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    Python for Data Science Automation: Let's Do This!
  • Урок 2. 00:00:46
    The Game Plan: Data Analysis Foundations
  • Урок 3. 00:00:44
    The Business Case: Building an Automated Forecast System
  • Урок 4. 00:00:45
    Course Project Zip [File Download]
  • Урок 5. 00:02:27
    Course Workflow: Tying Specific Actions to the Business Process
  • Урок 6. 00:04:03
    Ultimate Python Cheat Sheet: Python Ecosystem in 2 Pages
  • Урок 7. 00:03:35
    The Transactional Database Model [PDF Download]
  • Урок 8. 00:02:38
    Anaconda Installation
  • Урок 9. 00:02:19
    IDE (Integrated Development Environment) Options
  • Урок 10. 00:01:38
    VSCode Installation
  • Урок 11. 00:01:13
    Connect VSCode to Your Course Project Files
  • Урок 12. 00:04:41
    Conda Env Create: Make the Python Course Environment
  • Урок 13. 00:00:41
    Python Select Interpreter: Connect VSCode to Your Python Environment
  • Урок 14. 00:01:44
    Conda Env Update: Add Python Packages to Your Environment
  • Урок 15. 00:01:12
    Conda Env Export: Review & Share Your Environment
  • Урок 16. 00:01:22
    Conda Env List & Remove: List Available Environments & Remove Unnecessary Envs
  • Урок 17. 00:01:15
    Getting to Know VSCode
  • Урок 18. 00:02:07
    VSCode Theme Customization
  • Урок 19. 00:00:44
    VSCode Icon Themes
  • Урок 20. 00:04:16
    VSCode User & Workspace Settings
  • Урок 21. 00:01:17
    VSCode Keyboard Shortcuts
  • Урок 22. 00:03:23
    VSCode Python Extensions
  • Урок 23. 00:02:05
    VSCode Jupyter Extension - Jupyter Notebook Support
  • Урок 24. 00:03:35
    VSCode Jupyter Extension - Interactive Python
  • Урок 25. 00:02:31
    [Optional VSCode Setting] Jupyter: Send Selection to Interactive Window
  • Урок 26. 00:01:01
    VSCode Excel Viewer
  • Урок 27. 00:02:43
    VSCode Markdown & PDF Extensions
  • Урок 28. 00:01:09
    VSCode Path Intellisense
  • Урок 29. 00:00:41
    VSCode SQLite Extension
  • Урок 30. 00:01:27
    [Optional] VSCode Extensions for R Users
  • Урок 31. 00:03:53
    Python Environment Checkpoint [File Download]
  • Урок 32. 00:04:08
    Getting Started [File Download]
  • Урок 33. 00:01:26
    Using the Cheat Sheet
  • Урок 34. 00:03:34
    Import: pandas, numpy, matplotlib.pyplot
  • Урок 35. 00:04:42
    Importing From: plotnine, miziani
  • Урок 36. 00:02:10
    Importing Functions and Submodules: os, rich
  • Урок 37. 00:02:45
    Setting Up Python Interactive
  • Урок 38. 00:02:31
    [Reminder | Optional VSCode Setting] Jupyter: Send Selection to Interactive Window
  • Урок 39. 00:02:47
    Getting Help Documentation
  • Урок 40. 00:06:35
    IMPORTANT VSCODE SETTING: File Paths | jupyter.notebookFileRoot
  • Урок 41. 00:06:46
    Reading the Excel Files
  • Урок 42. 00:05:10
    Reviewing the Data Model
  • Урок 43. 00:03:55
    Exploratory 1: Top 5 Most Frequent Descriptions
  • Урок 44. 00:06:23
    Exploratory 2: Plotting the Top 5 Bike Descriptions
  • Урок 45. 00:03:05
    Preparing Orderlines for Merge: Drop Column
  • Урок 46. 00:03:34
    Merging the Bikes DataFrame
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    Merging the Bikeshops Data Frame
  • Урок 48. 00:04:52
    Datetime: Converting Order Date | Copy vs No Copy
  • Урок 49. 00:07:27
    Splitting the Description: Category 1, Category 2, and Frame Material
  • Урок 50. 00:03:05
    Splitting Location: City, State
  • Урок 51. 00:02:55
    Create the Total Price Column
  • Урок 52. 00:04:44
    Reorganizing the Columns
  • Урок 53. 00:04:07
    Renaming Columns
  • Урок 54. 00:01:12
    Reviewing the Data Transformations
  • Урок 55. 00:03:50
    Save Your Work: Pickle it.
  • Урок 56. 00:02:44
    Pandas Datetime Accessors
  • Урок 57. 00:07:26
    Resampling: Working with Pandas Offsets
  • Урок 58. 00:01:41
    Quick Plot: Plotting Single Time Series w/ Pandas Matplotlib Backend
  • Урок 59. 00:05:53
    Plotnine Visualization: Sales By Month, Part 1 - Geometries
  • Урок 60. 00:05:51
    Plotnine Visualization: Sales by Month, Part 2 - Scales & Themes
  • Урок 61. 00:09:23
    Resampling Groups: Combine groupby() and resample()
  • Урок 62. 00:07:24
    Quick Plot: Plotting Multiple Time Series w/ Pandas Matplotlib Backend
  • Урок 63. 00:08:58
    Plotnine Visualization, Part 1: Facetted Sales By Date & Category2 (Group)
  • Урок 64. 00:08:53
    Plotnine Visualization, Part 2: Adding Themes & Scales
  • Урок 65. 00:04:42
    Writing Files: Pickle, CSV, Excel
  • Урок 66. 00:02:35
    Congrats. That was a fun whirlwind. Let's recap.
  • Урок 67. 00:01:22
    Getting Started [File Download]
  • Урок 68. 00:03:41
    Pickle Files
  • Урок 69. 00:04:00
    CSV Files
  • Урок 70. 00:03:26
    Excel Files
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    SQL Databases
  • Урок 72. 00:03:02
    Pandas I/O & SQL Alchemy Overviews
  • Урок 73. 00:01:24
    Make Database Directory
  • Урок 74. 00:04:20
    Create the SQLite Database
  • Урок 75. 00:03:04
    Read the Excel Files
  • Урок 76. 00:07:12
    Create the Database Tables
  • Урок 77. 00:00:54
    Close the Connection
  • Урок 78. 00:02:08
    Connect to the Database
  • Урок 79. 00:02:36
    Getting the Database Table Names
  • Урок 80. 00:01:48
    Reading from the Tables with f-strings
  • Урок 81. 00:03:05
    [Bonus] VSCode SQLite Extension
  • Урок 82. 00:06:40
    Making collect_data(), Part 1: Function Setup
  • Урок 83. 00:08:39
    Making collect_data(), Part 2: Read Tables from the Database
  • Урок 84. 00:01:15
    Making collect_data(), Part 3: Test the Database Import
  • Урок 85. 00:08:26
    Making collect_data(), Part 4: Joining the Data
  • Урок 86. 00:07:15
    Making collect_data(), Part 5: Cleaning the Data 1
  • Урок 87. 00:06:49
    Making collect_data(), Part 6: Cleaning the Data 2
  • Урок 88. 00:03:59
    Making collect_data(), Part 7: VSCode Docstring Generator
  • Урок 89. 00:04:42
    Making a Package (my_pandas_extensions): Adding the database module
  • Урок 90. 00:01:07
    🥳Congrats! You're learning really powerful concepts.
  • Урок 91. 00:02:25
    Getting Started [File Download]
  • Урок 92. 00:01:13
    [VSCode Setting] Jupyter: Send Selection to Interactive Window
  • Урок 93. 00:01:29
    Package & Function Imports
  • Урок 94. 00:01:29
    My Pandas Extensions: Fix FutureWarning Message (regex)
  • Урок 95. 00:05:30
    How Python Works: Objects
  • Урок 96. 00:02:52
    Pandas DataFrame & Series
  • Урок 97. 00:04:09
    Numpy Arrays
  • Урок 98. 00:05:54
    Python Builtin Data Structures: Dictionary, List, Tuple
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    Python Builtin Data Types: Int, Float, Str, Bool,
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    Casting Basics: Numeric & String Conversions
  • Урок 101. 00:02:41
    Casting Sequences: To List, Numpy Array, Pandas Series, & DataFrame
  • Урок 102. 00:01:44
    Pandas Series Dtype Conversion
  • Урок 103. 00:02:09
    Pandas Data Wrangling Setup
  • Урок 104. 00:02:17
    Subsetting Columns by Name
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    Subsetting by Column Index (Position): iloc[]
  • Урок 106. 00:03:38
    Subsetting Columns with Regex (Regular Expressions)
  • Урок 107. 00:02:17
    Rearranging a Single Column (Column Subsetting)
  • Урок 108. 00:01:44
    Rearranging Multiple Columns (Repetitive Way First)
  • Урок 109. 00:02:51
    Rearranging Multiple Columns (List Comprehension)
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    Data Frame Rearrange: Select Dtypes, Concat, & Drop
  • Урок 111. 00:03:07
    Sort Values
  • Урок 112. 00:03:55
    Simple Filters with Boolean Series
  • Урок 113. 00:03:48
    Query Filters
  • Урок 114. 00:03:42
    Filtering with isin() and
  • Урок 115. 00:02:42
    Index slicing with df.iloc[]
  • Урок 116. 00:01:44
    Getting Distinct Values: Drop duplicates
  • Урок 117. 00:02:15
    N-Largest and N-Smallest
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    Random Samples
  • Урок 119. 00:02:26
    DataFrame Column Assignment: Calculated Columns
  • Урок 120. 00:03:11
    Assign Basics: Lambda Functions
  • Урок 121. 00:03:32
    Assign Cookbook: Making a Log Transformation
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    Assign Cookbook: Searching Text (Boolean Flags)
  • Урок 123. 00:03:46
    Assign Cookbook: Even-Width Binning with pd.cut()
  • Урок 124. 00:03:01
    Visualizing Binning Strategies with a Pandas Heat Table
  • Урок 125. 00:02:36
    Assign Cookbook: Quantile Binning with pd.qcut()
  • Урок 126. 00:05:49
    Aggregation Basics (Summarizations)
  • Урок 127. 00:04:11
    Common Summary Functions
  • Урок 128. 00:05:27
    Groupby + Aggregate Basics (Summarizations)
  • Урок 129. 00:03:14
    Groupby + Agg Cookbook (Summary DF 1): Sum & Median Total Price By Category 1 & 2
  • Урок 130. 00:03:24
    Groupby + Agg Cookbook (Summary DF 2): Sum Total Price & Quantity By Category 1 & 2
  • Урок 131. 00:02:01
    Groupby + Agg Details: Examining the Multilevel Column Index
  • Урок 132. 00:04:12
    Groupby + Agg Cookbook (Summary DF 3): Grouping Time Series with Groupby & Resample
  • Урок 133. 00:03:42
    Groupby + Apply Basics (Transformations)
  • Урок 134. 00:02:35
    Groupby + Apply Cookbook: Transform All Columns by Group
  • Урок 135. 00:03:25
    Groupby + Apply Cookbook: Filtering Slices by Group
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    Renaming Basics: Renaming All Columns with Lambda
  • Урок 137. 00:01:21
    Renaming Basics: Targeting Specific Columns
  • Урок 138. 00:05:57
    Advanced Renaming: Renaming Multi-Index Columns
  • Урок 139. 00:05:00
    Set Up Summarized Data: Revenue by Category 1
  • Урок 140. 00:06:42
    Pivot: To Wide Format
  • Урок 141. 00:06:09
    Export a Stylized Pandas Table to Excel (Wide Data)
  • Урок 142. 00:03:31
    Melt: To Long Format
  • Урок 143. 00:04:34
    Plotnine - Making a Faceted Horizontal Bar Chart (Tidy Long Data)
  • Урок 144. 00:06:09
    Intro to Categorical Data: Sorting the Plotnine Plot
  • Урок 145. 00:07:42
    Pivot Table (An awesome function for BI Tables)
  • Урок 146. 00:04:10
    Unstack: A programmatic version of pivot()
  • Урок 147. 00:02:25
    Stack: A programmatic version of melt()
  • Урок 148. 00:04:12
    Merge: Data Frame Joins
  • Урок 149. 00:04:27
    Concat: Binding DataFrames Rowwise & Columnwise
  • Урок 150. 00:03:08
    Splitting Text Columns
  • Урок 151. 00:01:07
    Combining Text Columns
  • Урок 152. 00:03:02
    Set Up Summarized Data: Sales by Category 2 Daily
  • Урок 153. 00:02:22
    Apply: Lambda Aggregations vs Transformations
  • Урок 154. 00:01:53
    Apply: Broadcasting Aggregations
  • Урок 155. 00:02:24
    Grouped Apply: Broadcasting
  • Урок 156. 00:02:03
    Grouped Transform: Alternative to Grouped Apply (Fixes Index Issue)
  • Урок 157. 00:06:08
    Making a "Data Frame" Function: add_columns()
  • Урок 158. 00:03:12
    Pipe: Method chaining our custom function using the pipe
  • Урок 159. 00:01:17
    Challenge #1: Data Wrangling with Pandas [File Download]
  • Урок 160. 00:02:25
    Method 1: Jupyter VSCode Integration
  • Урок 161. 00:02:07
    Method 2: Jupyter Notebooks (Legacy Method)
  • Урок 162. 00:03:16
    Method 3: JupyterLab (Next Generation of Jupyter)
  • Урок 163. 00:03:08
    Challenge Objectives
  • Урок 164. 00:05:10
    Getting Started: Syncing Your JupyterLab Current Working Directory (%cd and %pwd)
  • Урок 165. 00:03:18
    Challenge Tasks
  • Урок 166. 00:08:40
    Challenge Solution
  • Урок 167. 00:01:37
    Congrats! You've finished your first challenge.
  • Урок 168. 00:01:40
    Automating Time Series Forecasting
  • Урок 169. 00:01:49
    Getting Started [File Download]
  • Урок 170. 00:01:41
    VSCode Extension: Browser Preview
  • Урок 171. 00:01:40
    Package Imports
  • Урок 172. 00:01:10
    The ProfileReport() Class
  • Урок 173. 00:03:19
    Section 1: Profile Overview
  • Урок 174. 00:06:03
    Section 2A: Numeric & Date Variables
  • Урок 175. 00:05:02
    Section 2B: Categorical (Text) Variables
  • Урок 176. 00:02:52
    Sections 3-6: Interactions, Correlations, Missing Values, & Sample
  • Урок 177. 00:03:09
    Pandas Extension: df.profile_report()
  • Урок 178. 00:01:36
    Exporting the Profile Report as HTML
  • Урок 179. 00:00:50
    Getting Started
  • Урок 180. 00:02:59
    TimeStamp & Period Conversions
  • Урок 181. 00:01:56
    Pandas Datetime Accessors
  • Урок 182. 00:02:39
    Date Math: Offsetting Time with TimeDelta's
  • Урок 183. 00:03:28
    Date Math: Getting Duration between Two TimeStamps
  • Урок 184. 00:03:09
    Creating Date Sequences: pd.date_range()
  • Урок 185. 00:07:58
    Periods (In-Depth)
  • Урок 186. 00:06:24
    Resampling (In-Depth): bike_sales_m_df
  • Урок 187. 00:06:38
    Grouped Resampling (In-Depth): bike_sales_cat2_m_wide_df
  • Урок 188. 00:01:31
    Reorganizing: Adding Comments
  • Урок 189. 00:05:39
    Differencing with Lags (Single Time Series)
  • Урок 190. 00:02:05
    Differencing with Lags (Multiple Time Series)
  • Урок 191. 00:01:43
    Difference from First (Single Time Series)
  • Урок 192. 00:00:58
    Difference From First (Multiple Time Series)
  • Урок 193. 00:03:21
    Cumulative Expanding Windows (Single Time Series)
  • Урок 194. 00:01:39
    Cumulative Expanding Windows (Multiple Time Series)
  • Урок 195. 00:08:15
    Moving Average (Single Time Series)
  • Урок 196. 00:04:37
    Moving Average (Multiple Time Series)
  • Урок 197. 00:01:17
    Next Steps (Where we are headed)
  • Урок 198. 00:01:37
    Getting Started [File Download]
  • Урок 199. 00:00:46
    Setup: Python Imports & Data
  • Урок 200. 00:03:53
    Function Anatomy: pd.Series.max()
  • Урок 201. 00:01:03
    Errors (Exceptions)
  • Урок 202. 00:01:17
    Function Names
  • Урок 203. 00:05:13
    Function Anatomy: **kwargs
  • Урок 204. 00:02:19
    Detect Outliers: Function Setup
  • Урок 205. 00:03:37
    IQR Outlier Method, Part 1
  • Урок 206. 00:04:07
    IQR Method, Part 2
  • Урок 207. 00:01:47
    New Argument: IQR Multiplier
  • Урок 208. 00:02:36
    New Argument: How? (Both, Upper, Lower)
  • Урок 209. 00:02:11
    Checking for Pandas Series Input
  • Урок 210. 00:02:54
    Checking IQR Multiplier for Int or Float Type
  • Урок 211. 00:01:10
    Checking that IQR Multiplier is a Positive Value
  • Урок 212. 00:02:19
    Checking that How is a Valid Option: both, lower, upper
  • Урок 213. 00:07:12
    Informative Help Documentation: Adding a Docstring
  • Урок 214. 00:03:05
    Testing Our Function: Detecting Outliers within Groups
  • Урок 215. 00:02:12
    Extending the Pandas Series Class
  • Урок 216. 00:04:00
    Summarize By Time: A handy function for time series wrangling
  • Урок 217. 00:04:56
    Setting Up the "Summarize By Time" Function
  • Урок 218. 00:01:31
    Handling the Date Column Input
  • Урок 219. 00:02:03
    Handling Groups Input
  • Урок 220. 00:04:14
    Handling the Time Series Resample
  • Урок 221. 00:03:16
    Handling the Aggregation Function Input
  • Урок 222. 00:01:40
    Handling the Value Column Input
  • Урок 223. 00:02:44
    Forcing the Value Column Input to a List (to generate a data frame)
  • Урок 224. 00:02:26
    Bug! Thinking through a solution
  • Урок 225. 00:03:52
    Solution: Converting to a Function Dictionary with Zip + Dict
  • Урок 226. 00:02:02
    Handling the Unstack
  • Урок 227. 00:02:51
    Handling the Period Conversion
  • Урок 228. 00:02:25
    Add Fill Missing Capability
  • Урок 229. 00:01:25
    Review the Core Functionality
  • Урок 230. 00:01:50
    Check Incoming Data: Raising a TypeError
  • Урок 231. 00:07:28
    Adding the Docstring
  • Урок 232. 00:06:23
    Pandas Flavor: Extending Pandas DataFrame Class
  • Урок 233. 00:03:03
    Getting Started [File Download]
  • Урок 234. 00:04:36
    Sktime Documentation
  • Урок 235. 00:01:35
    How to Google Search like a Pro
  • Урок 236. 00:02:41
    Set Up & Imports
  • Урок 237. 00:05:00
    Summarizing to get Total Revenue by Month
  • Урок 238. 00:02:42
    Summarizing to get Total Revenue by Category 2 & Month
  • Урок 239. 00:04:59
    What is AutoARIMA?
  • Урок 240. 00:08:25
    AutoARIMA Applied: Forecaster, Fit, Predict
  • Урок 241. 00:02:41
    Adding Confidence Intervals (Prediction Intervals)
  • Урок 242. 00:02:39
    Tuple Unpacking (Predictions, Confidence Intervals)
  • Урок 243. 00:05:28
    Forecast Visualization
  • Урок 244. 00:00:24
    Code Housekeeping
  • Урок 245. 00:03:10
    Multiple Time Series Forecasting: AutoARIMA()
  • Урок 246. 00:02:20
    For Loop: Iterate Across the DataFrame Columns
  • Урок 247. 00:05:23
    For Loop: Modeling AutoARIMA()
  • Урок 248. 00:01:32
    For-Loop: Getting the Confidence Intervals
  • Урок 249. 00:04:12
    For-Loop: Combine with DataFrame | Actual Values, Predictions, & CIs
  • Урок 250. 00:03:36
    For-Loop: Storing the Results (as a Dictionary)
  • Урок 251. 00:01:53
    Housekeeping: Appending Variable Types to Variable Names
  • Урок 252. 00:02:43
    Visual Forecast Assessment
  • Урок 253. 00:03:41
    TQDM: Progress Bars
  • Урок 254. 00:03:45
    Setting up the ARIMA Automation Function
  • Урок 255. 00:03:19
    Making arima_forecast() | Function Definition
  • Урок 256. 00:04:41
    Function Body | Setting Up the Iteration
  • Урок 257. 00:03:02
    Training the AutoARIMA() Models
  • Урок 258. 00:01:12
    Controlling Progress Bars: tqdm(min_interval)
  • Урок 259. 00:02:09
    Making Predictions and Confidence Intervals
  • Урок 260. 00:02:24
    Combine Results into a DataFrame
  • Урок 261. 00:01:50
    Compose a Prediction Dictionary
  • Урок 262. 00:02:37
    Return Results as a Single DataFrame | Rowwise Concatenation
  • Урок 263. 00:09:16
    Setting the Column Names of the Output
  • Урок 264. 00:02:51
    Drop remaining columns beginning with "level_"
  • Урок 265. 00:02:05
    Testing the arima_forecast() function
  • Урок 266. 00:03:44
    Creating the forecasting.py module
  • Урок 267. 00:01:32
    Docstring: arima_forecast()
  • Урок 268. 00:06:35
    Adding Checks: arima_forecast()
  • Урок 269. 00:02:29
    Finally - Check Your Forecasts with Grouped Pandas Plotting
  • Урок 270. 00:01:10
    Recap: You've just made an ARIMA Forecast Automation!
  • Урок 271. 00:02:07
    Introduction to ETS Forecasting (Exponential Smoothing)
  • Урок 272. 00:06:07
    Challenge 2 [File Download]
  • Урок 273. 00:05:18
    Solution
  • Урок 274. 00:01:25
    Part 3: Visualization & Reporting
  • Урок 275. 00:00:32
    Getting Started [File Download]
  • Урок 276. 00:03:15
    Plotnine Documentation
  • Урок 277. 00:02:57
    Plotnine Anatomy: Imports
  • Урок 278. 00:02:54
    Data Summarization: For Plotting Annual Bike Sales
  • Урок 279. 00:07:13
    The Plot Canvas: Mapping Columns to Plot Components
  • Урок 280. 00:04:00
    Plotnine Geometries
  • Урок 281. 00:03:00
    Adding a Trend Line: geom_smooth()
  • Урок 282. 00:01:53
    Formatting Plots
  • Урок 283. 00:01:42
    Expand Limits
  • Урок 284. 00:03:52
    Scales: Dollar Format for Y-Axis
  • Урок 285. 00:02:15
    Scales: Date Format for X-Axis
  • Урок 286. 00:02:58
    Labs and Themes
  • Урок 287. 00:01:18
    Saving the ggplot
  • Урок 288. 00:02:24
    Exploring the Plotnine Object
  • Урок 289. 00:02:20
    Setting Up
  • Урок 290. 00:02:52
    Scatter Plot: Data Manipulation
  • Урок 291. 00:03:18
    Scatter Plot: Visualization
  • Урок 292. 00:02:08
    Line Plot: Data Manipulation
  • Урок 293. 00:05:29
    Line Plot: Visualization
  • Урок 294. 00:02:49
    Data Manipulation, Part 1: No Categorical Ordering
  • Урок 295. 00:01:35
    Visualization, Part 1: Without Categorical Ordering
  • Урок 296. 00:09:55
    Aside: Introduction to Plotting using Categorical Data Type
  • Урок 297. 00:01:20
    Finalizing the Horizontal Bar Chart
  • Урок 298. 00:02:48
    Histogram: Data Manipulation
  • Урок 299. 00:02:15
    Histogram: Visualization
  • Урок 300. 00:02:51
    Histogram: Using Fill Aesthetic to Explore Differences by a Category
  • Урок 301. 00:02:48
    Histogram: Using Facet Grids to Compare Distributions by Category
  • Урок 302. 00:02:55
    Density Plots: Kernel Density Estimation (KDE) using geom_density()
  • Урок 303. 00:02:20
    Box Plot: Data Manipulation
  • Урок 304. 00:07:24
    Box Plot: Visualization
  • Урок 305. 00:03:32
    Violin Plot with Jitter: geom_violin() and geom_jitter()
  • Урок 306. 00:06:09
    Data Manipulation: Add a Total Price Text Column with USD Dollar Format
  • Урок 307. 00:03:11
    Creating the Bar Plot: geom_col() and geom_smooth()
  • Урок 308. 00:05:35
    Adding Text to a Bar Plot: geom_text()
  • Урок 309. 00:05:58
    Highlighting an Outlier with a Label: geom_label()
  • Урок 310. 00:03:39
    Finalizing the Plot with Scales and Themes
  • Урок 311. 00:04:41
    Sales by Month and Category 2: Data Manipulation
  • Урок 312. 00:06:55
    Facets: Adding subplots "facets" with facet_wrap()
  • Урок 313. 00:04:34
    Scales: Applying scales to alter x, y, and color mappings
  • Урок 314. 00:03:54
    Themes: Theme Customization with Pre-Built Themes | theme_matplotlib()
  • Урок 315. 00:05:33
    Theme Elements: Customization with theme()
  • Урок 316. 00:04:44
    Plot Title and X/Y-Axis Labels: labs()
  • Урок 317. 00:01:21
    Getting Started
  • Урок 318. 00:02:10
    Package Imports
  • Урок 319. 00:05:00
    Our Forecasting Workflow Recap
  • Урок 320. 00:04:51
    Data Preparation: Melting the Value and Prediction Columns
  • Урок 321. 00:03:04
    Data Preparation: Fixing the FutureWarning
  • Урок 322. 00:03:35
    Visualization: Setting up the canvas with ggplot()
  • Урок 323. 00:05:43
    Visualization: Adding geoms and facets
  • Урок 324. 00:05:12
    Visualization: Scales and Theme Minimal
  • Урок 325. 00:04:22
    Visualization: Customizing the Theme Elements
  • Урок 326. 00:03:23
    Making the plot_forecast() Function Definition
  • Урок 327. 00:04:38
    Data Wrangling: Implementing the Melt
  • Урок 328. 00:08:52
    Handling the Time-Based Column: Converting to TimeStamp
  • Урок 329. 00:08:56
    Visualization: Parameterizing the Plot
  • Урок 330. 00:07:10
    Testing the Forecast Plot Function Parameters
  • Урок 331. 00:01:30
    Testing the Automation Workflow
  • Урок 332. 00:06:15
    Reordering the Subplots using Cat Tools
  • Урок 333. 00:03:37
    Adding the plot_forecast() function to our forecasting module
  • Урок 334. 00:03:30
    Docstring | Testing Our Imported plot_forecast() Function
  • Урок 335. 00:01:56
    Getting Started [File Download]
  • Урок 336. 00:02:33
    Package Imports
  • Урок 337. 00:01:37
    Reviewing Our Files
  • Урок 338. 00:05:26
    Generating the Forecasting Workflow
  • Урок 339. 00:01:39
    Generating the Forecast Visualization
  • Урок 340. 00:01:27
    Overview of the Database I/O Process
  • Урок 341. 00:05:44
    Preparing the Forecast for Update
  • Урок 342. 00:06:11
    Validating the Column Names
  • Урок 343. 00:01:15
    Testing the Prep Forecast for Database Function
  • Урок 344. 00:05:28
    Setting Up the Write Forecast to Database Function
  • Урок 345. 00:01:19
    Modularizing the Data Preparation Step
  • Урок 346. 00:06:47
    Specifying SQL Data Types
  • Урок 347. 00:06:50
    Write to Database
  • Урок 348. 00:00:55
    Close Connection
  • Урок 349. 00:04:29
    Testing Our Function
  • Урок 350. 00:06:00
    Creating our Read Forecast Function
  • Урок 351. 00:04:19
    Adding Functions to Database Module
  • Урок 352. 00:03:07
    Docstrings
  • Урок 353. 00:02:44
    Automation Workflow with Database I/O
  • Урок 354. 00:04:43
    Forecasting 1: Total Revenue
  • Урок 355. 00:04:30
    Fix #1: Reorder Columns in Prep Data Function
  • Урок 356. 00:01:25
    Plotting Total Revenue Forecast
  • Урок 357. 00:05:33
    Forecasting 2: Revenue by Category 1
  • Урок 358. 00:04:37
    Forecasting 3: Revenue by Category 2
  • Урок 359. 00:05:34
    Forecasting 4: Forecast Quarterly Revenue by Customer
  • Урок 360. 00:01:33
    Fix #2: Prep Data | Add timestamp conversion
  • Урок 361. 00:03:20
    Rerun Our Workflow: Success!
  • Урок 362. 00:03:15
    Writing to the Database
  • Урок 363. 00:01:46
    Pro-Tip: Saving Intermediate Data
  • Урок 364. 00:07:00
    Utility Function: Convert to Datetime
  • Урок 365. 00:03:46
    Rerun the Forecast Workflow
  • Урок 366. 00:02:02
    Read Forecast from Database
  • Урок 367. 00:03:42
    Recap: Debugging is a Skill
  • Урок 368. 00:01:16
    Jupyter Automated Reporting
  • Урок 369. 00:02:54
    Getting Started [File Download]
  • Урок 370. 00:05:49
    The Updated Database Script: Automatically Run Forecasts
  • Урок 371. 00:03:28
    python update_database.py
  • Урок 372. 00:01:23
    SQLite Explorer
  • Урок 373. 00:06:14
    Setting Up the Working Directory
  • Урок 374. 00:06:12
    Importing Data and Parameterizing a Header with Markdown
  • Урок 375. 00:03:56
    Parameterizing a Paragraph with Markdown
  • Урок 376. 00:05:35
    Performance Summary: Pivot Table, Part 1
  • Урок 377. 00:02:14
    Performance Summary: Pivot Table, Part 2
  • Урок 378. 00:02:04
    Plotting the Forecast: plot_forecast()
  • Урок 379. 00:01:16
    Papermill Setup
  • Урок 380. 00:02:12
    Package Imports
  • Урок 381. 00:03:19
    Papermill Documentation
  • Урок 382. 00:02:11
    Developing Parameters: Game Plan
  • Урок 383. 00:03:21
    Making ID Sets, Part 1
  • Урок 384. 00:04:32
    Making ID Sets, Part 2
  • Урок 385. 00:03:02
    Part 1: Intro to Pathlib and OS
  • Урок 386. 00:04:42
    Part 2: Detecting Directories Exist & Making New Directories
  • Урок 387. 00:02:57
    Jupyter Template Setup
  • Урок 388. 00:03:49
    Parameterizing the Jupyter Template
  • Урок 389. 00:03:47
    Finishing the Juyter Template Parameterization
  • Урок 390. 00:04:06
    The pm.exectute_notebook() function
  • Урок 391. 00:06:02
    Setting Up Key Parameters
  • Урок 392. 00:06:09
    Iterating without a For-Loop
  • Урок 393. 00:06:01
    Iterating with a For-Loop
  • Урок 394. 00:01:08
    Getting Started
  • Урок 395. 00:03:33
    Setting Up the Report Parameters
  • Урок 396. 00:01:29
    Creating a Resource Path
  • Урок 397. 00:06:32
    String Transformation: Make File Names from Report Titles
  • Урок 398. 00:02:46
    Setting Up run_reports()
  • Урок 399. 00:04:49
    Make the Report Directory
  • Урок 400. 00:06:23
    Setting Up the For-Loop Parameters
  • Урок 401. 00:04:02
    Setting Up Jupyter Notebook Execution (Inside of For-Loop)
  • Урок 402. 00:06:04
    Package Resources: Setting Up the Template Path
  • Урок 403. 00:04:35
    Integrating the Run Reports Function into Our Package
  • Урок 404. 00:03:32
    Getting Started [File Download]
  • Урок 405. 00:01:50
    NB Convert Documentation & Installation Requirements
  • Урок 406. 00:01:06
    Step 1: Pandoc Installation
  • Урок 407. 00:01:06
    Step 2: Tex Installation (MikTex Windows Shown | Mac Use MacTex)
  • Урок 408. 00:01:34
    HTML Report Conversion
  • Урок 409. 00:01:04
    PDF Report Conversion
  • Урок 410. 00:04:22
    Setup & Imports
  • Урок 411. 00:04:23
    Making the Config()
  • Урок 412. 00:03:21
    Locating Files with Glob
  • Урок 413. 00:06:39
    Exporting an HTML Report Programmatically
  • Урок 414. 00:05:51
    HTML Automation: Using a For-Loop to Convert All 4 Reports
  • Урок 415. 00:06:00
    PDF Automation: Using a For-Loop to Convert All 4 Reports
  • Урок 416. 00:02:42
    Getting Set Up
  • Урок 417. 00:02:47
    Integrating glob: Pulling the Jupyter Notebook File Paths
  • Урок 418. 00:04:12
    Integrate "Convert to HTML" Report Automation
  • Урок 419. 00:02:32
    Test "Convert to HTML" Report Automation
  • Урок 420. 00:01:47
    Integrate "Convert to PDF" Report Automation
  • Урок 421. 00:03:40
    Test "Convert to PDF" Report Automation
  • Урок 422. 00:06:32
    My Pandas Extensions: Upgrade reporting.py with HTML & PDF Reports, Part 1
  • Урок 423. 00:04:01
    My Pandas Extensions: Upgrade reporting.py with HTML & PDF Reports, Part 2
  • Урок 424. 00:05:08
    Run Forecast Reports Py: Part 1 - The main() function
  • Урок 425. 00:06:02
    Run Forecast Reports Py: Part 2 - Adding Timestamps to Folders
  • Урок 426. 00:02:57
    Run Forecast Reports Py: Part 3 - Running Reports
  • Урок 427. 00:03:14
    Run Forecast Reports Py: Part 4 - Adjusting Folder Automation
  • Урок 428. 00:00:28
    Scheduling Python Scripts Bonus!!!
  • Урок 429. 00:02:26
    Making the Batch File (.bat) to run our Python Script
  • Урок 430. 00:02:14
    Setting up Automated Tasks with Windows Task Scheduler
  • Урок 431. 00:00:40
    Debugging Windows Task Scheduler Tasks with Pause
  • Урок 432. 00:01:53
    Fixing the SQL Alchemy Connection
  • Урок 433. 00:00:24
    Removing the Automation: Disable & Delete
  • Урок 434. 00:02:31
    Python Script Setup | SQL Database Absolute Path
  • Урок 435. 00:03:23
    The Mac Automator
  • Урок 436. 00:02:01
    Scheduling the Automator App with Calendar
  • Урок 437. 00:01:11
    Congratulations!!!
  • Урок 438. 01:33:11
    Forecasting 100 Time Series in Python with Sktime
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