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PYTHON-PANDAS.AP1.0L0
Pandas for Everyone: Python Data Analysis Labs
Self-paced · 1 year access
30 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
01 / Lessons & labs
See exactly what you will learn and practice
Lessons
46 Interactive Lessons · 143 topics01 Pandas DataFrame Basics 6 topics · 1 LiveLab +
- Introduction
- Load Your First Data Set
- Look at Columns, Rows, and Cells
- Grouped and Aggregated Calculations
- Basic Plot
- Conclusion
1 LiveLab in this lesson — see the labs panel →
02 Pandas Data Structures Basics 6 topics · 1 LiveLab +
- Create Your Own Data
- The Series
- The DataFrame
- Making Changes to Series and DataFrames
- Exporting and Importing Data
- Conclusion
1 LiveLab in this lesson — see the labs panel →
03 Plotting Basics 6 topics · 2 LiveLab +
- Why Visualize Data?
- Matplotlib Basics
- Statistical Graphics Using matplotlib
- Seaborn
- Pandas Plotting Method
- Conclusion
2 LiveLab in this lesson — see the labs panel →
04 Tidy Data 4 topics · 1 LiveLab +
- Columns Contain Values, Not Variables
- Columns Contain Multiple Variables
- Variables in Both Rows and Columns
- Conclusion
1 LiveLab in this lesson — see the labs panel →
05 Apply Functions 5 topics · 2 LiveLab +
- Primer on Functions
- Apply (Basics)
- Vectorized Functions
- Lambda Functions (Anonymous Functions)
- Conclusion
2 LiveLab in this lesson — see the labs panel →
06 Data Assembly 5 topics · 2 LiveLab +
- Combine Data Sets
- Concatenation
- Observational Units Across Multiple Tables
- Merge Multiple Data Sets
- Conclusion
2 LiveLab in this lesson — see the labs panel →
07 Data Normalization 2 topics · 1 LiveLab +
- Multiple Observational Units in a Table (Normalization)
- Conclusion
1 LiveLab in this lesson — see the labs panel →
08 Groupby Operations: Split-Apply-Combine 6 topics · 3 LiveLab +
- Aggregate
- Transform
- Filter
- The pandas.core.groupby. DataFrameGroupBy object
- Working With a MultiIndex
- Conclusion
3 LiveLab in this lesson — see the labs panel →
09 Missing Data 5 topics · 1 LiveLab +
- What Is a NaN Value?
- Where Do Missing Values Come From?
- Working With Missing Data
- Pandas Built-In NA Missing
- Conclusion
1 LiveLab in this lesson — see the labs panel →
10 Data Types 4 topics · 1 LiveLab +
- Data Types
- Converting Types
- Categorical Data
- Conclusion
1 LiveLab in this lesson — see the labs panel →
11 Strings and Text Data 8 topics · 1 LiveLab +
- Introduction
- Strings
- String Methods
- More String Methods
- String Formatting (F-Strings)
- Regular Expressions (RegEx)
- The regex Library
- Conclusion
1 LiveLab in this lesson — see the labs panel →
12 Dates and Times 14 topics · 4 LiveLab +
- 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
4 LiveLab in this lesson — see the labs panel →
13 Linear Regression (Continuous Outcome Variable) 5 topics · 2 LiveLab +
- Simple Linear Regression
- Multiple Regression
- Models with Categorical Variables
- One-Hot Encoding in scikit-learn with Transformer Pipelines
- Conclusion
2 LiveLab in this lesson — see the labs panel →
14 Generalized Linear Models 5 topics · 2 LiveLab +
- About This Lesson
- Logistic Regression (Binary Outcome Variable)
- Poisson Regression (Count Outcome Variable)
- More Generalized Linear Models
- Conclusion
2 LiveLab in this lesson — see the labs panel →
15 Survival Analysis 4 topics · 1 LiveLab +
- Survival Data
- Kaplan Meier Curves
- Cox Proportional Hazard Model
- Conclusion
1 LiveLab in this lesson — see the labs panel →
16 Model Diagnostics 4 topics · 1 LiveLab +
- Residuals
- Comparing Multiple Models
- k-Fold Cross-Validation
- Conclusion
1 LiveLab in this lesson — see the labs panel →
17 Regularization 6 topics · 2 LiveLab +
- Why Regularize?
- LASSO Regression
- Ridge Regression
- Elastic Net
- Cross-Validation
- Conclusion
2 LiveLab in this lesson — see the labs panel →
18 Clustering 3 topics · 2 LiveLab +
- k-Means
- Hierarchical Clustering
- Conclusion
2 LiveLab in this lesson — see the labs panel →
19 Life Outside of Pandas 12 topics +
- The (Scientific) Computing Stack
- Performance
- Dask
- Siuba
- Ibis
- Polars
- PyJanitor
- Pandera
- Machine Learning
- Publishing
- Dashboards
- Conclusion
20 It’s Dangerous To Go Alone! 6 topics +
- Local Meetups
- Conferences
- The Carpentries
- Podcasts
- Other Resources
- Conclusion
21 Appendix A: Concept Maps +
22 Appendix B: Installation and Setup 3 topics +
- B.1 Install Python
- B.2 Install Python Packages
- B.3 Download Book Data
23 Appendix C: Command Line 2 topics +
- C.1 Installation
- C.2 Basics
24 Appendix D: Project Templates +
25 Appendix E: Using Python 4 topics +
- E.1 Command Line and Text Editor
- E.2 Python and IPython
- E.3 Jupyter
- E.4 Integrated Development Environments (IDEs)
26 Appendix F: Working Directories +
27 Appendix G: Environments 2 topics +
- G.1 Conda Environments
- G.2 Pyenv + Pipenv
28 Appendix H: Install Packages 1 topics +
- H.1 Updating Packages
29 Appendix I: Importing Libraries +
30 Appendix J: Code Style 1 topics +
- J.1 Line Breaks in Code
31 Appendix K: Containers: Lists, Tuples, and Dictionaries 3 topics +
- K.1 Lists
- K.2 Tuples
- K.3 Dictionaries
32 Appendix L: Slice Values +
33 Appendix M: Loops +
34 Appendix N: Comprehensions +
35 Appendix O: Functions 2 topics +
- O.1 Default Parameters
- O.2 Arbitrary Parameters
36 Appendix P: Ranges and Generators +
37 Appendix Q: Multiple Assignment +
38 Appendix R: Numpy ndarray +
39 Appendix S: Classes +
40 Appendix T: SettingWithCopyWarning 3 topics +
- T.1 Modifying a Subset of Data
- T.2 Replacing a Value
- T.3 More Resources
41 Appendix U: Method Chaining +
42 Appendix V: Timing Code +
43 Appendix W: String Formatting 3 topics +
- W.1 C-Style
- W.2 String Formatting: .format() Method
- W.3 Formatting Numbers
44 Appendix X: Conditionals (if-elif-else) +
45 Appendix Y: New York ACS Logistic Regression Example +
46 Appendix Z: Replicating Results in R 3 topics +
- Z.1 Linear Regression
- Z.2 Logistic Regression
- Z.3 Poisson Regression
Hands-On Labs Our edge
30 LiveLabs- 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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