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Урок 1.
00:02:19
Python for Data Science Automation: Let's Do This!
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Урок 2.
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The Game Plan: Data Analysis Foundations
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Урок 3.
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The Business Case: Building an Automated Forecast System
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Урок 4.
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Course Project Zip [File Download]
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Урок 5.
00:02:27
Course Workflow: Tying Specific Actions to the Business Process
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Урок 6.
00:04:03
Ultimate Python Cheat Sheet: Python Ecosystem in 2 Pages
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Урок 7.
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The Transactional Database Model [PDF Download]
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Урок 8.
00:02:38
Anaconda Installation
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Урок 9.
00:02:19
IDE (Integrated Development Environment) Options
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Урок 10.
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VSCode Installation
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Урок 11.
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Connect VSCode to Your Course Project Files
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Урок 12.
00:04:41
Conda Env Create: Make the Python Course Environment
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Урок 13.
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Python Select Interpreter: Connect VSCode to Your Python Environment
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Урок 14.
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Conda Env Update: Add Python Packages to Your Environment
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Урок 15.
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Conda Env Export: Review & Share Your Environment
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Урок 16.
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Conda Env List & Remove: List Available Environments & Remove Unnecessary Envs
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Урок 17.
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Getting to Know VSCode
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Урок 18.
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VSCode Theme Customization
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Урок 19.
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VSCode Icon Themes
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Урок 20.
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VSCode User & Workspace Settings
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Урок 21.
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VSCode Keyboard Shortcuts
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Урок 22.
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VSCode Python Extensions
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Урок 23.
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VSCode Jupyter Extension - Jupyter Notebook Support
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Урок 24.
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VSCode Jupyter Extension - Interactive Python
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Урок 25.
00:02:31
[Optional VSCode Setting] Jupyter: Send Selection to Interactive Window
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Урок 26.
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VSCode Excel Viewer
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Урок 27.
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VSCode Markdown & PDF Extensions
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Урок 28.
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VSCode Path Intellisense
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Урок 29.
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VSCode SQLite Extension
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Урок 30.
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[Optional] VSCode Extensions for R Users
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Урок 31.
00:03:53
Python Environment Checkpoint [File Download]
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Урок 32.
00:04:08
Getting Started [File Download]
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Урок 33.
00:01:26
Using the Cheat Sheet
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Урок 34.
00:03:34
Import: pandas, numpy, matplotlib.pyplot
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Урок 35.
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Importing From: plotnine, miziani
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Урок 36.
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Importing Functions and Submodules: os, rich
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Урок 37.
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Setting Up Python Interactive
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Урок 38.
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[Reminder | Optional VSCode Setting] Jupyter: Send Selection to Interactive Window
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Урок 39.
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Getting Help Documentation
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Урок 40.
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IMPORTANT VSCODE SETTING: File Paths | jupyter.notebookFileRoot
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Урок 41.
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Reading the Excel Files
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Урок 42.
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Reviewing the Data Model
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Урок 43.
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Exploratory 1: Top 5 Most Frequent Descriptions
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Урок 44.
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Exploratory 2: Plotting the Top 5 Bike Descriptions
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Урок 45.
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Preparing Orderlines for Merge: Drop Column
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Урок 46.
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Merging the Bikes DataFrame
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Урок 47.
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Merging the Bikeshops Data Frame
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Урок 48.
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Datetime: Converting Order Date | Copy vs No Copy
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Урок 49.
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Splitting the Description: Category 1, Category 2, and Frame Material
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Урок 50.
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Splitting Location: City, State
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Урок 51.
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Create the Total Price Column
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Урок 52.
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Reorganizing the Columns
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Урок 53.
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Renaming Columns
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Урок 54.
00:01:12
Reviewing the Data Transformations
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Урок 55.
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Save Your Work: Pickle it.
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Урок 56.
00:02:44
Pandas Datetime Accessors
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Урок 57.
00:07:26
Resampling: Working with Pandas Offsets
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Урок 58.
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Quick Plot: Plotting Single Time Series w/ Pandas Matplotlib Backend
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Урок 59.
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Plotnine Visualization: Sales By Month, Part 1 - Geometries
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Урок 60.
00:05:51
Plotnine Visualization: Sales by Month, Part 2 - Scales & Themes
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Урок 61.
00:09:23
Resampling Groups: Combine groupby() and resample()
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Урок 62.
00:07:24
Quick Plot: Plotting Multiple Time Series w/ Pandas Matplotlib Backend
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Урок 63.
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Plotnine Visualization, Part 1: Facetted Sales By Date & Category2 (Group)
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Урок 64.
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Plotnine Visualization, Part 2: Adding Themes & Scales
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Урок 65.
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Writing Files: Pickle, CSV, Excel
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Урок 66.
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Congrats. That was a fun whirlwind. Let's recap.
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Урок 67.
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Getting Started [File Download]
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Урок 68.
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Pickle Files
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Урок 69.
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CSV Files
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Урок 70.
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Excel Files
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Урок 71.
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SQL Databases
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Урок 72.
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Pandas I/O & SQL Alchemy Overviews
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Урок 73.
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Make Database Directory
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Урок 74.
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Create the SQLite Database
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Урок 75.
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Read the Excel Files
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Урок 76.
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Create the Database Tables
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Урок 77.
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Close the Connection
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Урок 78.
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Connect to the Database
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Урок 79.
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Getting the Database Table Names
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Урок 80.
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Reading from the Tables with f-strings
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Урок 81.
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[Bonus] VSCode SQLite Extension
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Урок 82.
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Making collect_data(), Part 1: Function Setup
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Урок 83.
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Making collect_data(), Part 2: Read Tables from the Database
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Урок 84.
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Making collect_data(), Part 3: Test the Database Import
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Урок 85.
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Making collect_data(), Part 4: Joining the Data
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Урок 86.
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Making collect_data(), Part 5: Cleaning the Data 1
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Урок 87.
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Making collect_data(), Part 6: Cleaning the Data 2
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Урок 88.
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Making collect_data(), Part 7: VSCode Docstring Generator
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Урок 89.
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Making a Package (my_pandas_extensions): Adding the database module
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Урок 90.
00:01:07
🥳Congrats! You're learning really powerful concepts.
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Урок 91.
00:02:25
Getting Started [File Download]
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Урок 92.
00:01:13
[VSCode Setting] Jupyter: Send Selection to Interactive Window
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Урок 93.
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Package & Function Imports
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Урок 94.
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My Pandas Extensions: Fix FutureWarning Message (regex)
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Урок 95.
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How Python Works: Objects
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Урок 96.
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Pandas DataFrame & Series
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Урок 97.
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Numpy Arrays
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Урок 98.
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Python Builtin Data Structures: Dictionary, List, Tuple
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Урок 99.
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Python Builtin Data Types: Int, Float, Str, Bool,
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Урок 100.
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Casting Basics: Numeric & String Conversions
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Урок 101.
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Casting Sequences: To List, Numpy Array, Pandas Series, & DataFrame
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Урок 102.
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Pandas Series Dtype Conversion
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Урок 103.
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Pandas Data Wrangling Setup
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Урок 104.
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Subsetting Columns by Name
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Урок 105.
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Subsetting by Column Index (Position): iloc[]
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Урок 106.
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Subsetting Columns with Regex (Regular Expressions)
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Урок 107.
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Rearranging a Single Column (Column Subsetting)
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Урок 108.
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Rearranging Multiple Columns (Repetitive Way First)
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Урок 109.
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Rearranging Multiple Columns (List Comprehension)
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Урок 110.
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Data Frame Rearrange: Select Dtypes, Concat, & Drop
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Урок 111.
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Sort Values
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Урок 112.
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Simple Filters with Boolean Series
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Урок 113.
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Query Filters
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Урок 114.
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Filtering with isin() and
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Урок 115.
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Index slicing with df.iloc[]
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Урок 116.
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Getting Distinct Values: Drop duplicates
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Урок 117.
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N-Largest and N-Smallest
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Урок 118.
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Random Samples
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Урок 119.
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DataFrame Column Assignment: Calculated Columns
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Урок 120.
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Assign Basics: Lambda Functions
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Урок 121.
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Assign Cookbook: Making a Log Transformation
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Урок 122.
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Assign Cookbook: Searching Text (Boolean Flags)
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Урок 123.
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Assign Cookbook: Even-Width Binning with pd.cut()
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Урок 124.
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Visualizing Binning Strategies with a Pandas Heat Table
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Урок 125.
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Assign Cookbook: Quantile Binning with pd.qcut()
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Урок 126.
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Aggregation Basics (Summarizations)
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Урок 127.
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Common Summary Functions
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Урок 128.
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Groupby + Aggregate Basics (Summarizations)
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Урок 129.
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Groupby + Agg Cookbook (Summary DF 1): Sum & Median Total Price By Category 1 & 2
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Урок 130.
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Groupby + Agg Cookbook (Summary DF 2): Sum Total Price & Quantity By Category 1 & 2
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Урок 131.
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Groupby + Agg Details: Examining the Multilevel Column Index
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Урок 132.
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Groupby + Agg Cookbook (Summary DF 3): Grouping Time Series with Groupby & Resample
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Урок 133.
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Groupby + Apply Basics (Transformations)
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Урок 134.
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Groupby + Apply Cookbook: Transform All Columns by Group
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Урок 135.
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Groupby + Apply Cookbook: Filtering Slices by Group
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Урок 136.
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Renaming Basics: Renaming All Columns with Lambda
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Урок 137.
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Renaming Basics: Targeting Specific Columns
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Урок 138.
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Advanced Renaming: Renaming Multi-Index Columns
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Урок 139.
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Set Up Summarized Data: Revenue by Category 1
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Урок 140.
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Pivot: To Wide Format
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Урок 141.
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Export a Stylized Pandas Table to Excel (Wide Data)
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Урок 142.
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Melt: To Long Format
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Урок 143.
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Plotnine - Making a Faceted Horizontal Bar Chart (Tidy Long Data)
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Урок 144.
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Intro to Categorical Data: Sorting the Plotnine Plot
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Урок 145.
00:07:42
Pivot Table (An awesome function for BI Tables)
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Урок 146.
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Unstack: A programmatic version of pivot()
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Урок 147.
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Stack: A programmatic version of melt()
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Урок 148.
00:04:12
Merge: Data Frame Joins
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Урок 149.
00:04:27
Concat: Binding DataFrames Rowwise & Columnwise
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Урок 150.
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Splitting Text Columns
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Урок 151.
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Combining Text Columns
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Урок 152.
00:03:02
Set Up Summarized Data: Sales by Category 2 Daily
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Урок 153.
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Apply: Lambda Aggregations vs Transformations
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Урок 154.
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Apply: Broadcasting Aggregations
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Урок 155.
00:02:24
Grouped Apply: Broadcasting
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Урок 156.
00:02:03
Grouped Transform: Alternative to Grouped Apply (Fixes Index Issue)
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Урок 157.
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Making a "Data Frame" Function: add_columns()
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Урок 158.
00:03:12
Pipe: Method chaining our custom function using the pipe
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Урок 159.
00:01:17
Challenge #1: Data Wrangling with Pandas [File Download]
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Урок 160.
00:02:25
Method 1: Jupyter VSCode Integration
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Урок 161.
00:02:07
Method 2: Jupyter Notebooks (Legacy Method)
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Урок 162.
00:03:16
Method 3: JupyterLab (Next Generation of Jupyter)
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Урок 163.
00:03:08
Challenge Objectives
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Урок 164.
00:05:10
Getting Started: Syncing Your JupyterLab Current Working Directory (%cd and %pwd)
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Урок 165.
00:03:18
Challenge Tasks
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Урок 166.
00:08:40
Challenge Solution
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Урок 167.
00:01:37
Congrats! You've finished your first challenge.
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Урок 168.
00:01:40
Automating Time Series Forecasting
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Урок 169.
00:01:49
Getting Started [File Download]
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Урок 170.
00:01:41
VSCode Extension: Browser Preview
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Урок 171.
00:01:40
Package Imports
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Урок 172.
00:01:10
The ProfileReport() Class
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Урок 173.
00:03:19
Section 1: Profile Overview
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Урок 174.
00:06:03
Section 2A: Numeric & Date Variables
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Урок 175.
00:05:02
Section 2B: Categorical (Text) Variables
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Урок 176.
00:02:52
Sections 3-6: Interactions, Correlations, Missing Values, & Sample
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Урок 177.
00:03:09
Pandas Extension: df.profile_report()
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Урок 178.
00:01:36
Exporting the Profile Report as HTML
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Урок 179.
00:00:50
Getting Started
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Урок 180.
00:02:59
TimeStamp & Period Conversions
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Урок 181.
00:01:56
Pandas Datetime Accessors
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Урок 182.
00:02:39
Date Math: Offsetting Time with TimeDelta's
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Урок 183.
00:03:28
Date Math: Getting Duration between Two TimeStamps
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Урок 184.
00:03:09
Creating Date Sequences: pd.date_range()
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Урок 185.
00:07:58
Periods (In-Depth)
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Урок 186.
00:06:24
Resampling (In-Depth): bike_sales_m_df
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Урок 187.
00:06:38
Grouped Resampling (In-Depth): bike_sales_cat2_m_wide_df
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Урок 188.
00:01:31
Reorganizing: Adding Comments
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Урок 189.
00:05:39
Differencing with Lags (Single Time Series)
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Урок 190.
00:02:05
Differencing with Lags (Multiple Time Series)
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Урок 191.
00:01:43
Difference from First (Single Time Series)
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Урок 192.
00:00:58
Difference From First (Multiple Time Series)
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Урок 193.
00:03:21
Cumulative Expanding Windows (Single Time Series)
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Урок 194.
00:01:39
Cumulative Expanding Windows (Multiple Time Series)
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Урок 195.
00:08:15
Moving Average (Single Time Series)
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Урок 196.
00:04:37
Moving Average (Multiple Time Series)
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Урок 197.
00:01:17
Next Steps (Where we are headed)
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Урок 198.
00:01:37
Getting Started [File Download]
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Урок 199.
00:00:46
Setup: Python Imports & Data
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Урок 200.
00:03:53
Function Anatomy: pd.Series.max()
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Урок 201.
00:01:03
Errors (Exceptions)
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Урок 202.
00:01:17
Function Names
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Урок 203.
00:05:13
Function Anatomy: **kwargs
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Урок 204.
00:02:19
Detect Outliers: Function Setup
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Урок 205.
00:03:37
IQR Outlier Method, Part 1
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Урок 206.
00:04:07
IQR Method, Part 2
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Урок 207.
00:01:47
New Argument: IQR Multiplier
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Урок 208.
00:02:36
New Argument: How? (Both, Upper, Lower)
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Урок 209.
00:02:11
Checking for Pandas Series Input
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Урок 210.
00:02:54
Checking IQR Multiplier for Int or Float Type
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Урок 211.
00:01:10
Checking that IQR Multiplier is a Positive Value
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Урок 212.
00:02:19
Checking that How is a Valid Option: both, lower, upper
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Урок 213.
00:07:12
Informative Help Documentation: Adding a Docstring
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Урок 214.
00:03:05
Testing Our Function: Detecting Outliers within Groups
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Урок 215.
00:02:12
Extending the Pandas Series Class
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Урок 216.
00:04:00
Summarize By Time: A handy function for time series wrangling
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Урок 217.
00:04:56
Setting Up the "Summarize By Time" Function
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Урок 218.
00:01:31
Handling the Date Column Input
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Урок 219.
00:02:03
Handling Groups Input
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Урок 220.
00:04:14
Handling the Time Series Resample
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Урок 221.
00:03:16
Handling the Aggregation Function Input
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Урок 222.
00:01:40
Handling the Value Column Input
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Урок 223.
00:02:44
Forcing the Value Column Input to a List (to generate a data frame)
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Урок 224.
00:02:26
Bug! Thinking through a solution
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Урок 225.
00:03:52
Solution: Converting to a Function Dictionary with Zip + Dict
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Урок 226.
00:02:02
Handling the Unstack
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Урок 227.
00:02:51
Handling the Period Conversion
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Урок 228.
00:02:25
Add Fill Missing Capability
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Урок 229.
00:01:25
Review the Core Functionality
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Урок 230.
00:01:50
Check Incoming Data: Raising a TypeError
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Урок 231.
00:07:28
Adding the Docstring
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Урок 232.
00:06:23
Pandas Flavor: Extending Pandas DataFrame Class
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Урок 233.
00:03:03
Getting Started [File Download]
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Урок 234.
00:04:36
Sktime Documentation
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Урок 235.
00:01:35
How to Google Search like a Pro
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Урок 236.
00:02:41
Set Up & Imports
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Урок 237.
00:05:00
Summarizing to get Total Revenue by Month
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Урок 238.
00:02:42
Summarizing to get Total Revenue by Category 2 & Month
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Урок 239.
00:04:59
What is AutoARIMA?
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Урок 240.
00:08:25
AutoARIMA Applied: Forecaster, Fit, Predict
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Урок 241.
00:02:41
Adding Confidence Intervals (Prediction Intervals)
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Урок 242.
00:02:39
Tuple Unpacking (Predictions, Confidence Intervals)
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Урок 243.
00:05:28
Forecast Visualization
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Урок 244.
00:00:24
Code Housekeeping
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Урок 245.
00:03:10
Multiple Time Series Forecasting: AutoARIMA()
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Урок 246.
00:02:20
For Loop: Iterate Across the DataFrame Columns
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Урок 247.
00:05:23
For Loop: Modeling AutoARIMA()
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Урок 248.
00:01:32
For-Loop: Getting the Confidence Intervals
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Урок 249.
00:04:12
For-Loop: Combine with DataFrame | Actual Values, Predictions, & CIs
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Урок 250.
00:03:36
For-Loop: Storing the Results (as a Dictionary)
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Урок 251.
00:01:53
Housekeeping: Appending Variable Types to Variable Names
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Урок 252.
00:02:43
Visual Forecast Assessment
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Урок 253.
00:03:41
TQDM: Progress Bars
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Урок 254.
00:03:45
Setting up the ARIMA Automation Function
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Урок 255.
00:03:19
Making arima_forecast() | Function Definition
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Урок 256.
00:04:41
Function Body | Setting Up the Iteration
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Урок 257.
00:03:02
Training the AutoARIMA() Models
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Урок 258.
00:01:12
Controlling Progress Bars: tqdm(min_interval)
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Урок 259.
00:02:09
Making Predictions and Confidence Intervals
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Урок 260.
00:02:24
Combine Results into a DataFrame
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Урок 261.
00:01:50
Compose a Prediction Dictionary
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Урок 262.
00:02:37
Return Results as a Single DataFrame | Rowwise Concatenation
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Урок 263.
00:09:16
Setting the Column Names of the Output
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Урок 264.
00:02:51
Drop remaining columns beginning with "level_"
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Урок 265.
00:02:05
Testing the arima_forecast() function
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Урок 266.
00:03:44
Creating the forecasting.py module
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Урок 267.
00:01:32
Docstring: arima_forecast()
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Урок 268.
00:06:35
Adding Checks: arima_forecast()
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Урок 269.
00:02:29
Finally - Check Your Forecasts with Grouped Pandas Plotting
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Урок 270.
00:01:10
Recap: You've just made an ARIMA Forecast Automation!
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Урок 271.
00:02:07
Introduction to ETS Forecasting (Exponential Smoothing)
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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]
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Урок 276.
00:03:15
Plotnine Documentation
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Урок 277.
00:02:57
Plotnine Anatomy: Imports
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Урок 278.
00:02:54
Data Summarization: For Plotting Annual Bike Sales
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Урок 279.
00:07:13
The Plot Canvas: Mapping Columns to Plot Components
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Урок 280.
00:04:00
Plotnine Geometries
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Урок 281.
00:03:00
Adding a Trend Line: geom_smooth()
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Урок 282.
00:01:53
Formatting Plots
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Урок 283.
00:01:42
Expand Limits
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Урок 284.
00:03:52
Scales: Dollar Format for Y-Axis
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Урок 285.
00:02:15
Scales: Date Format for X-Axis
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Урок 286.
00:02:58
Labs and Themes
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Урок 287.
00:01:18
Saving the ggplot
-
Урок 288.
00:02:24
Exploring the Plotnine Object
-
Урок 289.
00:02:20
Setting Up
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Урок 290.
00:02:52
Scatter Plot: Data Manipulation
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Урок 291.
00:03:18
Scatter Plot: Visualization
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Урок 292.
00:02:08
Line Plot: Data Manipulation
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Урок 293.
00:05:29
Line Plot: Visualization
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Урок 294.
00:02:49
Data Manipulation, Part 1: No Categorical Ordering
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Урок 295.
00:01:35
Visualization, Part 1: Without Categorical Ordering
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Урок 296.
00:09:55
Aside: Introduction to Plotting using Categorical Data Type
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Урок 297.
00:01:20
Finalizing the Horizontal Bar Chart
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Урок 298.
00:02:48
Histogram: Data Manipulation
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Урок 299.
00:02:15
Histogram: Visualization
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Урок 300.
00:02:51
Histogram: Using Fill Aesthetic to Explore Differences by a Category
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Урок 301.
00:02:48
Histogram: Using Facet Grids to Compare Distributions by Category
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Урок 302.
00:02:55
Density Plots: Kernel Density Estimation (KDE) using geom_density()
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Урок 303.
00:02:20
Box Plot: Data Manipulation
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Урок 304.
00:07:24
Box Plot: Visualization
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Урок 305.
00:03:32
Violin Plot with Jitter: geom_violin() and geom_jitter()
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Урок 306.
00:06:09
Data Manipulation: Add a Total Price Text Column with USD Dollar Format
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Урок 307.
00:03:11
Creating the Bar Plot: geom_col() and geom_smooth()
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Урок 308.
00:05:35
Adding Text to a Bar Plot: geom_text()
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Урок 309.
00:05:58
Highlighting an Outlier with a Label: geom_label()
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Урок 310.
00:03:39
Finalizing the Plot with Scales and Themes
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Урок 311.
00:04:41
Sales by Month and Category 2: Data Manipulation
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Урок 312.
00:06:55
Facets: Adding subplots "facets" with facet_wrap()
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Урок 313.
00:04:34
Scales: Applying scales to alter x, y, and color mappings
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Урок 314.
00:03:54
Themes: Theme Customization with Pre-Built Themes | theme_matplotlib()
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Урок 315.
00:05:33
Theme Elements: Customization with theme()
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Урок 316.
00:04:44
Plot Title and X/Y-Axis Labels: labs()
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Урок 317.
00:01:21
Getting Started
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Урок 318.
00:02:10
Package Imports
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Урок 319.
00:05:00
Our Forecasting Workflow Recap
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Урок 320.
00:04:51
Data Preparation: Melting the Value and Prediction Columns
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Урок 321.
00:03:04
Data Preparation: Fixing the FutureWarning
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Урок 322.
00:03:35
Visualization: Setting up the canvas with ggplot()
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Урок 323.
00:05:43
Visualization: Adding geoms and facets
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Урок 324.
00:05:12
Visualization: Scales and Theme Minimal
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Урок 325.
00:04:22
Visualization: Customizing the Theme Elements
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Урок 326.
00:03:23
Making the plot_forecast() Function Definition
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Урок 327.
00:04:38
Data Wrangling: Implementing the Melt
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Урок 328.
00:08:52
Handling the Time-Based Column: Converting to TimeStamp
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Урок 329.
00:08:56
Visualization: Parameterizing the Plot
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Урок 330.
00:07:10
Testing the Forecast Plot Function Parameters
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Урок 331.
00:01:30
Testing the Automation Workflow
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Урок 332.
00:06:15
Reordering the Subplots using Cat Tools
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Урок 333.
00:03:37
Adding the plot_forecast() function to our forecasting module
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Урок 334.
00:03:30
Docstring | Testing Our Imported plot_forecast() Function
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Урок 335.
00:01:56
Getting Started [File Download]
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Урок 336.
00:02:33
Package Imports
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Урок 337.
00:01:37
Reviewing Our Files
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Урок 338.
00:05:26
Generating the Forecasting Workflow
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Урок 339.
00:01:39
Generating the Forecast Visualization
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Урок 340.
00:01:27
Overview of the Database I/O Process
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Урок 341.
00:05:44
Preparing the Forecast for Update
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Урок 342.
00:06:11
Validating the Column Names
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Урок 343.
00:01:15
Testing the Prep Forecast for Database Function
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Урок 344.
00:05:28
Setting Up the Write Forecast to Database Function
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Урок 345.
00:01:19
Modularizing the Data Preparation Step
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Урок 346.
00:06:47
Specifying SQL Data Types
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Урок 347.
00:06:50
Write to Database
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Урок 348.
00:00:55
Close Connection
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Урок 349.
00:04:29
Testing Our Function
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Урок 350.
00:06:00
Creating our Read Forecast Function
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Урок 351.
00:04:19
Adding Functions to Database Module
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Урок 352.
00:03:07
Docstrings
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Урок 353.
00:02:44
Automation Workflow with Database I/O
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Урок 354.
00:04:43
Forecasting 1: Total Revenue
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Урок 355.
00:04:30
Fix #1: Reorder Columns in Prep Data Function
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Урок 356.
00:01:25
Plotting Total Revenue Forecast
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Урок 357.
00:05:33
Forecasting 2: Revenue by Category 1
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Урок 358.
00:04:37
Forecasting 3: Revenue by Category 2
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Урок 359.
00:05:34
Forecasting 4: Forecast Quarterly Revenue by Customer
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Урок 360.
00:01:33
Fix #2: Prep Data | Add timestamp conversion
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Урок 361.
00:03:20
Rerun Our Workflow: Success!
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Урок 362.
00:03:15
Writing to the Database
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Урок 363.
00:01:46
Pro-Tip: Saving Intermediate Data
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Урок 364.
00:07:00
Utility Function: Convert to Datetime
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Урок 365.
00:03:46
Rerun the Forecast Workflow
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Урок 366.
00:02:02
Read Forecast from Database
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Урок 367.
00:03:42
Recap: Debugging is a Skill
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Урок 368.
00:01:16
Jupyter Automated Reporting
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Урок 369.
00:02:54
Getting Started [File Download]
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Урок 370.
00:05:49
The Updated Database Script: Automatically Run Forecasts
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Урок 371.
00:03:28
python update_database.py
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Урок 372.
00:01:23
SQLite Explorer
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Урок 373.
00:06:14
Setting Up the Working Directory
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Урок 374.
00:06:12
Importing Data and Parameterizing a Header with Markdown
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Урок 375.
00:03:56
Parameterizing a Paragraph with Markdown
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Урок 376.
00:05:35
Performance Summary: Pivot Table, Part 1
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Урок 377.
00:02:14
Performance Summary: Pivot Table, Part 2
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Урок 378.
00:02:04
Plotting the Forecast: plot_forecast()
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Урок 379.
00:01:16
Papermill Setup
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Урок 380.
00:02:12
Package Imports
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Урок 381.
00:03:19
Papermill Documentation
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Урок 382.
00:02:11
Developing Parameters: Game Plan
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Урок 383.
00:03:21
Making ID Sets, Part 1
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Урок 384.
00:04:32
Making ID Sets, Part 2
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Урок 385.
00:03:02
Part 1: Intro to Pathlib and OS
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Урок 386.
00:04:42
Part 2: Detecting Directories Exist & Making New Directories
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Урок 387.
00:02:57
Jupyter Template Setup
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Урок 388.
00:03:49
Parameterizing the Jupyter Template
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Урок 389.
00:03:47
Finishing the Juyter Template Parameterization
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Урок 390.
00:04:06
The pm.exectute_notebook() function
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Урок 391.
00:06:02
Setting Up Key Parameters
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Урок 392.
00:06:09
Iterating without a For-Loop
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Урок 393.
00:06:01
Iterating with a For-Loop
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Урок 394.
00:01:08
Getting Started
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Урок 395.
00:03:33
Setting Up the Report Parameters
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Урок 396.
00:01:29
Creating a Resource Path
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Урок 397.
00:06:32
String Transformation: Make File Names from Report Titles
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Урок 398.
00:02:46
Setting Up run_reports()
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Урок 399.
00:04:49
Make the Report Directory
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Урок 400.
00:06:23
Setting Up the For-Loop Parameters
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Урок 401.
00:04:02
Setting Up Jupyter Notebook Execution (Inside of For-Loop)
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Урок 402.
00:06:04
Package Resources: Setting Up the Template Path
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Урок 403.
00:04:35
Integrating the Run Reports Function into Our Package
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Урок 404.
00:03:32
Getting Started [File Download]
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Урок 405.
00:01:50
NB Convert Documentation & Installation Requirements
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Урок 406.
00:01:06
Step 1: Pandoc Installation
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Урок 407.
00:01:06
Step 2: Tex Installation (MikTex Windows Shown | Mac Use MacTex)
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Урок 408.
00:01:34
HTML Report Conversion
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Урок 409.
00:01:04
PDF Report Conversion
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Урок 410.
00:04:22
Setup & Imports
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Урок 411.
00:04:23
Making the Config()
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Урок 412.
00:03:21
Locating Files with Glob
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Урок 413.
00:06:39
Exporting an HTML Report Programmatically
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Урок 414.
00:05:51
HTML Automation: Using a For-Loop to Convert All 4 Reports
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Урок 415.
00:06:00
PDF Automation: Using a For-Loop to Convert All 4 Reports
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Урок 416.
00:02:42
Getting Set Up
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Урок 417.
00:02:47
Integrating glob: Pulling the Jupyter Notebook File Paths
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Урок 418.
00:04:12
Integrate "Convert to HTML" Report Automation
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Урок 419.
00:02:32
Test "Convert to HTML" Report Automation
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Урок 420.
00:01:47
Integrate "Convert to PDF" Report Automation
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Урок 421.
00:03:40
Test "Convert to PDF" Report Automation
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Урок 422.
00:06:32
My Pandas Extensions: Upgrade reporting.py with HTML & PDF Reports, Part 1
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Урок 423.
00:04:01
My Pandas Extensions: Upgrade reporting.py with HTML & PDF Reports, Part 2
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Урок 424.
00:05:08
Run Forecast Reports Py: Part 1 - The main() function
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Урок 425.
00:06:02
Run Forecast Reports Py: Part 2 - Adding Timestamps to Folders
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Урок 426.
00:02:57
Run Forecast Reports Py: Part 3 - Running Reports
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Урок 427.
00:03:14
Run Forecast Reports Py: Part 4 - Adjusting Folder Automation
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Урок 428.
00:00:28
Scheduling Python Scripts Bonus!!!
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Урок 429.
00:02:26
Making the Batch File (.bat) to run our Python Script
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Урок 430.
00:02:14
Setting up Automated Tasks with Windows Task Scheduler
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Урок 431.
00:00:40
Debugging Windows Task Scheduler Tasks with Pause
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Урок 432.
00:01:53
Fixing the SQL Alchemy Connection
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Урок 433.
00:00:24
Removing the Automation: Disable & Delete
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Урок 434.
00:02:31
Python Script Setup | SQL Database Absolute Path
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Урок 435.
00:03:23
The Mac Automator
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Урок 436.
00:02:01
Scheduling the Automator App with Calendar
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Урок 437.
00:01:11
Congratulations!!!
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Урок 438.
01:33:11
Forecasting 100 Time Series in Python with Sktime
https://university.business-science.io/p/python-for-data-science-automation-ds4b-101p
Секция "4.2.2 Periods & Time-Based Groupings (Resampling)" , то там список видео такой
====
Periods (In-Depth) (7:57)
Resampling (In-Depth): ♻️ bike_sales_m_df (6:23)
Grouped Resampling (In-Depth): ♻️ bike_sales_cat2_m_wide_df (6:37)
...
===
У вас идет так:
===
Creating Date Sequences - pd.date_range() (здесь закончилась предыдущая секция)
Resampling (In-Depth) - bike_sales_m_df
Grouped Resampling (In-Depth) - bike_sales_cat2_m_wide_df
===
Видео "Periods (In-Depth) (7:57)" отсутствует.
Еще почему то в архиве 2 одинаковых видео файла:
lesson38.ts
lesson38.mp4
Насколько я могу судить - по содержанию они идентичные, только расширение различается