#random module in numpy python

[data-science-lovers]

!! Numpy Random Module !!

[izicodes]

Any good python modules I can learn now that I'm familiar with the basics?

Hiya 💗

Yep, here's a bunch you can import them into your program to play around with!

math: Provides mathematical functions and constants.

random: Enables generation of random numbers, choices, and shuffling.

datetime: Offers classes for working with dates and times.

os: Allows interaction with the operating system, such as file and directory manipulation.

sys: Provides access to system-specific parameters and functions.

json: Enables working with JSON (JavaScript Object Notation) data.

csv: Simplifies reading and writing CSV (Comma-Separated Values) files.

re: Provides regular expression matching operations.

requests: Allows making HTTP requests to interact with web servers.

matplotlib: A popular plotting library for creating visualizations.

numpy: Enables numerical computations and working with arrays.

pandas: Provides data structures and analysis tools for data manipulation.

turtle: Allows creating graphics and simple games using turtle graphics.

time: Offers functions for time-related operations.

argparse: Simplifies creating command-line interfaces with argument parsing.

How to actually import to your program?

Just in case you don't know, or those reading who don't know:

Use the 'import' keyword, preferably at the top of the page, and the name of the module you want to import. OPTIONAL: you could add 'as [shortname you want to name it in your program]' at the end to use the shortname instead of the whole module name

Hope this helps, good luck with your Python programming! 🙌🏾

[abhinav3045]

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K-Means Clustering in Python: Step-by-Step Example

by Zach BobbittPosted on August 31, 2022

One of the most common clustering algorithms in machine learning is known as k-means clustering.

K-means clustering is a technique in which we place each observation in a dataset into one of K clusters.

The end goal is to have K clusters in which the observations within each cluster are quite similar to each other while the observations in different clusters are quite different from each other.

In practice, we use the following steps to perform K-means clustering:

1. Choose a value for K.

First, we must decide how many clusters we’d like to identify in the data. Often we have to simply test several different values for K and analyze the results to see which number of clusters seems to make the most sense for a given problem.

2. Randomly assign each observation to an initial cluster, from 1 to K.

3. Perform the following procedure until the cluster assignments stop changing.

For each of the K clusters, compute the cluster centroid. This is simply the vector of the p feature means for the observations in the kth cluster.

Assign each observation to the cluster whose centroid is closest. Here, closest is defined using Euclidean distance.

The following step-by-step example shows how to perform k-means clustering in Python by using the KMeans function from the sklearn module.

Step 1: Import Necessary Modules

First, we’ll import all of the modules that we will need to perform k-means clustering:import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.cluster import KMeans from sklearn.preprocessing import StandardScaler

Step 2: Create the DataFrame

Next, we’ll create a DataFrame that contains the following three variables for 20 different basketball players:

points

assists

rebounds

The following code shows how to create this pandas DataFrame:#create DataFrame df = pd.DataFrame({'points': [18, np.nan, 19, 14, 14, 11, 20, 28, 30, 31, 35, 33, 29, 25, 25, 27, 29, 30, 19, 23], 'assists': [3, 3, 4, 5, 4, 7, 8, 7, 6, 9, 12, 14, np.nan, 9, 4, 3, 4, 12, 15, 11], 'rebounds': [15, 14, 14, 10, 8, 14, 13, 9, 5, 4, 11, 6, 5, 5, 3, 8, 12, 7, 6, 5]}) #view first five rows of DataFrame print(df.head()) points assists rebounds 0 18.0 3.0 15 1 NaN 3.0 14 2 19.0 4.0 14 3 14.0 5.0 10 4 14.0 4.0 8

We will use k-means clustering to group together players that are similar based on these three metrics.

Step 3: Clean & Prep the DataFrame

Next, we’ll perform the following steps:

Use dropna() to drop rows with NaN values in any column

Use StandardScaler() to scale each variable to have a mean of 0 and a standard deviation of 1

The following code shows how to do so:#drop rows with NA values in any columns df = df.dropna() #create scaled DataFrame where each variable has mean of 0 and standard dev of 1 scaled_df = StandardScaler().fit_transform(df) #view first five rows of scaled DataFrame print(scaled_df[:5]) [[-0.86660275 -1.22683918 1.72722524] [-0.72081911 -0.96077767 1.45687694] [-1.44973731 -0.69471616 0.37548375] [-1.44973731 -0.96077767 -0.16521285] [-1.88708823 -0.16259314 1.45687694]]

Note: We use scaling so that each variable has equal importance when fitting the k-means algorithm. Otherwise, the variables with the widest ranges would have too much influence.

Step 4: Find the Optimal Number of Clusters

To perform k-means clustering in Python, we can use the KMeans function from the sklearn module.

This function uses the following basic syntax:

KMeans(init=’random’, n_clusters=8, n_init=10, random_state=None)

where:

init: Controls the initialization technique.

n_clusters: The number of clusters to place observations in.

n_init: The number of initializations to perform. The default is to run the k-means algorithm 10 times and return the one with the lowest SSE.

random_state: An integer value you can pick to make the results of the algorithm reproducible. 

The most important argument in this function is n_clusters, which specifies how many clusters to place the observations in.

However, we don’t know beforehand how many clusters is optimal so we must create a plot that displays the number of clusters along with the SSE (sum of squared errors) of the model.

Typically when we create this type of plot we look for an “elbow” where the sum of squares begins to “bend” or level off. This is typically the optimal number of clusters.

The following code shows how to create this type of plot that displays the number of clusters on the x-axis and the SSE on the y-axis:#initialize kmeans parameters kmeans_kwargs = { "init": "random", "n_init": 10, "random_state": 1, } #create list to hold SSE values for each k sse = [] for k in range(1, 11): kmeans = KMeans(n_clusters=k, **kmeans_kwargs) kmeans.fit(scaled_df) sse.append(kmeans.inertia_) #visualize results plt.plot(range(1, 11), sse) plt.xticks(range(1, 11)) plt.xlabel("Number of Clusters") plt.ylabel("SSE") plt.show()

In this plot it appears that there is an elbow or “bend” at k = 3 clusters.

Thus, we will use 3 clusters when fitting our k-means clustering model in the next step.

Note: In the real-world, it’s recommended to use a combination of this plot along with domain expertise to pick how many clusters to use.

Step 5: Perform K-Means Clustering with Optimal K

The following code shows how to perform k-means clustering on the dataset using the optimal value for k of 3:#instantiate the k-means class, using optimal number of clusters kmeans = KMeans(init="random", n_clusters=3, n_init=10, random_state=1) #fit k-means algorithm to data kmeans.fit(scaled_df) #view cluster assignments for each observation kmeans.labels_ array([1, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0])

The resulting array shows the cluster assignments for each observation in the DataFrame.

To make these results easier to interpret, we can add a column to the DataFrame that shows the cluster assignment of each player:#append cluster assingments to original DataFrame df['cluster'] = kmeans.labels_ #view updated DataFrame print(df) points assists rebounds cluster 0 18.0 3.0 15 1 2 19.0 4.0 14 1 3 14.0 5.0 10 1 4 14.0 4.0 8 1 5 11.0 7.0 14 1 6 20.0 8.0 13 1 7 28.0 7.0 9 2 8 30.0 6.0 5 2 9 31.0 9.0 4 0 10 35.0 12.0 11 0 11 33.0 14.0 6 0 13 25.0 9.0 5 0 14 25.0 4.0 3 2 15 27.0 3.0 8 2 16 29.0 4.0 12 2 17 30.0 12.0 7 0 18 19.0 15.0 6 0 19 23.0 11.0 5 0

The cluster column contains a cluster number (0, 1, or 2) that each player was assigned to.

Players that belong to the same cluster have roughly similar values for the points, assists, and rebounds columns.

Note: You can find the complete documentation for the KMeans function from sklearn here.

Additional Resources

The following tutorials explain how to perform other common tasks in Python:

How to Perform Linear Regression in Python How to Perform Logistic Regression in Python How to Perform K-Fold Cross Validation in Python

[pandeypankaj]

How can I learn artificial intelligence with a little bit of knowledge of Python?

Learning AI with Python: A Beginner's Guide

Great choice to start with Python! It is one of the most used languages in AI and machine learning. Here is a roadmap to help you get started with AI using Python.

1. Strengthen Your Python Fundamentals

Learn the basics: variable type, data types, control flow-if-else, loops, functions, and modules.

Practice: Solve coding challenges at HackerRank or LeetCode.

Understand NumPy and Pandas: Both libraries are great to work with while manipulating and analyzing data.

2. Master the Fundamentals of AI

Machine Learning: Explore the world of supervised-learning (classification, regression), unsupervised-learning (clustering, dimensionality reduction), and reinforcement learning.

Deep Learning: Learn neural networks, backpropagation, and popular architectures like CNNs, RNNs, and transformers.

AI Algorithms: Understand algorithms such as linear regression, decision trees, random forests, support vector machines, and k-nearest neighbors.

3. Leverage Online Resources

Coursera: Uses specific courses from elite universities.

Lejhro: Provides access to a variety of courses from industry professionals.

Fast.ai: For practical deep learning courses. 

Kaggle: Compete in data science competitions to put knowledge into practice. 

YouTube: Channels such as TensorFlow, Keras, and Andrew Ng's DeepLearning.AI tutorials are great. 

4. Hands-on Projects

 Start Small: Avail simple projects where your goal will be to predict house prices or classifying images. 

Use Libraries: Avail the libraries at hand such as TensorFlow, Keras, PyTorch, and Scikit-learn. 

Experimentation: The goal is to try different algorithms and different techniques to study their effect. 

5. Online Communities

 Socialize with Others: Engage yourself in forums, discussion groups, and meetups.

Ask questions. Don't be afraid to ask the experts for help. 

Share your work: Make something and share; people will give you feedback. Some Recommended Libraries in Python: NumPy for numerical operations; Pandas for data manipulation and analysis; Matplotlib and Seaborn for data visualizations; Scikit-learn for machine learning algorithms.   TensorFlow and Keras for deep learning; 

PyTorch: This is another very popular deep learning framework. And remember, learning AI takes time and practice. So, just be patient, stay curious, and above all, have fun along the way!

[tech-insides]

Data Science Interview Preparation: Resources, Courses, and Study Plans

Preparing for a data science interview can be a daunting task, but with the right resources, courses, and study plans, you can tackle it with confidence. Whether you're aiming for a job at a FAANG+ company or another top-tier tech firm, having a structured approach is key. Let's dive into how you can best prepare for your data science interview.

1. Essential Resources

a. Books:

"Python Data Science Handbook" by Jake VanderPlas: A comprehensive guide to essential data science tools in Python.

"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron: Perfect for practical, hands-on learning.

"Data Science for Business" by Foster Provost and Tom Fawcett: Great for understanding the application of data science in business contexts.

b. Online Resources:

Kaggle: Participate in competitions and explore datasets to sharpen your skills.

Towards Data Science (Medium): Read articles and tutorials on a wide range of data science topics.

GitHub: Browse through repositories to see real-world applications of data science projects.

c. Practice Platforms:

LeetCode: Focus on data structure and algorithm problems.

HackerRank: Practice coding challenges specifically tailored for data science.

Interview Kickstart: Get access to curated interview questions and mock interviews to simulate the real experience.

2. Recommended Courses

a. Online Courses:

Coursera: "Applied Data Science with Python": Offers a series of courses that cover various aspects of data science.

edX: "Data Science MicroMasters": A more intensive program that covers data science fundamentals in depth.

Udacity: "Data Scientist Nanodegree": Provides hands-on projects and mentorship.

b. Specialized Programs:

Interview Kickstart: Data Science Interview Preparation Course: Tailored specifically for those aiming for FAANG+ companies, this course includes comprehensive modules on technical skills, mock interviews, and personalized feedback.

Springboard: Data Science Career Track: Offers a job guarantee and one-on-one mentorship.

3. Structured Study Plans

a. 3-Month Study Plan:

Month 1: Basics and Fundamentals

Week 1-2: Revise Python programming and essential libraries (NumPy, Pandas).

Week 3: Dive into statistics and probability.

Week 4: Basic machine learning algorithms and concepts.

Month 2: Intermediate Topics and Practice

Week 1-2: Advanced machine learning algorithms (SVM, Random Forest, XGBoost).

Week 3: Deep learning basics (neural networks, TensorFlow/Keras).

Week 4: Practice coding challenges on LeetCode and HackerRank.

Month 3: Mock Interviews and Revision

Week 1-2: Behavioral and situational interview preparation.

Week 3: Mock interviews with peers or mentors.

Week 4: Review projects and fine-tune your portfolio.

b. Daily Study Routine:

1-2 hours of coding practice: Focus on algorithms and data structures.

1 hour of reading: Articles, research papers, or book chapters.

1 hour of hands-on project work: Apply what you've learned in a real-world context.

30 minutes of revision: Go over key concepts and formulas.

4. Tips for Success

Stay Consistent: Regular practice is more effective than cramming.

Join a Study Group: Collaborate with peers to keep motivated and gain new insights.

Seek Feedback: Regularly review your work with mentors or experienced professionals.

Simulate Real Interviews: Use mock interviews to get used to the pressure and format of real interviews.

By leveraging these resources, courses, and study plans, you'll be well-prepared to tackle your data science interview. Remember, preparation is the key to confidence. Good luck, and may you ace your interview!

[develeran]

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Basic Syntax, Operators

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Functions and Recurrsion

Object Oriented Programming

File Handling

Exception Handling

Web Scrapping

Libraries

Database Connectivity

SQL

The SQL module equips beginners with fundamental skills for managing and querying relational databases, making it an excellent starting point for Data Science and Analyst jobs.

Introduction To Databases

Introduction To SQL

SQL Data Manipulation Language

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The AI, Deep Learning, and NLP module takes your skills to the State of the Art, enabling you to program Deep Learning machines and NLP-like computers with expert guidance and easy-to-understand resources.

Introduction to Artificial Intelligence

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Artificial Neural Network (ANN)

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[edcater]

Cracking the Code: A Guide to Intermediate Python Programming Proficiency

Introduction

Python, with its simplicity and versatility, has become one of the most popular programming languages. After mastering the basics, many programmers find themselves at a crossroads, unsure of how to progress to the next level. This article aims to guide you through the journey of reaching intermediate proficiency in Python programming, providing insights, strategies, and hands-on tips to crack the code and elevate your skills.

1. Strengthening Fundamentals

Before delving into intermediate concepts, it's crucial to reinforce your understanding of the fundamentals. Brush up on topics like data types, control flow, and functions. This foundation is essential for building more complex programs later on. Online platforms like Codecademy, HackerRank, and LeetCode offer exercises to solidify your basics.

2. Mastering Functions and Modules

Functions are the building blocks of Python programs, and mastering them is key to writing efficient and modular code. Learn about function parameters, return values, and scope. Additionally, explore Python's extensive library of modules, such as math, random, and datetime, to enhance your ability to leverage pre-built functionality.

3. Understanding Object-Oriented Programming (OOP)

Intermediate Python programming involves a deep dive into OOP concepts. Grasp the principles of encapsulation, inheritance, and polymorphism. Practice creating classes and objects, understanding how they contribute to code organization and reusability. Projects like building a simple game or a class hierarchy for a real-world scenario can solidify your OOP skills.

4. Exploring File Handling and Data Persistence

Working with files and data persistence is a crucial skill for any intermediate Python programmer. Learn how to read and write files, manipulate data structures like CSV and JSON, and interact with databases using libraries like SQLite or SQLAlchemy. This knowledge is invaluable when dealing with real-world applications that involve data storage and retrieval.

5. Conquering Exception Handling

As your programs become more complex, robust error handling becomes essential. Dive into exception handling in Python to gracefully manage errors and prevent crashes. Understand the try-except blocks, and learn to raise and catch exceptions effectively. Real-world scenarios often require code that can gracefully handle unexpected situations.

6. Embracing Decorators and Generators

Decorators and generators are advanced features that can significantly enhance your Python programming skills. Decorators allow you to modify or extend the behavior of functions, providing a powerful tool for code organization and reuse. Generators, on the other hand, enable efficient handling of large datasets and lazy evaluation. Mastering these features will set you apart as an intermediate Python programmer.

7. Harnessing the Power of Libraries and Frameworks

Python's strength lies in its rich ecosystem of libraries and frameworks. Explore popular libraries like NumPy for numerical computing, Pandas for data manipulation, and Matplotlib for data visualization. Familiarize yourself with web frameworks like Flask or Django for building robust web applications. This exposure to different tools broadens your skill set and prepares you for diverse programming challenges.

8. Collaborating with Version Control Systems

As a professional programmer, collaboration is inevitable. Learn to use version control systems like Git to track changes, collaborate with team members, and manage project versions effectively. Platforms like GitHub provide an excellent environment to showcase your projects, collaborate with others, and contribute to open-source software.

Conclusion

Achieving intermediate proficiency in Python programming is a rewarding journey that requires dedication, practice, and a willingness to explore new concepts. Strengthen your foundation, delve into advanced topics, and apply your knowledge through hands-on projects. By mastering functions, OOP, file handling, and other intermediate concepts, you'll be well on your way to cracking the code and becoming a proficient Python programmer. Keep coding, stay curious, and embrace the challenges that come with advancing your skills in the world of Python programming

[edujournalblogs]

Power of Python

We have seen a lot of python developers worldwide who are able to leverage the functionality of modules/libraries, that are built by people at the individual level and also with standard built-in libraries available in Python, to do lot of interesting projects. We are able to fast track our progress and do a lot of really good projects pretty quickly. The projects can be done by

Developing your own module

Utilize the Built-in Standard modules/libraries provided by python. You can import the library and use it. eg random() that generates random numbers and datetime() function that can do date and time details.

Python Community: There is a huge community worldwide for Python. For eg. The libraries that we use in Data Science regularly such as Pandas, Numpy, Matplotlib etc are not available instandard built-in Python library. They are developed by developers and shared within the community. You only need to use 'pip3 install ' command to to install it in your system and use the import it thereafter and include them in your code.

Check out our master program in Data Science and ASP.NET- Complete Beginner to Advanced course and boost your confidence and knowledge.

URL: www.edujournal.com

[truepdf]

Bottom-up approach to crack your python interview

Key Features

Get the answer for the most common and challenging Python question

Learn to trace the code and answer the question correctly

Explore the solutions of GUI and DBMS in Python

Gain sufficient understanding on Machine Learning library and Pandas

Description This book covers all possible interview questions and coding in Python. It presents written theory as well as practical questions as all the interviewers do not follow the same pattern. Questions are jumbled and compiled.

Practical questions may help you to understand the logic and will help you to fight the technical round. Simple questions with deep coding are the hallmark of this book.

With over 242 questions in this book, you will be able to crack your Python interview. The book covers the following topics: Variable, Datatype, type conversion, Operators, if-else, loops ,List , Tuples, Set ,Dictionary, Functions, Array, classes and objects, constructor , Inheritance, Encapsulation, keywords , regular expression, Random Module, Sys Module , OS Module , Statistics Module, widgets of Tkinter , Multithreading, other GUI Framework , work on multiple Tkinter windows , File Input-output , file handling with GUI, MySQL , SQLite , MongoDB , Redis, connectivity with GUI, Matplotlib Library, Django, Flask.

What you will learn

Become a Python Developer without having to spend a lot of money on theoretical content.

You will achieve the confidence to tackle the most challenging questions on Python.

You will develop a strong understanding around the entire ecosystem of Python programming.

Who this book is for This book is targeted at Python Developers, Technical specialist, Beginners who want to stand out in a Python coding interview.

Table of Contents 1. Core Concept 2. OOPs Concept 3. Python Module 4. Python GUI 5. File Handling 6. Python Database 7. NumPy, Pandas 8. Django, Flask

[underoosstark]

HI absolutely no worries if you're not up for like an in-depth discussion about this or anything but I just saw your post on analysing sound with Fourier transforms and was wondering if you used python to do it? I'm about to start a really similar project and thought it was super cool to see this on my dash! :) if you did use python, what modules did you end up using, esp for the visual representation portion?

(Also, congrats on finishing all those lab reports! That's really awesome!)

anon, you do not understand just how excited this ask made me

i did use python!! i play the string bass, so i recorded myself playing a scale and then saved it as a wav file & then uploaded that. [ from scipy.io import wavfile , and then read it into the code with  rate, data = wavfile.read('file-name-here') ]

for the visual stuff, we did two parts! the first was a Fast Fourier Transform (FFT) which tbh I still don’t entirely understand lmao. [that’s part of the numpy library,  fourier_transform = np.fft.fft(data) ]  if you graph the frequency (using the sample rate you got when you read in the wav file & the number of data entries) vs. the absolute value of that FFT, you get this really funky looking graph!

these are the frequencies (aka different pitches) that were most common in my data! if you read in a sound file that was just one single note, you should ideally have a really tall peak that would let you figure out what note it was

the second way we visualized it was with a spectrogram - basically, as great as the FFT is, it doesn’t tell us anything about when any of this nonsense actually happened. was it a scale that went up? was it a scale that went down? was it just random notes? a spectrogram shows us the time also, which is v fun.

spectrograms are part of the matplotlib.pyplot library so you can import that and then do [ plt.specgram and then graph it with plt.colorbar() ]  here’s a zoomed in version of my spectrogram!

you can see that the frequencies go up, starting around 100 and ending somewhere close to 200. (if you look at a table online, you should be able figure out what scale i played!)

this was probably SO much longer than you anticipated, and simultaneously not long enough to accurately explain things, but if you ever want to dm me i would genuinely love to share my code with you & chat about whatever !!

[generatour1]

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[gayatrisc]

Top 8 Python Libraries for Data Science

Python language is popular and most commonly used by developers in creating mobile apps, games and other applications. A Python library is nothing but a collection of functions and methods which helps in solving complex data science-related functions. Python also helps in saving an amount of time while completing specific tasks.

Python has more than 130,000 libraries that are intended for different uses. Like python imaging library is used for image manipulation whereas Tensorflow is used for the development of Deep Learning models using python.

There are multiple python libraries available for data science and some of them are already popular, remaining are improving day-by-day to reach their acceptance level by developers

Read: HOW TO SHAPE YOUR CAREER WITH DATA SCIENCE COURSE IN BANGALORE?

Here we are discussing some Python libraries which are used for Data Science:

1. Numpy

NumPy is the most popular library among developers working on data science. It is used for performing scientific computations like a random number, linear algebra and Fourier transformation. It can also be used for binary operations and for creating images. If you are in the field of Machine Learning or Data Science, you must have good knowledge of NumPy to process your real-time data sets. It is a perfect tool for basic and advanced array operations.

2. Pandas

PANDAS is an open-source library developed over Numpy and it contains Data Frame as its main data structure. It is used in high-performance data structures and analysis tools. With Data Frame, we can manage and store data from tables by performing manipulation over rows and columns. Panda library makes it easier for a developer to work with relational data. Panda offers fast, expressive and flexible data structures.

Translating complex data operations using mere one or two commands is one of the most powerful features of pandas and it also features time-series functionality.

3. Matplotlib

This is a two-dimensional plotting library of Python a programming language that is very famous among data scientists. Matplotlib is capable of producing data visualizations such as plots, bar charts, scatterplots, and non-Cartesian coordinate graphs.

It is one of the important plotting libraries’ useful in data science projects. This is one of the important library because of which Python can compete with scientific tools like MatLab or Mathematica.

4. SciPy

SciPy library is based on the NumPy concept to solve complex mathematical problems. It comes with multiple modules for statistics, integration, linear algebra and optimization. This library also allows data scientist and engineers to deal with image processing, signal processing, Fourier transforms etc.

If you are going to start your career in the data science field, SciPy will be very helpful to guide you for the whole numerical computations thing.

5. Scikit Learn

Scikit-Learn is open-sourced and most rapidly developing Python libraries. It is used as a tool for data analysis and data mining. Mainly it is used by developers and data scientists for classification, regression and clustering, stock pricing, image recognition, model selection and pre-processing, drug response, customer segmentation and many more.

6. TensorFlow

It is a popular python framework used in deep learning and machine learning and it is developed by Google. It is an open-source math library for mathematical computations. Tensorflow allows python developers to install computations to multiple CPU or GPU in desktop, or server without rewriting the code. Some popular Google products like Google Voice Search and Google Photos are built using the Tensorflow library.

7. Keras

Keras is one of the most expressive and flexible python libraries for research. It is considered as one of the coolest machine learning Python libraries offer the easiest mechanism for expressing neural networks and having all portable models. Keras is written in python and it has the ability to run on top of Theano and TensorFlow.

Compared to other Python libraries, Keras is a bit slow, as it creates a computational graph using the backend structure and then performs operations.

8. Seaborn

It is a data visualization library for a python based on Matplotlib is also integrated with pandas data structures. Seaborn offers a high-level interface for drawing statistical graphs. In simple words, Seaborn is an extension of Matplotlib with advanced features.

Matplotlib is used for basic plotting such as bars, pies, lines, scatter plots and Seaborn is used for a variety of visualization patterns with few syntaxes and less complexity.

With the development of data science and machine learning, Python data science libraries are also advancing day by day. If you are interested in learning python libraries in-depth, get NearLearn’s Best Python Training in Bangalore with real-time projects and live case studies. Other than python, we provide training on Data Science, Machine learning, Blockchain and React JS course. Contact us to get to know about our upcoming training batches and fees.

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[pandeypankaj]

What will we study in Data Science with Python and AI with Python, and what is its scope in the future?

Data Science using Python and AI is an up-and-coming field that merges the power of Python programming with superior AI techniques to extract insightful information from big data.

Key Areas of Study

Python Programming

 Fundamentals: Variables, data types, control flow, functions, modules.

 Libraries: NumPy, Pandas, Matplotlib, Seaborn for data manipulation, analysis, and visualization.

Statistics and Mathematics

 Descriptive Statistics: Mean, median, mode, standard deviation.

 Probability: Distributions, hypothesis testing.

 Linear Algebra: Matrices, vectors, operations.

 Calculus: Derivatives, integrals.

Machine Learning

 Supervised Learning: Regression-Linear, logistic; classification-decision trees, random forests, support vector machines.

 Unsupervised Learning: Clustering, K-means, hierarchical; dimensionality reduction, PCA, t-SNE.

 Deep Learning: Neural networks-convolutional, recurrent; TensorFlow, PyTorch.

Data Cleaning and Preprocessing

 Handling Missing Values: Imputation techniques

 Outlier Detection: Identification and treatment

 Feature Engineering: New feature creation from existing ones.

Natural Language Processing

 Text Preprocessing: Tokenization; stemming; lemmatization

 Sentiment Analysis: Determining the sentiment of a text.

 Text Classification: Categorization of texts into predefined categories.

Computer Vision

 Image Processing: Filtering, Segmentation, Feature Extraction 

 Object Detection: The capability of recognizing objects from images and videos. 

 Image Classification: Classifying images into groups based on their content. 

Future Scope of Data Science with Python and   AI Automation: AI will perform routine tasks from the data scientist and will leave them for more significant roles to play. 

Deep Learning: Breakthroughs in deep learning will make the models' architecture even more complicated and enable them to be more accurate. 

Ethics: Bias, Privacy, and explainability are some of the crucial concerns for AI applications.

Interdisciplinary Applications: Data Science integrated with healthcare, finance, and manufacturing. 

Emerging Technologies: A foray into new areas of generative AI, reinforcement learning, and quantum machine learning.

Conceptual clarity on these topics and an update on the current trends could take you far towards a really rewarding career in data science and AI.

[tonkitk]

Numpy random permute

#Numpy random permute full

Returns outndarray Permuted sequence or array range. axisint, optional The axis which x is shuffled along. If x is an array, make a copy and shuffle the elements randomly. Parameters xint or arraylike If x is an integer, randomly permute np.arange (x). To permute both rows and columns I think you either have to run it twice, or pull some ugly shenanigans with numpy.unravel_index that hurts my head to think about. Randomly permute a sequence, or return a permuted range. PVec0, pVec1 = calcMyPermutationVectors()Īrr.take(pVec0, axis=0, out=arr, mode="clip")Īrr.take(pVec1, axis=1, out=arr, mode="clip") Numba supports top-level functions from the numpy.random module. So in total you call should look something like the following: #Inplace Rearrange On a final note, take seems to be a array method, so instead of np.take(i, rr, axis=0) If x is an integer, randomly permute np.arange (x). If x is a multi-dimensional array, it is only shuffled along its first index. If you don't set the mode it will make a copy to restore the array state on a Python exception (see here). Randomly permute a sequence, or return a permuted range. To do it in-place, all you need to do is specify the "out" parameter to be the same as the input array AND you have to set the mode="clip" or mode="wrap". Here is an example of doing a random permutation of an identity matrix's rows: import numpy as npĪrray(, We are using Excel’s RANDBETWEEN functions for Column B here. axisint, optional Slices of x in this axis are shuffled. Parameters xarraylike, at least one-dimensional Array to be shuffled. Unlike shuffle, each slice along the given axis is shuffled independently of the others. The trick here is we pre- generate the random m’s. method (x, axisNone, outNone) Randomly permute x along axis axis. You can do this in-place with numpy's take() function, but it requires a bit of hoop jumping. For simplicity, we assume N 5, but this methodology works with any N (as long as Excel can hold it) First, you need to set up k’s and m’s.

#Numpy random permute full

Warning: The below example works properly, but using the full set of parameters suggested at the post end exposes a bug, or at least an "undocumented feature" in the numpy.take() function. I currently copy matrices this large unnecessarily, but I would hope this could be easily avoided for permutation. Actually I would like to use matrices as large as possible, but I don't want to deal with the inconvenience of not being able to hold them in RAM, and I do O(N^3) LAPACK operations on the matrix which would also limit the practical matrix size. The matrix is small enough to fit into desktop RAM but big enough that I do not want to copy it thoughtlessly. shuffle: the order of the sub-arrays along the first axis, but the content remains the same, which is equivalent to. Or they mean counting or enumerating all possible permutations. Sometimes when people talk about permutations, they only mean the sampling of random permutations, for example as part of a procedure to obtain p-values in statistics. Also for some simple situations it may be sufficient to just separately track an index permutation, but this is not convenient in my case. Something like this might be possible with numpy's "advanced indexing" but my understanding is that such a solution would not be in-place. Maybe I am just failing at searching the internet. Now, let x be a permutation of the elements of x. Right now I am manually swapping rows and columns, but I would have expected numpy to have a nice function f(M, v) where M has n rows and columns, and v has n entries, so that f(M, v) updates M according to the index permutation v. Write a Python program to print all permutations of a given string (including. Given a 2D array, I would like to permute this array row-wise. Mathematically this corresponds to pre-multiplying the matrix by the permutation matrix P and post-multiplying it by P^-1 = P^T, but this is not a computationally reasonable solution. Efficiently permute array in row wise using Numpy. np.ed is a function, which you need to call, not assign to it. Julia sample without replacement.I want to modify a dense square transition matrix in-place by changing the order of several of its rows and columns, using python's numpy library.

[swarnalata31techiio]

About GitHub :GitHub, Inc. is a provider of Internet hosting for software development and version control using Git. It offers the distributed version control and source code management (SCM) functionality of Git, plus its own features. About GitHub Tensorflow :GitHub TensorFlow hosts various archives on Machine learning as a. Libraries to assemble powerfully, progressed information models b. Apparatuses to empower easier/quicker execution of Tensor codes and Tensor work processes, to imagine the working of Tensor projects and investigate the issues, to do consider the possibility that examination on the Tensor models to upgrade its adequacy c. Top GitHub repositories related to Tensorflow 1. Matplotlib:It is hosted in GitHub and the developments, issues are tracked systematically. 2. Pandas: It is a python library that handles data analysis and manipulation effectively and it manages big volumes of data by splitting them into subsets based on some conditions and forming multiple decision trees. 3. Numpy: This package facilitates mathematical and scientific computing in Python. Several mathematical functions, algebra routines, Fourier conversions and Random number functions are offered as part of this tool. 4. Scipy: SciPy offers vibrant Mathematical, Engineering and Science modules for video/image processing. 5. Scikit-learn: It offers Machine learning modules in Python and it is built over data models built on NumPy, SciPy and matplotlib. GitHub Tensorflow learning: Neural Structured learning (NSL) uses structured signals along with feature inputs to train neural networks. Structures can be a graph in the explicit model or they can be adversarial perturbation in the implicit model.

[pickai]

What Are the Best Books on Data Science?

The general public's interest in data science classes has skyrocketed. What was once a niche topic has suddenly become a hot topic in the news, politics, international law, and our social media feed. Furthermore, consumers enter data points into massive business intelligence systems daily, and data literacy is becoming a highly sought-after skill in all fields. This article contains a selection of publications that may help newcomers navigate the field of data science. They may decide whether they want to stay up to speed on the data boom or wish to launch their data science or data literacy route.

Which Data Science-related Books Are the Best?

To advance your skills, it's essential to stay up to date and pertinent research topics. These books are available for reading by data scientists of various skill levels. You may start your career and advance it with the aid of these books.

Practical Statistics for Data Scientists

Author: Peter Bruce and Andrew Bruce; Publisher: O' Reilly

If you're brand new to data science, this book will thoroughly introduce all the concepts you'll need to understand. Even though the book isn't very popular amongst readers, it does cover all the essential ideas, including bias in sample selection and distribution as well as randomization and sampling. Each has an extensive explanation with examples and explanations of how these principles relate to data science. The book also includes a study of artificial intelligence models.

Every subject a data scientist would need to know is in this book. Unfortunately, the descriptions and examples are not enough to help the reader fully understand the concepts, but it is a valuable and quick reference.

R for Data Science

Author: Hadley Wickham & Garrett Grolemund; Publisher: O' Reilly

This data science book introduces R, RStudio, and the tidyverse, a collection of R modules. This makes it easier and more fun to analyze data. Even if the student has no programming experience, R for Data Science is designed to start you doing data science as soon as feasible.

The writers will guide you through the steps of importing, manipulating, researching, modelling and distributing your data. You'll discover the big picture of the data science cycle and the critical equipment you'll require to handle the details. Activities are included in every chapter of the book to assist you in applying what you have learned.

Python Data Science Handbook

Author: Jake VanderPlas; Publisher: O 'Reilly

This book is for you if you're taking a data science course. In addition to improving their command of Python for data science and letting it work, it necessitates a fast reference manual for all the methods and library functions. The book covers the following topics extensively and in-depth: I Python (Interactive Python), Numpy, Pandas' data processing, Matplotlib visualization, and Sci-kit-Learn taught and unsupervised machine learning approaches. Your ability to hone your skills in the early phases of any data science project cycle will be significantly helped by the quantity and quality of knowledge supplied in these areas.

Big Data: A Revolution That Will Transform How We Live, Work, and Think

Author: Viktor Mayer-Schonberger and Kenneth Cukier

This book is an excellent introduction to business analytics and big data. The target audience for the book is organizational professionals who want to learn more about big data and how it affects data policies, techniques, and analytical decision-making in diverse industries. It is crammed with examples highlighting big data solutions and their impact on many sectors. The authors did well in describing the many levels of value that may be produced by leveraging big data. It also covers the drawbacks of business analytics and the organizational safeguards that must be implemented to address all these worries.

Introduction to Machine Learning with Python: A Guide for Data Scientists

Author: Andreas C. Müller and Sarah Guido

You may use this book to master Python and machine learning, two essential concepts in the Data Science course. The explanation of ideas is in simple language and gets the support of several instances to facilitate understanding. The language is plain and friendly. Although machine learning is a complicated subject, you should be able to create your ML models after using the book as a practice. You will gain a thorough knowledge of machine learning subjects. Although the book includes examples in Python, reading it doesn't require any prior understanding of math or programming. This book covers the foundations and is written with beginners in mind. But reading this book alone won't be enough when you go further into machine learning and coding.

Data Science for Dummies (2nd Edition)

Author: Lillian Pierson

Although it isn't new (Publication year: 2017), Data Science for Dummies is an excellent place for beginners to start. Lillian Pierson's Data Science book includes chapters on MPP platforms, Spark, machine learning, NoSQL, Hadoop, big data analytics, MapReduce, and artificial intelligence, to name just a few. Because technical students and IT professionals are its intended audience, the term could be deceptive. The book provides a thorough overview of data science rather than a step-by-step lesson, making the complex subject easier to understand.

Data Science from the Scratch: First Principles with Python 

Author: Joel Gurus

It is one of those crucial books in data science which help teach arithmetic and statistics. These topics are essential to data science courses. You'll also learn the hacking skills required to start working as a data scientist. The topics addressed in the books are how to implement k-nearest neighbours, Nave Bayes, linear and logistic regression, decision trees, and clustering models. Additionally, readers will learn about network analysis, natural language processing, and other subjects.

The Art of Data Science

Author: Roger D. Peng and Elizabeth Matsui

The focus of "The Art of Data Science" is on the skill of sifting through and finding new information inside any data set that is available to you. It highlights the process of scrutinizing and enhancing data to see the actual narrative. The authors draw on their knowledge to help experts and beginners through data science analysis. Both authors have experience leading data projects and analysts in a professional setting. They discuss their observations of what regularly results in outstanding results and what risks guaranteeing a data endeavour's failure.

Conclusion

A great way to learn about the subject or hone your skills is to read the best books possible while enrolled in a Data Science course. Do not let the abundance of literature on data science scare you. There is no need for you read them all. You may create practical models and gain a thorough grasp of data science, nevertheless, with the help of some of the books included in this blog. Additional reference books that may be helpful include "Data Analytics made Accessibly," "The Hundred-page Machine Learning Book," "Teach Yourself SQL," and "Teach Yourself SQL, Too Big to Ignore." To kick start your data science career, read these books.