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Python for Data Science Automation: Let's Do This!
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The Game Plan: Data Analysis Foundations
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The Business Case: Building an Automated Forecast System
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Course Project Zip [File Download]
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Course Workflow: Tying Specific Actions to the Business Process
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Ultimate Python Cheat Sheet: Python Ecosystem in 2 Pages
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The Transactional Database Model [PDF Download]
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Anaconda Installation
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IDE (Integrated Development Environment) Options
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VSCode Installation
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Connect VSCode to Your Course Project Files
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Conda Env Create: Make the Python Course Environment
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Python Select Interpreter: Connect VSCode to Your Python Environment
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Conda Env Update: Add Python Packages to Your Environment
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Conda Env Export: Review & Share Your Environment
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Conda Env List & Remove: List Available Environments & Remove Unnecessary Envs
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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
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VSCode Keyboard Shortcuts
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VSCode Python Extensions
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VSCode Jupyter Extension - Jupyter Notebook Support
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VSCode Jupyter Extension - Interactive Python
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[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
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Python Environment Checkpoint [File Download]
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Getting Started [File Download]
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Using the Cheat Sheet
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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
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[Reminder | Optional VSCode Setting] Jupyter: Send Selection to Interactive Window
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Getting Help Documentation
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IMPORTANT VSCODE SETTING: File Paths | jupyter.notebookFileRoot
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Reading the Excel Files
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Reviewing the Data Model
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Exploratory 1: Top 5 Most Frequent Descriptions
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Exploratory 2: Plotting the Top 5 Bike Descriptions
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Preparing Orderlines for Merge: Drop Column
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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
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Splitting the Description: Category 1, Category 2, and Frame Material
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Splitting Location: City, State
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Create the Total Price Column
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Reorganizing the Columns
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Renaming Columns
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Reviewing the Data Transformations
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Save Your Work: Pickle it.
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Pandas Datetime Accessors
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Resampling: Working with Pandas Offsets
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Quick Plot: Plotting Single Time Series w/ Pandas Matplotlib Backend
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Plotnine Visualization: Sales By Month, Part 1 - Geometries
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Plotnine Visualization: Sales by Month, Part 2 - Scales & Themes
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Resampling Groups: Combine groupby() and resample()
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Quick Plot: Plotting Multiple Time Series w/ Pandas Matplotlib Backend
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Plotnine Visualization, Part 1: Facetted Sales By Date & Category2 (Group)
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Plotnine Visualization, Part 2: Adding Themes & Scales
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Writing Files: Pickle, CSV, Excel
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Congrats. That was a fun whirlwind. Let's recap.
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Getting Started [File Download]
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Pickle Files
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CSV Files
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Excel Files
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SQL Databases
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Pandas I/O & SQL Alchemy Overviews
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Make Database Directory
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Create the SQLite Database
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Read the Excel Files
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Create the Database Tables
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Close the Connection
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Connect to the Database
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Getting the Database Table Names
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Reading from the Tables with f-strings
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[Bonus] VSCode SQLite Extension
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Making collect_data(), Part 1: Function Setup
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Making collect_data(), Part 2: Read Tables from the Database
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Making collect_data(), Part 3: Test the Database Import
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Making collect_data(), Part 4: Joining the Data
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Making collect_data(), Part 5: Cleaning the Data 1
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Making collect_data(), Part 6: Cleaning the Data 2
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Making collect_data(), Part 7: VSCode Docstring Generator
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Making a Package (my_pandas_extensions): Adding the database module
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🥳Congrats! You're learning really powerful concepts.
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Getting Started [File Download]
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[VSCode Setting] Jupyter: Send Selection to Interactive Window
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Package & Function Imports
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My Pandas Extensions: Fix FutureWarning Message (regex)
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How Python Works: Objects
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Pandas DataFrame & Series
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Numpy Arrays
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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
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Casting Sequences: To List, Numpy Array, Pandas Series, & DataFrame
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Pandas Series Dtype Conversion
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Pandas Data Wrangling Setup
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Subsetting Columns by Name
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Subsetting by Column Index (Position): iloc[]
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Subsetting Columns with Regex (Regular Expressions)
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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[]
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Getting Distinct Values: Drop duplicates
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N-Largest and N-Smallest
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Random Samples
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DataFrame Column Assignment: Calculated Columns
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Assign Basics: Lambda Functions
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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()
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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)
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Common Summary Functions
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Groupby + Aggregate Basics (Summarizations)
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Groupby + Agg Cookbook (Summary DF 1): Sum & Median Total Price By Category 1 & 2
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Groupby + Agg Cookbook (Summary DF 2): Sum Total Price & Quantity By Category 1 & 2
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Groupby + Agg Details: Examining the Multilevel Column Index
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Groupby + Agg Cookbook (Summary DF 3): Grouping Time Series with Groupby & Resample
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Groupby + Apply Basics (Transformations)
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Groupby + Apply Cookbook: Transform All Columns by Group
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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
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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
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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
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Splitting Text Columns
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Combining Text Columns
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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
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Grouped Transform: Alternative to Grouped Apply (Fixes Index Issue)
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Making a "Data Frame" Function: add_columns()
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Pipe: Method chaining our custom function using the pipe
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Challenge #1: Data Wrangling with Pandas [File Download]
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Method 1: Jupyter VSCode Integration
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Method 2: Jupyter Notebooks (Legacy Method)
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Method 3: JupyterLab (Next Generation of Jupyter)
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Challenge Objectives
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Getting Started: Syncing Your JupyterLab Current Working Directory (%cd and %pwd)
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Challenge Tasks
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Challenge Solution
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Congrats! You've finished your first challenge.
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Automating Time Series Forecasting
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Getting Started [File Download]
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VSCode Extension: Browser Preview
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Package Imports
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The ProfileReport() Class
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Section 1: Profile Overview
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Section 2A: Numeric & Date Variables
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Section 2B: Categorical (Text) Variables
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Sections 3-6: Interactions, Correlations, Missing Values, & Sample
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Pandas Extension: df.profile_report()
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Exporting the Profile Report as HTML
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Getting Started
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TimeStamp & Period Conversions
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Pandas Datetime Accessors
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Date Math: Offsetting Time with TimeDelta's
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Date Math: Getting Duration between Two TimeStamps
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Creating Date Sequences: pd.date_range()
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Periods (In-Depth)
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Resampling (In-Depth): bike_sales_m_df
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Grouped Resampling (In-Depth): bike_sales_cat2_m_wide_df
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Reorganizing: Adding Comments
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Differencing with Lags (Single Time Series)
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Differencing with Lags (Multiple Time Series)
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Difference from First (Single Time Series)
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Difference From First (Multiple Time Series)
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Cumulative Expanding Windows (Single Time Series)
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Cumulative Expanding Windows (Multiple Time Series)
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Moving Average (Single Time Series)
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Moving Average (Multiple Time Series)
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Next Steps (Where we are headed)
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Getting Started [File Download]
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Setup: Python Imports & Data
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Function Anatomy: pd.Series.max()
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Errors (Exceptions)
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Function Names
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Function Anatomy: **kwargs
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Detect Outliers: Function Setup
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IQR Outlier Method, Part 1
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IQR Method, Part 2
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New Argument: IQR Multiplier
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New Argument: How? (Both, Upper, Lower)
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Checking for Pandas Series Input
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Checking IQR Multiplier for Int or Float Type
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Checking that IQR Multiplier is a Positive Value
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Checking that How is a Valid Option: both, lower, upper
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Informative Help Documentation: Adding a Docstring
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Testing Our Function: Detecting Outliers within Groups
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Extending the Pandas Series Class
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Summarize By Time: A handy function for time series wrangling
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Setting Up the "Summarize By Time" Function
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Handling the Date Column Input
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Handling Groups Input
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Handling the Time Series Resample
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Handling the Aggregation Function Input
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Handling the Value Column Input
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Forcing the Value Column Input to a List (to generate a data frame)
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Bug! Thinking through a solution
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Solution: Converting to a Function Dictionary with Zip + Dict
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Handling the Unstack
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Handling the Period Conversion
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Add Fill Missing Capability
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Review the Core Functionality
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Check Incoming Data: Raising a TypeError
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Adding the Docstring
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Pandas Flavor: Extending Pandas DataFrame Class
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Getting Started [File Download]
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Sktime Documentation
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How to Google Search like a Pro
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Set Up & Imports
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Summarizing to get Total Revenue by Month
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Summarizing to get Total Revenue by Category 2 & Month
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What is AutoARIMA?
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AutoARIMA Applied: Forecaster, Fit, Predict
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Adding Confidence Intervals (Prediction Intervals)
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Tuple Unpacking (Predictions, Confidence Intervals)
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Forecast Visualization
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Code Housekeeping
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Multiple Time Series Forecasting: AutoARIMA()
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For Loop: Iterate Across the DataFrame Columns
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For Loop: Modeling AutoARIMA()
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For-Loop: Getting the Confidence Intervals
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For-Loop: Combine with DataFrame | Actual Values, Predictions, & CIs
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For-Loop: Storing the Results (as a Dictionary)
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Housekeeping: Appending Variable Types to Variable Names
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Visual Forecast Assessment
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TQDM: Progress Bars
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Setting up the ARIMA Automation Function
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Making arima_forecast() | Function Definition
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Function Body | Setting Up the Iteration
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Training the AutoARIMA() Models
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Controlling Progress Bars: tqdm(min_interval)
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Making Predictions and Confidence Intervals
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Combine Results into a DataFrame
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Compose a Prediction Dictionary
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Return Results as a Single DataFrame | Rowwise Concatenation
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Setting the Column Names of the Output
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Drop remaining columns beginning with "level_"
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Testing the arima_forecast() function
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Creating the forecasting.py module
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Docstring: arima_forecast()
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Adding Checks: arima_forecast()
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Finally - Check Your Forecasts with Grouped Pandas Plotting
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Recap: You've just made an ARIMA Forecast Automation!
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Introduction to ETS Forecasting (Exponential Smoothing)
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Challenge 2 [File Download]
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Solution
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Part 3: Visualization & Reporting
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Getting Started [File Download]
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Plotnine Documentation
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Plotnine Anatomy: Imports
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Data Summarization: For Plotting Annual Bike Sales
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The Plot Canvas: Mapping Columns to Plot Components
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Plotnine Geometries
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Adding a Trend Line: geom_smooth()
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Formatting Plots
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Expand Limits
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Scales: Dollar Format for Y-Axis
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Scales: Date Format for X-Axis
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Labs and Themes
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Saving the ggplot
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Exploring the Plotnine Object
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Setting Up
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Scatter Plot: Data Manipulation
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Scatter Plot: Visualization
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Line Plot: Data Manipulation
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Line Plot: Visualization
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Data Manipulation, Part 1: No Categorical Ordering
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Visualization, Part 1: Without Categorical Ordering
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Aside: Introduction to Plotting using Categorical Data Type
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Finalizing the Horizontal Bar Chart
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Histogram: Data Manipulation
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Histogram: Visualization
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Histogram: Using Fill Aesthetic to Explore Differences by a Category
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Histogram: Using Facet Grids to Compare Distributions by Category
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Density Plots: Kernel Density Estimation (KDE) using geom_density()
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Box Plot: Data Manipulation
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Box Plot: Visualization
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Violin Plot with Jitter: geom_violin() and geom_jitter()
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Data Manipulation: Add a Total Price Text Column with USD Dollar Format
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Creating the Bar Plot: geom_col() and geom_smooth()
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Adding Text to a Bar Plot: geom_text()
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Highlighting an Outlier with a Label: geom_label()
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Finalizing the Plot with Scales and Themes
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Sales by Month and Category 2: Data Manipulation
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Facets: Adding subplots "facets" with facet_wrap()
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Scales: Applying scales to alter x, y, and color mappings
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Themes: Theme Customization with Pre-Built Themes | theme_matplotlib()
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Theme Elements: Customization with theme()
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Plot Title and X/Y-Axis Labels: labs()
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Getting Started
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Package Imports
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Our Forecasting Workflow Recap
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Data Preparation: Melting the Value and Prediction Columns
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Data Preparation: Fixing the FutureWarning
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Visualization: Setting up the canvas with ggplot()
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Visualization: Adding geoms and facets
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Visualization: Scales and Theme Minimal
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Visualization: Customizing the Theme Elements
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Making the plot_forecast() Function Definition
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Data Wrangling: Implementing the Melt
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Handling the Time-Based Column: Converting to TimeStamp
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Visualization: Parameterizing the Plot
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Testing the Forecast Plot Function Parameters
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Testing the Automation Workflow
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Reordering the Subplots using Cat Tools
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Adding the plot_forecast() function to our forecasting module
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Docstring | Testing Our Imported plot_forecast() Function
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Getting Started [File Download]
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Package Imports
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Reviewing Our Files
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Generating the Forecasting Workflow
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Generating the Forecast Visualization
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Overview of the Database I/O Process
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Preparing the Forecast for Update
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Validating the Column Names
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Testing the Prep Forecast for Database Function
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Setting Up the Write Forecast to Database Function
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Modularizing the Data Preparation Step
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Specifying SQL Data Types
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Write to Database
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Close Connection
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Testing Our Function
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Creating our Read Forecast Function
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Adding Functions to Database Module
Урок 352. 00:03:07
Docstrings
Урок 353. 00:02:44
Automation Workflow with Database I/O
Урок 354. 00:04:43
Forecasting 1: Total Revenue
Урок 355. 00:04:30
Fix #1: Reorder Columns in Prep Data Function
Урок 356. 00:01:25
Plotting Total Revenue Forecast
Урок 357. 00:05:33
Forecasting 2: Revenue by Category 1
Урок 358. 00:04:37
Forecasting 3: Revenue by Category 2
Урок 359. 00:05:34
Forecasting 4: Forecast Quarterly Revenue by Customer
Урок 360. 00:01:33
Fix #2: Prep Data | Add timestamp conversion
Урок 361. 00:03:20
Rerun Our Workflow: Success!
Урок 362. 00:03:15
Writing to the Database
Урок 363. 00:01:46
Pro-Tip: Saving Intermediate Data
Урок 364. 00:07:00
Utility Function: Convert to Datetime
Урок 365. 00:03:46
Rerun the Forecast Workflow
Урок 366. 00:02:02
Read Forecast from Database
Урок 367. 00:03:42
Recap: Debugging is a Skill
Урок 368. 00:01:16
Jupyter Automated Reporting
Урок 369. 00:02:54
Getting Started [File Download]
Урок 370. 00:05:49
The Updated Database Script: Automatically Run Forecasts
Урок 371. 00:03:28
python update_database.py
Урок 372. 00:01:23
SQLite Explorer
Урок 373. 00:06:14
Setting Up the Working Directory
Урок 374. 00:06:12
Importing Data and Parameterizing a Header with Markdown
Урок 375. 00:03:56
Parameterizing a Paragraph with Markdown
Урок 376. 00:05:35
Performance Summary: Pivot Table, Part 1
Урок 377. 00:02:14
Performance Summary: Pivot Table, Part 2
Урок 378. 00:02:04
Plotting the Forecast: plot_forecast()
Урок 379. 00:01:16
Papermill Setup
Урок 380. 00:02:12
Package Imports
Урок 381. 00:03:19
Papermill Documentation
Урок 382. 00:02:11
Developing Parameters: Game Plan
Урок 383. 00:03:21
Making ID Sets, Part 1
Урок 384. 00:04:32
Making ID Sets, Part 2
Урок 385. 00:03:02
Part 1: Intro to Pathlib and OS
Урок 386. 00:04:42
Part 2: Detecting Directories Exist & Making New Directories
Урок 387. 00:02:57
Jupyter Template Setup
Урок 388. 00:03:49
Parameterizing the Jupyter Template
Урок 389. 00:03:47
Finishing the Juyter Template Parameterization
Урок 390. 00:04:06
The pm.exectute_notebook() function
Урок 391. 00:06:02
Setting Up Key Parameters
Урок 392. 00:06:09
Iterating without a For-Loop
Урок 393. 00:06:01
Iterating with a For-Loop
Урок 394. 00:01:08
Getting Started
Урок 395. 00:03:33
Setting Up the Report Parameters
Урок 396. 00:01:29
Creating a Resource Path
Урок 397. 00:06:32
String Transformation: Make File Names from Report Titles
Урок 398. 00:02:46
Setting Up run_reports()
Урок 399. 00:04:49
Make the Report Directory
Урок 400. 00:06:23
Setting Up the For-Loop Parameters
Урок 401. 00:04:02
Setting Up Jupyter Notebook Execution (Inside of For-Loop)
Урок 402. 00:06:04
Package Resources: Setting Up the Template Path
Урок 403. 00:04:35
Integrating the Run Reports Function into Our Package
Урок 404. 00:03:32
Getting Started [File Download]
Урок 405. 00:01:50
NB Convert Documentation & Installation Requirements
Урок 406. 00:01:06
Step 1: Pandoc Installation
Урок 407. 00:01:06
Step 2: Tex Installation (MikTex Windows Shown | Mac Use MacTex)
Урок 408. 00:01:34
HTML Report Conversion
Урок 409. 00:01:04
PDF Report Conversion
Урок 410. 00:04:22
Setup & Imports
Урок 411. 00:04:23
Making the Config()
Урок 412. 00:03:21
Locating Files with Glob
Урок 413. 00:06:39
Exporting an HTML Report Programmatically
Урок 414. 00:05:51
HTML Automation: Using a For-Loop to Convert All 4 Reports
Урок 415. 00:06:00
PDF Automation: Using a For-Loop to Convert All 4 Reports
Урок 416. 00:02:42
Getting Set Up
Урок 417. 00:02:47
Integrating glob: Pulling the Jupyter Notebook File Paths
Урок 418. 00:04:12
Integrate "Convert to HTML" Report Automation
Урок 419. 00:02:32
Test "Convert to HTML" Report Automation
Урок 420. 00:01:47
Integrate "Convert to PDF" Report Automation
Урок 421. 00:03:40
Test "Convert to PDF" Report Automation
Урок 422. 00:06:32
My Pandas Extensions: Upgrade reporting.py with HTML & PDF Reports, Part 1
Урок 423. 00:04:01
My Pandas Extensions: Upgrade reporting.py with HTML & PDF Reports, Part 2
Урок 424. 00:05:08
Run Forecast Reports Py: Part 1 - The main() function
Урок 425. 00:06:02
Run Forecast Reports Py: Part 2 - Adding Timestamps to Folders
Урок 426. 00:02:57
Run Forecast Reports Py: Part 3 - Running Reports
Урок 427. 00:03:14
Run Forecast Reports Py: Part 4 - Adjusting Folder Automation
Урок 428. 00:00:28
Scheduling Python Scripts Bonus!!!
Урок 429. 00:02:26
Making the Batch File (.bat) to run our Python Script
Урок 430. 00:02:14
Setting up Automated Tasks with Windows Task Scheduler
Урок 431. 00:00:40
Debugging Windows Task Scheduler Tasks with Pause
Урок 432. 00:01:53
Fixing the SQL Alchemy Connection
Урок 433. 00:00:24
Removing the Automation: Disable & Delete
Урок 434. 00:02:31
Python Script Setup | SQL Database Absolute Path
Урок 435. 00:03:23
The Mac Automator
Урок 436. 00:02:01
Scheduling the Automator App with Calendar
Урок 437. 00:01:11
Congratulations!!!
Урок 438. 01:33:11
Forecasting 100 Time Series in Python with Sktime
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.