Это пробный урок. Оформите подписку, чтобы получить доступ ко всем материалам курса. Премиум

  1. Урок 1. 00:02:19
    Python for Data Science Automation: Let's Do This!
  2. Урок 2. 00:00:46
    The Game Plan: Data Analysis Foundations
  3. Урок 3. 00:00:44
    The Business Case: Building an Automated Forecast System
  4. Урок 4. 00:00:45
    Course Project Zip [File Download]
  5. Урок 5. 00:02:27
    Course Workflow: Tying Specific Actions to the Business Process
  6. Урок 6. 00:04:03
    Ultimate Python Cheat Sheet: Python Ecosystem in 2 Pages
  7. Урок 7. 00:03:35
    The Transactional Database Model [PDF Download]
  8. Урок 8. 00:02:38
    Anaconda Installation
  9. Урок 9. 00:02:19
    IDE (Integrated Development Environment) Options
  10. Урок 10. 00:01:38
    VSCode Installation
  11. Урок 11. 00:01:13
    Connect VSCode to Your Course Project Files
  12. Урок 12. 00:04:41
    Conda Env Create: Make the Python Course Environment
  13. Урок 13. 00:00:41
    Python Select Interpreter: Connect VSCode to Your Python Environment
  14. Урок 14. 00:01:44
    Conda Env Update: Add Python Packages to Your Environment
  15. Урок 15. 00:01:12
    Conda Env Export: Review & Share Your Environment
  16. Урок 16. 00:01:22
    Conda Env List & Remove: List Available Environments & Remove Unnecessary Envs
  17. Урок 17. 00:01:15
    Getting to Know VSCode
  18. Урок 18. 00:02:07
    VSCode Theme Customization
  19. Урок 19. 00:00:44
    VSCode Icon Themes
  20. Урок 20. 00:04:16
    VSCode User & Workspace Settings
  21. Урок 21. 00:01:17
    VSCode Keyboard Shortcuts
  22. Урок 22. 00:03:23
    VSCode Python Extensions
  23. Урок 23. 00:02:05
    VSCode Jupyter Extension - Jupyter Notebook Support
  24. Урок 24. 00:03:35
    VSCode Jupyter Extension - Interactive Python
  25. Урок 25. 00:02:31
    [Optional VSCode Setting] Jupyter: Send Selection to Interactive Window
  26. Урок 26. 00:01:01
    VSCode Excel Viewer
  27. Урок 27. 00:02:43
    VSCode Markdown & PDF Extensions
  28. Урок 28. 00:01:09
    VSCode Path Intellisense
  29. Урок 29. 00:00:41
    VSCode SQLite Extension
  30. Урок 30. 00:01:27
    [Optional] VSCode Extensions for R Users
  31. Урок 31. 00:03:53
    Python Environment Checkpoint [File Download]
  32. Урок 32. 00:04:08
    Getting Started [File Download]
  33. Урок 33. 00:01:26
    Using the Cheat Sheet
  34. Урок 34. 00:03:34
    Import: pandas, numpy, matplotlib.pyplot
  35. Урок 35. 00:04:42
    Importing From: plotnine, miziani
  36. Урок 36. 00:02:10
    Importing Functions and Submodules: os, rich
  37. Урок 37. 00:02:45
    Setting Up Python Interactive
  38. Урок 38. 00:02:31
    [Reminder | Optional VSCode Setting] Jupyter: Send Selection to Interactive Window
  39. Урок 39. 00:02:47
    Getting Help Documentation
  40. Урок 40. 00:06:35
    IMPORTANT VSCODE SETTING: File Paths | jupyter.notebookFileRoot
  41. Урок 41. 00:06:46
    Reading the Excel Files
  42. Урок 42. 00:05:10
    Reviewing the Data Model
  43. Урок 43. 00:03:55
    Exploratory 1: Top 5 Most Frequent Descriptions
  44. Урок 44. 00:06:23
    Exploratory 2: Plotting the Top 5 Bike Descriptions
  45. Урок 45. 00:03:05
    Preparing Orderlines for Merge: Drop Column
  46. Урок 46. 00:03:34
    Merging the Bikes DataFrame
  47. Урок 47. 00:03:27
    Merging the Bikeshops Data Frame
  48. Урок 48. 00:04:52
    Datetime: Converting Order Date | Copy vs No Copy
  49. Урок 49. 00:07:27
    Splitting the Description: Category 1, Category 2, and Frame Material
  50. Урок 50. 00:03:05
    Splitting Location: City, State
  51. Урок 51. 00:02:55
    Create the Total Price Column
  52. Урок 52. 00:04:44
    Reorganizing the Columns
  53. Урок 53. 00:04:07
    Renaming Columns
  54. Урок 54. 00:01:12
    Reviewing the Data Transformations
  55. Урок 55. 00:03:50
    Save Your Work: Pickle it.
  56. Урок 56. 00:02:44
    Pandas Datetime Accessors
  57. Урок 57. 00:07:26
    Resampling: Working with Pandas Offsets
  58. Урок 58. 00:01:41
    Quick Plot: Plotting Single Time Series w/ Pandas Matplotlib Backend
  59. Урок 59. 00:05:53
    Plotnine Visualization: Sales By Month, Part 1 - Geometries
  60. Урок 60. 00:05:51
    Plotnine Visualization: Sales by Month, Part 2 - Scales & Themes
  61. Урок 61. 00:09:23
    Resampling Groups: Combine groupby() and resample()
  62. Урок 62. 00:07:24
    Quick Plot: Plotting Multiple Time Series w/ Pandas Matplotlib Backend
  63. Урок 63. 00:08:58
    Plotnine Visualization, Part 1: Facetted Sales By Date & Category2 (Group)
  64. Урок 64. 00:08:53
    Plotnine Visualization, Part 2: Adding Themes & Scales
  65. Урок 65. 00:04:42
    Writing Files: Pickle, CSV, Excel
  66. Урок 66. 00:02:35
    Congrats. That was a fun whirlwind. Let's recap.
  67. Урок 67. 00:01:22
    Getting Started [File Download]
  68. Урок 68. 00:03:41
    Pickle Files
  69. Урок 69. 00:04:00
    CSV Files
  70. Урок 70. 00:03:26
    Excel Files
  71. Урок 71. 00:01:47
    SQL Databases
  72. Урок 72. 00:03:02
    Pandas I/O & SQL Alchemy Overviews
  73. Урок 73. 00:01:24
    Make Database Directory
  74. Урок 74. 00:04:20
    Create the SQLite Database
  75. Урок 75. 00:03:04
    Read the Excel Files
  76. Урок 76. 00:07:12
    Create the Database Tables
  77. Урок 77. 00:00:54
    Close the Connection
  78. Урок 78. 00:02:08
    Connect to the Database
  79. Урок 79. 00:02:36
    Getting the Database Table Names
  80. Урок 80. 00:01:48
    Reading from the Tables with f-strings
  81. Урок 81. 00:03:05
    [Bonus] VSCode SQLite Extension
  82. Урок 82. 00:06:40
    Making collect_data(), Part 1: Function Setup
  83. Урок 83. 00:08:39
    Making collect_data(), Part 2: Read Tables from the Database
  84. Урок 84. 00:01:15
    Making collect_data(), Part 3: Test the Database Import
  85. Урок 85. 00:08:26
    Making collect_data(), Part 4: Joining the Data
  86. Урок 86. 00:07:15
    Making collect_data(), Part 5: Cleaning the Data 1
  87. Урок 87. 00:06:49
    Making collect_data(), Part 6: Cleaning the Data 2
  88. Урок 88. 00:03:59
    Making collect_data(), Part 7: VSCode Docstring Generator
  89. Урок 89. 00:04:42
    Making a Package (my_pandas_extensions): Adding the database module
  90. Урок 90. 00:01:07
    🥳Congrats! You're learning really powerful concepts.
  91. Урок 91. 00:02:25
    Getting Started [File Download]
  92. Урок 92. 00:01:13
    [VSCode Setting] Jupyter: Send Selection to Interactive Window
  93. Урок 93. 00:01:29
    Package & Function Imports
  94. Урок 94. 00:01:29
    My Pandas Extensions: Fix FutureWarning Message (regex)
  95. Урок 95. 00:05:30
    How Python Works: Objects
  96. Урок 96. 00:02:52
    Pandas DataFrame & Series
  97. Урок 97. 00:04:09
    Numpy Arrays
  98. Урок 98. 00:05:54
    Python Builtin Data Structures: Dictionary, List, Tuple
  99. Урок 99. 00:03:42
    Python Builtin Data Types: Int, Float, Str, Bool,
  100. Урок 100. 00:04:10
    Casting Basics: Numeric & String Conversions
  101. Урок 101. 00:02:41
    Casting Sequences: To List, Numpy Array, Pandas Series, & DataFrame
  102. Урок 102. 00:01:44
    Pandas Series Dtype Conversion
  103. Урок 103. 00:02:09
    Pandas Data Wrangling Setup
  104. Урок 104. 00:02:17
    Subsetting Columns by Name
  105. Урок 105. 00:01:36
    Subsetting by Column Index (Position): iloc[]
  106. Урок 106. 00:03:38
    Subsetting Columns with Regex (Regular Expressions)
  107. Урок 107. 00:02:17
    Rearranging a Single Column (Column Subsetting)
  108. Урок 108. 00:01:44
    Rearranging Multiple Columns (Repetitive Way First)
  109. Урок 109. 00:02:51
    Rearranging Multiple Columns (List Comprehension)
  110. Урок 110. 00:06:33
    Data Frame Rearrange: Select Dtypes, Concat, & Drop
  111. Урок 111. 00:03:07
    Sort Values
  112. Урок 112. 00:03:55
    Simple Filters with Boolean Series
  113. Урок 113. 00:03:48
    Query Filters
  114. Урок 114. 00:03:42
    Filtering with isin() and
  115. Урок 115. 00:02:42
    Index slicing with df.iloc[]
  116. Урок 116. 00:01:44
    Getting Distinct Values: Drop duplicates
  117. Урок 117. 00:02:15
    N-Largest and N-Smallest
  118. Урок 118. 00:01:53
    Random Samples
  119. Урок 119. 00:02:26
    DataFrame Column Assignment: Calculated Columns
  120. Урок 120. 00:03:11
    Assign Basics: Lambda Functions
  121. Урок 121. 00:03:32
    Assign Cookbook: Making a Log Transformation
  122. Урок 122. 00:05:27
    Assign Cookbook: Searching Text (Boolean Flags)
  123. Урок 123. 00:03:46
    Assign Cookbook: Even-Width Binning with pd.cut()
  124. Урок 124. 00:03:01
    Visualizing Binning Strategies with a Pandas Heat Table
  125. Урок 125. 00:02:36
    Assign Cookbook: Quantile Binning with pd.qcut()
  126. Урок 126. 00:05:49
    Aggregation Basics (Summarizations)
  127. Урок 127. 00:04:11
    Common Summary Functions
  128. Урок 128. 00:05:27
    Groupby + Aggregate Basics (Summarizations)
  129. Урок 129. 00:03:14
    Groupby + Agg Cookbook (Summary DF 1): Sum & Median Total Price By Category 1 & 2
  130. Урок 130. 00:03:24
    Groupby + Agg Cookbook (Summary DF 2): Sum Total Price & Quantity By Category 1 & 2
  131. Урок 131. 00:02:01
    Groupby + Agg Details: Examining the Multilevel Column Index
  132. Урок 132. 00:04:12
    Groupby + Agg Cookbook (Summary DF 3): Grouping Time Series with Groupby & Resample
  133. Урок 133. 00:03:42
    Groupby + Apply Basics (Transformations)
  134. Урок 134. 00:02:35
    Groupby + Apply Cookbook: Transform All Columns by Group
  135. Урок 135. 00:03:25
    Groupby + Apply Cookbook: Filtering Slices by Group
  136. Урок 136. 00:04:28
    Renaming Basics: Renaming All Columns with Lambda
  137. Урок 137. 00:01:21
    Renaming Basics: Targeting Specific Columns
  138. Урок 138. 00:05:57
    Advanced Renaming: Renaming Multi-Index Columns
  139. Урок 139. 00:05:00
    Set Up Summarized Data: Revenue by Category 1
  140. Урок 140. 00:06:42
    Pivot: To Wide Format
  141. Урок 141. 00:06:09
    Export a Stylized Pandas Table to Excel (Wide Data)
  142. Урок 142. 00:03:31
    Melt: To Long Format
  143. Урок 143. 00:04:34
    Plotnine - Making a Faceted Horizontal Bar Chart (Tidy Long Data)
  144. Урок 144. 00:06:09
    Intro to Categorical Data: Sorting the Plotnine Plot
  145. Урок 145. 00:07:42
    Pivot Table (An awesome function for BI Tables)
  146. Урок 146. 00:04:10
    Unstack: A programmatic version of pivot()
  147. Урок 147. 00:02:25
    Stack: A programmatic version of melt()
  148. Урок 148. 00:04:12
    Merge: Data Frame Joins
  149. Урок 149. 00:04:27
    Concat: Binding DataFrames Rowwise & Columnwise
  150. Урок 150. 00:03:08
    Splitting Text Columns
  151. Урок 151. 00:01:07
    Combining Text Columns
  152. Урок 152. 00:03:02
    Set Up Summarized Data: Sales by Category 2 Daily
  153. Урок 153. 00:02:22
    Apply: Lambda Aggregations vs Transformations
  154. Урок 154. 00:01:53
    Apply: Broadcasting Aggregations
  155. Урок 155. 00:02:24
    Grouped Apply: Broadcasting
  156. Урок 156. 00:02:03
    Grouped Transform: Alternative to Grouped Apply (Fixes Index Issue)
  157. Урок 157. 00:06:08
    Making a "Data Frame" Function: add_columns()
  158. Урок 158. 00:03:12
    Pipe: Method chaining our custom function using the pipe
  159. Урок 159. 00:01:17
    Challenge #1: Data Wrangling with Pandas [File Download]
  160. Урок 160. 00:02:25
    Method 1: Jupyter VSCode Integration
  161. Урок 161. 00:02:07
    Method 2: Jupyter Notebooks (Legacy Method)
  162. Урок 162. 00:03:16
    Method 3: JupyterLab (Next Generation of Jupyter)
  163. Урок 163. 00:03:08
    Challenge Objectives
  164. Урок 164. 00:05:10
    Getting Started: Syncing Your JupyterLab Current Working Directory (%cd and %pwd)
  165. Урок 165. 00:03:18
    Challenge Tasks
  166. Урок 166. 00:08:40
    Challenge Solution
  167. Урок 167. 00:01:37
    Congrats! You've finished your first challenge.
  168. Урок 168. 00:01:40
    Automating Time Series Forecasting
  169. Урок 169. 00:01:49
    Getting Started [File Download]
  170. Урок 170. 00:01:41
    VSCode Extension: Browser Preview
  171. Урок 171. 00:01:40
    Package Imports
  172. Урок 172. 00:01:10
    The ProfileReport() Class
  173. Урок 173. 00:03:19
    Section 1: Profile Overview
  174. Урок 174. 00:06:03
    Section 2A: Numeric & Date Variables
  175. Урок 175. 00:05:02
    Section 2B: Categorical (Text) Variables
  176. Урок 176. 00:02:52
    Sections 3-6: Interactions, Correlations, Missing Values, & Sample
  177. Урок 177. 00:03:09
    Pandas Extension: df.profile_report()
  178. Урок 178. 00:01:36
    Exporting the Profile Report as HTML
  179. Урок 179. 00:00:50
    Getting Started
  180. Урок 180. 00:02:59
    TimeStamp & Period Conversions
  181. Урок 181. 00:01:56
    Pandas Datetime Accessors
  182. Урок 182. 00:02:39
    Date Math: Offsetting Time with TimeDelta's
  183. Урок 183. 00:03:28
    Date Math: Getting Duration between Two TimeStamps
  184. Урок 184. 00:03:09
    Creating Date Sequences: pd.date_range()
  185. Урок 185. 00:07:58
    Periods (In-Depth)
  186. Урок 186. 00:06:24
    Resampling (In-Depth): bike_sales_m_df
  187. Урок 187. 00:06:38
    Grouped Resampling (In-Depth): bike_sales_cat2_m_wide_df
  188. Урок 188. 00:01:31
    Reorganizing: Adding Comments
  189. Урок 189. 00:05:39
    Differencing with Lags (Single Time Series)
  190. Урок 190. 00:02:05
    Differencing with Lags (Multiple Time Series)
  191. Урок 191. 00:01:43
    Difference from First (Single Time Series)
  192. Урок 192. 00:00:58
    Difference From First (Multiple Time Series)
  193. Урок 193. 00:03:21
    Cumulative Expanding Windows (Single Time Series)
  194. Урок 194. 00:01:39
    Cumulative Expanding Windows (Multiple Time Series)
  195. Урок 195. 00:08:15
    Moving Average (Single Time Series)
  196. Урок 196. 00:04:37
    Moving Average (Multiple Time Series)
  197. Урок 197. 00:01:17
    Next Steps (Where we are headed)
  198. Урок 198. 00:01:37
    Getting Started [File Download]
  199. Урок 199. 00:00:46
    Setup: Python Imports & Data
  200. Урок 200. 00:03:53
    Function Anatomy: pd.Series.max()
  201. Урок 201. 00:01:03
    Errors (Exceptions)
  202. Урок 202. 00:01:17
    Function Names
  203. Урок 203. 00:05:13
    Function Anatomy: **kwargs
  204. Урок 204. 00:02:19
    Detect Outliers: Function Setup
  205. Урок 205. 00:03:37
    IQR Outlier Method, Part 1
  206. Урок 206. 00:04:07
    IQR Method, Part 2
  207. Урок 207. 00:01:47
    New Argument: IQR Multiplier
  208. Урок 208. 00:02:36
    New Argument: How? (Both, Upper, Lower)
  209. Урок 209. 00:02:11
    Checking for Pandas Series Input
  210. Урок 210. 00:02:54
    Checking IQR Multiplier for Int or Float Type
  211. Урок 211. 00:01:10
    Checking that IQR Multiplier is a Positive Value
  212. Урок 212. 00:02:19
    Checking that How is a Valid Option: both, lower, upper
  213. Урок 213. 00:07:12
    Informative Help Documentation: Adding a Docstring
  214. Урок 214. 00:03:05
    Testing Our Function: Detecting Outliers within Groups
  215. Урок 215. 00:02:12
    Extending the Pandas Series Class
  216. Урок 216. 00:04:00
    Summarize By Time: A handy function for time series wrangling
  217. Урок 217. 00:04:56
    Setting Up the "Summarize By Time" Function
  218. Урок 218. 00:01:31
    Handling the Date Column Input
  219. Урок 219. 00:02:03
    Handling Groups Input
  220. Урок 220. 00:04:14
    Handling the Time Series Resample
  221. Урок 221. 00:03:16
    Handling the Aggregation Function Input
  222. Урок 222. 00:01:40
    Handling the Value Column Input
  223. Урок 223. 00:02:44
    Forcing the Value Column Input to a List (to generate a data frame)
  224. Урок 224. 00:02:26
    Bug! Thinking through a solution
  225. Урок 225. 00:03:52
    Solution: Converting to a Function Dictionary with Zip + Dict
  226. Урок 226. 00:02:02
    Handling the Unstack
  227. Урок 227. 00:02:51
    Handling the Period Conversion
  228. Урок 228. 00:02:25
    Add Fill Missing Capability
  229. Урок 229. 00:01:25
    Review the Core Functionality
  230. Урок 230. 00:01:50
    Check Incoming Data: Raising a TypeError
  231. Урок 231. 00:07:28
    Adding the Docstring
  232. Урок 232. 00:06:23
    Pandas Flavor: Extending Pandas DataFrame Class
  233. Урок 233. 00:03:03
    Getting Started [File Download]
  234. Урок 234. 00:04:36
    Sktime Documentation
  235. Урок 235. 00:01:35
    How to Google Search like a Pro
  236. Урок 236. 00:02:41
    Set Up & Imports
  237. Урок 237. 00:05:00
    Summarizing to get Total Revenue by Month
  238. Урок 238. 00:02:42
    Summarizing to get Total Revenue by Category 2 & Month
  239. Урок 239. 00:04:59
    What is AutoARIMA?
  240. Урок 240. 00:08:25
    AutoARIMA Applied: Forecaster, Fit, Predict
  241. Урок 241. 00:02:41
    Adding Confidence Intervals (Prediction Intervals)
  242. Урок 242. 00:02:39
    Tuple Unpacking (Predictions, Confidence Intervals)
  243. Урок 243. 00:05:28
    Forecast Visualization
  244. Урок 244. 00:00:24
    Code Housekeeping
  245. Урок 245. 00:03:10
    Multiple Time Series Forecasting: AutoARIMA()
  246. Урок 246. 00:02:20
    For Loop: Iterate Across the DataFrame Columns
  247. Урок 247. 00:05:23
    For Loop: Modeling AutoARIMA()
  248. Урок 248. 00:01:32
    For-Loop: Getting the Confidence Intervals
  249. Урок 249. 00:04:12
    For-Loop: Combine with DataFrame | Actual Values, Predictions, & CIs
  250. Урок 250. 00:03:36
    For-Loop: Storing the Results (as a Dictionary)
  251. Урок 251. 00:01:53
    Housekeeping: Appending Variable Types to Variable Names
  252. Урок 252. 00:02:43
    Visual Forecast Assessment
  253. Урок 253. 00:03:41
    TQDM: Progress Bars
  254. Урок 254. 00:03:45
    Setting up the ARIMA Automation Function
  255. Урок 255. 00:03:19
    Making arima_forecast() | Function Definition
  256. Урок 256. 00:04:41
    Function Body | Setting Up the Iteration
  257. Урок 257. 00:03:02
    Training the AutoARIMA() Models
  258. Урок 258. 00:01:12
    Controlling Progress Bars: tqdm(min_interval)
  259. Урок 259. 00:02:09
    Making Predictions and Confidence Intervals
  260. Урок 260. 00:02:24
    Combine Results into a DataFrame
  261. Урок 261. 00:01:50
    Compose a Prediction Dictionary
  262. Урок 262. 00:02:37
    Return Results as a Single DataFrame | Rowwise Concatenation
  263. Урок 263. 00:09:16
    Setting the Column Names of the Output
  264. Урок 264. 00:02:51
    Drop remaining columns beginning with "level_"
  265. Урок 265. 00:02:05
    Testing the arima_forecast() function
  266. Урок 266. 00:03:44
    Creating the forecasting.py module
  267. Урок 267. 00:01:32
    Docstring: arima_forecast()
  268. Урок 268. 00:06:35
    Adding Checks: arima_forecast()
  269. Урок 269. 00:02:29
    Finally - Check Your Forecasts with Grouped Pandas Plotting
  270. Урок 270. 00:01:10
    Recap: You've just made an ARIMA Forecast Automation!
  271. Урок 271. 00:02:07
    Introduction to ETS Forecasting (Exponential Smoothing)
  272. Урок 272. 00:06:07
    Challenge 2 [File Download]
  273. Урок 273. 00:05:18
    Solution
  274. Урок 274. 00:01:25
    Part 3: Visualization & Reporting
  275. Урок 275. 00:00:32
    Getting Started [File Download]
  276. Урок 276. 00:03:15
    Plotnine Documentation
  277. Урок 277. 00:02:57
    Plotnine Anatomy: Imports
  278. Урок 278. 00:02:54
    Data Summarization: For Plotting Annual Bike Sales
  279. Урок 279. 00:07:13
    The Plot Canvas: Mapping Columns to Plot Components
  280. Урок 280. 00:04:00
    Plotnine Geometries
  281. Урок 281. 00:03:00
    Adding a Trend Line: geom_smooth()
  282. Урок 282. 00:01:53
    Formatting Plots
  283. Урок 283. 00:01:42
    Expand Limits
  284. Урок 284. 00:03:52
    Scales: Dollar Format for Y-Axis
  285. Урок 285. 00:02:15
    Scales: Date Format for X-Axis
  286. Урок 286. 00:02:58
    Labs and Themes
  287. Урок 287. 00:01:18
    Saving the ggplot
  288. Урок 288. 00:02:24
    Exploring the Plotnine Object
  289. Урок 289. 00:02:20
    Setting Up
  290. Урок 290. 00:02:52
    Scatter Plot: Data Manipulation
  291. Урок 291. 00:03:18
    Scatter Plot: Visualization
  292. Урок 292. 00:02:08
    Line Plot: Data Manipulation
  293. Урок 293. 00:05:29
    Line Plot: Visualization
  294. Урок 294. 00:02:49
    Data Manipulation, Part 1: No Categorical Ordering
  295. Урок 295. 00:01:35
    Visualization, Part 1: Without Categorical Ordering
  296. Урок 296. 00:09:55
    Aside: Introduction to Plotting using Categorical Data Type
  297. Урок 297. 00:01:20
    Finalizing the Horizontal Bar Chart
  298. Урок 298. 00:02:48
    Histogram: Data Manipulation
  299. Урок 299. 00:02:15
    Histogram: Visualization
  300. Урок 300. 00:02:51
    Histogram: Using Fill Aesthetic to Explore Differences by a Category
  301. Урок 301. 00:02:48
    Histogram: Using Facet Grids to Compare Distributions by Category
  302. Урок 302. 00:02:55
    Density Plots: Kernel Density Estimation (KDE) using geom_density()
  303. Урок 303. 00:02:20
    Box Plot: Data Manipulation
  304. Урок 304. 00:07:24
    Box Plot: Visualization
  305. Урок 305. 00:03:32
    Violin Plot with Jitter: geom_violin() and geom_jitter()
  306. Урок 306. 00:06:09
    Data Manipulation: Add a Total Price Text Column with USD Dollar Format
  307. Урок 307. 00:03:11
    Creating the Bar Plot: geom_col() and geom_smooth()
  308. Урок 308. 00:05:35
    Adding Text to a Bar Plot: geom_text()
  309. Урок 309. 00:05:58
    Highlighting an Outlier with a Label: geom_label()
  310. Урок 310. 00:03:39
    Finalizing the Plot with Scales and Themes
  311. Урок 311. 00:04:41
    Sales by Month and Category 2: Data Manipulation
  312. Урок 312. 00:06:55
    Facets: Adding subplots "facets" with facet_wrap()
  313. Урок 313. 00:04:34
    Scales: Applying scales to alter x, y, and color mappings
  314. Урок 314. 00:03:54
    Themes: Theme Customization with Pre-Built Themes | theme_matplotlib()
  315. Урок 315. 00:05:33
    Theme Elements: Customization with theme()
  316. Урок 316. 00:04:44
    Plot Title and X/Y-Axis Labels: labs()
  317. Урок 317. 00:01:21
    Getting Started
  318. Урок 318. 00:02:10
    Package Imports
  319. Урок 319. 00:05:00
    Our Forecasting Workflow Recap
  320. Урок 320. 00:04:51
    Data Preparation: Melting the Value and Prediction Columns
  321. Урок 321. 00:03:04
    Data Preparation: Fixing the FutureWarning
  322. Урок 322. 00:03:35
    Visualization: Setting up the canvas with ggplot()
  323. Урок 323. 00:05:43
    Visualization: Adding geoms and facets
  324. Урок 324. 00:05:12
    Visualization: Scales and Theme Minimal
  325. Урок 325. 00:04:22
    Visualization: Customizing the Theme Elements
  326. Урок 326. 00:03:23
    Making the plot_forecast() Function Definition
  327. Урок 327. 00:04:38
    Data Wrangling: Implementing the Melt
  328. Урок 328. 00:08:52
    Handling the Time-Based Column: Converting to TimeStamp
  329. Урок 329. 00:08:56
    Visualization: Parameterizing the Plot
  330. Урок 330. 00:07:10
    Testing the Forecast Plot Function Parameters
  331. Урок 331. 00:01:30
    Testing the Automation Workflow
  332. Урок 332. 00:06:15
    Reordering the Subplots using Cat Tools
  333. Урок 333. 00:03:37
    Adding the plot_forecast() function to our forecasting module
  334. Урок 334. 00:03:30
    Docstring | Testing Our Imported plot_forecast() Function
  335. Урок 335. 00:01:56
    Getting Started [File Download]
  336. Урок 336. 00:02:33
    Package Imports
  337. Урок 337. 00:01:37
    Reviewing Our Files
  338. Урок 338. 00:05:26
    Generating the Forecasting Workflow
  339. Урок 339. 00:01:39
    Generating the Forecast Visualization
  340. Урок 340. 00:01:27
    Overview of the Database I/O Process
  341. Урок 341. 00:05:44
    Preparing the Forecast for Update
  342. Урок 342. 00:06:11
    Validating the Column Names
  343. Урок 343. 00:01:15
    Testing the Prep Forecast for Database Function
  344. Урок 344. 00:05:28
    Setting Up the Write Forecast to Database Function
  345. Урок 345. 00:01:19
    Modularizing the Data Preparation Step
  346. Урок 346. 00:06:47
    Specifying SQL Data Types
  347. Урок 347. 00:06:50
    Write to Database
  348. Урок 348. 00:00:55
    Close Connection
  349. Урок 349. 00:04:29
    Testing Our Function
  350. Урок 350. 00:06:00
    Creating our Read Forecast Function
  351. Урок 351. 00:04:19
    Adding Functions to Database Module
  352. Урок 352. 00:03:07
    Docstrings
  353. Урок 353. 00:02:44
    Automation Workflow with Database I/O
  354. Урок 354. 00:04:43
    Forecasting 1: Total Revenue
  355. Урок 355. 00:04:30
    Fix #1: Reorder Columns in Prep Data Function
  356. Урок 356. 00:01:25
    Plotting Total Revenue Forecast
  357. Урок 357. 00:05:33
    Forecasting 2: Revenue by Category 1
  358. Урок 358. 00:04:37
    Forecasting 3: Revenue by Category 2
  359. Урок 359. 00:05:34
    Forecasting 4: Forecast Quarterly Revenue by Customer
  360. Урок 360. 00:01:33
    Fix #2: Prep Data | Add timestamp conversion
  361. Урок 361. 00:03:20
    Rerun Our Workflow: Success!
  362. Урок 362. 00:03:15
    Writing to the Database
  363. Урок 363. 00:01:46
    Pro-Tip: Saving Intermediate Data
  364. Урок 364. 00:07:00
    Utility Function: Convert to Datetime
  365. Урок 365. 00:03:46
    Rerun the Forecast Workflow
  366. Урок 366. 00:02:02
    Read Forecast from Database
  367. Урок 367. 00:03:42
    Recap: Debugging is a Skill
  368. Урок 368. 00:01:16
    Jupyter Automated Reporting
  369. Урок 369. 00:02:54
    Getting Started [File Download]
  370. Урок 370. 00:05:49
    The Updated Database Script: Automatically Run Forecasts
  371. Урок 371. 00:03:28
    python update_database.py
  372. Урок 372. 00:01:23
    SQLite Explorer
  373. Урок 373. 00:06:14
    Setting Up the Working Directory
  374. Урок 374. 00:06:12
    Importing Data and Parameterizing a Header with Markdown
  375. Урок 375. 00:03:56
    Parameterizing a Paragraph with Markdown
  376. Урок 376. 00:05:35
    Performance Summary: Pivot Table, Part 1
  377. Урок 377. 00:02:14
    Performance Summary: Pivot Table, Part 2
  378. Урок 378. 00:02:04
    Plotting the Forecast: plot_forecast()
  379. Урок 379. 00:01:16
    Papermill Setup
  380. Урок 380. 00:02:12
    Package Imports
  381. Урок 381. 00:03:19
    Papermill Documentation
  382. Урок 382. 00:02:11
    Developing Parameters: Game Plan
  383. Урок 383. 00:03:21
    Making ID Sets, Part 1
  384. Урок 384. 00:04:32
    Making ID Sets, Part 2
  385. Урок 385. 00:03:02
    Part 1: Intro to Pathlib and OS
  386. Урок 386. 00:04:42
    Part 2: Detecting Directories Exist & Making New Directories
  387. Урок 387. 00:02:57
    Jupyter Template Setup
  388. Урок 388. 00:03:49
    Parameterizing the Jupyter Template
  389. Урок 389. 00:03:47
    Finishing the Juyter Template Parameterization
  390. Урок 390. 00:04:06
    The pm.exectute_notebook() function
  391. Урок 391. 00:06:02
    Setting Up Key Parameters
  392. Урок 392. 00:06:09
    Iterating without a For-Loop
  393. Урок 393. 00:06:01
    Iterating with a For-Loop
  394. Урок 394. 00:01:08
    Getting Started
  395. Урок 395. 00:03:33
    Setting Up the Report Parameters
  396. Урок 396. 00:01:29
    Creating a Resource Path
  397. Урок 397. 00:06:32
    String Transformation: Make File Names from Report Titles
  398. Урок 398. 00:02:46
    Setting Up run_reports()
  399. Урок 399. 00:04:49
    Make the Report Directory
  400. Урок 400. 00:06:23
    Setting Up the For-Loop Parameters
  401. Урок 401. 00:04:02
    Setting Up Jupyter Notebook Execution (Inside of For-Loop)
  402. Урок 402. 00:06:04
    Package Resources: Setting Up the Template Path
  403. Урок 403. 00:04:35
    Integrating the Run Reports Function into Our Package
  404. Урок 404. 00:03:32
    Getting Started [File Download]
  405. Урок 405. 00:01:50
    NB Convert Documentation & Installation Requirements
  406. Урок 406. 00:01:06
    Step 1: Pandoc Installation
  407. Урок 407. 00:01:06
    Step 2: Tex Installation (MikTex Windows Shown | Mac Use MacTex)
  408. Урок 408. 00:01:34
    HTML Report Conversion
  409. Урок 409. 00:01:04
    PDF Report Conversion
  410. Урок 410. 00:04:22
    Setup & Imports
  411. Урок 411. 00:04:23
    Making the Config()
  412. Урок 412. 00:03:21
    Locating Files with Glob
  413. Урок 413. 00:06:39
    Exporting an HTML Report Programmatically
  414. Урок 414. 00:05:51
    HTML Automation: Using a For-Loop to Convert All 4 Reports
  415. Урок 415. 00:06:00
    PDF Automation: Using a For-Loop to Convert All 4 Reports
  416. Урок 416. 00:02:42
    Getting Set Up
  417. Урок 417. 00:02:47
    Integrating glob: Pulling the Jupyter Notebook File Paths
  418. Урок 418. 00:04:12
    Integrate "Convert to HTML" Report Automation
  419. Урок 419. 00:02:32
    Test "Convert to HTML" Report Automation
  420. Урок 420. 00:01:47
    Integrate "Convert to PDF" Report Automation
  421. Урок 421. 00:03:40
    Test "Convert to PDF" Report Automation
  422. Урок 422. 00:06:32
    My Pandas Extensions: Upgrade reporting.py with HTML & PDF Reports, Part 1
  423. Урок 423. 00:04:01
    My Pandas Extensions: Upgrade reporting.py with HTML & PDF Reports, Part 2
  424. Урок 424. 00:05:08
    Run Forecast Reports Py: Part 1 - The main() function
  425. Урок 425. 00:06:02
    Run Forecast Reports Py: Part 2 - Adding Timestamps to Folders
  426. Урок 426. 00:02:57
    Run Forecast Reports Py: Part 3 - Running Reports
  427. Урок 427. 00:03:14
    Run Forecast Reports Py: Part 4 - Adjusting Folder Automation
  428. Урок 428. 00:00:28
    Scheduling Python Scripts Bonus!!!
  429. Урок 429. 00:02:26
    Making the Batch File (.bat) to run our Python Script
  430. Урок 430. 00:02:14
    Setting up Automated Tasks with Windows Task Scheduler
  431. Урок 431. 00:00:40
    Debugging Windows Task Scheduler Tasks with Pause
  432. Урок 432. 00:01:53
    Fixing the SQL Alchemy Connection
  433. Урок 433. 00:00:24
    Removing the Automation: Disable & Delete
  434. Урок 434. 00:02:31
    Python Script Setup | SQL Database Absolute Path
  435. Урок 435. 00:03:23
    The Mac Automator
  436. Урок 436. 00:02:01
    Scheduling the Automator App with Calendar
  437. Урок 437. 00:01:11
    Congratulations!!!
  438. Урок 438. 01:33:11
    Forecasting 100 Time Series in Python with Sktime