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Management Modeling. Python. Google Cloud Platform - jcmachicao

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Qué pasa con los programadores en la era de la IA

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Qué pasa con los programadores en la era de la IA

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CUF LLM Ejemplos de Casos de Análisis Cognitivo

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I never understood Tensors intuitively...until now

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Jamie CuffeHead of Self-Serve and New Products

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LangChain’s suite of products supports developers along each step of their development journey.

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La evolución de la confianza (algoritmo)

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La evolución de la confianza (algoritmo)

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an interactive guide to the game theory of why & how we trust each other

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Contribute to jcmachicao/cuf__programacion development by creating an account on GitHub.

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Python se ha convertido en un lenguaje imprescindible para la ciencia de datos, no debido a sus propias capacidades, sino gracias al poder de sus bibliotecas especializadas en el procesamiento numérico y de datos. Dos de las más prominentes son NumPy y Pandas. Estas bibliotecas no son rivales, sino compañeras, cada una con sus propias fortalezas y casos de uso. Sumergámonos en el mundo del análisis de datos en Python con NumPy y Pandas, y comprendamos cómo elegir la herramienta adecuada para tus tareas de datos.

¿Quieres crear rápidamente visualizaciones de datos en Python?

PyGWalker es un proyecto de Python de código abierto que puede ayudar a acelerar el flujo de trabajo de análisis y visualización de datos directamente en entornos basados en Jupyter Notebook.

PyGWalker (opens in a new tab) convierte tu DataFrame de Pandas (o DataFrame de Polars) en una interfaz de usuario visual donde puedes arrastrar y soltar variables para crear gráficos con facilidad. Simplemente usa el siguiente código:

Puedes ejecutar PyGWalker ahora mismo en estos cuadernos en línea:

¡Y no olvides darnos una ⭐️ en GitHub!

Comprendiendo NumPy

NumPy, abreviatura de Numerical Python, se lanzó como proyecto de código abierto en 2005 con el objetivo de llevar la computación científica a Python. Se basaba en dos paquetes anteriores, Numeric y Numarray, y su fortaleza radica en su capacidad para trabajar con objetos de matrices multidimensionales.

NumPy proporciona herramientas para ordenar, buscar, filtrar y aplicar álgebra lineal y transformadas de Fourier. Aprovecha Basic Linear Algebra Subprogram (BLAS) y Linear Algebra PACKage (LAPACK) para potenciar sus capacidades de álgebra lineal. Esto lo convierte en una potente herramienta para manejar grandes cantidades de datos mucho más rápido que las funciones incorporadas de Python.

El Poder de Pandas

Mientras que NumPy destaca en el análisis y las simulaciones numéricas, cuando se trata de análisis y manipulación de datos, trabajando con una amplia gama de fuentes de datos, ahí es donde Pandas brilla.

Pandas fue desarrollado en 2008 por Wes McKinney, quien buscaba una herramienta poderosa y flexible para el análisis cuantitativo de datos financieros. Nombrado así por los datos de panel tridimensionales con los que trabaja, Pandas se hizo de código abierto al año siguiente.

Pandas simplifica el proceso de trabajo con datos al proporcionar métodos para cargar, remodelar, pivotar, fusionar y unir datos. También ofrece herramientas para manejar datos faltantes. Destaca en el trabajo con datos tabulares, lo que lo convierte en la elección preferida para tareas de análisis de datos.

NumPy vs Pandas: Profundizando

Explicación de NumPy

La funcionalidad principal de NumPy gira en torno a sus objetos de matrices n-dimensionales. Estas matrices son homogéneas, lo que significa que todos los elementos son del mismo tipo, generalmente enteros o números de punto flotante. Esto hace que NumPy sea especialmente útil para tareas que requieren operaciones matemáticas en conjuntos de datos grandes.

Por ejemplo, si estás trabajando en un proyecto que implica simulaciones o análisis numérico, las matrices multidimensionales eficientes de NumPy y las funciones matemáticas pueden ser extremadamente útiles. También es una gran elección para proyectos que requieren integración con código C/C++ o Fortran, ya que NumPy proporciona interoperabilidad sin problemas y rápida.

Explorando las Fortalezas de Pandas

Por otro lado, Pandas está diseñado para trabajar con estructuras de datos complejas y manipular datos. Proporciona dos estructuras de datos clave: Series y DataFrame. Una Serie es un objeto similar a una matriz unidimensional que puede contener cualquier tipo de datos, mientras que un DataFrame es una estructura de datos etiquetada bidimensional con columnas potencialmente de diferentes tipos.

Pandas destaca cuando se trata de manipulación y preparación de datos. Proporciona amplias capacidades para remodelar, dividir, cortar y agregar datos. También es una excelente herramienta para manejar datos de series temporales. Si tu proyecto involucra análisis de datos, limpieza de datos o visualización de datos, es probable que Pandas sea la herramienta adecuada para el trabajo.

NumPy vs Pandas: Consideraciones de Rendimiento

Si bien Pandas tiene un poco de sobrecarga debido a sus características adicionales, también implementa varias funciones optimizadas con C y Cython. Esto significa que, para conjuntos de datos muy grandes, algunas operaciones en Pandas pueden ser más rápidas que sus equivalentes en NumPy.

Sin embargo, para conjuntos de datos más pequeños o tareas que implican principalmente cálculos numéricos, NumPy puede ser la opción más eficiente. También vale la pena destacar que dado que Pandas se construye sobre NumPy, a menudo se pueden utilizar las dos bibliotecas juntas, aprovechando las fortalezas de cada una según sea necesario.

NumPy vs Pandas: Elegir la herramienta adecuada

Pandas se construye sobre NumPy, lo que significa que la mayoría de los métodos de NumPy están disponibles a través de Pandas. Sin embargo, esto también representa una sobrecarga en términos de rendimiento y curva de aprendizaje. Las capacidades de Pandas tienen un costo de complejidad. Sin embargo, implementa varias funciones optimizadas con C y Cython, que pueden ser más rápidas que el equivalente de NumPy para conjuntos de datos muy grandes.

El consenso general parece ser comenzar con NumPy y buscar las características que es más probable que necesite. Si esa búsqueda te lleva a Pandas, entonces ahí está tu respuesta. No se trata de elegir uno sobre el otro, sino más bien de elegir la herramienta adecuada para la tarea en cuestión.

Preguntas frecuentes

1. ¿Es Pandas mejor que NumPy?

No se trata de que uno sea mejor que el otro. Tanto Pandas como NumPy tienen sus propias fortalezas. NumPy es más eficiente para operaciones numéricas en conjuntos de datos grandes, mientras que Pandas es más adecuado para la manipulación y análisis de datos.

2. ¿Puedo usar NumPy y Pandas juntos?

¡Absolutamente! De hecho, dado que Pandas se construye sobre NumPy, ya estás utilizando NumPy cuando usas Pandas. Ciertamente puedes usar ambas bibliotecas en el mismo proyecto, aprovechando las fortalezas de cada una según sea necesario.

3. ¿Debería aprender NumPy o Pandas primero?

En general, se recomienda comenzar con NumPy, ya que proporciona las estructuras de datos y operaciones fundamentales sobre las que Pandas se basa. Una vez que te sientas cómodo con NumPy, puedes pasar a aprender Pandas.

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NumPy vs Pandas: Explicando la Diferencia en Términos Sencillos

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Sumérgete en el mundo del análisis de datos en Python con NumPy y Pandas. Comprende sus fortalezas, diferencias y cómo elegir la herramienta adecuada para tus tareas de datos.

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programacion/2025 🔥 Juegos Simples para Aprender Python.pdf at main · jcmachicaocuf/programacion

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Contribute to jcmachicaocuf/programacion development by creating an account on GitHub.

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Contribute to jcmachicaocuf/programacion development by creating an account on GitHub.

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A function is a block of code which only runs when it is called.

You can pass data, known as parameters, into a function.

A function can return data as a result.

Creating a Function

In Python a function is defined using the def keyword:

Example

Calling a Function

To call a function, use the function name followed by parenthesis:

Example

my_function()

Try it Yourself "

Arguments

Information can be passed into functions as arguments.

Arguments are specified after the function name, inside the parentheses. You can add as many arguments as you want, just separate them with a comma.

The following example has a function with one argument (fname). When the function is called, we pass along a first name, which is used inside the function to print the full name:

Example

Try it Yourself "

Arguments are often shortened to args in Python documentations.

Parameters or Arguments?

The terms parameter and argument can be used for the same thing: information that are passed into a function.

From a function's perspective:

A parameter is the variable listed inside the parentheses in the function definition.

An argument is the value that is sent to the function when it is called.

Number of Arguments

By default, a function must be called with the correct number of arguments. Meaning that if your function expects 2 arguments, you have to call the function with 2 arguments, not more, and not less.

If you try to call the function with 1 or 3 arguments, you will get an error:

Arbitrary Arguments, *args

If you do not know how many arguments that will be passed into your function, add a * before the parameter name in the function definition.

This way the function will receive a tuple of arguments, and can access the items accordingly:

Example

If the number of arguments is unknown, add a * before the parameter name:

Try it Yourself "

Arbitrary Arguments are often shortened to *args in Python documentations.

Keyword Arguments

You can also send arguments with the key = value syntax.

This way the order of the arguments does not matter.

The phrase Keyword Arguments are often shortened to kwargs in Python documentations.

Arbitrary Keyword Arguments, **kwargs

If you do not know how many keyword arguments that will be passed into your function, add two asterisk: ** before the parameter name in the function definition.

This way the function will receive a dictionary of arguments, and can access the items accordingly:

Example

If the number of keyword arguments is unknown, add a double ** before the parameter name:

Try it Yourself "

Arbitrary Kword Arguments are often shortened to **kwargs in Python documentations.

Default Parameter Value

The following example shows how to use a default parameter value.

If we call the function without argument, it uses the default value:

Example

Try it Yourself "

Passing a List as an Argument

You can send any data types of argument to a function (string, number, list, dictionary etc.), and it will be treated as the same data type inside the function.

E.g. if you send a List as an argument, it will still be a List when it reaches the function:

Example

fruits = ["apple", "banana", "cherry"]

my_function(fruits)

Try it Yourself "

Return Values

To let a function return a value, use the return statement:

Example

Try it Yourself "

The pass Statement

function definitions cannot be empty, but if you for some reason have a function definition with no content, put in the pass statement to avoid getting an error.

Positional-Only Arguments

You can specify that a function can have ONLY positional arguments, or ONLY keyword arguments.

To specify that a function can have only positional arguments, add , / after the arguments:

Without the , / you are actually allowed to use keyword arguments even if the function expects positional arguments:

But when adding the , / you will get an error if you try to send a keyword argument:

Keyword-Only Arguments

To specify that a function can have only keyword arguments, add *, before the arguments:

Without the *, you are allowed to use positionale arguments even if the function expects keyword arguments:

But when adding the *, / you will get an error if you try to send a positional argument:

Combine Positional-Only and Keyword-Only

You can combine the two argument types in the same function.

Any argument before the / , are positional-only, and any argument after the *, are keyword-only.

Recursion

Python also accepts function recursion, which means a defined function can call itself.

Recursion is a common mathematical and programming concept. It means that a function calls itself. This has the benefit of meaning that you can loop through data to reach a result.

The developer should be very careful with recursion as it can be quite easy to slip into writing a function which never terminates, or one that uses excess amounts of memory or processor power. However, when written correctly recursion can be a very efficient and mathematically-elegant approach to programming.

In this example, tri_recursion() is a function that we have defined to call itself ("recurse"). We use the k variable as the data, which decrements (-1) every time we recurse. The recursion ends when the condition is not greater than 0 (i.e. when it is 0).

To a new developer it can take some time to work out how exactly this works, best way to find out is by testing and modifying it.

Example

Recursion Example

Try it Yourself "

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W3Schools offers free online tutorials, references and exercises in all the major languages of the web. Covering popular subjects like HTML, CSS, JavaScript, Python, SQL, Java, and many, many more.

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scikit-learn comes with a few small standard datasets that do not require to download any file from some external website.

They can be loaded using the following functions:

These datasets are useful to quickly illustrate the behavior of the various algorithms implemented in scikit-learn. They are however often too small to be representative of real world machine learning tasks.

7.1.1. Boston house prices dataset
Data Set Characteristics:
This is a copy of UCI ML housing dataset. https://archive.ics.uci.edu/ml/machine-learning-databases/housing/

This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics ...', Wiley, 1980. N.B. Various transformations are used in the table on pages 244-261 of the latter.

The Boston house-price data has been used in many machine learning papers that address regression problems.

References

7.1.2. Iris plants dataset
Data Set Characteristics:
The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken from Fisher's paper. Note that it's the same as in R, but not as in the UCI Machine Learning Repository, which has two wrong data points.

This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.

References

7.1.3. Diabetes dataset

Ten baseline variables, age, sex, body mass index, average blood pressure, and six blood serum measurements were obtained for each of n = 442 diabetes patients, as well as the response of interest, a quantitative measure of disease progression one year after baseline.
Data Set Characteristics:

Number of Instances

Number of Attributes

First 10 columns are numeric predictive values

Target

Column 11 is a quantitative measure of disease progression one year after baseline

Attribute Information

Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times (i.e. the sum of squares of each column totals 1).

Source URL: https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html

For more information see: Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) "Least Angle Regression," Annals of Statistics (with discussion), 407-499. ( https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)

7.1.4. Optical recognition of handwritten digits dataset
Data Set Characteristics:
This is a copy of the test set of the UCI ML hand-written digits datasets https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits

The data set contains images of hand-written digits: 10 classes where each class refers to a digit.

Preprocessing programs made available by NIST were used to extract normalized bitmaps of handwritten digits from a preprinted form. From a total of 43 people, 30 contributed to the training set and different 13 to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of 4x4 and the number of on pixels are counted in each block. This generates an input matrix of 8x8 where each element is an integer in the range 0..16. This reduces dimensionality and gives invariance to small distortions.

For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G. T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C. L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469, 1994.

References

7.1.5. Linnerrud dataset
Data Set Characteristics:
The Linnerud dataset is a multi-output regression dataset. It consists of three exercise (data) and three physiological (target) variables collected from twenty middle-aged men in a fitness club:

physiological - CSV containing 20 observations on 3 physiological variables:

Weight, Waist and Pulse.

exercise - CSV containing 20 observations on 3 exercise variables:

Chins, Situps and Jumps.

7.1.6. Wine recognition dataset
Data Set Characteristics:
This is a copy of UCI ML Wine recognition datasets. https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data

The data is the results of a chemical analysis of wines grown in the same region in Italy by three different cultivators. There are thirteen different measurements taken for different constituents found in the three types of wine.

Original Owners:

Forina, M. et al, PARVUS - An Extendible Package for Data Exploration, Classification and Correlation. Institute of Pharmaceutical and Food Analysis and Technologies, Via Brigata Salerno, 16147 Genoa, Italy.

Citation:

Lichman, M. (2013). UCI Machine Learning Repository [ https://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

References

7.1.7. Breast cancer wisconsin (diagnostic) dataset
Data Set Characteristics:
This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets. https://goo.gl/U2Uwz2

Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image.

Separating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree Construction Via Linear Programming." Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.

The actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].

This database is also available through the UW CS ftp server:

ftp ftp.cs.wisc.edu cd math-prog/cpo-dataset/machine-learn/WDBC/

References

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scikit-learn comes with a few small standard datasets that do not require to download any file from some external website. They can be loaded using the following functions: These datasets are useful to quickly illustrate the behavior of the various algorithms implemented in scikit-learn.

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Contribute to jcmachicao/Modelamiento_Complejo_ModCplx development by creating an account on GitHub.

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Reduce inference costs by 71% and drive scale out using PyTorch, TorchServe, and AWS Inferentia.

Pushing the state of the art in NLP and Multi-task learning.

Using PyTorch's flexibility to efficiently research new algorithmic approaches.

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An open source machine learning framework that accelerates the path from research prototyping to production deployment.

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Streamlit vs Gradio: La batalla definitiva para los paneles de Python

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Explora la comparación entre Streamlit y Gradio para crear cautivadores paneles de Python. ¡Descubre la mejor herramienta para tus necesidades de proyecto!

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Hugging Face - The AI community building the future.

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We're on a journey to advance and democratize artificial intelligence through open source and open science.

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Get a custom spaCy pipeline, tailor-made for your NLP problem by spaCy's core developers.

Streamlined. Nobody knows spaCy better than we do. Send us your pipeline requirements and we'll be ready to start producing your solution in no time at all.

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spaCy · Industrial-strength Natural Language Processing in Python

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spaCy is a free open-source library for Natural Language Processing in Python. It features NER, POS tagging, dependency parsing, word vectors and more.