | }, 131 | { 132 | "cell_type": "markdown", 133 | "metadata": {}, 134 | "source": [ 135 | "For the next example, we will use the text file Example1.txt. The file is shown as follow:" 136 | ] 137 | }, 138 | { 139 | "cell_type": "markdown", 140 | "metadata": {}, 141 | "source": [ 142 | "" 143 | ] 144 | }, 145 | { 146 | "cell_type": "markdown", 147 | "metadata": {}, 148 | "source": [ 149 | " We read the file: " 150 | ] 151 | }, 152 | { 153 | "cell_type": "code", 154 | "execution_count": 6, 155 | "metadata": {}, 156 | "outputs": [], 157 | "source": [ 158 | "# Read the Example1.txt\n", 159 | "\n", 160 | "example1 = \"/resources/data/Example1.txt\"\n", 161 | "file1 = open(example1, \"r\")" 162 | ] 163 | }, 164 | { 165 | "cell_type": "markdown", 166 | "metadata": {}, 167 | "source": [ 168 | " We can view the attributes of the file." 169 | ] 170 | }, 171 | { 172 | "cell_type": "markdown", 173 | "metadata": {}, 174 | "source": [ 175 | "The name of the file:" 176 | ] 177 | }, 178 | { 179 | "cell_type": "code", 180 | "execution_count": 7, 181 | "metadata": {}, 182 | "outputs": [ 183 | { 184 | "data": { 185 | "text/plain": [ 186 | "'/resources/data/Example1.txt'" 187 | ] 188 | }, 189 | "execution_count": 7, 190 | "metadata": {}, 191 | "output_type": "execute_result" 192 | } 193 | ], 194 | "source": [ 195 | "# Print the path of file\n", 196 | "\n", 197 | "file1.name" 198 | ] 199 | }, 200 | { 201 | "cell_type": "markdown", 202 | "metadata": {}, 203 | "source": [ 204 | " The mode the file object is in:" 205 | ] 206 | }, 207 | { 208 | "cell_type": "code", 209 | "execution_count": 8, 210 | "metadata": {}, 211 | "outputs": [ 212 | { 213 | "data": { 214 | "text/plain": [ 215 | "'r'" 216 | ] 217 | }, 218 | "execution_count": 8, 219 | "metadata": {}, 220 | "output_type": "execute_result" 221 | } 222 | ], 223 | "source": [ 224 | "# Print the mode of file, either 'r' or 'w'\n", 225 | "\n", 226 | "file1.mode" 227 | ] 228 | }, 229 | { 230 | "cell_type": "markdown", 231 | "metadata": {}, 232 | "source": [ 233 | "We can read the file and assign it to a variable :" 234 | ] 235 | }, 236 | { 237 | "cell_type": "code", 238 | "execution_count": 9, 239 | "metadata": {}, 240 | "outputs": [ 241 | { 242 | "data": { 243 | "text/plain": [ 244 | "'This is line 1 \\nThis is line 2\\nThis is line 3'" 245 | ] 246 | }, 247 | "execution_count": 9, 248 | "metadata": {}, 249 | "output_type": "execute_result" 250 | } 251 | ], 252 | "source": [ 253 | "# Read the file\n", 254 | "\n", 255 | "FileContent = file1.read()\n", 256 | "FileContent" 257 | ] 258 | }, 259 | { 260 | "cell_type": "markdown", 261 | "metadata": {}, 262 | "source": [ 263 | "The /n means that there is a new line. " 264 | ] 265 | }, 266 | { 267 | "cell_type": "markdown", 268 | "metadata": {}, 269 | "source": [ 270 | "We can print the file: " 271 | ] 272 | }, 273 | { 274 | "cell_type": "code", 275 | "execution_count": 10, 276 | "metadata": {}, 277 | "outputs": [ 278 | { 279 | "name": "stdout", 280 | "output_type": "stream", 281 | "text": [ 282 | "This is line 1 \n", 283 | "This is line 2\n", 284 | "This is line 3\n" 285 | ] 286 | } 287 | ], 288 | "source": [ 289 | "# Print the file with '\\n' as a new line\n", 290 | "\n", 291 | "print(FileContent)" 292 | ] 293 | }, 294 | { 295 | "cell_type": "markdown", 296 | "metadata": {}, 297 | "source": [ 298 | "The file is of type string:" 299 | ] 300 | }, 301 | { 302 | "cell_type": "code", 303 | "execution_count": 11, 304 | "metadata": {}, 305 | "outputs": [ 306 | { 307 | "data": { 308 | "text/plain": [ 309 | "str" 310 | ] 311 | }, 312 | "execution_count": 11, 313 | "metadata": {}, 314 | "output_type": "execute_result" 315 | } 316 | ], 317 | "source": [ 318 | "# Type of file content\n", 319 | "\n", 320 | "type(FileContent)" 321 | ] 322 | }, 323 | { 324 | "cell_type": "markdown", 325 | "metadata": {}, 326 | "source": [ 327 | " We must close the file object:" 328 | ] 329 | }, 330 | { 331 | "cell_type": "code", 332 | "execution_count": 12, 333 | "metadata": {}, 334 | "outputs": [], 335 | "source": [ 336 | "# Close file after finish\n", 337 | "\n", 338 | "file1.close()" 339 | ] 340 | }, 341 | { 342 | "cell_type": "markdown", 343 | "metadata": {}, 344 | "source": [ 345 | "
" 346 | ] 347 | }, 348 | { 349 | "cell_type": "markdown", 350 | "metadata": {}, 351 | "source": [ 352 | "

A Better Way to Open a File

" 353 | ] 354 | }, 355 | { 356 | "cell_type": "markdown", 357 | "metadata": {}, 358 | "source": [ 359 | "Using the with statement is better practice, it automatically closes the file even if the code encounters an exception. The code will run everything in the indent block then close the file object. " 360 | ] 361 | }, 362 | { 363 | "cell_type": "code", 364 | "execution_count": 13, 365 | "metadata": {}, 366 | "outputs": [ 367 | { 368 | "name": "stdout", 369 | "output_type": "stream", 370 | "text": [ 371 | "This is line 1 \n", 372 | "This is line 2\n", 373 | "This is line 3\n" 374 | ] 375 | } 376 | ], 377 | "source": [ 378 | "# Open file using with\n", 379 | "\n", 380 | "with open(example1, \"r\") as file1:\n", 381 | " FileContent = file1.read()\n", 382 | " print(FileContent)" 383 | ] 384 | }, 385 | { 386 | "cell_type": "markdown", 387 | "metadata": {}, 388 | "source": [ 389 | "The file object is closed, you can verify it by running the following cell: " 390 | ] 391 | }, 392 | { 393 | "cell_type": "code", 394 | "execution_count": 14, 395 | "metadata": {}, 396 | "outputs": [ 397 | { 398 | "data": { 399 | "text/plain": [ 400 | "True" 401 | ] 402 | }, 403 | "execution_count": 14, 404 | "metadata": {}, 405 | "output_type": "execute_result" 406 | } 407 | ], 408 | "source": [ 409 | "# Verify if the file is closed\n", 410 | "\n", 411 | "file1.closed" 412 | ] 413 | }, 414 | { 415 | "cell_type": "markdown", 416 | "metadata": {}, 417 | "source": [ 418 | " We can see the info in the file:" 419 | ] 420 | }, 421 | { 422 | "cell_type": "code", 423 | "execution_count": 15, 424 | "metadata": {}, 425 | "outputs": [ 426 | { 427 | "name": "stdout", 428 | "output_type": "stream", 429 | "text": [ 430 | "This is line 1 \n", 431 | "This is line 2\n", 432 | "This is line 3\n" 433 | ] 434 | } 435 | ], 436 | "source": [ 437 | "# See the content of file\n", 438 | "\n", 439 | "print(FileContent)" 440 | ] 441 | }, 442 | { 443 | "cell_type": "markdown", 444 | "metadata": {}, 445 | "source": [ 446 | "The syntax is a little confusing as the file object is after the as statement. We also don’t explicitly close the file. Therefore we summarize the steps in a figure:" 447 | ] 448 | }, 449 | { 450 | "cell_type": "markdown", 451 | "metadata": {}, 452 | "source": [ 453 | "" 454 | ] 455 | }, 456 | { 457 | "cell_type": "markdown", 458 | "metadata": {}, 459 | "source": [ 460 | "We don’t have to read the entire file, for example, we can read the first 4 characters by entering three as a parameter to the method **.read()**:\n" 461 | ] 462 | }, 463 | { 464 | "cell_type": "code", 465 | "execution_count": 16, 466 | "metadata": {}, 467 | "outputs": [ 468 | { 469 | "name": "stdout", 470 | "output_type": "stream", 471 | "text": [ 472 | "This\n" 473 | ] 474 | } 475 | ], 476 | "source": [ 477 | "# Read first four characters\n", 478 | "\n", 479 | "with open(example1, \"r\") as file1:\n", 480 | " print(file1.read(4))" 481 | ] 482 | }, 483 | { 484 | "cell_type": "markdown", 485 | "metadata": {}, 486 | "source": [ 487 | "Once the method .read(4) is called the first 4 characters are called. If we call the method again, the next 4 characters are called. The output for the following cell will demonstrate the process for different inputs to the method read():" 488 | ] 489 | }, 490 | { 491 | "cell_type": "code", 492 | "execution_count": 17, 493 | "metadata": {}, 494 | "outputs": [ 495 | { 496 | "name": "stdout", 497 | "output_type": "stream", 498 | "text": [ 499 | "This\n", 500 | " is \n", 501 | "line 1 \n", 502 | "\n", 503 | "This is line 2\n" 504 | ] 505 | } 506 | ], 507 | "source": [ 508 | "# Read certain amount of characters\n", 509 | "\n", 510 | "with open(example1, \"r\") as file1:\n", 511 | " print(file1.read(4))\n", 512 | " print(file1.read(4))\n", 513 | " print(file1.read(7))\n", 514 | " print(file1.read(15))" 515 | ] 516 | }, 517 | { 518 | "cell_type": "markdown", 519 | "metadata": {}, 520 | "source": [ 521 | "The process is illustrated in the below figure, and each color represents the part of the file read after the method read() is called:" 522 | ] 523 | }, 524 | { 525 | "cell_type": "markdown", 526 | "metadata": {}, 527 | "source": [ 528 | "" 529 | ] 530 | }, 531 | { 532 | "cell_type": "markdown", 533 | "metadata": {}, 534 | "source": [ 535 | " Here is an example using the same file, but instead we read 16, 5, and then 9 characters at a time: " 536 | ] 537 | }, 538 | { 539 | "cell_type": "code", 540 | "execution_count": 18, 541 | "metadata": {}, 542 | "outputs": [ 543 | { 544 | "name": "stdout", 545 | "output_type": "stream", 546 | "text": [ 547 | "This is line 1 \n", 548 | "\n", 549 | "This \n", 550 | "is line 2\n" 551 | ] 552 | } 553 | ], 554 | "source": [ 555 | "# Read certain amount of characters\n", 556 | "\n", 557 | "with open(example1, \"r\") as file1:\n", 558 | " print(file1.read(16))\n", 559 | " print(file1.read(5))\n", 560 | " print(file1.read(9))" 561 | ] 562 | }, 563 | { 564 | "cell_type": "markdown", 565 | "metadata": {}, 566 | "source": [ 567 | "We can also read one line of the file at a time using the method readline(): " 568 | ] 569 | }, 570 | { 571 | "cell_type": "code", 572 | "execution_count": 19, 573 | "metadata": {}, 574 | "outputs": [ 575 | { 576 | "name": "stdout", 577 | "output_type": "stream", 578 | "text": [ 579 | "first line: This is line 1 \n", 580 | "\n" 581 | ] 582 | } 583 | ], 584 | "source": [ 585 | "# Read one line\n", 586 | "\n", 587 | "with open(example1, \"r\") as file1:\n", 588 | " print(\"first line: \" + file1.readline())" 589 | ] 590 | }, 591 | { 592 | "cell_type": "markdown", 593 | "metadata": {}, 594 | "source": [ 595 | " We can use a loop to iterate through each line: \n" 596 | ] 597 | }, 598 | { 599 | "cell_type": "code", 600 | "execution_count": 20, 601 | "metadata": {}, 602 | "outputs": [ 603 | { 604 | "name": "stdout", 605 | "output_type": "stream", 606 | "text": [ 607 | "Iteration 0 : This is line 1 \n", 608 | "\n", 609 | "Iteration 1 : This is line 2\n", 610 | "\n", 611 | "Iteration 2 : This is line 3\n" 612 | ] 613 | } 614 | ], 615 | "source": [ 616 | "# Iterate through the lines\n", 617 | "\n", 618 | "with open(example1,\"r\") as file1:\n", 619 | " i = 0;\n", 620 | " for line in file1:\n", 621 | " print(\"Iteration\", str(i), \": \", line)\n", 622 | " i = i + 1;" 623 | ] 624 | }, 625 | { 626 | "cell_type": "markdown", 627 | "metadata": {}, 628 | "source": [ 629 | "We can use the method readlines() to save the text file to a list: " 630 | ] 631 | }, 632 | { 633 | "cell_type": "code", 634 | "execution_count": 22, 635 | "metadata": {}, 636 | "outputs": [], 637 | "source": [ 638 | "# Read all lines and save as a list\n", 639 | "\n", 640 | "with open(example1, \"r\") as file1:\n", 641 | " FileasList = file1.readlines()" 642 | ] 643 | }, 644 | { 645 | "cell_type": "markdown", 646 | "metadata": {}, 647 | "source": [ 648 | " Each element of the list corresponds to a line of text:" 649 | ] 650 | }, 651 | { 652 | "cell_type": "code", 653 | "execution_count": 23, 654 | "metadata": {}, 655 | "outputs": [ 656 | { 657 | "data": { 658 | "text/plain": [ 659 | "'This is line 1 \\n'" 660 | ] 661 | }, 662 | "execution_count": 23, 663 | "metadata": {}, 664 | "output_type": "execute_result" 665 | } 666 | ], 667 | "source": [ 668 | "# Print the first line\n", 669 | "\n", 670 | "FileasList[0]" 671 | ] 672 | }, 673 | { 674 | "cell_type": "code", 675 | "execution_count": 24, 676 | "metadata": {}, 677 | "outputs": [ 678 | { 679 | "data": { 680 | "text/plain": [ 681 | "'This is line 2\\n'" 682 | ] 683 | }, 684 | "execution_count": 24, 685 | "metadata": {}, 686 | "output_type": "execute_result" 687 | } 688 | ], 689 | "source": [ 690 | "# Print the second line\n", 691 | "\n", 692 | "FileasList[1]" 693 | ] 694 | }, 695 | { 696 | "cell_type": "code", 697 | "execution_count": 25, 698 | "metadata": {}, 699 | "outputs": [ 700 | { 701 | "data": { 702 | "text/plain": [ 703 | "'This is line 3'" 704 | ] 705 | }, 706 | "execution_count": 25, 707 | "metadata": {}, 708 | "output_type": "execute_result" 709 | } 710 | ], 711 | "source": [ 712 | "# Print the third line\n", 713 | "\n", 714 | "FileasList[2]" 715 | ] 716 | }, 717 | { 718 | "cell_type": "markdown", 719 | "metadata": {}, 720 | "source": [ 721 | "
\n", 722 | "

The last exercise!

\n", 723 | "

Congratulations, you have completed your first lesson and hands-on lab in Python. However, there is one more thing you need to do. The Data Science community encourages sharing work. The best way to share and showcase your work is to share it on GitHub. By sharing your notebook on GitHub you are not only building your reputation with fellow data scientists, but you can also show it off when applying for a job. Even though this was your first piece of work, it is never too early to start building good habits. So, please read and follow this article to learn how to share your work.\n", 724 | "

" 725 | ] 726 | }, 727 | { 728 | "cell_type": "markdown", 729 | "metadata": {}, 730 | "source": [ 731 | "
\n", 732 | "

Get IBM Watson Studio free of charge!

\n", 733 | "

\n", 734 | "
" 735 | ] 736 | }, 737 | { 738 | "cell_type": "markdown", 739 | "metadata": {}, 740 | "source": [ 741 | "

About the Authors:

\n", 742 | "

Joseph Santarcangelo is a Data Scientist at IBM, and holds a PhD in Electrical Engineering. His research focused on using Machine Learning, Signal Processing, and Computer Vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

" 743 | ] 744 | }, 745 | { 746 | "cell_type": "markdown", 747 | "metadata": {}, 748 | "source": [ 749 | "Other contributors: Mavis Zhou" 750 | ] 751 | }, 752 | { 753 | "cell_type": "markdown", 754 | "metadata": {}, 755 | "source": [ 756 | "
" 757 | ] 758 | }, 759 | { 760 | "cell_type": "markdown", 761 | "metadata": {}, 762 | "source": [ 763 | "

Copyright © 2018 IBM Developer Skills Network. This notebook and its source code are released under the terms of the MIT License.

" 764 | ] 765 | } 766 | ], 767 | "metadata": { 768 | "kernelspec": { 769 | "display_name": "Python", 770 | "language": "python", 771 | "name": "conda-env-python-py" 772 | }, 773 | "language_info": { 774 | "codemirror_mode": { 775 | "name": "ipython", 776 | "version": 3 777 | }, 778 | "file_extension": ".py", 779 | "mimetype": "text/x-python", 780 | "name": "python", 781 | "nbconvert_exporter": "python", 782 | "pygments_lexer": "ipython3", 783 | "version": "3.6.10" 784 | } 785 | }, 786 | "nbformat": 4, 787 | "nbformat_minor": 4 788 | } 789 | -------------------------------------------------------------------------------- /Python for Data Science, AI & Development/PY0101EN-4-2-WriteFile.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "
\n", 8 | " \n", 9 | " \n", 10 | " \n", 11 | "
" 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "metadata": {}, 17 | "source": [ 18 | "\n", 19 | " \n", 20 | "" 21 | ] 22 | }, 23 | { 24 | "cell_type": "markdown", 25 | "metadata": {}, 26 | "source": [ 27 | "

Write and Save Files in Python

" 28 | ] 29 | }, 30 | { 31 | "cell_type": "markdown", 32 | "metadata": {}, 33 | "source": [ 34 | "

Welcome! This notebook will teach you about write the text to file in the Python Programming Language. By the end of this lab, you'll know how to write to file and copy the file.

" 35 | ] 36 | }, 37 | { 38 | "cell_type": "markdown", 39 | "metadata": {}, 40 | "source": [ 41 | "

Table of Contents

\n", 42 | "
\n", 43 | " \n", 47 | "

\n", 48 | " Estimated time needed: 15 min\n", 49 | "

\n", 50 | "
\n", 51 | "\n", 52 | "
" 53 | ] 54 | }, 55 | { 56 | "cell_type": "markdown", 57 | "metadata": {}, 58 | "source": [ 59 | "

Writing Files

" 60 | ] 61 | }, 62 | { 63 | "cell_type": "markdown", 64 | "metadata": {}, 65 | "source": [ 66 | " We can open a file object using the method write() to save the text file to a list. To write the mode, argument must be set to write w. Let’s write a file Example2.txt with the line: “This is line A”" 67 | ] 68 | }, 69 | { 70 | "cell_type": "code", 71 | "execution_count": 2, 72 | "metadata": {}, 73 | "outputs": [], 74 | "source": [ 75 | "# Write line to file\n", 76 | "\n", 77 | "with open('/resources/data/Example2.txt', 'w') as writefile:\n", 78 | " writefile.write(\"This is line A\")" 79 | ] 80 | }, 81 | { 82 | "cell_type": "markdown", 83 | "metadata": {}, 84 | "source": [ 85 | " We can read the file to see if it worked:" 86 | ] 87 | }, 88 | { 89 | "cell_type": "code", 90 | "execution_count": 3, 91 | "metadata": { 92 | "collapsed": false, 93 | "jupyter": { 94 | "outputs_hidden": false 95 | } 96 | }, 97 | "outputs": [ 98 | { 99 | "name": "stdout", 100 | "output_type": "stream", 101 | "text": [ 102 | "This is line A\n" 103 | ] 104 | } 105 | ], 106 | "source": [ 107 | "# Read file\n", 108 | "\n", 109 | "with open('/resources/data/Example2.txt', 'r') as testwritefile:\n", 110 | " print(testwritefile.read())" 111 | ] 112 | }, 113 | { 114 | "cell_type": "markdown", 115 | "metadata": {}, 116 | "source": [ 117 | "We can write multiple lines:" 118 | ] 119 | }, 120 | { 121 | "cell_type": "code", 122 | "execution_count": 5, 123 | "metadata": {}, 124 | "outputs": [], 125 | "source": [ 126 | "# Write lines to file\n", 127 | "\n", 128 | "with open('/resources/data/Example2.txt', 'w') as writefile:\n", 129 | " writefile.write(\"This is line A\\n\")\n", 130 | " writefile.write(\"This is line B\\n\")" 131 | ] 132 | }, 133 | { 134 | "cell_type": "markdown", 135 | "metadata": {}, 136 | "source": [ 137 | "The method .write() works similar to the method .readline(), except instead of reading a new line it writes a new line. The process is illustrated in the figure , the different colour coding of the grid represents a new line added to the file after each method call." 138 | ] 139 | }, 140 | { 141 | "cell_type": "markdown", 142 | "metadata": {}, 143 | "source": [ 144 | "" 145 | ] 146 | }, 147 | { 148 | "cell_type": "markdown", 149 | "metadata": {}, 150 | "source": [ 151 | "You can check the file to see if your results are correct " 152 | ] 153 | }, 154 | { 155 | "cell_type": "code", 156 | "execution_count": 6, 157 | "metadata": { 158 | "collapsed": false, 159 | "jupyter": { 160 | "outputs_hidden": false 161 | } 162 | }, 163 | "outputs": [ 164 | { 165 | "name": "stdout", 166 | "output_type": "stream", 167 | "text": [ 168 | "This is line A\n", 169 | "This is line B\n", 170 | "\n" 171 | ] 172 | } 173 | ], 174 | "source": [ 175 | "# Check whether write to file\n", 176 | "\n", 177 | "with open('/resources/data/Example2.txt', 'r') as testwritefile:\n", 178 | " print(testwritefile.read())" 179 | ] 180 | }, 181 | { 182 | "cell_type": "markdown", 183 | "metadata": {}, 184 | "source": [ 185 | " By setting the mode argument to append **a** you can append a new line as follows:" 186 | ] 187 | }, 188 | { 189 | "cell_type": "code", 190 | "execution_count": 7, 191 | "metadata": { 192 | "collapsed": false, 193 | "jupyter": { 194 | "outputs_hidden": false 195 | } 196 | }, 197 | "outputs": [], 198 | "source": [ 199 | "# Write a new line to text file\n", 200 | "\n", 201 | "with open('/resources/data/Example2.txt', 'a') as testwritefile:\n", 202 | " testwritefile.write(\"This is line C\\n\")" 203 | ] 204 | }, 205 | { 206 | "cell_type": "markdown", 207 | "metadata": {}, 208 | "source": [ 209 | " You can verify the file has changed by running the following cell:" 210 | ] 211 | }, 212 | { 213 | "cell_type": "code", 214 | "execution_count": 8, 215 | "metadata": { 216 | "collapsed": false, 217 | "jupyter": { 218 | "outputs_hidden": false 219 | } 220 | }, 221 | "outputs": [ 222 | { 223 | "name": "stdout", 224 | "output_type": "stream", 225 | "text": [ 226 | "This is line A\n", 227 | "This is line B\n", 228 | "This is line C\n", 229 | "\n" 230 | ] 231 | } 232 | ], 233 | "source": [ 234 | "# Verify if the new line is in the text file\n", 235 | "\n", 236 | "with open('/resources/data/Example2.txt', 'r') as testwritefile:\n", 237 | " print(testwritefile.read())" 238 | ] 239 | }, 240 | { 241 | "cell_type": "markdown", 242 | "metadata": {}, 243 | "source": [ 244 | " We write a list to a .txt file as follows:" 245 | ] 246 | }, 247 | { 248 | "cell_type": "code", 249 | "execution_count": 9, 250 | "metadata": { 251 | "collapsed": false, 252 | "jupyter": { 253 | "outputs_hidden": false 254 | } 255 | }, 256 | "outputs": [ 257 | { 258 | "data": { 259 | "text/plain": [ 260 | "['This is line A\\n', 'This is line B\\n', 'This is line C\\n']" 261 | ] 262 | }, 263 | "execution_count": 9, 264 | "metadata": {}, 265 | "output_type": "execute_result" 266 | } 267 | ], 268 | "source": [ 269 | "# Sample list of text\n", 270 | "\n", 271 | "Lines = [\"This is line A\\n\", \"This is line B\\n\", \"This is line C\\n\"]\n", 272 | "Lines" 273 | ] 274 | }, 275 | { 276 | "cell_type": "code", 277 | "execution_count": 10, 278 | "metadata": { 279 | "collapsed": false, 280 | "jupyter": { 281 | "outputs_hidden": false 282 | } 283 | }, 284 | "outputs": [ 285 | { 286 | "name": "stdout", 287 | "output_type": "stream", 288 | "text": [ 289 | "This is line A\n", 290 | "\n", 291 | "This is line B\n", 292 | "\n", 293 | "This is line C\n", 294 | "\n" 295 | ] 296 | } 297 | ], 298 | "source": [ 299 | "# Write the strings in the list to text file\n", 300 | "\n", 301 | "with open('Example2.txt', 'w') as writefile:\n", 302 | " for line in Lines:\n", 303 | " print(line)\n", 304 | " writefile.write(line)" 305 | ] 306 | }, 307 | { 308 | "cell_type": "markdown", 309 | "metadata": {}, 310 | "source": [ 311 | " We can verify the file is written by reading it and printing out the values: " 312 | ] 313 | }, 314 | { 315 | "cell_type": "code", 316 | "execution_count": 11, 317 | "metadata": { 318 | "collapsed": false, 319 | "jupyter": { 320 | "outputs_hidden": false 321 | } 322 | }, 323 | "outputs": [ 324 | { 325 | "name": "stdout", 326 | "output_type": "stream", 327 | "text": [ 328 | "This is line A\n", 329 | "This is line B\n", 330 | "This is line C\n", 331 | "\n" 332 | ] 333 | } 334 | ], 335 | "source": [ 336 | "# Verify if writing to file is successfully executed\n", 337 | "\n", 338 | "with open('Example2.txt', 'r') as testwritefile:\n", 339 | " print(testwritefile.read())" 340 | ] 341 | }, 342 | { 343 | "cell_type": "markdown", 344 | "metadata": {}, 345 | "source": [ 346 | "We can again append to the file by changing the second parameter to a. This adds the code:" 347 | ] 348 | }, 349 | { 350 | "cell_type": "code", 351 | "execution_count": 12, 352 | "metadata": { 353 | "collapsed": false, 354 | "jupyter": { 355 | "outputs_hidden": false 356 | } 357 | }, 358 | "outputs": [], 359 | "source": [ 360 | "# Append the line to the file\n", 361 | "\n", 362 | "with open('Example2.txt', 'a') as testwritefile:\n", 363 | " testwritefile.write(\"This is line D\\n\")" 364 | ] 365 | }, 366 | { 367 | "cell_type": "markdown", 368 | "metadata": {}, 369 | "source": [ 370 | "We can see the results of appending the file: " 371 | ] 372 | }, 373 | { 374 | "cell_type": "code", 375 | "execution_count": 13, 376 | "metadata": { 377 | "collapsed": false, 378 | "jupyter": { 379 | "outputs_hidden": false 380 | } 381 | }, 382 | "outputs": [ 383 | { 384 | "name": "stdout", 385 | "output_type": "stream", 386 | "text": [ 387 | "This is line A\n", 388 | "This is line B\n", 389 | "This is line C\n", 390 | "This is line D\n", 391 | "\n" 392 | ] 393 | } 394 | ], 395 | "source": [ 396 | "# Verify if the appending is successfully executed\n", 397 | "\n", 398 | "with open('Example2.txt', 'r') as testwritefile:\n", 399 | " print(testwritefile.read())" 400 | ] 401 | }, 402 | { 403 | "cell_type": "markdown", 404 | "metadata": {}, 405 | "source": [ 406 | "
" 407 | ] 408 | }, 409 | { 410 | "cell_type": "markdown", 411 | "metadata": {}, 412 | "source": [ 413 | "

Copy a File

" 414 | ] 415 | }, 416 | { 417 | "cell_type": "markdown", 418 | "metadata": {}, 419 | "source": [ 420 | "Let's copy the file Example2.txt to the file Example3.txt:" 421 | ] 422 | }, 423 | { 424 | "cell_type": "code", 425 | "execution_count": 14, 426 | "metadata": { 427 | "collapsed": false, 428 | "jupyter": { 429 | "outputs_hidden": false 430 | } 431 | }, 432 | "outputs": [], 433 | "source": [ 434 | "# Copy file to another\n", 435 | "\n", 436 | "with open('Example2.txt','r') as readfile:\n", 437 | " with open('Example3.txt','w') as writefile:\n", 438 | " for line in readfile:\n", 439 | " writefile.write(line)" 440 | ] 441 | }, 442 | { 443 | "cell_type": "markdown", 444 | "metadata": {}, 445 | "source": [ 446 | "We can read the file to see if everything works:" 447 | ] 448 | }, 449 | { 450 | "cell_type": "code", 451 | "execution_count": 15, 452 | "metadata": { 453 | "collapsed": false, 454 | "jupyter": { 455 | "outputs_hidden": false 456 | } 457 | }, 458 | "outputs": [ 459 | { 460 | "name": "stdout", 461 | "output_type": "stream", 462 | "text": [ 463 | "This is line A\n", 464 | "This is line B\n", 465 | "This is line C\n", 466 | "This is line D\n", 467 | "\n" 468 | ] 469 | } 470 | ], 471 | "source": [ 472 | "# Verify if the copy is successfully executed\n", 473 | "\n", 474 | "with open('Example3.txt','r') as testwritefile:\n", 475 | " print(testwritefile.read())" 476 | ] 477 | }, 478 | { 479 | "cell_type": "markdown", 480 | "metadata": {}, 481 | "source": [ 482 | " After reading files, we can also write data into files and save them in different file formats like **.txt, .csv, .xls (for excel files) etc**. Let's take a look at some examples." 483 | ] 484 | }, 485 | { 486 | "cell_type": "markdown", 487 | "metadata": {}, 488 | "source": [ 489 | "Now go to the directory to ensure the .txt file exists and contains the summary data that we wrote." 490 | ] 491 | }, 492 | { 493 | "cell_type": "markdown", 494 | "metadata": {}, 495 | "source": [ 496 | "
\n", 497 | "

The last exercise!

\n", 498 | "

Congratulations, you have completed your first lesson and hands-on lab in Python. However, there is one more thing you need to do. The Data Science community encourages sharing work. The best way to share and showcase your work is to share it on GitHub. By sharing your notebook on GitHub you are not only building your reputation with fellow data scientists, but you can also show it off when applying for a job. Even though this was your first piece of work, it is never too early to start building good habits. So, please read and follow this article to learn how to share your work.\n", 499 | "

" 500 | ] 501 | }, 502 | { 503 | "cell_type": "markdown", 504 | "metadata": {}, 505 | "source": [ 506 | "
\n", 507 | "

Get IBM Watson Studio free of charge!

\n", 508 | "

\n", 509 | "
" 510 | ] 511 | }, 512 | { 513 | "cell_type": "markdown", 514 | "metadata": {}, 515 | "source": [ 516 | "

About the Authors:

\n", 517 | "

Joseph Santarcangelo is a Data Scientist at IBM, and holds a PhD in Electrical Engineering. His research focused on using Machine Learning, Signal Processing, and Computer Vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

" 518 | ] 519 | }, 520 | { 521 | "cell_type": "markdown", 522 | "metadata": {}, 523 | "source": [ 524 | "Other contributors: Mavis Zhou" 525 | ] 526 | }, 527 | { 528 | "cell_type": "markdown", 529 | "metadata": {}, 530 | "source": [ 531 | "
" 532 | ] 533 | }, 534 | { 535 | "cell_type": "markdown", 536 | "metadata": {}, 537 | "source": [ 538 | "

Copyright © 2018 IBM Developer Skills Network. This notebook and its source code are released under the terms of the MIT License.

" 539 | ] 540 | } 541 | ], 542 | "metadata": { 543 | "kernelspec": { 544 | "display_name": "Python", 545 | "language": "python", 546 | "name": "conda-env-python-py" 547 | }, 548 | "language_info": { 549 | "codemirror_mode": { 550 | "name": "ipython", 551 | "version": 3 552 | }, 553 | "file_extension": ".py", 554 | "mimetype": "text/x-python", 555 | "name": "python", 556 | "nbconvert_exporter": "python", 557 | "pygments_lexer": "ipython3", 558 | "version": "3.6.10" 559 | } 560 | }, 561 | "nbformat": 4, 562 | "nbformat_minor": 4 563 | } 564 | -------------------------------------------------------------------------------- /Python for Data Science, AI & Development/PY0101EN-5.1_Intro_API.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "
\n", 8 | " \"cognitiveclass.ai\n", 9 | "
\n", 10 | "\n", 11 | "# Application Programming Interface\n", 12 | "\n", 13 | "Estimated time needed: **15** minutes\n", 14 | "\n", 15 | "## Objectives\n", 16 | "\n", 17 | "After completing this lab you will be able to:\n", 18 | "\n", 19 | "* Create and Use APIs in Python\n" 20 | ] 21 | }, 22 | { 23 | "cell_type": "markdown", 24 | "metadata": {}, 25 | "source": [ 26 | "### Introduction\n", 27 | "\n", 28 | "An API lets two pieces of software talk to each other. Just like a function, you don’t have to know how the API works only its inputs and outputs. An essential type of API is a REST API that allows you to access resources via the internet. In this lab, we will review the Pandas Library in the context of an API, we will also review a basic REST API\n" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "## Table of Contents\n", 36 | "\n", 37 | "
\n", 38 | "
  • Pandas is an API
    • \n", 39 | "
  • REST APIs Basics
    • \n", 40 | "
  • Quiz on Tuples
    • \n", 41 | "\n", 42 | "
    \n", 43 | "\n", 44 | "
    \n" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 4, 50 | "metadata": { 51 | "collapsed": false, 52 | "jupyter": { 53 | "outputs_hidden": false 54 | } 55 | }, 56 | "outputs": [ 57 | { 58 | "name": "stdout", 59 | "output_type": "stream", 60 | "text": [ 61 | "Requirement already satisfied: pycoingecko in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (2.2.0)\n", 62 | "Requirement already satisfied: requests in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from pycoingecko) (2.25.1)\n", 63 | "Requirement already satisfied: idna<3,>=2.5 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->pycoingecko) (2.10)\n", 64 | "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->pycoingecko) (1.26.5)\n", 65 | "Requirement already satisfied: certifi>=2017.4.17 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->pycoingecko) (2020.12.5)\n", 66 | "Requirement already satisfied: chardet<5,>=3.0.2 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->pycoingecko) (4.0.0)\n", 67 | "Requirement already satisfied: plotly in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (4.14.3)\n", 68 | "Requirement already satisfied: retrying>=1.3.3 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from plotly) (1.3.3)\n", 69 | "Requirement already satisfied: six in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from plotly) (1.16.0)\n", 70 | "Requirement already satisfied: mplfinance in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (0.12.7a17)\n", 71 | "Requirement already satisfied: pandas in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from mplfinance) (1.1.5)\n", 72 | "Requirement already satisfied: matplotlib in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from mplfinance) (3.3.4)\n", 73 | "Requirement already satisfied: pytz>=2017.2 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from pandas->mplfinance) (2021.1)\n", 74 | "Requirement already satisfied: python-dateutil>=2.7.3 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from pandas->mplfinance) (2.8.1)\n", 75 | "Requirement already satisfied: numpy>=1.15.4 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from pandas->mplfinance) (1.19.5)\n", 76 | "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from matplotlib->mplfinance) (2.4.7)\n", 77 | "Requirement already satisfied: pillow>=6.2.0 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from matplotlib->mplfinance) (8.2.0)\n", 78 | "Requirement already satisfied: kiwisolver>=1.0.1 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from matplotlib->mplfinance) (1.3.1)\n", 79 | "Requirement already satisfied: cycler>=0.10 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/cycler-0.10.0-py3.6.egg (from matplotlib->mplfinance) (0.10.0)\n", 80 | "Requirement already satisfied: six>=1.5 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from python-dateutil>=2.7.3->pandas->mplfinance) (1.16.0)\n" 81 | ] 82 | } 83 | ], 84 | "source": [ 85 | "!pip install pycoingecko\n", 86 | "!pip install plotly\n", 87 | "!pip install mplfinance" 88 | ] 89 | }, 90 | { 91 | "cell_type": "markdown", 92 | "metadata": {}, 93 | "source": [ 94 | "

    Pandas is an API

    \n" 95 | ] 96 | }, 97 | { 98 | "cell_type": "markdown", 99 | "metadata": {}, 100 | "source": [ 101 | "Pandas is actually set of software components , much of which is not even written in Python.\n" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": 5, 107 | "metadata": { 108 | "collapsed": false, 109 | "jupyter": { 110 | "outputs_hidden": false 111 | } 112 | }, 113 | "outputs": [], 114 | "source": [ 115 | "import pandas as pd\n", 116 | "import numpy as np\n", 117 | "import plotly.graph_objects as go\n", 118 | "from plotly.offline import plot\n", 119 | "import matplotlib.pyplot as plt\n", 120 | "import datetime\n", 121 | "from pycoingecko import CoinGeckoAPI\n", 122 | "from mplfinance.original_flavor import candlestick2_ohlc" 123 | ] 124 | }, 125 | { 126 | "cell_type": "markdown", 127 | "metadata": {}, 128 | "source": [ 129 | "You create a dictionary, this is just data.\n" 130 | ] 131 | }, 132 | { 133 | "cell_type": "code", 134 | "execution_count": 6, 135 | "metadata": { 136 | "collapsed": false, 137 | "jupyter": { 138 | "outputs_hidden": false 139 | } 140 | }, 141 | "outputs": [], 142 | "source": [ 143 | "dict_={'a':[11,21,31],'b':[12,22,32]}" 144 | ] 145 | }, 146 | { 147 | "cell_type": "markdown", 148 | "metadata": {}, 149 | "source": [ 150 | "When you create a Pandas object with the Dataframe constructor in API lingo, this is an \"instance\". The data in the dictionary is passed along to the pandas API. You then use the dataframe to communicate with the API.\n" 151 | ] 152 | }, 153 | { 154 | "cell_type": "code", 155 | "execution_count": 7, 156 | "metadata": { 157 | "collapsed": false, 158 | "jupyter": { 159 | "outputs_hidden": false 160 | } 161 | }, 162 | "outputs": [ 163 | { 164 | "data": { 165 | "text/plain": [ 166 | "pandas.core.frame.DataFrame" 167 | ] 168 | }, 169 | "execution_count": 7, 170 | "metadata": {}, 171 | "output_type": "execute_result" 172 | } 173 | ], 174 | "source": [ 175 | "df=pd.DataFrame(dict_)\n", 176 | "type(df)" 177 | ] 178 | }, 179 | { 180 | "cell_type": "markdown", 181 | "metadata": {}, 182 | "source": [ 183 | "\"logistic\n" 184 | ] 185 | }, 186 | { 187 | "cell_type": "markdown", 188 | "metadata": {}, 189 | "source": [ 190 | "When you call the method head the dataframe communicates with the API displaying the first few rows of the dataframe.\n" 191 | ] 192 | }, 193 | { 194 | "cell_type": "code", 195 | "execution_count": 8, 196 | "metadata": { 197 | "collapsed": false, 198 | "jupyter": { 199 | "outputs_hidden": false 200 | } 201 | }, 202 | "outputs": [ 203 | { 204 | "data": { 205 | "text/html": [ 206 | "
    \n", 207 | "\n", 220 | "\n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | "
    ab
    01112
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    \n", 246 | "
    " 247 | ], 248 | "text/plain": [ 249 | " a b\n", 250 | "0 11 12\n", 251 | "1 21 22\n", 252 | "2 31 32" 253 | ] 254 | }, 255 | "execution_count": 8, 256 | "metadata": {}, 257 | "output_type": "execute_result" 258 | } 259 | ], 260 | "source": [ 261 | "df.head()" 262 | ] 263 | }, 264 | { 265 | "cell_type": "markdown", 266 | "metadata": {}, 267 | "source": [ 268 | "When you call the method mean,the API will calculate the mean and return the value.\n" 269 | ] 270 | }, 271 | { 272 | "cell_type": "code", 273 | "execution_count": 9, 274 | "metadata": { 275 | "collapsed": false, 276 | "jupyter": { 277 | "outputs_hidden": false 278 | } 279 | }, 280 | "outputs": [ 281 | { 282 | "data": { 283 | "text/plain": [ 284 | "a 21.0\n", 285 | "b 22.0\n", 286 | "dtype: float64" 287 | ] 288 | }, 289 | "execution_count": 9, 290 | "metadata": {}, 291 | "output_type": "execute_result" 292 | } 293 | ], 294 | "source": [ 295 | "df.mean()" 296 | ] 297 | }, 298 | { 299 | "cell_type": "markdown", 300 | "metadata": {}, 301 | "source": [ 302 | "

    REST APIs

    \n" 303 | ] 304 | }, 305 | { 306 | "cell_type": "markdown", 307 | "metadata": {}, 308 | "source": [ 309 | "Rest API’s function by sending a request, the request is communicated via HTTP message. The HTTP message usually contains a JSON file. This contains instructions for what operation we would like the service or resource to perform. In a similar manner, API returns a response, via an HTTP message, this response is usually contained within a JSON.\n", 310 | "\n", 311 | "In cryptocurrency a popular method to display the movements of the price of a currency.\n", 312 | "\n", 313 | "![Example Candlestick Graph](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/candlestick.png)\n", 314 | "\n", 315 | "Here is a description of the candle sticks.\n", 316 | "\n", 317 | "\"Candle\n", 318 | "\n", 319 | "In this lab, we will be using the CoinGecko API to create one of these candlestick graphs for Bitcoin. We will use the API to get the price data for 30 days with 24 observation per day, 1 per hour. We will find the max, min, open, and close price per day meaning we will have 30 candlesticks and use that to generate the candlestick graph. Although we are using the CoinGecko API we will use a Python client/wrapper for the API called PyCoinGecko. PyCoinGecko will make performing the requests easy and it will deal with the enpoint targeting.\n" 320 | ] 321 | }, 322 | { 323 | "cell_type": "markdown", 324 | "metadata": {}, 325 | "source": [ 326 | "Lets start off by getting the data we need. Using the get_coin_market_chart_by_id(id, vs_currency, days). id is the name of the coin you want, vs_currency is the currency you want the price in, and days is how many days back from today you want.\n" 327 | ] 328 | }, 329 | { 330 | "cell_type": "code", 331 | "execution_count": 10, 332 | "metadata": {}, 333 | "outputs": [], 334 | "source": [ 335 | "cg = CoinGeckoAPI()\n", 336 | "\n", 337 | "bitcoin_data = cg.get_coin_market_chart_by_id(id='bitcoin', vs_currency='usd', days=30)" 338 | ] 339 | }, 340 | { 341 | "cell_type": "code", 342 | "execution_count": 11, 343 | "metadata": {}, 344 | "outputs": [ 345 | { 346 | "data": { 347 | "text/plain": [ 348 | "dict" 349 | ] 350 | }, 351 | "execution_count": 11, 352 | "metadata": {}, 353 | "output_type": "execute_result" 354 | } 355 | ], 356 | "source": [ 357 | "type(bitcoin_data )" 358 | ] 359 | }, 360 | { 361 | "cell_type": "markdown", 362 | "metadata": {}, 363 | "source": [ 364 | "The response we get is in the form of a JSON which includes the price, market caps, and total volumes along with timestamps for each observation. We are focused on the prices so we will select that data.\n" 365 | ] 366 | }, 367 | { 368 | "cell_type": "code", 369 | "execution_count": 12, 370 | "metadata": {}, 371 | "outputs": [ 372 | { 373 | "data": { 374 | "text/plain": [ 375 | "[[1621836341055, 35363.48814978805],\n", 376 | " [1621841089336, 36251.58007239732],\n", 377 | " [1621843700749, 36778.04698988238],\n", 378 | " [1621847103260, 36651.72609163723],\n", 379 | " [1621850802945, 36339.00905215398]]" 380 | ] 381 | }, 382 | "execution_count": 12, 383 | "metadata": {}, 384 | "output_type": "execute_result" 385 | } 386 | ], 387 | "source": [ 388 | "bitcoin_price_data = bitcoin_data['prices']\n", 389 | "\n", 390 | "bitcoin_price_data[0:5]" 391 | ] 392 | }, 393 | { 394 | "cell_type": "markdown", 395 | "metadata": {}, 396 | "source": [ 397 | "Finally lets turn this data into a Pandas DataFrame.\n" 398 | ] 399 | }, 400 | { 401 | "cell_type": "code", 402 | "execution_count": 13, 403 | "metadata": {}, 404 | "outputs": [], 405 | "source": [ 406 | "data = pd.DataFrame(bitcoin_price_data, columns=['TimeStamp', 'Price'])" 407 | ] 408 | }, 409 | { 410 | "cell_type": "markdown", 411 | "metadata": {}, 412 | "source": [ 413 | "Now that we have the DataFrame we will convert the timestamp to datetime and save it as a column called Date. We will map our unix_to_datetime to each timestamp and convert it to a readable datetime.\n" 414 | ] 415 | }, 416 | { 417 | "cell_type": "code", 418 | "execution_count": 14, 419 | "metadata": {}, 420 | "outputs": [], 421 | "source": [ 422 | "data['date'] = data['TimeStamp'].apply(lambda d: datetime.date.fromtimestamp(d/1000.0))\n" 423 | ] 424 | }, 425 | { 426 | "cell_type": "markdown", 427 | "metadata": {}, 428 | "source": [ 429 | "Using this modified dataset we can now group by the Date and find the min, max, open, and close for the candlesticks.\n" 430 | ] 431 | }, 432 | { 433 | "cell_type": "code", 434 | "execution_count": 15, 435 | "metadata": {}, 436 | "outputs": [], 437 | "source": [ 438 | "candlestick_data = data.groupby(data.date, as_index=False).agg({\"Price\": ['min', 'max', 'first', 'last']})" 439 | ] 440 | }, 441 | { 442 | "cell_type": "markdown", 443 | "metadata": {}, 444 | "source": [ 445 | "Finally we are now ready to use plotly to create our Candlestick Chart.\n" 446 | ] 447 | }, 448 | { 449 | "cell_type": "code", 450 | "execution_count": null, 451 | "metadata": {}, 452 | "outputs": [], 453 | "source": [ 454 | "fig = go.Figure(data=[go.Candlestick(x=candlestick_data['date'],\n", 455 | " open=candlestick_data['Price']['first'], \n", 456 | " high=candlestick_data['Price']['max'],\n", 457 | " low=candlestick_data['Price']['min'], \n", 458 | " close=candlestick_data['Price']['last'])\n", 459 | " ])\n", 460 | "\n", 461 | "fig.update_layout(xaxis_rangeslider_visible=False)\n", 462 | "\n", 463 | "fig.show()" 464 | ] 465 | }, 466 | { 467 | "cell_type": "markdown", 468 | "metadata": {}, 469 | "source": [ 470 | "## Authors:\n", 471 | "\n", 472 | "## Change Log\n", 473 | "\n", 474 | "| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n", 475 | "|---|---|---|---|\n", 476 | "| 2020-11-23 | 3.0 | Azim Hirjani | New API |\n", 477 | "| 2020-09-09 | 2.1 | Malika Singla | Spell Check |\n", 478 | "| 2020-08-26 | 2.0 | Lavanya | Moved lab to course repo in GitLab |\n", 479 | "| | | | |\n", 480 | "\n", 481 | "
    \n", 482 | "\n", 483 | "##

    © IBM Corporation 2020. All rights reserved.

    \n" 484 | ] 485 | }, 486 | { 487 | "cell_type": "code", 488 | "execution_count": null, 489 | "metadata": {}, 490 | "outputs": [], 491 | "source": [] 492 | } 493 | ], 494 | "metadata": { 495 | "kernelspec": { 496 | "display_name": "Python", 497 | "language": "python", 498 | "name": "conda-env-python-py" 499 | }, 500 | "language_info": { 501 | "codemirror_mode": { 502 | "name": "ipython", 503 | "version": 3 504 | }, 505 | "file_extension": ".py", 506 | "mimetype": "text/x-python", 507 | "name": "python", 508 | "nbconvert_exporter": "python", 509 | "pygments_lexer": "ipython3", 510 | "version": "3.6.13" 511 | } 512 | }, 513 | "nbformat": 4, 514 | "nbformat_minor": 4 515 | } 516 | -------------------------------------------------------------------------------- /Python for Data Science, AI & Development/README.md: -------------------------------------------------------------------------------- 1 | 2 | [![View Repositories](https://img.shields.io/badge/View-My_Repositories-blue?logo=GitHub)](https://github.com/ndleah?tab=repositories) 3 | [![View My Profile](https://img.shields.io/badge/View-My_Profile-green?logo=GitHub)](https://github.com/ndleah) 4 | 5 | # Databases and SQL for Data Science 6 | 7 | ## About this Course 8 | 9 | This course will take you from zero to programming in Python in a matter of hours—no prior programming experience necessary! You will learn Python fundamentals, including data structures and data analysis, complete hands-on exercises throughout the course modules, and create a final project to demonstrate your new skills. 10 | 11 | By the end of this course, you’ll feel comfortable creating basic programs, working with data, and solving real-world problems in Python. You’ll gain a strong foundation for more advanced learning in the field, and develop skills to help advance your career. 12 | 13 | ## Modules 14 | 15 | * ### **Module 1 - Python Basics** 16 | This module teaches the basics of Python and begins by exploring some of the different data types such as integers, real numbers, and strings. 17 | 18 | * Your first program 19 | * Types 20 | * Expressions and Variables 21 | * String Operations 22 | * ### **Module 2 - Python Data Structures** 23 | This module begins a journey into Python data structures by explaining the use of lists and tuples and how they are able to store collections of data in a single variable. 24 | 25 | * Lists and Tuples 26 | * Sets 27 | * Dictionaries 28 | * ### **Module 3 - Python Programming Fundamentals** 29 | This module discusses Python fundamentals and begins with the concepts of conditions and branching. 30 | 31 | * Conditions and Branching 32 | * Loops 33 | * Functions 34 | * Objects and Classes 35 | * ### **Module 4 - Working with Data in Python** 36 | This module explains the basics of working with data in Python and begins the path with learning how to read and write files. 37 | 38 | * Reading files with open 39 | * Writing files with open 40 | * Loading data with Pandas 41 | * Numpy 42 | * ### **Module 5 - APIs, and Data Collection** 43 | This module delves into the unique ways to collect data by the use of APIs and webscraping. 44 | 45 | * Simple APIs 46 | * REST APIs & HTTP Requests 47 | * HTML for Webscraping 48 | * Webscraping -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![Star Badge](https://img.shields.io/static/v1?label=%F0%9F%8C%9F&message=If%20Useful&style=style=flat&color=BC4E99) 2 | [![View Repositories](https://img.shields.io/badge/View-My_Repositories-blue?logo=GitHub)](https://github.com/ndleah?tab=repositories) 3 | [![View My Profile](https://img.shields.io/badge/View-My_Profile-green?logo=GitHub)](https://github.com/ndleah) 4 | 5 | # IBM Data Analyst Professional 6 | 7 | ## 📍 About this Professional Certificate 8 | Gain the **job-ready skills** for an entry-level data analyst role through this eight-course Professional Certificate from IBM and position yourself competitively in the thriving job market for data analysts, which will see a 20% growth until 2028 (U.S. Bureau of Labor Statistics). 9 | 10 | Power your data analyst career by learning the core principles of data analysis and gaining hands-on skills practice. You’ll work with a variety of data sources, project scenarios, and data analysis tools, including Excel, SQL, Python, Jupyter Notebooks, and Cognos Analytics, gaining practical experience with data manipulation and applying analytical techniques. 11 | 12 | --- 13 | 14 | ## 🥇 Professional Certificate 15 | 16 |

    17 | 18 | 19 | --- 20 | 21 | ## 📙 Course Structures 22 | 23 | There are 9 Courses in this Professional Certificate Specialization are as follows: 24 | 25 | - [x] [__Introduction to Data Analytics__](https://github.com/ndleah/IBM-Data-Analyst-Professional/blob/main/certificate/Intro%20to%20Data%20Analysis-1.png) 26 | 27 | This course presents a gentle introduction into the concepts of data analysis, the role of a Data Analyst, and the tools that are used to perform daily functions. You will gain an understanding of the data ecosystem and the fundamentals of data analysis, such as data gathering or data mining. 28 | 29 |

    30 | 31 | 32 | - [x] [__Excel Basics for Data Analysis__](https://github.com/ndleah/IBM-Data-Analyst-Professional/tree/main/Excel%20Basics%20for%20Data%20Analysis) 33 | 34 | This course is designed to provide you with basic working knowledge for using Excel spreadsheets for Data Analysis. It covers some of the first steps for working with spreadsheets and their usage in the process of analyzing data. It includes plenty of videos, demos, and examples for you to learn, followed by step-by-step instructions for you to apply and practice on a live spreadsheet. 35 | 36 |

    37 | 38 | 39 | 40 | - [X] [__Data Visualization and Dashboards with Excel and Cognos__ ](https://github.com/ndleah/IBM-Data-Analyst-Professional/tree/main/Data%20Visualization%20Dashboards%20Excel%20Cognos) 41 | 42 | This course covers some of the first steps in the development of data visualizations using spreadsheets and dashboards. 43 | 44 |

    45 | 46 | 47 | - [x] [__Python for Data Science and AI__](https://github.com/ndleah/IBM-Data-Analyst-Professional/tree/main/Python%20for%20Data%20Science%2C%20AI%20%26%20Development) 48 | 49 | Kickstart your learning of Python for data science, as well as programming in general, with this beginner-friendly introduction to Python. Python is one of the world’s most popular programming languages, and there has never been greater demand for professionals with the ability to apply Python fundamentals to drive business solutions across industries. 50 | 51 |

    52 | 53 | 54 | - [X] [__Python Project for Data Science__](https://github.com/ndleah/IBM-Data-Analyst-Professional/tree/main/Python%20Project%20for%20Data%20Science) 55 | 56 | This mini-course is intended to for you to demonstrate foundational Python skills for working with data. The completion of this course involves working on a hands-on project where you will develop a simple dashboard using Python. 57 | 58 |

    59 | 60 | 61 | - [x] [__Databases and SQL for Data Science__](https://github.com/ndleah/IBM-Data-Analyst-Professional/tree/main/Databases%20and%20SQL%20for%20Data%20Science%20with%20Python) 62 | 63 | The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment. 64 | 65 |

    66 | 67 | 68 | - [x] [__Data Analysis with Python__](https://github.com/ndleah/IBM-Data-Analyst-Professional/tree/main/Data%20Analysis%20With%20Python) 69 | 70 | Learn how to analyze data using Python. Topics covered: 71 | 72 | 1) Importing Datasets 73 | 2) Cleaning the Data 74 | 3) Data frame manipulation 75 | 4) Summarizing the Data 76 | 5) Building machine learning Regression models 77 | 6) Building data pipelines 78 | 79 |

    80 | 81 | 82 | - [x] [__Data Visualization with Python__](https://github.com/ndleah/IBM-Data-Analyst-Professional/tree/main/Data%20Visualization%20with%20Python) 83 | 84 | The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium. 85 | 86 |

    87 | 88 | 89 | - [x] [__IBM Data Analyst Capstone Project__](https://github.com/ndleah/IBM-Data-Analyst-Professional/tree/main/Data%20Analyst%20Capstone%20Project) 90 | 91 | In this course you will apply various Data Analytics skills and techniques that you have learned as part of the previous courses in the IBM Data Analyst Professional Certificate. You will assume the role of an Associate Data Analyst who has recently joined the organization and be presented with a business challenge that requires data analysis to be performed on real-world datasets. 92 | 93 |

    94 | 95 | 96 | --- 97 |

    © 2021 Leah Nguyen

    98 | -------------------------------------------------------------------------------- /certificate/8H6UT8SHWYPD-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/certificate/8H6UT8SHWYPD-1.png -------------------------------------------------------------------------------- /certificate/Data Analysis with Python-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/certificate/Data Analysis with Python-1.png -------------------------------------------------------------------------------- /certificate/Data Visualization and Dashboards with Excel and Cognos-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/certificate/Data Visualization and Dashboards with Excel and Cognos-1.png -------------------------------------------------------------------------------- /certificate/Data Visualization with Python-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/certificate/Data Visualization with Python-1.png -------------------------------------------------------------------------------- /certificate/Database SQL Data Science-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/certificate/Database SQL Data Science-1.png -------------------------------------------------------------------------------- /certificate/Excel Basic for Data Analysis-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/certificate/Excel Basic for Data Analysis-1.png -------------------------------------------------------------------------------- /certificate/IBM Data Analyst Capstone Project-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/certificate/IBM Data Analyst Capstone Project-1.png -------------------------------------------------------------------------------- /certificate/Intro to Data Analysis-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/certificate/Intro to Data Analysis-1.png -------------------------------------------------------------------------------- /certificate/Python Project for Data Science AI-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/certificate/Python Project for Data Science AI-1.png -------------------------------------------------------------------------------- /certificate/Python for AI, Data Science Development-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndleah/IBM-data-analyst-professional/708fa3e19d49d501f888be623d77170719c8d046/certificate/Python for AI, Data Science Development-1.png --------------------------------------------------------------------------------