• Урок 1. 00:02:19
    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
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    VSCode Theme Customization
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    VSCode Icon Themes
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    VSCode User & Workspace Settings
  • Урок 21. 00:01:17
    VSCode Keyboard Shortcuts
  • Урок 22. 00:03:23
    VSCode Python Extensions
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    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
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    VSCode Excel Viewer
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    VSCode Markdown & PDF Extensions
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    VSCode Path Intellisense
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    VSCode SQLite Extension
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    [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
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    Importing From: plotnine, miziani
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    Importing Functions and Submodules: os, rich
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    Setting Up Python Interactive
  • Урок 38. 00:02:31
    [Reminder | Optional VSCode Setting] Jupyter: Send Selection to Interactive Window
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    Getting Help Documentation
  • Урок 40. 00:06:35
    IMPORTANT VSCODE SETTING: File Paths | jupyter.notebookFileRoot
  • Урок 41. 00:06:46
    Reading the Excel Files
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    Reviewing the Data Model
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    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
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    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
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    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
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    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
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    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)
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    Rearranging Multiple Columns (Repetitive Way First)
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    Rearranging Multiple Columns (List Comprehension)
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    Data Frame Rearrange: Select Dtypes, Concat, & Drop
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    Sort Values
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    Simple Filters with Boolean Series
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    Query Filters
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    Filtering with isin() and
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    Index slicing with df.iloc[]
  • Урок 116. 00:01:44
    Getting Distinct Values: Drop duplicates
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    N-Largest and N-Smallest
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    Random Samples
  • Урок 119. 00:02:26
    DataFrame Column Assignment: Calculated Columns
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    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)
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    Assign Cookbook: Even-Width Binning with pd.cut()
  • Урок 124. 00:03:01
    Visualizing Binning Strategies with a Pandas Heat Table
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    Assign Cookbook: Quantile Binning with pd.qcut()
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    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
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    Renaming Basics: Targeting Specific Columns
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    Advanced Renaming: Renaming Multi-Index Columns
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    Set Up Summarized Data: Revenue by Category 1
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    Pivot: To Wide Format
  • Урок 141. 00:06:09
    Export a Stylized Pandas Table to Excel (Wide Data)
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    Melt: To Long Format
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    Plotnine - Making a Faceted Horizontal Bar Chart (Tidy Long Data)
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    Intro to Categorical Data: Sorting the Plotnine Plot
  • Урок 145. 00:07:42
    Pivot Table (An awesome function for BI Tables)
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    Unstack: A programmatic version of pivot()
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    Stack: A programmatic version of melt()
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    Merge: Data Frame Joins
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    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
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    Apply: Lambda Aggregations vs Transformations
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    Apply: Broadcasting Aggregations
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    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)
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    Method 3: JupyterLab (Next Generation of Jupyter)
  • Урок 163. 00:03:08
    Challenge Objectives
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    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. 00:06:24
    Resampling (In-Depth): bike_sales_m_df
  • Урок 186. 00:06:38
    Grouped Resampling (In-Depth): bike_sales_cat2_m_wide_df
  • Урок 187. 00:01:31
    Reorganizing: Adding Comments
  • Урок 188. 00:05:39
    Differencing with Lags (Single Time Series)
  • Урок 189. 00:02:05
    Differencing with Lags (Multiple Time Series)
  • Урок 190. 00:01:43
    Difference from First (Single Time Series)
  • Урок 191. 00:00:58
    Difference From First (Multiple Time Series)
  • Урок 192. 00:03:21
    Cumulative Expanding Windows (Single Time Series)
  • Урок 193. 00:01:39
    Cumulative Expanding Windows (Multiple Time Series)
  • Урок 194. 00:08:15
    Moving Average (Single Time Series)
  • Урок 195. 00:04:37
    Moving Average (Multiple Time Series)
  • Урок 196. 00:01:17
    Next Steps (Where we are headed)
  • Урок 197. 00:01:37
    Getting Started [File Download]
  • Урок 198. 00:00:46
    Setup: Python Imports & Data
  • Урок 199. 00:03:53
    Function Anatomy: pd.Series.max()
  • Урок 200. 00:01:03
    Errors (Exceptions)
  • Урок 201. 00:01:17
    Function Names
  • Урок 202. 00:05:13
    Function Anatomy: **kwargs
  • Урок 203. 00:02:19
    Detect Outliers: Function Setup
  • Урок 204. 00:03:37
    IQR Outlier Method, Part 1
  • Урок 205. 00:04:07
    IQR Method, Part 2
  • Урок 206. 00:01:47
    New Argument: IQR Multiplier
  • Урок 207. 00:02:36
    New Argument: How? (Both, Upper, Lower)
  • Урок 208. 00:02:11
    Checking for Pandas Series Input
  • Урок 209. 00:02:54
    Checking IQR Multiplier for Int or Float Type
  • Урок 210. 00:01:10
    Checking that IQR Multiplier is a Positive Value
  • Урок 211. 00:02:19
    Checking that How is a Valid Option: both, lower, upper
  • Урок 212. 00:07:12
    Informative Help Documentation: Adding a Docstring
  • Урок 213. 00:03:05
    Testing Our Function: Detecting Outliers within Groups
  • Урок 214. 00:02:12
    Extending the Pandas Series Class
  • Урок 215. 00:04:00
    Summarize By Time: A handy function for time series wrangling
  • Урок 216. 00:04:56
    Setting Up the "Summarize By Time" Function
  • Урок 217. 00:01:31
    Handling the Date Column Input
  • Урок 218. 00:02:03
    Handling Groups Input
  • Урок 219. 00:04:14
    Handling the Time Series Resample
  • Урок 220. 00:03:16
    Handling the Aggregation Function Input
  • Урок 221. 00:01:40
    Handling the Value Column Input
  • Урок 222. 00:02:44
    Forcing the Value Column Input to a List (to generate a data frame)
  • Урок 223. 00:02:26
    Bug! Thinking through a solution
  • Урок 224. 00:03:52
    Solution: Converting to a Function Dictionary with Zip + Dict
  • Урок 225. 00:02:02
    Handling the Unstack
  • Урок 226. 00:02:51
    Handling the Period Conversion
  • Урок 227. 00:02:25
    Add Fill Missing Capability
  • Урок 228. 00:01:25
    Review the Core Functionality
  • Урок 229. 00:01:50
    Check Incoming Data: Raising a TypeError
  • Урок 230. 00:07:28
    Adding the Docstring
  • Урок 231. 00:06:23
    Pandas Flavor: Extending Pandas DataFrame Class
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