Pandas for Everyone: Python Data Analysis
(PYTHON-PANDAS.AP1)
/ ISBN: 978-1-64459-413-1
This course includes
Lessons
TestPrep
LiveLab
Mentoring (Add-on)
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$140
Pandas for Everyone: Python Data Analysis
Pandas is an open-source Python library for data analysis. The Pandas for Everyone: Python Data Analysis course focuses on loading data into Python with the help of the Pandas library. This course contains interactive lessons with knowledge checks, quizzes, and hands-on labs to get a deeper understanding of the concepts such as Pandas DataFrame and Data Structure Basics, Plotting Basics, Tidy Data, Data Assembly, Data Normalization, linear regression, survival models, and so on.
Lessons
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47+ Lessons
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100+ Exercises
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90+ Quizzes
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109+ Flashcards
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109+ Glossary of terms
TestPrep
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50+ Pre Assessment Questions
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50+ Post Assessment Questions
LiveLab
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30+ LiveLab
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20+ Video tutorials
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43+ Minutes
- Breakdown of the Course
- How to Read This Course
- Setup
- Introduction
- Load Your First Data Set
- Look at Columns, Rows, and Cells
- Grouped and Aggregated Calculations
- Basic Plot
- Conclusion
- Create Your Own Data
- The Series
- The DataFrame
- Making Changes to Series and DataFrames
- Exporting and Importing Data
- Conclusion
- Why Visualize Data?
- Matplotlib Basics
- Statistical Graphics Using matplotlib
- Seaborn
- Pandas Plotting Method
- Conclusion
- Columns Contain Values, Not Variables
- Columns Contain Multiple Variables
- Variables in Both Rows and Columns
- Conclusion
- Primer on Functions
- Apply (Basics)
- Vectorized Functions
- Lambda Functions (Anonymous Functions)
- Conclusion
- Combine Data Sets
- Concatenation
- Observational Units Across Multiple Tables
- Merge Multiple Data Sets
- Conclusion
- Multiple Observational Units in a Table (Normalization)
- Conclusion
- Aggregate
- Transform
- Filter
- The pandas.core.groupby. DataFrameGroupBy object
- Working With a MultiIndex
- Conclusion
- What Is a NaN Value?
- Where Do Missing Values Come From?
- Working With Missing Data
- Pandas Built-In NA Missing
- Conclusion
- Data Types
- Converting Types
- Categorical Data
- Conclusion
- Introduction
- Strings
- String Methods
- More String Methods
- String Formatting (F-Strings)
- Regular Expressions (RegEx)
- The regex Library
- Conclusion
- Python's datetime Object
- Converting to datetime
- Loading Data That Include Dates
- Extracting Date Components
- Date Calculations and Timedeltas
- Datetime Methods
- Getting Stock Data
- Subsetting Data Based on Dates
- Date Ranges
- Shifting Values
- Resampling
- Time Zones
- Arrow for Better Dates and Times
- Conclusion
- Simple Linear Regression
- Multiple Regression
- Models with Categorical Variables
- One-Hot Encoding in scikit-learn with Transformer Pipelines
- Conclusion
- About This Lesson
- Logistic Regression (Binary Outcome Variable)
- Poisson Regression (Count Outcome Variable)
- More Generalized Linear Models
- Conclusion
- Survival Data
- Kaplan Meier Curves
- Cox Proportional Hazard Model
- Conclusion
- Residuals
- Comparing Multiple Models
- k-Fold Cross-Validation
- Conclusion
- Why Regularize?
- LASSO Regression
- Ridge Regression
- Elastic Net
- Cross-Validation
- Conclusion
- k-Means
- Hierarchical Clustering
- Conclusion
- The (Scientific) Computing Stack
- Performance
- Dask
- Siuba
- Ibis
- Polars
- PyJanitor
- Pandera
- Machine Learning
- Publishing
- Dashboards
- Conclusion
- Local Meetups
- Conferences
- The Carpentries
- Podcasts
- Other Resources
- Conclusion
- B.1 Install Python
- B.2 Install Python Packages
- B.3 Download Book Data
- C.1 Installation
- C.2 Basics
- E.1 Command Line and Text Editor
- E.2 Python and IPython
- E.3 Jupyter
- E.4 Integrated Development Environments (IDEs)
- G.1 Conda Environments
- G.2 Pyenv + Pipenv
- H.1 Updating Packages
- J.1 Line Breaks in Code
- K.1 Lists
- K.2 Tuples
- K.3 Dictionaries
- O.1 Default Parameters
- O.2 Arbitrary Parameters
- T.1 Modifying a Subset of Data
- T.2 Replacing a Value
- T.3 More Resources
- W.1 C-Style
- W.2 String Formatting: .format() Method
- W.3 Formatting Numbers
- Z.1 Linear Regression
- Z.2 Logistic Regression
- Z.3 Poisson Regression
Hands on Activities (Live Labs)
- Performing Grouped and Aggregated Calculations Using the .groupby() Method
- Creating a DataFrame and Making Changes to it
- Creating a Scatter Plot Using Multivariate Data
- Creating a Density Plot Using Bivariate Data
- Using Functions and Methods to Process and Tidy Data
- Performing Calculations Across DataFrames
- Vectorizing Functions
- Performing Concatenation Using the concat() Function
- Merging Multiple Data Sets Using the .merge() Function
- Understanding Multiple Observational Units in a Data Set
- Performing Data Summarization Using Group-by Operations
- Performing Boolean Subsetting on the Data
- Performing Operations on Grouped Objects
- Finding and Cleaning Missing Data
- Performing Data Type Conversion
- Finding and Substituting a Pattern
- Converting an Object Type into a datetime Type
- Extracting Date Components from the Data
- Getting Stock Data and Subsetting it Based on Dates
- Resampling Dates Using the .resample() Method
- Performing Linear Regression
- Performing Multiple Regression
- Performing Logistic Regression
- Performing Poisson Regression Using the poisson() Function
- Performing Survival Analysis Using the KaplanMeierFitter() Function
- Comparing Models Using Cross-Validation
- Performing L1 Regularization Using the Lasso() Function
- Performing L2 Regularization Using the Ridge() Function
- Performing k-Means Clustering
- Using Hierarchical Clustering Algorithms
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