PYTHON-PANDAS.AP1
Pandas for Everyone: Python Data Analysis
Sharing practical insights into solving real-world data science problems using Pandas library and Python programming language.
- Practice in 30 Laboratorios prácticos — nothing to install
- 47 Lecciones interactivas y 146 topics mapped to the official exam objectives
- 170 Preguntas del examen de práctica
Intermediate A tu propio ritmo · 1 año de acceso
30 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
47Lecciones interactivas
146Topics
30Laboratorio en vivo
170Preguntas del examen de práctica
109Tarjetas didácticas
109Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
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No se requiere tarjeta de crédito
This course, Pandas for Everyone: Python Data Analysis, teaches how to tackle real-world data analysis problems using the popular Pandas library. You'll begin with the fundamentals, learning how to load data sets, explore their structure, and create basic visualizations. As you progress, you'll explore data manipulation techniques and be introduced to powerful data cleaning and transformation tools. Finally, the course will briefly introduce you to the broader Python data science ecosystem, touching on tools like scikit-learn for machine learning and visualization libraries like Seaborn.
- Load, explore, and manipulate data using Pandas DataFrames
- Create basic data visualizations in pandas labs
- Combine and clean messy datasets
- Handle missing values and work with different data types
- Perform groupby operations and data normalization
- Apply functions and regular expressions for data transformation
- Conduct statistical modeling using techniques like linear regression and logistic regression
- Gain exposure to the broader Python data science ecosystem
Target Career Roles
- Desarrollador de Python
- Analista de Datos
- Científico de Datos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
47 Lecciones interactivas · 146 topics01 Preface 3 topics +
- Breakdown of the Course
- How to Read This Course
- Setup
02 Pandas DataFrame Basics 6 topics · 1 Laboratorio en vivo +
- Introduction
- Load Your First Data Set
- Look at Columns, Rows, and Cells
- Grouped and Aggregated Calculations
- Basic Plot
- Conclusion
1 Laboratorio en vivo in this lesson — see the labs panel →
03 Pandas Data Structures Basics 6 topics · 1 Laboratorio en vivo +
- Create Your Own Data
- The Series
- The DataFrame
- Making Changes to Series and DataFrames
- Exporting and Importing Data
- Conclusion
1 Laboratorio en vivo in this lesson — see the labs panel →
04 Plotting Basics 6 topics · 2 Laboratorio en vivo +
- Why Visualize Data?
- Matplotlib Basics
- Statistical Graphics Using matplotlib
- Seaborn
- Pandas Plotting Method
- Conclusion
2 Laboratorio en vivo in this lesson — see the labs panel →
05 Tidy Data 4 topics · 1 Laboratorio en vivo +
- Columns Contain Values, Not Variables
- Columns Contain Multiple Variables
- Variables in Both Rows and Columns
- Conclusion
1 Laboratorio en vivo in this lesson — see the labs panel →
06 Apply Functions 5 topics · 2 Laboratorio en vivo +
- Primer on Functions
- Apply (Basics)
- Vectorized Functions
- Lambda Functions (Anonymous Functions)
- Conclusion
2 Laboratorio en vivo in this lesson — see the labs panel →
07 Data Assembly 5 topics · 2 Laboratorio en vivo +
- Combine Data Sets
- Concatenation
- Observational Units Across Multiple Tables
- Merge Multiple Data Sets
- Conclusion
2 Laboratorio en vivo in this lesson — see the labs panel →
08 Data Normalization 2 topics · 1 Laboratorio en vivo +
- Multiple Observational Units in a Table (Normalization)
- Conclusion
1 Laboratorio en vivo in this lesson — see the labs panel →
09 Groupby Operations: Split-Apply-Combine 6 topics · 3 Laboratorio en vivo +
- Aggregate
- Transform
- Filter
- The pandas.core.groupby. DataFrameGroupBy object
- Working With a MultiIndex
- Conclusion
3 Laboratorio en vivo in this lesson — see the labs panel →
10 Missing Data 5 topics · 1 Laboratorio en vivo +
- What Is a NaN Value?
- Where Do Missing Values Come From?
- Working With Missing Data
- Pandas Built-In NA Missing
- Conclusion
1 Laboratorio en vivo in this lesson — see the labs panel →
11 Data Types 4 topics · 1 Laboratorio en vivo +
- Data Types
- Converting Types
- Categorical Data
- Conclusion
1 Laboratorio en vivo in this lesson — see the labs panel →
12 Strings and Text Data 8 topics · 1 Laboratorio en vivo +
- Introduction
- Strings
- String Methods
- More String Methods
- String Formatting (F-Strings)
- Regular Expressions (RegEx)
- The regex Library
- Conclusion
1 Laboratorio en vivo in this lesson — see the labs panel →
13 Dates and Times 14 topics · 4 Laboratorio en vivo +
- 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 Laboratorio en vivo in this lesson — see the labs panel →
14 Linear Regression (Continuous Outcome Variable) 5 topics · 2 Laboratorio en vivo +
- Simple Linear Regression
- Multiple Regression
- Models with Categorical Variables
- One-Hot Encoding in scikit-learn with Transformer Pipelines
- Conclusion
2 Laboratorio en vivo in this lesson — see the labs panel →
15 Generalized Linear Models 5 topics · 2 Laboratorio en vivo +
- About This Lesson
- Logistic Regression (Binary Outcome Variable)
- Poisson Regression (Count Outcome Variable)
- More Generalized Linear Models
- Conclusion
2 Laboratorio en vivo in this lesson — see the labs panel →
16 Survival Analysis 4 topics · 1 Laboratorio en vivo +
- Survival Data
- Kaplan Meier Curves
- Cox Proportional Hazard Model
- Conclusion
1 Laboratorio en vivo in this lesson — see the labs panel →
17 Model Diagnostics 4 topics · 1 Laboratorio en vivo +
- Residuals
- Comparing Multiple Models
- k-Fold Cross-Validation
- Conclusion
1 Laboratorio en vivo in this lesson — see the labs panel →
18 Regularization 6 topics · 2 Laboratorio en vivo +
- Why Regularize?
- LASSO Regression
- Ridge Regression
- Elastic Net
- Cross-Validation
- Conclusion
2 Laboratorio en vivo in this lesson — see the labs panel →
19 Clustering 3 topics · 2 Laboratorio en vivo +
- k-Means
- Hierarchical Clustering
- Conclusion
2 Laboratorio en vivo in this lesson — see the labs panel →
20 Life Outside of Pandas 12 topics +
- The (Scientific) Computing Stack
- Performance
- Dask
- Siuba
- Ibis
- Polars
- PyJanitor
- Pandera
- Machine Learning
- Publishing
- Dashboards
- Conclusion
21 It’s Dangerous To Go Alone! 6 topics +
- Local Meetups
- Conferences
- The Carpentries
- Podcasts
- Other Resources
- Conclusion
22 Appendix A: Concept Maps +
23 Appendix B: Installation and Setup 3 topics +
- B.1 Install Python
- B.2 Install Python Packages
- B.3 Download Book Data
24 Appendix C: Command Line 2 topics +
- C.1 Installation
- C.2 Basics
25 Appendix D: Project Templates +
26 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)
27 Appendix F: Working Directories +
28 Appendix G: Environments 2 topics +
- G.1 Conda Environments
- G.2 Pyenv + Pipenv
29 Appendix H: Install Packages 1 topics +
- H.1 Updating Packages
30 Appendix I: Importing Libraries +
31 Appendix J: Code Style 1 topics +
- J.1 Line Breaks in Code
32 Appendix K: Containers: Lists, Tuples, and Dictionaries 3 topics +
- K.1 Lists
- K.2 Tuples
- K.3 Dictionaries
33 Appendix L: Slice Values +
34 Appendix M: Loops +
35 Appendix N: Comprehensions +
36 Appendix O: Functions 2 topics +
- O.1 Default Parameters
- O.2 Arbitrary Parameters
37 Appendix P: Ranges and Generators +
38 Appendix Q: Multiple Assignment +
39 Appendix R: Numpy ndarray +
40 Appendix S: Classes +
41 Appendix T: SettingWithCopyWarning 3 topics +
- T.1 Modifying a Subset of Data
- T.2 Replacing a Value
- T.3 More Resources
42 Appendix U: Method Chaining +
43 Appendix V: Timing Code +
44 Appendix W: String Formatting 3 topics +
- W.1 C-Style
- W.2 String Formatting: .format() Method
- W.3 Formatting Numbers
45 Appendix X: Conditionals (if-elif-else) +
46 Appendix Y: New York ACS Logistic Regression Example +
47 Appendix Z: Replicating Results in R 3 topics +
- Z.1 Linear Regression
- Z.2 Logistic Regression
- Z.3 Poisson Regression
Laboratorios prácticos Our edge
30 Laboratorio en vivos- 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
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
What are Pandas in Python? +
Pandas in Python are a powerful open-source library for data analysis. It offers data structures like DataFrames and tools to manipulate, clean, and visualize that data.
Is Python good for data analysis?+
Yes, Python is excellent for data analysis. It's easy to learn, has versatile libraries (like Pandas), and a large, supportive community. Python's flexibility makes it useful for various data science tasks.
Do you need any prior programming experience to take this course? +
While some basic programming experience can be helpful, this course is designed to be accessible for beginners. We'll start with the fundamentals of Python and Pandas, gradually building your skills throughout the course.
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- 1 año de acceso completo
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No se requiere tarjeta de crédito