Data Analysis with Pandas and Python [2026]
Analyze data quickly and easily with Python's powerful pandas library! All datasets included --- beginners welcome!
Lo que serás capaz de hacer
** Newly recorded in 2026 for the release of Pandas 3 ** Student Testimonials: The instructor knows the material, and has detailed explanation on every topic he discusses. Has clarity too, and warns students of potential pitfalls. He has a very logical explanation, and it is easy to follow him. I highly recommend this class, and would look into taking a new class from him. - Diana This is excellent, and I cannot complement the instructor enough. Extremely clear, relevant, and high quality - with helpful practical tips and advice. Would recommend this to anyone wanting to learn pandas. Lessons are well constructed. I'm actually surprised at how well done this is. I don't give many 5 stars, but this has earned it so far. - Michael This course is very thorough, clear, and well thought out. This is the best Udemy course I have taken thus far. (This is my third course.) The instruction is excellent! - James Welcome to the most comprehensive Pandas course available on Udemy! An excellent choice for both beginners and experts looking to expand their knowledge on one of the most popular Python libraries in the world! This course has been re-recorded from scratch in 2026 for the release of Pandas 3. Data Analysis with Pandas and Python offers 19+ hours of in-depth video tutorials on the most powerful data analysis toolkit available today. Lessons include: installing sorting filtering grouping aggregating de-duplicating pivoting munging deleting merging visualizing and more! Why learn pandas? If you've spent time in a spreadsheet software like Microsoft Excel, Apple Numbers, or Google Sheets and are eager to take your data analysis skills to the next level, this course is for you! Data Analysis with Pandas and Python introduces you to the popular Pandas library built on top of the Python programming language. Pandas is a powerhouse tool that allows you to do anything and everything with colossal data sets -- analyzing, organizing, sorting, filtering, pivoting, aggregating, munging, cleaning, calculating, and more! I call it "Excel on steroids"! Over the course of more than 19 hours, I'll take you step-by-step through Pandas, from installation to visualization! We'll cover hundreds of different methods, attributes, features, and functionalities packed away inside this awesome library. We'll dive into tons of different datasets, short and long, broken and pristine, to demonstrate the incredible versatility and efficiency of this package. Data Analysis with Pandas and Python is bundled with dozens of datasets for you to use. Dive right in and follow along with my lessons to see how easy it is to get started with pandas! Whether you're a new data analyst or have spent years (*cough* too long *cough*) in Excel, Data Analysis with pandas and Python offers you an incredible introduction to one of the most powerful data toolkits available today!
- Perform a multitude of data operations in Python's popular pandas library including grouping, pivoting, joining and more!
- Learn hundreds of methods and attributes across numerous pandas objects
- Possess a strong understanding of manipulating 1D, 2D, and 3D data sets
- Resolve common issues in broken or incomplete data sets
Temario
UNIDAD 1 - Installation and Setup · 12 leccionesUNIDAD 1 - Installation and Setup
UNIDAD 1 - Installation and Setup
- Introduction to the Course
- [macOS] Intro to the Terminal
- [macOS] Install uv, a Python package and project manager
- [macOS] Download Course Materials and Setup Project
- [Windows] Intro to PowerShell
- [Windows] Install uv, a Python package and project manager
- [Windows] Download Course Materials and Setup Project
- Jupyter Lab Startup and Shutdown
- Intro to Jupyter Lab
- Setting Up Ruff Formatter in Jupyter Lab
- Import Libraries into Jupyter Lab
- Installation and Setup
UNIDAD 2 - Python Crash Course · 23 leccionesUNIDAD 2 - Python Crash Course
UNIDAD 2 - Python Crash Course
- Comments
- Data Types
- Operators
- Equality and Inequality Operators
- Variables
- Declare Variables
- Built-In Functions
- Built-in Functions
- Custom Functions
- Custom Functions
- String Methods
- String Methods
- Lists
- Creating Lists
- Index Positions and Slicing
- Index Positions and Slicing
- Tuples
- Dictionaries
- Creating Dictionaries
- Classes and Objects
- Importing Modules
- Importing Libraries
- Python Crash Course
UNIDAD 3 - Series · 34 leccionesUNIDAD 3 - Series
UNIDAD 3 - Series
- Create A Series Object from a List
- Create A Series Object from a Dictionary
- Create a Series Object
- Intro to Series Methods
- Intro to Series Attributes
- Attributes and Methods on a Series
- Parameters and Arguments
- Parameters and Arguments
- Import Series with the pd.read_csv Function
- Import Series with the read_csv Function
- The head and tail Methods
- The head and tail Methods
- Passing Series to Python Built-In Functions
- Check for Inclusion with Python's in Keyword
- The sort_values Method
- The sort_values Method
- The sort_index Method
- The sort_index Method
- Check for Inclusion with Python's in Keyword
- Extract Series Values by Index Position
- Extract Series Values by Index Label
- Extract Series Values by Index Position or Index Label
- The get Method
- Overwrite a Series Value
- Copy-on-Write and the copy Method
- Math Methods on Series
- Broadcasting
- Math Methods on Series
- Pandas Aligns by Index Labels
- The value_counts method
- The value_counts Method
- The apply Method
- The map Method
- Series
UNIDAD 4 - DataFrames I: Introduction · 26 leccionesUNIDAD 4 - DataFrames I: Introduction
UNIDAD 4 - DataFrames I: Introduction
- Methods and Attributes between Series and DataFrames
- Differences between Shared Methods
- Select One Column from a DataFrame
- Select One Column from a DataFrame
- Select Multiple Columns from a DataFrame
- Select Multiple Columns from a DataFrame
- Add New Column to DataFrame
- Create Columns with the assign Method
- Add Column to DataFrame
- Drop Rows with Missing Values
- Drop DataFrame Rows with Missing Values
- Fill in Missing Values with the fillna Method
- Fill Missing Values with a Forward Fill or Back Fill
- Convert Data Types with the astype Method
- Convert to Numbers with the to_numeric Function
- Select Columns by Type with the select_dtypes Method
- Convert Data Types with the astype Method II: Categories
- Attribute Namespaces
- The astype Method
- Sort a DataFrame with the sort_values Method I
- Sort a DataFrame with the sort_values Method II: Multiple Columns
- Sort with Ordered Categories
- Sort a DataFrame with the sort_values Method
- Sort a DataFrame by its Index
- Rank Values with the rank Method
- DataFrames I
UNIDAD 5 - DataFrames II: Filtering Data · 18 leccionesUNIDAD 5 - DataFrames II: Filtering Data
UNIDAD 5 - DataFrames II: Filtering Data
- This Module's Dataset + Memory Optimization
- Filter a DataFrame Based on a Condition
- Filter a DataFrame Based on a Condition
- Filter with More than One Condition (AND - &)
- Filter DataFrame with More than One Condition (AND - &)
- Filter with More than One Condition (OR - |)
- Filter DataFrame with More than One Condition (OR - |)
- The isin Method
- The isin Method
- The isnull and notnull Methods
- The between Method
- The between Method
- The duplicated Method
- The drop_duplicates Method
- The drop_duplicates Method
- The where Method
- The query Method
- DataFrames II
UNIDAD 6 - DataFrames III: Data Extraction · 11 leccionesUNIDAD 6 - DataFrames III: Data Extraction
UNIDAD 6 - DataFrames III: Data Extraction
- This Module's Dataset
- The set_index and reset_index Methods
- Retrieve Rows by Index Position and Index Label
- Second Arguments to loc and iloc Accessors
- Overwrite Value in a DataFrame
- Rename Index Labels or Columns in a DataFrame
- Delete Rows or Columns from a DataFrame
- Create Random Sample with the sample Method
- The nlargest and nsmallest Methods
- The clip Method
- The apply Method with DataFrames
UNIDAD 7 - Working with Text Data · 8 leccionesUNIDAD 7 - Working with Text Data
UNIDAD 7 - Working with Text Data
- This Module's Dataset
- Common String Methods
- Common String Methods
- Filtering with String Methods
- String Methods on Index and Columns
- The split Method
- The expand and n Parameters of the split Method
- The explode Method
UNIDAD 8 - MultiIndex · 15 leccionesUNIDAD 8 - MultiIndex
UNIDAD 8 - MultiIndex
- Intro to the MultiIndex Module
- Create a MultiIndex
- Extract and Rename Index Level Values
- Extract Index Level Values
- Extract Rows from a MultiIndex DataFrame
- Extract Rows from a MultiIndex DataFrame
- The xs Method
- The swaplevel Method
- The transpose Method
- The stack Method
- The unstack Method
- The pivot Method
- The melt Method
- The melt Method
- The pivot_table Method
UNIDAD 9 - GroupBy · 6 leccionesUNIDAD 9 - GroupBy
UNIDAD 9 - GroupBy
- The GroupBy Object
- Retrieve Groups and Rows
- Methods on the GroupBy Object
- The agg Method
- Grouping by Multiple Columns
- Iterating over Groups
UNIDAD 10 - Merging DataFrames · 10 leccionesUNIDAD 10 - Merging DataFrames
UNIDAD 10 - Merging DataFrames
- Intro to the Merging DataFrames Module
- The pd.concat Function I
- The pd.concat Function II
- Left Joins
- The left_on and right_on Parameters
- Inner Joins
- Matching across Multiple Columns
- Full/Outer Joins
- Merging by Indexes with the left_index and right_index Parameters
- The join Method
UNIDAD 11 - Working with Dates and Times · 8 leccionesUNIDAD 11 - Working with Dates and Times
UNIDAD 11 - Working with Dates and Times
- Intro to the Working with Dates and Times Module and Review of Python's datetime
- The dt Attribute
- The Timestamp Type
- Extracting Datetime Rows from a DataFrame
- The DateOffset Object
- The date_range Function
- Specialized Date Offsets
- Timedeltas
UNIDAD 12 - Input and Output · 5 leccionesUNIDAD 12 - Input and Output
UNIDAD 12 - Input and Output
- Intro to the Input and Output Module
- Export DataFrame to CSV File
- Export DataFrame to CSV File
- Import Excel Workbook into pandas
- Export Excel Workbook from pandas
UNIDAD 13 - Visualization · 9 leccionesUNIDAD 13 - Visualization
UNIDAD 13 - Visualization
- The plot Method
- Modifying Plot Aesthetics with Templates
- Bar Charts
- Stacked Bar Charts
- Pie Charts
- Area Charts
- Histograms
- Scatter Plots
- Customizing Plots with Parameters
UNIDAD 14 - Table Styling · 5 leccionesUNIDAD 14 - Table Styling
UNIDAD 14 - Table Styling
- The Styler Object
- Hide Index or Column Labels
- Highlighting Values
- Using Color Gradients
- In-Cell Bar Charts
UNIDAD 15 - Options and Settings · 4 leccionesUNIDAD 15 - Options and Settings
UNIDAD 15 - Options and Settings
- Changing Options with Attributes
- Changing Options with Functions
- The precision Option
- Temporary Options with option_context
UNIDAD 16 - Conclusion · 2 leccionesUNIDAD 16 - Conclusion
UNIDAD 16 - Conclusion
- Congratulations!
- Bonus
Tu certificado lo emite la SEP, no solo una plataforma
Al demostrar la competencia recibes un certificado emitido por la Secretaría de Educación Pública a través del Gobierno del Estado de México: un documento oficial que puedes presentar con confianza ante cualquier empleador o institución.
Tu certificado con INEC
- Lo emite la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México
- Documento oficial con folio verificable en línea
- Lo otorga la Universidad Tecnológica Fidel Velázquez
Otras plataformas en línea
- Constancia privada emitida por la misma plataforma
- Solo acredita horas cursadas o asistencia
- Sin respaldo de una autoridad educativa
Tu itinerario, de la inscripción al certificado
- 1
Inscripción con pago único
Pagas $2,800 MXN una sola vez y entras de inmediato al curso.
- 2
Estudias a tu ritmo
16 unidades con videos, lecturas y actividades, disponibles 24/7 durante 12 meses.
- 3
Cuestionarios por unidad
5 cuestionarios para comprobar lo aprendido.
- 4
Certificado
Emitido por la SEP a través del Gobierno del Estado de México, con folio verificable.
Un certificado oficial de la SEP
![Muestra del certificado: Data Analysis with Pandas and Python [2026]](https://www.institutoimec.edu.mx/wp-content/uploads/inec-landing/certificado-muestra.jpg)
Muestra · verificable por folio y QR
- Emitido por la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México. No es una constancia privada: es un documento oficial, y ese respaldo marca la diferencia frente a otras plataformas en línea.
- Contenido impartido por Boris Paskhaver.
- Folio y QR verificables para compartirlo con quien te contrate.
Empieza hoy, a tu ritmo
Un solo pago y acceso inmediato al curso completo durante 12 meses.
- 16 unidades · 196 lecciones · 60 h
- 12 meses de acceso
- 5 cuestionarios de evaluación
- Certificado emitido por la SEP (Gob. del Estado de México) con folio verificable
Sobre tu inscripción
¿Cómo es el pago?
Un solo pago de $2,800 MXN. No hay mensualidades ni cargos recurrentes.
¿Cuánto tiempo tengo acceso?
12 meses desde tu inscripción, 24/7, para avanzar a tu ritmo.
¿Quién imparte el contenido?
El curso es impartido por Boris Paskhaver; INEC gestiona tu inscripción, evaluación y certificado.
¿Qué pasa cuando vence mi acceso?
Puedes renovar tu cuenta tú mismo desde la plataforma, sin trámites ni esperas.
