NumPy is a Python library used for working with multidimensional arrays and matrices for scientific computing. It allows fast operations on arrays through optimized C code and is the foundation of the Python scientific computing stack. NumPy arrays can be created in many ways and support operations like indexing, slicing, broadcasting, and universal functions. NumPy provides many useful features for linear algebra, Fourier transforms, random number generation and more.
This is the basic introduction of the pandas library, you can use it for teaching this library for machine learning introduction. This slide will be able to help to understand the basics of pandas to the students with no coding background.
Abstract: This PDSG workshop introduces the basics of Python libraries used in machine learning. Libraries covered are Numpy, Pandas and MathlibPlot.
Level: Fundamental
Requirements: One should have some knowledge of programming and some statistics.
( Python Training: https://www.edureka.co/python )
This Edureka Python Numpy tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what exactly is Numpy and how it is better than Lists. It also explains various Numpy operations with examples.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
This tutorial helps you to learn the following topics:
1. What is Numpy?
2. Numpy v/s Lists
3. Numpy Operations
4. Numpy Special Functions
This is the basic introduction of the pandas library, you can use it for teaching this library for machine learning introduction. This slide will be able to help to understand the basics of pandas to the students with no coding background.
Abstract: This PDSG workshop introduces the basics of Python libraries used in machine learning. Libraries covered are Numpy, Pandas and MathlibPlot.
Level: Fundamental
Requirements: One should have some knowledge of programming and some statistics.
( Python Training: https://www.edureka.co/python )
This Edureka Python Numpy tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what exactly is Numpy and how it is better than Lists. It also explains various Numpy operations with examples.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
This tutorial helps you to learn the following topics:
1. What is Numpy?
2. Numpy v/s Lists
3. Numpy Operations
4. Numpy Special Functions
This slide is used to do an introduction for the matplotlib library and this will be a very basic introduction. As matplotlib is a very used and famous library for machine learning this will be very helpful to teach a student with no coding background and they can start the plotting of maps from the ending of the slide by there own.
This presentation is all about various built in
datastructures which we have in python.
List
Dictionary
Tuple
Set
and various methods present in each data structure
This document is useful when use with Video session I have recorded today with execution, This is document no. 2 of course "Introduction of Data Science using Python". Which is a prerequisite of Artificial Intelligence course at Ethans Tech.
Disclaimer: Some of the Images and content have been taken from Multiple online sources and this presentation is intended only for Knowledge Sharing
A class is a code template for creating objects. Objects have member variables and have behaviour associated with them. In python a class is created by the keyword class.
An object is created using the constructor of the class. This object will then be called the instance of the class.
This Edureka Python Matplotlib tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what is data visualization and how to perform data visualization using Matplotlib. It also explains how to modify your plot and how to plot various types of graphs. Below are the topics covered in this tutorial:
1. Why Data Visualization?
2. What Is Data Visualization?
3. Various Types Of Plots
4. What Is Matplotlib?
6. How To Use Matplotlib?
Introduction to Pandas and Time Series Analysis [PyCon DE]Alexander Hendorf
Most data is allocated to a period or to some point in time. We can gain a lot of insight by analyzing what happened when. The better the quality and accuracy of our data, the better our predictions can become.
Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,…. It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists.
Dealing with periodical data can be a challenge. This talk will show to how you can deal with it with Pandas.
What is Multithreading In Python | Python Multithreading Tutorial | EdurekaEdureka!
YouTube Link: https://youtu.be/JnFfp81VbOs
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on 'Multithreading in Python'' will help you understand the concept of threading in python. Below are the topics covered in this live PPT:
What is multitasking in Python?
Types of multitasking
What is a thread?
How to achieve multithreading in Python?
When to use multithreading?
How to create threads in Python?
Advantages of multithreading
Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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This slide is used to do an introduction for the matplotlib library and this will be a very basic introduction. As matplotlib is a very used and famous library for machine learning this will be very helpful to teach a student with no coding background and they can start the plotting of maps from the ending of the slide by there own.
This presentation is all about various built in
datastructures which we have in python.
List
Dictionary
Tuple
Set
and various methods present in each data structure
This document is useful when use with Video session I have recorded today with execution, This is document no. 2 of course "Introduction of Data Science using Python". Which is a prerequisite of Artificial Intelligence course at Ethans Tech.
Disclaimer: Some of the Images and content have been taken from Multiple online sources and this presentation is intended only for Knowledge Sharing
A class is a code template for creating objects. Objects have member variables and have behaviour associated with them. In python a class is created by the keyword class.
An object is created using the constructor of the class. This object will then be called the instance of the class.
This Edureka Python Matplotlib tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what is data visualization and how to perform data visualization using Matplotlib. It also explains how to modify your plot and how to plot various types of graphs. Below are the topics covered in this tutorial:
1. Why Data Visualization?
2. What Is Data Visualization?
3. Various Types Of Plots
4. What Is Matplotlib?
6. How To Use Matplotlib?
Introduction to Pandas and Time Series Analysis [PyCon DE]Alexander Hendorf
Most data is allocated to a period or to some point in time. We can gain a lot of insight by analyzing what happened when. The better the quality and accuracy of our data, the better our predictions can become.
Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,…. It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists.
Dealing with periodical data can be a challenge. This talk will show to how you can deal with it with Pandas.
What is Multithreading In Python | Python Multithreading Tutorial | EdurekaEdureka!
YouTube Link: https://youtu.be/JnFfp81VbOs
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on 'Multithreading in Python'' will help you understand the concept of threading in python. Below are the topics covered in this live PPT:
What is multitasking in Python?
Types of multitasking
What is a thread?
How to achieve multithreading in Python?
When to use multithreading?
How to create threads in Python?
Advantages of multithreading
Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Effective Numerical Computation in NumPy and SciPyKimikazu Kato
Presented at PyCon JP 2014.
Video is available at
http://bit.ly/1tXYhw6
This talk explores case studies of effective usage of Numpy/Scipy and shows that the computational speed sometimes improves drastically with the appropriate derivation of formulas and performance-conscious implementation. I especially focus on scipy.sparse, the module for sparse matrices, which is often useful in the areas of machine learning and natural language processing.
Michal Mucha: Build and Deploy an End-to-end Streaming NLP Insight System | P...PyData
At this workshop, you will build your own messaging insights system - data ingestion from a live data source (Reddit), queueing, deploying a machine learning model, and serving messages with insights to your mobile phone!
Unit testing data with marbles - Jane Stewart Adams, Leif WalshPyData
In the same way that we need to make assertions about how code functions, we need to make assertions about data, and unit testing is a promising framework. In this talk, we'll explore what is unique about unit testing data, and see how Two Sigma's open source library Marbles addresses these unique challenges in several real-world scenarios.
The TileDB Array Data Storage Manager - Stavros Papadopoulos, Jake BolewskiPyData
TileDB is an open-source storage manager for multi-dimensional sparse and dense array data. It has a novel architecture that addresses some of the pain points in storing array data on “big-data” and “cloud” storage architectures. This talk will highlight TileDB’s design and its ability to integrate with analysis environments relevant to the PyData community such as Python, R, Julia, etc.
Using Embeddings to Understand the Variance and Evolution of Data Science... ...PyData
In this talk I will discuss exponential family embeddings, which are methods that extend the idea behind word embeddings to other data types. I will describe how we used dynamic embeddings to understand how data science skill-sets have transformed over the last 3 years using our large corpus of jobs. The key takeaway is that these models can enrich analysis of specialized datasets.
Deploying Data Science for Distribution of The New York Times - Anne BauerPyData
How many newspapers should be distributed to each store for sale every day? The data science group at The New York Times addresses this optimization problem using custom time series modeling and analytical solutions, while also incorporating qualitative business concerns. I'll describe our modeling and data engineering approaches, written in Python and hosted on Google Cloud Platform.
Graph Analytics - From the Whiteboard to Your Toolbox - Sam LermaPyData
However, the the graph theory jargon can make graph analytics seem more intimidating for self-study than is necessary. In this talk, the audience will be exposed to some of the basic concepts of graph theory (no prerequisite math knowledge needed!) and a few of the Python tools available for graph analysis.
Do Your Homework! Writing tests for Data Science and Stochastic Code - David ...PyData
To productionize data science work (and have it taken seriously by software engineers, CTOs, clients, or the open source community), you need to write tests! Except… how can you test code that performs nondeterministic tasks like natural language parsing and modeling? This talk presents an approach to testing probabilistic functions in code, illustrated with concrete examples written for Pytest.
RESTful Machine Learning with Flask and TensorFlow Serving - Carlo MazzaferroPyData
Those of us who use TensorFlow often focus on building the model that's most predictive, not the one that's most deployable. So how to put that hard work to work? In this talk, we'll walk through a strategy for taking your machine learning models from Jupyter Notebook into production and beyond.
Mining dockless bikeshare and dockless scootershare trip data - Stefanie Brod...PyData
In September 2017, dockless bikeshare joined the transportation options in the District of Columbia. In March 2018, scooter share followed. During the pilot of these technologies, Python has helped District Department of Transportation answer some critical questions. This talk will discuss how Python was used to answer research questions and how it supported the evaluation of this demonstration.
Avoiding Bad Database Surprises: Simulation and Scalability - Steven LottPyData
There are many stories of developers creating databases that don't operate at scale. The application is good, but the database won't work the realistic volumes of data. It's like a horror movie where they never looked behind the door, ran into the dark forest and night, and discovered the database was the monster killing their application. How can we leverage Python to avoid scaling problems?
Machine learning often requires us to think spatially and make choices about what it means for two instances to be close or far apart. So which is best - Euclidean? Manhattan? Cosine? It all depends! In this talk, we'll explore open source tools and visual diagnostic strategies for picking good distance metrics when doing machine learning on text.
End-to-End Machine learning pipelines for Python driven organizations - Nick ...PyData
The recent advances in machine learning and artificial intelligence are amazing! Yet, in order to have real value within a company, data scientists must be able to get their models off of their laptops and deployed within a company’s data pipelines and infrastructure. In this session, I'll demonstrate how one-off experiments can be transformed into scalable ML pipelines with minimal effort.
We will be using Beautiful Soup to Webscrape the IMDB website and create a function that will allow you to create a dictionary object on specific metadata of the IMDB profile for any IMDB ID you pass through as an argument.
1D Convolutional Neural Networks for Time Series Modeling - Nathan Janos, Jef...PyData
This talk describes an experimental approach to time series modeling using 1D convolution filter layers in a neural network architecture. This approach was developed at System1 for forecasting marketplace value of online advertising categories.
Extending Pandas with Custom Types - Will AydPyData
Pandas v.0.23 brought to life a new extension interface through which you can extend NumPy's type system. This talk will explain what that means in more detail and provide practical examples of how the new interface can be leveraged to drastically improve your reporting.
Machine learning models are increasingly used to make decisions that affect people’s lives. With this power comes a responsibility to ensure that model predictions are fair. In this talk I’ll introduce several common model fairness metrics, discuss their tradeoffs, and finally demonstrate their use with a case study analyzing anonymized data from one of Civis Analytics’s client engagements.
What's the Science in Data Science? - Skipper SeaboldPyData
The gold standard for validating any scientific assumption is to run an experiment. Data science isn’t any different. Unfortunately, it’s not always possible to design the perfect experiment. In this talk, we’ll take a realistic look at measurement using tools from the social sciences to conduct quasi-experiments with observational data.
Applying Statistical Modeling and Machine Learning to Perform Time-Series For...PyData
Forecasting time-series data has applications in many fields, including finance, health, etc. There are potential pitfalls when applying classic statistical and machine learning methods to time-series problems. This talk will give folks the basic toolbox to analyze time-series data and perform forecasting using statistical and machine learning models, as well as interpret and convey the outputs.
Solving very simple substitution ciphers algorithmically - Stephen Enright-WardPyData
A historical text may now be unreadable, because its language is unknown, or its script forgotten (or both), or because it was deliberately enciphered. Deciphering needs two steps: Identify the language, then map the unknown script to a familiar one. I’ll present an algorithm to solve a cartoon version of this problem, where the language is known, and the cipher is alphabet rearrangement.
The Face of Nanomaterials: Insightful Classification Using Deep Learning - An...PyData
Artificial intelligence is emerging as a new paradigm in materials science. This talk describes how physical intuition and (insightful) machine learning can solve the complicated task of structure recognition in materials at the nanoscale.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
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Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
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3. NumPy is a Python C extension library for array-oriented computing
Efficient
In-memory
Contiguous (or Strided)
Homogeneous (but types can be algebraic)
NumPy is suited to many applications
Image processing
Signal processing
Linear algebra
A plethora of others
4. NumPy is the foundation of theNumPy is the foundation of the
python scientific stackpython scientific stack
10. Array Element Type (dtype)Array Element Type (dtype)
NumPy arrays comprise elements of a single data type
The type object is accessible through the .dtype attribute
Here are a few of the most important attributes of dtype objects
dtype.byteorder — big or little endian
dtype.itemsize — element size of this dtype
dtype.name — a name for this dtype object
dtype.type — type object used to create scalars
There are many others...
11. Array dtypes are usually inferred automatically
But can also be specified explicitly
In [16]: a = np.array([1,2,3])
In [17]: a.dtype
Out[17]: dtype('int64')
In [18]: b = np.array([1,2,3,4.567])
In [19]: b.dtype
Out[19]: dtype('float64')
In [20]: a = np.array([1,2,3], dtype=np.float32)
In [21]: a.dtype
Out[21]: dtype('int64')
In [22]: a
Out[22]: array([ 1., 2., 3.], dtype=float32)
12. NumPy Builtin dtype HierarchyNumPy Builtin dtype Hierarchy
np.datetime64 is a new addition in NumPy 1.7
13. Array CreationArray Creation
Explicitly from a list of values
As a range of values
By specifying the number of elements
In [2]: np.array([1,2,3,4])
Out[2]: array([1, 2, 3, 4])
In [3]: np.arange(10)
Out[3]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
In [4]: np.linspace(0, 1, 5)
Out[4]: array([ 0. , 0.25, 0.5 , 0.75, 1. ])
20. Array ViewsArray Views
Simple assigments do not make copies of arrays (same semantics as
Python). Slicing operations do not make copies either; they return views
on the original array.
Array views contain a pointer to the original data, but may have different
shape or stride values. Views always have flags.owndata equal to
False.
In [2]: a = np.arange(10)
In [3]: b = a[3:7]
In [4]: b
Out[4]: array([3, 4, 5, 6])
In [5]: b[:] = 0
In [6]: a
Out[6]: array([0, 1, 3, 0, 0, 0, 0, 7, 8, 9])
In [7]: b.flags.owndata
Out[7]: False
21. Universal Functions (ufuncs)Universal Functions (ufuncs)
NumPy ufuncs are functions that operate element-wise on one or more
arrays
ufuncs dispatch to optimized C inner-loops based on array dtype
23. AxisAxis
Array method reductions take an optional axis parameter that specifies
over which axes to reduce
axis=None reduces into a single scalar
In [7]: a.sum
()
Out[7]: 105
25. axis=0 reduces into the zeroth dimension
axis=0 reduces into the first dimension
In [8]: a.sum(axis=0)
Out[8]: array([15, 18, 21, 24,
27])
In [9]: a.sum(axis=1)
Out[9]: array([10, 35, 60])
26. BroadcastingBroadcasting
A key feature of NumPy is broadcasting, where arrays with different, but
compatible shapes can be used as arguments to ufuncs
In this case an array scalar is broadcast to an array with shape (5, )
27. A slightly more involved broadcasting example in two dimensions
Here an array of shape (3, 1) is broadcast to an array with shape
(3, 2)
28. Broadcasting RulesBroadcasting Rules
In order for an operation to broadcast, the size of all the trailing
dimensions for both arrays must either:
be equal OR be one
A (1d array): 3
B (2d array): 2 x 3
Result (2d array): 2 x 3
A (2d array): 6 x 1
B (3d array): 1 x 6 x 4
Result (3d array): 1 x 6 x 4
A (4d array): 3 x 1 x 6 x 1
B (3d array): 2 x 1 x 4
Result (4d array): 3 x 2 x 6 x 4
29. Square Peg in a Round HoleSquare Peg in a Round Hole
If the dimensions do not match up, np.newaxis may be useful
In [16]: a = np.arange(6).reshape((2, 3))
In [17]: b = np.array([10, 100])
In [18]: a * b
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
in ()
----> 1 a * b
ValueError: operands could not be broadcast together with shapes (2,3) (2)
In [19]: b[:,np.newaxis].shape
Out[19]: (2, 1)
In [20]: a *b[:,np.newaxis]
Out[20]:
array([[ 0, 10, 20],
[300, 400, 500]])
31. Fancy IndexingFancy Indexing
NumPy arrays may be used to index into other arrays
In [2]: a = np.arange(15).reshape((3,5))
In [3]: a
Out[3]:
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])
In [4]: i = np.array([[0,1], [1, 2]])
In [5]: j = np.array([[2, 1], [4, 4]])
In [6]: a[i,j]
Out[6]:
array([[ 2, 6],
[ 9, 14]])
32. Boolean arrays can also be used as indices into other arrays
In [2]: a = np.arange(15).reshape((3,5))
In [3]: a
Out[3]:
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])
In [4]: b = (a % 3 == 0)
In [5]: b
Out[5]:
array([[ True, False, False, True, False],
[False, True, False, False, True],
[False, False, True, False, False]], dtype=bool)
In [6]: a[b]
Out[6]: array([ 0, 3, 6, 9, 12])
40. ResourcesResources
These slides are currently available at
http://docs.scipy.org/doc/numpy/reference/
http://docs.scipy.org/doc/numpy/user/index.html
http://www.scipy.org/Tentative_NumPy_Tutorial
http://www.scipy.org/Numpy_Example_List
https://github.com/ContinuumIO/tutorials/blob/master/IntrotoNumPy.pdf
41. The EndThe End
Many thanks toMany thanks to
Ben Zaitlin
Stéfan van der Walt
Amy Troschinetz
Maggie Mari
Travis Oliphant
Questions?Questions?