#Python Pandas Tutorial

[tutorialwithexample]

Unlock the Power of Pandas: Easy-to-Follow Python Tutorial for Newbies

Python Pandas is a powerful tool for working with data, making it a must-learn library for anyone starting in data analysis. With Pandas, you can effortlessly clean, organize, and analyze data to extract meaningful insights. This tutorial is perfect for beginners looking to get started with Pandas.

Pandas is a Python library designed specifically for data manipulation and analysis. It offers two main data structures: Series and DataFrame. A Series is like a single column of data, while a DataFrame is a table-like structure that holds rows and columns, similar to a spreadsheet.

Why use Pandas? First, it simplifies handling large datasets by providing easy-to-use functions for filtering, sorting, and grouping data. Second, it works seamlessly with other popular Python libraries, such as NumPy and Matplotlib, making it a versatile tool for data projects.

Getting started with Pandas is simple. After installing the library, you can load datasets from various sources like CSV files, Excel sheets, or even databases. Once loaded, Pandas lets you perform tasks like renaming columns, replacing missing values, or summarizing data in just a few lines of code.

If you're looking to dive deeper into how Pandas can make your data analysis journey smoother, explore this beginner-friendly guide: Python Pandas Tutorial. Start your journey today, and unlock the potential of data analysis with Python Pandas!

Whether you're a student or a professional, mastering Pandas will open doors to numerous opportunities in the world of data science.

[jtpoint]

Discover the Python Pandas Tutorial for Beginners and learn how to easily manage and analyze data. This beginner-friendly guide covers all the basics. For a detailed tutorial, visit TAE.

[ibarrau]

[Fabric] Leer PowerBi data con Notebooks - Semantic Link

El nombre del artículo puede sonar extraño puesto que va en contra del flujo de datos que muchos arquitectos pueden pensar para el desarrollo de soluciones. Sin embargo, las puertas a nuevos modos de conectividad entre herramientas y conjuntos de datos pueden ayudarnos a encontrar nuevos modos que fortalezcan los análisis de datos.

En este post vamos a mostrar dos sencillos modos que tenemos para leer datos de un Power Bi Semantic Model desde un Fabric Notebook con Python y SQL.

¿Qué son los Semantic Links? (vínculo semántico)

Como nos gusta hacer aquí en LaDataWeb, comencemos con un poco de teoría de la fuente directa.

Definición Microsoft: Vínculo semántico es una característica que permite establecer una conexión entre modelos semánticos y Ciencia de datos de Synapse en Microsoft Fabric. El uso del vínculo semántico solo se admite en Microsoft Fabric.

Dicho en criollo, nos facilita la conectividad de datos para simplificar el acceso a información. Si bién Microsoft lo enfoca como una herramienta para Científicos de datos, no veo porque no puede ser usada por cualquier perfil que tenga en mente la resolución de un problema leyendo datos limpios de un modelo semántico.

El límite será nuestra creatividad para resolver problemas que se nos presenten para responder o construir entorno a la lectura de estos modelos con notebooks que podrían luego volver a almacenarse en Onelake con un nuevo procesamiento enfocado en la solución.

Semantic Links ofrecen conectividad de datos con el ecosistema de Pandas de Python a través de la biblioteca de Python SemPy. SemPy proporciona funcionalidades que incluyen la recuperación de datos de tablas , cálculo de medidas y ejecución de consultas DAX y metadatos.

Para usar la librería primero necesitamos instalarla:

%pip install semantic-link

Lo primero que podríamos hacer es ver los modelos disponibles:

import sempy.fabric as fabric df_datasets = fabric.list_datasets()

Entrando en más detalle, también podemos listar las tablas de un modelo:

df_tables = fabric.list_tables("Nombre Modelo Semantico", include_columns=True)

Cuando ya estemos seguros de lo que necesitamos, podemos leer una tabla puntual:

df_table = fabric.read_table("Nombre Modelo Semantico", "Nombre Tabla")

Esto genera un FabricDataFrame con el cual podemos trabajar libremente.

Nota: FabricDataFrame es la estructura de datos principal de vínculo semántico. Realiza subclases de DataFrame de Pandas y agrega metadatos, como información semántica y linaje

Existen varias funciones que podemos investigar usando la librería. Una de las favoritas es la que nos permite entender las relaciones entre tablas. Podemos obtenerlas y luego usar otro apartado de la librería para plotearlo:

from sempy.relationships import plot_relationship_metadata relationships = fabric.list_relationships("Nombre Modelo Semantico") plot_relationship_metadata(relationships)

Un ejemplo de la respuesta:

Conector Nativo Semantic Link Spark

Adicional a la librería de Python para trabajar con Pandas, la característica nos trae un conector nativo para usar con Spark. El mismo permite a los usuarios de Spark acceder a las tablas y medidas de Power BI. El conector es independiente del lenguaje y admite PySpark, Spark SQL, R y Scala. Veamos lo simple que es usarlo:

spark.conf.set("spark.sql.catalog.pbi", "com.microsoft.azure.synapse.ml.powerbi.PowerBICatalog")

Basta con especificar esa línea para pronto nutrirnos de clásico SQL. Listamos tablas de un modelo:

%%sql SHOW TABLES FROM pbi.`Nombre Modelo Semantico`

Consulta a una tabla puntual:

%%sql SELECT * FROM pbi.`Nombre Modelo Semantico`.NombreTabla

Así de simple podemos ejecutar SparkSQL para consultar el modelo. En este caso es importante la participación del caracter " ` " comilla invertida que nos ayuda a leer espacios y otros caracteres.

Exploración con DAX

Como un tercer modo de lectura de datos incorporaron la lectura basada en DAX. Esta puede ayudarnos de distintas maneras, por ejemplo guardando en nuestro FabricDataFrame el resultado de una consulta:

df_dax = fabric.evaluate_dax( "Nombre Modelo Semantico", """ EVALUATE SUMMARIZECOLUMNS( 'State'[Region], 'Calendar'[Year], 'Calendar'[Month], "Total Revenue" , CALCULATE([Total Revenue] ) ) """ )

Otra manera es utilizando DAX puramente para consultar al igual que lo haríamos con SQL. Para ello, Fabric incorporó una nueva y poderosa etiqueta que lo facilita. Delimitación de celdas tipo "%%dax":

%%dax "Nombre Modelo Semantico" -w "Area de Trabajo" EVALUATE SUMMARIZECOLUMNS( 'State'[Region], 'Calendar'[Year], 'Calendar'[Month], "Total Revenue" , CALCULATE([Total Revenue] ) )

Hasta aquí llegamos con esos tres modos de leer datos de un Power Bi Semantic Model utilizando Fabric Notebooks. Espero que esto les revuelva la cabeza para redescubrir soluciones a problemas con un nuevo enfoque.

[unculturedai]

Learn the art of web scraping with Python! This beginner-friendly guide covers the basics, ethics, legal considerations, and a step-by-step tutorial with code examples. Uncover valuable data and become a digital explorer.

[codewithnazam]

Cleaning Dirty Data in Python: Practical Techniques with Pandas

I. Introduction Hey there! So, let’s talk about a really important step in data analysis: data cleaning. It’s basically like tidying up your room before a big party – you want everything to be neat and organized so you can find what you need, right? Now, when it comes to sorting through a bunch of messy data, you’ll be glad to have a tool like Pandas by your side. It’s like the superhero of…

[pandeypankaj]

How do I learn Python in depth?

Improving Your Python Skills

  Writing Python Programs Basics: Practice the basics solidly. 

  Syntax and Semantics: Make sure you are very strong in variables, data types, control flow, functions, and object-oriented programming. 

 Data Structures: Be able to work with lists, tuples, dictionaries, and sets, and know when to use which. 

 Modules and Packages: Study how to import and use built-in and third-party modules. 

Advanced Concepts

Generators and Iterators: Know how to develop efficient iterators and generators for memory-efficient code. 

Decorators: Learn how to dynamically alter functions using decorators. 

Metaclasses: Understand how classes are created and can be customized. 

Context Managers: Understand how contexts work with statements. 

Project Practice 

 Personal Projects: You will work on projects that you want to, whether building a web application, data analysis tool, or a game.

 Contributing to Open Source: Contribute to open-source projects in order to learn from senior developers. Get exposed to real-life code. 

 Online Challenges: Take part in coding challenges on HackerRank, LeetCode, or Project Euler. 

 Learn Various Libraries and Frameworks

 Scientific Computing: NumPy, SciPy, Pandas

 Data Visualization: Matplotlib, Seaborn

 Machine Learning: Scikit-learn, TensorFlow, PyTorch

 Web Development: Django, Flask

Data Analysis: Dask, Airflow

Read Pythonic Code

 Open Source Projects: Study the source code of a few popular Python projects. Go through their best practices and idiomatic Python. 

 Books and Tutorials: Read all the code examples in books and tutorials on Python. 

 Conferences and Workshops

  Attend conferences and workshops that will help you further your skills in Python. PyCon is an annual Python conference that includes talks, workshops, and even networking opportunities. Local meetups will let you connect with other Python developers in your area. 

Learn Continuously

 Follow Blogs and Podcasts: Keep reading blogs and listening to podcasts that will keep you updated with the latest trends and developments taking place within the Python community.

Online Courses: Advanced understanding in Python can be acquired by taking online courses on the subject.

 Try It Yourself: Trying new techniques and libraries expands one's knowledge.

Other Recommendations

 Readable-Clean Code: For code writing, it's essential to follow the style guide in Python, PEP 

Naming your variables and functions as close to their utilization as possible is also recommended.

 Test Your Code: Unit tests will help in establishing the correctness of your code.

 Coding with Others: Doing pair programming and code reviews would provide you with experience from other coders.

 You are not Afraid to Ask for Help: Never hesitate to ask for help when things are beyond your hand-on areas, be it online communities or mentors.

These steps, along with consistent practice, will help you become proficient in Python development and open a wide range of possibilities in your career.

[tutorialwithexample]

Mastering Data Analysis with Python Pandas: A Comprehensive Tutorial

Python Pandas is a popular library that is widely used for data manipulation, analysis, and visualization. With its powerful data structures and functions, Pandas makes it easy for developers to perform complex data operations with ease. In this Python Pandas tutorial, we will explore the basics of Python Pandas and learn how to use it for data analysis.

First, we will cover the fundamentals of Pandas data structures such as Series and DataFrames. We will also discuss how to create, manipulate, and merge these data structures. Then, we will move on to data analysis techniques such as filtering, sorting, grouping, and aggregation. We will also explore how to handle missing data and perform statistical computations.

Furthermore, we will demonstrate how to read and write data to various file formats, including CSV, Excel, and SQL databases. Finally, we will cover advanced topics such as time-series analysis, visualization, and machine learning using Pandas.

By the end of this tutorial, you will have a solid understanding of Python Pandas and be able to apply its powerful functionalities to your data analysis projects.

[jtpoint]

Python Pandas is a powerful library for data analysis, manipulation, and visualization in Python. Whether you're a beginner or an experienced programmer, this comprehensive tutorial covers all aspects of using Pandas for data analysis. From importing data to manipulating and visualizing it, you will learn how to efficiently work with data using Pandas data structures and popular Python libraries such as Matplotlib and Seaborn. Real-world examples and practical exercises will help you apply your skills to real data analysis scenarios.

[ibarrau]

[Fabric] Dataflows Gen2 destino “archivos” - Opción 2

Continuamos con la problematica de una estructura lakehouse del estilo “medallón” (bronze, silver, gold) con Fabric, en la cual, la herramienta de integración de datos de mayor conectividad, Dataflow gen2, no permite la inserción en este apartado de nuestro sistema de archivos, sino que su destino es un spark catalog. ¿Cómo podemos utilizar la herramienta para armar un flujo limpio que tenga nuestros datos crudos en bronze?

Veamos una opción más pythonesca donde podamos realizar la integración de datos mediante dos contenidos de Fabric

Como repaso de la problemática, veamos un poco la comparativa de las características de las herramientas de integración de Data Factory dentro de Fabric (Feb 2024)

Si nuestro origen solo puede ser leído con Dataflows Gen2 y queremos iniciar nuestro proceso de datos en Raw o Bronze de Archivos de un Lakehouse, no podríamos dado el impedimento de delimitar el destino en la herramienta.

Para solucionarlo planteamos un punto medio de stage y un shortcut en un post anterior. Pueden leerlo para tener más cercanía y contexto con esa alternativa.

Ahora vamos a verlo de otro modo. El planteo bajo el cual llegamos a esta solución fue conociendo en más profundidad la herramienta. Conociendo que Dataflows Gen2 tiene la característica de generar por si mismo un StagingLakehouse, ¿por qué no usarlo?. Si no sabes de que hablo, podes leer todo sobre staging de lakehouse en este post.

Ejemplo práctico. Cree dos dataflows que lean datos con "Enable Staging" activado pero sin destino. Un dataflow tiene dos tablas (InternetSales y Producto) y otro tiene una tabla (Product). De esa forma pensaba aprovechar este stage automático sin necesidad de crear uno. Sin embargo, al conectarme me encontre con lo siguiente:

Dataflow gen2 por defecto genera snapshots de cada actualización. Los dataflows corrieron dos veces entonces hay 6 tablas. Por si fuera aún más dificil, ocurre que las tablas no tienen metadata. Sus columnas están expresadas como "column1, column2, column3,...". Si prestamos atención en "Files" tenemos dos models. Cada uno de ellos son jsons con toda la información de cada dataflow.

Muy buena información pero de shortcut difícilmente podríamos solucionarlo. Sin perder la curiosidad hablo con un Data Engineer para preguntarle más en detalle sobre la información que podemos encontrar de Tablas Delta, puesto que Fabric almacena Delta por defecto en "Tables". Ahi me compartió que podemos ver la última fecha de modificación con lo que podríamos conocer cual de esos snapshots es el más reciente para moverlo a Bronze o Raw con un Notebook. El desafío estaba. Leer la tabla delta más reciente, leer su metadata en los json de files y armar un spark dataframe para llevarlo a Bronze de nuestro lakehouse. Algo así:

Si apreciamos las cajas con fondo gris, podremos ver el proceso. Primero tomar los datos con Dataflow Gen2 sin configurar destino asegurando tener "Enable Staging" activado. De esa forma llevamos los datos al punto intermedio. Luego construir un Notebook para leerlo, en mi caso el código está preparado para construir un Bronze de todas las tablas de un dataflow, es decir que sería un Notebook por cada Dataflow.

¿Qué encontraremos en el notebook?

Para no ir celda tras celda pegando imágenes, puede abrirlo de mi GitHub y seguir los pasos con el siguiente texto.

Trás importar las librerías haremos los siguientes pasos para conseguir nuestro objetivo.

1- Delimitar parámetros de Onelake origen y Onelake destino. Definir Dataflow a procesar.

Podemos tomar la dirección de los lake viendo las propiedades de carpetas cuando lo exploramos:

La dirección del dataflow esta delimitado en los archivos jsons dentro de la sección Files del StagingLakehouse. El parámetro sería más o menos así:

Files/models$50a92467_002D7193_002D4445_002D8ac5_002D00143959ff98/*.json

2- Armar una lista con nombre de los snapshots de tablas en Tables

3- Construimos una nueva lista con cada Tabla y su última fecha de modificación para conocer cual de los snapshots es el más reciente.

4- Creamos un pandas dataframe que tenga nombre de la tabla delta, el nombre semántico apropiado y la fecha de modificación

5- Buscamos la metadata (nombre de columnas) de cada Tabla puesto que, tal como mencioné antes, en sus logs delta no se encuentran.

6- Recorremos los nombre apropiados de tabla buscando su más reciente fecha para extraer el apropiado nombre del StagingLakehouse con su apropiada metadata y lo escribimos en destino.

Para más detalle cada línea de código esta documentada.

De esta forma llegaríamos a construir la arquitectura planteada arriba. Logramos así construir una integración de datos que nos permita conectarnos a orígenes SAP, Oracle, Teradata u otro onpremise que son clásicos y hoy Pipelines no puede, para continuar el flujo de llevarlos a Bronze/Raw de nuestra arquitectura medallón en un solo tramo. Dejamos así una arquitectura y paso del dato más limpio.

Por supuesto, esta solución tiene mucho potencial de mejora como por ejemplo no tener un notebook por dataflow, sino integrar de algún modo aún más la solución.

[mind-benderx]

📊 Welcome to Mind Benderx! In this video, we're on a mission to transform you into a complete data analyst. 🚀 Join us as we explore five phenomenal YouTube channels that will guide you through the intricate world of data analysis.

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Alex the Analyst: Dive into practical data analysis with Alex's comprehensive tutorials.

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Corey Schafer: Unravel the secrets of Pandas and data manipulation using Python.

🎓 Whether you're a beginner or looking to enhance your skills, these channels offer a treasure trove of knowledge to propel you on your data analyst journey.

[codewithnazam]

DataFrame in Pandas: Guide to Creating Awesome DataFrames

Explore how to create a dataframe in Pandas, including data input methods, customization options, and practical examples.

Data analysis used to be a daunting task, reserved for statisticians and mathematicians. But with the rise of powerful tools like Python and its fantastic library, Pandas, anyone can become a data whiz! Pandas, in particular, shines with its DataFrames, these nifty tables that organize and manipulate data like magic. But where do you start? Fear not, fellow data enthusiast, for this guide will…

[evilsexy]

hello, i hope that you're well. if it's not too much trouble, i'd really like it if you shared some advice about learning / pursuing a programming language like q or ocaml (you mentioned a bit about it relatively recently). i'm really interested and think that any knowledge you have to offer in regards to both learning and practical applications would be useful. thank you so much regardless. wishing you a pleasant day.

hm this is a really good question! disclaimer that this is going to be biased towards (big) data science applications since that’s what i know the most. i also learned these languages after i already finished a math/cs undergrad degree so if you are from a different background you may have a different learning experience

to get it out of the way: you need 1-2 semesters of college statistics/probability to really get juice out of these languages. their best use cases are basically using statistical methods to process, transform, and understand truly vast amounts of data. you really also need linear algebra basics to understand functional programming (everything is a matrix operation)

in a nutshell functional programming is best used for large, dense, well-structured datasets because it allows you to do transforms very quickly and efficiently

in practical use these languages are good because for example i will be working with a 2 terabyte dataset of millions of millions of entries and i need to find the average of X value every hour over the past Y years. or i want to find the top 5 users from a specific region who clicked X button the most amount of times each day and what they have in common. or when we have a new user, how many times do i think they will click the button based on data from 100,000 other users? or i want to visualize what happens before and after X event in a time series under 20 different conditions for the past decade. or i want to fit a high dimensional regression. and i can get that all in a few seconds and with just 1 line of code

i was raised in python land and i have used pandas extensively and it does not even hold a candle to the power of ocaml or q when you’re working with larger amounts of data. python can’t keep up. i do use it for creating the graphs/figures i use in reports though

now. for learning—

cmu has a principles of functional programming class that my coworker swears by that has the lecture slides available along with some other resources http://www.cs.cmu.edu/~15150/lect.html

cs3110 from cornell is a good course for ocaml and their book is free https://cs3110.github.io/textbook/cover.html which also has video lectures

q is harder because it’s not taught in universities as much and has a smaller community, i mostly learned it by reading the tutorial on the website (Q for Mortals) which is available for free https://code.kx.com/q4m3/ . I also personally found that the book Q Tips https://libgen.is/search.php?req=q+tips was really good. I read Machine Learning and Big Data for kdb/q and the first few chapters were helpful starting out as well

[jayanthinit]

Master Python: The Ultimate Training Resource for Aspiring Developers

Python is a versatile, high-level programming language renowned for its readability and efficiency. Its interpreted, interactive, and object-oriented nature makes it a preferred choice for beginners and seasoned developers. This article provides an in-depth overview of Python training, highlighting its features, applications, and the benefits of mastering this powerful language.

Understanding Python

Python's design philosophy emphasizes code readability, enabling developers to express concepts in fewer lines than languages like C++ or Java. Its syntax is clean and straightforward, reducing the learning curve for newcomers. Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming, offering flexibility in software development.

Objectives of the Course

To understand the concepts and constructs of Python

To create your own Python programs, know the machine learning algorithms in Python, and work on a real-time project running on Python.

Key Features of Python

Interpreted Language: Python code is executed line by line, facilitating easier debugging and dynamic typing.

Object-Oriented: Supports classes and objects, promoting code reuse and modularity.

High-Level Language: Abstracts complex details, allowing developers to focus on problem-solving rather than intricate hardware specifics.

Extensive Standard Library: Offers various modules and packages for various tasks, from web development to data analysis.

Applications of Python

Python's versatility extends across numerous domains:

Web Development: Frameworks like Django and Flask streamline the creation of dynamic web applications.

Data Analysis and Scientific Computing: Libraries such as NumPy, SciPy, and Pandas facilitate complex data manipulation and analysis.

Machine Learning and Artificial Intelligence: Tools like TensorFlow and sci-kit-learn enable the development of intelligent systems.

Automation and Scripting: Python's simplicity makes it ideal for automating repetitive tasks and scripting.

Game Development: Libraries like Pygame support the creation of simple games and multimedia applications.

Benefits of Python Training

Engaging in Python training offers several advantages:

Ease of Learning: Python's clear syntax and readability make it accessible to beginners.

Community Support: A vast, active community provides extensive resources, tutorials, and third-party modules.

Career Opportunities: Proficiency in Python opens doors to various roles in web development, data science, automation, and more.

Cross-Platform Compatibility: Python runs seamlessly on different operating systems, enhancing its applicability.

Python Training Curriculum Overview

A comprehensive Python training program typically covers:

Introduction to Python: Understanding the basics, installation, and setting up the development environment.

Data Types and Variables: Exploring different data types, variables, and basic operations.

Control Structures: Implementing decision-making and looping constructs.

Functions and Modules: Defining functions, importing modules, and understanding scope.

Object-Oriented Programming: Creating classes, objects, and understanding inheritance and polymorphism.

File Handling: Reading from and writing to files.

Exception Handling: Managing errors and exceptions gracefully.

Libraries and Frameworks: Introduction to essential libraries for web development, data analysis, and more.

Project Work: Applying learned concepts to real-world projects to solidify understanding.

Conclusion

Python's simplicity, versatility, and powerful libraries make it an invaluable tool in today's technology landscape. Whether you're aiming to develop web applications, delve into data analysis, or automate tasks, Python provides the foundation to achieve your goals. Investing in Python training equips you with the skills to harness this language's full potential, paving the way for a successful career in various tech domains.