This document provides an overview of different types of charts used for data visualization, including column charts, bar charts, pie charts, doughnut charts, line charts, area charts, scatter charts, spider/radar charts, gauge charts, and comparison charts. It describes the purpose and use of each chart type, highlighting when each type is most effective to visualize different kinds of data relationships. The document aims to help readers select the most appropriate chart type based on their data and visualization goals.
The document discusses various data visualization techniques using Matplotlib in Python. It covers creating basic line plots and scatter plots, customizing plots by adding labels, legends, colors and styles. It also discusses different chart types like pie charts, bar charts, histograms and boxplots. Advanced techniques like showing correlations and time series analysis are also covered. The document provides code examples for each visualization technique.
From Data to Knowledge thru Grailog Visualizationgiurca
Visualization of Data & Knowledge: Graphs Remove Entry Barrier to Logic: From 1-dimensional symbol-logic knowledge specification to 2-dimensional graph-logic visualization in a systematic 2D syntax; Supports human in the loop across knowledge elicitation, specification, validation, and reasoning; Combinable with graph transformation, (‘associative’) indexing & parallel processing for efficient implementation of specifications
A brief introduction to data visualisation using R. It contains both basic and advanced visualisation techniques with sample codes. The datasets being used are mostly available with RStudio.
This document discusses different types of graphs and charts, their purposes and guidelines for use. It defines the key difference between graphs and charts, with graphs representing relationships between objects and charts representing data through symbols. Common chart types are described like line charts to show changes over time, bar charts to compare categories, and pie charts to show proportions of a whole. The document provides examples and guidelines for effective graph and chart creation.
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.
PYTHON-Chapter 4-Plotting and Data Science PyLab - MAULIK BORSANIYAMaulik Borsaniya
This document discusses data visualization and Matplotlib. It begins with an introduction to data visualization and its importance. It then covers basic visualization rules like labeling axes and adding titles. It discusses what Matplotlib is and how to install it. It provides examples of common plot types in Matplotlib like sine waves, scatter plots, bar charts, and pie charts. It also discusses working with data science and Pandas, including how to create Pandas Series and DataFrames from various data sources.
This project is based on Library Management. Python and MySQL are the programming platforms which are used in making of this project.
Subject-Informatics Practices
Class-11/12
This document provides an overview of different types of charts used for data visualization, including column charts, bar charts, pie charts, doughnut charts, line charts, area charts, scatter charts, spider/radar charts, gauge charts, and comparison charts. It describes the purpose and use of each chart type, highlighting when each type is most effective to visualize different kinds of data relationships. The document aims to help readers select the most appropriate chart type based on their data and visualization goals.
The document discusses various data visualization techniques using Matplotlib in Python. It covers creating basic line plots and scatter plots, customizing plots by adding labels, legends, colors and styles. It also discusses different chart types like pie charts, bar charts, histograms and boxplots. Advanced techniques like showing correlations and time series analysis are also covered. The document provides code examples for each visualization technique.
From Data to Knowledge thru Grailog Visualizationgiurca
Visualization of Data & Knowledge: Graphs Remove Entry Barrier to Logic: From 1-dimensional symbol-logic knowledge specification to 2-dimensional graph-logic visualization in a systematic 2D syntax; Supports human in the loop across knowledge elicitation, specification, validation, and reasoning; Combinable with graph transformation, (‘associative’) indexing & parallel processing for efficient implementation of specifications
A brief introduction to data visualisation using R. It contains both basic and advanced visualisation techniques with sample codes. The datasets being used are mostly available with RStudio.
This document discusses different types of graphs and charts, their purposes and guidelines for use. It defines the key difference between graphs and charts, with graphs representing relationships between objects and charts representing data through symbols. Common chart types are described like line charts to show changes over time, bar charts to compare categories, and pie charts to show proportions of a whole. The document provides examples and guidelines for effective graph and chart creation.
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.
PYTHON-Chapter 4-Plotting and Data Science PyLab - MAULIK BORSANIYAMaulik Borsaniya
This document discusses data visualization and Matplotlib. It begins with an introduction to data visualization and its importance. It then covers basic visualization rules like labeling axes and adding titles. It discusses what Matplotlib is and how to install it. It provides examples of common plot types in Matplotlib like sine waves, scatter plots, bar charts, and pie charts. It also discusses working with data science and Pandas, including how to create Pandas Series and DataFrames from various data sources.
This project is based on Library Management. Python and MySQL are the programming platforms which are used in making of this project.
Subject-Informatics Practices
Class-11/12
One of the best ways to analyze any process is to plot the data. Different graphs can reveal different characteristics of your data such as the central tendency, the dispersion and the general shape for thedistribution.
Week-3 – System RSupplemental material1Recap •.docxhelzerpatrina
Week-3 – System R
Supplemental material
1
Recap
• R - workhorse data structures
• Data frame
• List
• Matrix / Array
• Vector
• System-R – Input and output
• read() function
• read.table and read.csv
• scan() function
• typeof() function
• Setwd() function
• print()
• Factor variables
• Used in category analysis and statistical modelling
• Contains predefined set value called levels
• Descriptive statistics
• ls() – list of named objects
• str() – structure of the data and not the data itself
• summary() – provides a summary of data
• Plot() – Simple plot
2
Descriptive statistics - continued
• Summary of commands with single-value result. These commands will work on variables
containing numeric value.
• max() ---- It shows the maximum value in the vector
• min() ----- It shows the minimum value in the vector
• sum() ----- It shows the sum of all the vector elements.
• mean() ---- It shows the arithmetic mean for the entire vector
• median() – It shows the median value of the vector
• sd() – It shows the standard deviation
• var() – It show the variance
3
Descriptive statistics - single value results -
example
temp is the name of the vector
containing all numeric values
4
• log(dataset) – Shows log value for each
element.
• summary(dataset) –shows the summary
of values
• quantile() - Shows the quantiles by
default—the 0%, 25%, 50%, 75%, and
100% quantiles. It is possible to select
other quantiles also.
Descriptive statistics - multiple value results -
example
5
Descriptive Statistics in R for Data Frames
• Max(frame) – Returns the largest value in the entire data frame.
• Min(frame) – Returns the smallest value in the entire data frame.
• Sum(frame) – Returns the sum of the entire data frame.
• Fivenum(frame) – Returns the Tukey summary values for the entire
data frame.
• Length(frame)- Returns the number of columns in the data frame.
• Summary(frame) – Returns the summary for each column.
6
Descriptive Statistics in R for Data Frames -
Example
7
Descriptive Statistics in R for Data Frames –
RowMeans example
8
Descriptive Statistics in R for Data Frames –
ColMeans example
9
Graphical analysis - simple linear regression model
in R
• Logistic regression is implemented to understand if the dependent
variable is a linear function of the independent variable.
• Logistic regression is used for fitting the regression curve.
• Pre-requisite for implementing linear regression:
• Dependent variable should conform to normal distribution
• Cars dataset that is part of the R-Studio will be used as an example to
explain linear regression model.
10
Creating a simple linear model
• cars is a dataset preloaded into
System-R studio.
• head() function prints the first
few rows of the list/df
• cars dataset contains two major
columns
• X = speed (cars$speed)
• Y = dist (cars$dist)
• data() function is used to list all
the active datasets in the
environment.
• ...
Data Visualization using different python libraries.pptxHamzaAli998966
This document discusses data visualization using Python libraries like Pandas, NumPy, and Matplotlib. It covers various types of charts that can be created like line charts, bar charts, and histograms to visualize different aspects of stock market data. Descriptive statistics are calculated on the stock data and various visualizations are created to analyze trends in closing prices, moving averages, daily returns, and correlations between stocks. Finally, it discusses predicting future closing stock prices of Apple using an LSTM model.
From data to diagrams: an introduction to basic graphs and chartsSchool of Data
This document provides training on data visualization and transforming data into diagrams. It discusses choosing the appropriate type of visualization based on the data and questions, including pie charts to show parts of a whole, bar charts to compare categories, line graphs to show changes over time, and maps to relate data to geography. Guidelines are provided for effectively designing each type of visualization, such as limiting the number of pie chart segments and starting bar and line graphs at zero. The importance of telling a story and engaging readers is also emphasized.
The document discusses different types of charts including column charts, bar charts, pie charts, line charts, area charts, stock charts, radar charts, bubble charts, scatter charts, and combo charts. For each chart type, the document outlines typical uses, advantages, and disadvantages. It provides an example of each chart type to illustrate how the chart can be constructed and interpreted.
Design Patterns
Christian Behrens
https://www.behance.net/gallery/29576487/The-Form-of-Facts-and-Figures
Christopher Alexander
The term design patterns was originally coined about three decades ago by Christopher Alexander, an architect and critic who envisioned a generic and modular “language” of methods to describe the process of construction and urban planning by means of recurring problems that are well-known in a specific context, and respective solutions that have been proved and tested in the past and can therefore be seen as a safe choice to tackle a certain design challenge. Although it never made its breakthrough in the field of architecture, the basic idea of design patterns was adopted by other engineering disciplines, most notably software development in the early 1990s. A second wave of success seems to have appeared recently, when several projects were launched to build up pattern libraries for digital user interfaces. https://en.wikipedia.org/wiki/Christopher_Alexander
2
Design Patterns
Rejected by Architects, Adopted by Software Engineers,
…and the field of user interface design.
Although Alexander’s book became a bestseller and is a de-facto standard read for architecture students until today, it received much criticism and invoked sceptical reactions among the architecture community. Looking back at it some thirty years later, Alexander’s pattern language can be described as a success story on a detour. While widely rejected by architects and urban planners, the concept was picked up by computer scientist in the late 1980s and became a huge success in the wake of the rise of object-oriented programming languages such as Java
3
Design Patterns
Rejected by Architects, Adopted by Software Engineers,
…and the field of user interface design.
http://zurb.com/patterntap
http://patternry.com/
useful for the general description of common design problems, and provide solutions based on the relationships and behaviors of objects Companies and institutions that deal with interface design problems, have also launched own projects that aim at streamlining the development of new products and services by means of a comprehensive design pattern collection.
Design Patterns can help to tackle commonly known recurring design problems with well-proven solutions. A single pattern provides a brief description of one particular design problem. This problem can be a physical attribute of an application (for instance a dropdown menu), or a functional behavior (e.g. the login dialog of a website). A pattern typically consists of a description of the problem, and a solution that has been proven before and is generally recognized. Usually, a pattern provides additional information like an example of a real-world scenario in which it has been successfully applied as well as a rationale to briefly describe the benefit the usage this patterns bears.
4
Discrete Quantities:
Simple Bar Chart
Snapshot:
they do not display con.
This document discusses multi-dimensional modeling and data warehousing implementation. It describes prediction cubes, which store prediction models in a multidimensional space to enable predictive analytics in an OLAP manner. It also covers attribute-oriented induction for data generalization, including attribute removal, generalization, and thresholding. Regarding data warehouse implementation, it outlines efficient data cube computation through cuboid materialization and indexing techniques like bitmap indexes and join indices to speed up OLAP queries.
Graphs are used to visually represent data and relationships between variables. There are various types of graphs that can be used for different purposes. Histograms represent the distribution of continuous variables. Bar graphs display the distribution of categorical variables or allow for comparisons between categories. Line graphs show trends and patterns over time. Pie charts summarize categorical data as percentages of a whole. Cubic graphs refer to graphs where all vertices have a degree of three. Response surface plots visualize the relationship between multiple independent variables and a response variable.
Visualization and Matplotlib using Python.pptxSharmilaMore5
This document provides an overview of Matplotlib, a Python data visualization library. It discusses Matplotlib's pyplot and OO APIs, how to install Matplotlib, create basic plots using functions like plot(), and customize plots using markers and line styles. It also covers displaying plots, the Matplotlib user interface, Matplotlib's relationships with NumPy and Pandas, and examples of different types of graphs and charts like line plots that can be created with Matplotlib.
This document discusses different types of graphs and charts, their uses, and provides examples. It summarizes 6 common types: line graphs show trends over time; bar charts compare categorical data with bars; pie charts illustrate proportional data with slices; histograms show distributions of continuous data with columns; scatter plots show relationships between two variables with x-y axes; and Venn charts visualize logical relationships between groups with overlapping circles. The document provides examples and descriptions of when each type would be useful.
This document provides summaries of various data visualization techniques in Python including:
Seaborn is a Python library for statistical graphics and is built on matplotlib. It supports NumPy and Pandas data structures.
FacetGrid allows plotting multiple axes showing the same relationship conditioned on different levels of variables from a Pandas DataFrame. It can condition on up to three variables.
Kdeplot fits and plots univariate or bivariate kernel density estimates and allows customizing colors, shading, and adding a colorbar.
Jointplot provides a wrapper for the JointGrid class to create scatter plots, regression plots, residual plots, and histograms of two variables jointly.
Heatmap plots a hierarchically clustered heatmap of a matrix dataset
Matplotlib is a popular Python library used for data visualization and creating plots from data. It allows creating different types of charts including line charts, bar charts, and pie charts. Line charts connect data points with straight lines and are used to show trends over time. Bar charts represent categorical data with rectangular bars of different heights. Pie charts show proportions between categories by dividing a circle into wedge-shaped slices. Matplotlib provides functions and methods to customize charts with options like colors, labels, titles and more.
Me 443 4 plotting curves Erdi Karaçal Mechanical Engineer University of Gaz...Erdi Karaçal
- The document discusses various plotting and graphics capabilities in Mathematica, including plotting functions, parametric plots, plotting lists of data, options for controlling plot appearance, and two-dimensional and three-dimensional graphics.
- It describes how to control aspects of plots like scales, sampling of functions, and colors using options. It also covers various graphics primitives and directives for customizing plots.
This short text will get you up to speed in no time on creating visualizations using R's ggplot2 package. It was developed as part of a training to those who had no prior experience in R and had limited knowledge on general programming concepts. It's a must have initial guide for those exploring the field of Data Science
The document discusses different types of graphs and charts used to visualize data, including their purposes and appropriate uses. It explains that line charts show trends over time, column charts compare items within categories, and pie charts show proportional relationships. Scatter plots and bubble charts illustrate correlations between two variables, while area charts emphasize changes between data points. Combination charts allow comparison of multiple categories. Proper visualization selection depends on the problem and data to be depicted.
A Subgraph Pattern Search over Graph DatabasesIJMER
The document discusses methods for continuous subgraph pattern searching over graph databases and graph streams. It proposes using Node-Neighbor Trees (NNTs) to represent local graph structures, and projecting NNTs to numerical vectors to enable approximate subgraph isomorphism checking. It also describes how to handle uncertain graph streams by computing probability upper bounds to filter out graph stream-query pairs that are unlikely to match. The overall approach conducts structural filtering followed by probability pruning to reduce the search space when capturing patterns over uncertain graph streams.
One of the best ways to analyze any process is to plot the data. Different graphs can reveal different characteristics of your data such as the central tendency, the dispersion and the general shape for thedistribution.
Week-3 – System RSupplemental material1Recap •.docxhelzerpatrina
Week-3 – System R
Supplemental material
1
Recap
• R - workhorse data structures
• Data frame
• List
• Matrix / Array
• Vector
• System-R – Input and output
• read() function
• read.table and read.csv
• scan() function
• typeof() function
• Setwd() function
• print()
• Factor variables
• Used in category analysis and statistical modelling
• Contains predefined set value called levels
• Descriptive statistics
• ls() – list of named objects
• str() – structure of the data and not the data itself
• summary() – provides a summary of data
• Plot() – Simple plot
2
Descriptive statistics - continued
• Summary of commands with single-value result. These commands will work on variables
containing numeric value.
• max() ---- It shows the maximum value in the vector
• min() ----- It shows the minimum value in the vector
• sum() ----- It shows the sum of all the vector elements.
• mean() ---- It shows the arithmetic mean for the entire vector
• median() – It shows the median value of the vector
• sd() – It shows the standard deviation
• var() – It show the variance
3
Descriptive statistics - single value results -
example
temp is the name of the vector
containing all numeric values
4
• log(dataset) – Shows log value for each
element.
• summary(dataset) –shows the summary
of values
• quantile() - Shows the quantiles by
default—the 0%, 25%, 50%, 75%, and
100% quantiles. It is possible to select
other quantiles also.
Descriptive statistics - multiple value results -
example
5
Descriptive Statistics in R for Data Frames
• Max(frame) – Returns the largest value in the entire data frame.
• Min(frame) – Returns the smallest value in the entire data frame.
• Sum(frame) – Returns the sum of the entire data frame.
• Fivenum(frame) – Returns the Tukey summary values for the entire
data frame.
• Length(frame)- Returns the number of columns in the data frame.
• Summary(frame) – Returns the summary for each column.
6
Descriptive Statistics in R for Data Frames -
Example
7
Descriptive Statistics in R for Data Frames –
RowMeans example
8
Descriptive Statistics in R for Data Frames –
ColMeans example
9
Graphical analysis - simple linear regression model
in R
• Logistic regression is implemented to understand if the dependent
variable is a linear function of the independent variable.
• Logistic regression is used for fitting the regression curve.
• Pre-requisite for implementing linear regression:
• Dependent variable should conform to normal distribution
• Cars dataset that is part of the R-Studio will be used as an example to
explain linear regression model.
10
Creating a simple linear model
• cars is a dataset preloaded into
System-R studio.
• head() function prints the first
few rows of the list/df
• cars dataset contains two major
columns
• X = speed (cars$speed)
• Y = dist (cars$dist)
• data() function is used to list all
the active datasets in the
environment.
• ...
Data Visualization using different python libraries.pptxHamzaAli998966
This document discusses data visualization using Python libraries like Pandas, NumPy, and Matplotlib. It covers various types of charts that can be created like line charts, bar charts, and histograms to visualize different aspects of stock market data. Descriptive statistics are calculated on the stock data and various visualizations are created to analyze trends in closing prices, moving averages, daily returns, and correlations between stocks. Finally, it discusses predicting future closing stock prices of Apple using an LSTM model.
From data to diagrams: an introduction to basic graphs and chartsSchool of Data
This document provides training on data visualization and transforming data into diagrams. It discusses choosing the appropriate type of visualization based on the data and questions, including pie charts to show parts of a whole, bar charts to compare categories, line graphs to show changes over time, and maps to relate data to geography. Guidelines are provided for effectively designing each type of visualization, such as limiting the number of pie chart segments and starting bar and line graphs at zero. The importance of telling a story and engaging readers is also emphasized.
The document discusses different types of charts including column charts, bar charts, pie charts, line charts, area charts, stock charts, radar charts, bubble charts, scatter charts, and combo charts. For each chart type, the document outlines typical uses, advantages, and disadvantages. It provides an example of each chart type to illustrate how the chart can be constructed and interpreted.
Design Patterns
Christian Behrens
https://www.behance.net/gallery/29576487/The-Form-of-Facts-and-Figures
Christopher Alexander
The term design patterns was originally coined about three decades ago by Christopher Alexander, an architect and critic who envisioned a generic and modular “language” of methods to describe the process of construction and urban planning by means of recurring problems that are well-known in a specific context, and respective solutions that have been proved and tested in the past and can therefore be seen as a safe choice to tackle a certain design challenge. Although it never made its breakthrough in the field of architecture, the basic idea of design patterns was adopted by other engineering disciplines, most notably software development in the early 1990s. A second wave of success seems to have appeared recently, when several projects were launched to build up pattern libraries for digital user interfaces. https://en.wikipedia.org/wiki/Christopher_Alexander
2
Design Patterns
Rejected by Architects, Adopted by Software Engineers,
…and the field of user interface design.
Although Alexander’s book became a bestseller and is a de-facto standard read for architecture students until today, it received much criticism and invoked sceptical reactions among the architecture community. Looking back at it some thirty years later, Alexander’s pattern language can be described as a success story on a detour. While widely rejected by architects and urban planners, the concept was picked up by computer scientist in the late 1980s and became a huge success in the wake of the rise of object-oriented programming languages such as Java
3
Design Patterns
Rejected by Architects, Adopted by Software Engineers,
…and the field of user interface design.
http://zurb.com/patterntap
http://patternry.com/
useful for the general description of common design problems, and provide solutions based on the relationships and behaviors of objects Companies and institutions that deal with interface design problems, have also launched own projects that aim at streamlining the development of new products and services by means of a comprehensive design pattern collection.
Design Patterns can help to tackle commonly known recurring design problems with well-proven solutions. A single pattern provides a brief description of one particular design problem. This problem can be a physical attribute of an application (for instance a dropdown menu), or a functional behavior (e.g. the login dialog of a website). A pattern typically consists of a description of the problem, and a solution that has been proven before and is generally recognized. Usually, a pattern provides additional information like an example of a real-world scenario in which it has been successfully applied as well as a rationale to briefly describe the benefit the usage this patterns bears.
4
Discrete Quantities:
Simple Bar Chart
Snapshot:
they do not display con.
This document discusses multi-dimensional modeling and data warehousing implementation. It describes prediction cubes, which store prediction models in a multidimensional space to enable predictive analytics in an OLAP manner. It also covers attribute-oriented induction for data generalization, including attribute removal, generalization, and thresholding. Regarding data warehouse implementation, it outlines efficient data cube computation through cuboid materialization and indexing techniques like bitmap indexes and join indices to speed up OLAP queries.
Graphs are used to visually represent data and relationships between variables. There are various types of graphs that can be used for different purposes. Histograms represent the distribution of continuous variables. Bar graphs display the distribution of categorical variables or allow for comparisons between categories. Line graphs show trends and patterns over time. Pie charts summarize categorical data as percentages of a whole. Cubic graphs refer to graphs where all vertices have a degree of three. Response surface plots visualize the relationship between multiple independent variables and a response variable.
Visualization and Matplotlib using Python.pptxSharmilaMore5
This document provides an overview of Matplotlib, a Python data visualization library. It discusses Matplotlib's pyplot and OO APIs, how to install Matplotlib, create basic plots using functions like plot(), and customize plots using markers and line styles. It also covers displaying plots, the Matplotlib user interface, Matplotlib's relationships with NumPy and Pandas, and examples of different types of graphs and charts like line plots that can be created with Matplotlib.
This document discusses different types of graphs and charts, their uses, and provides examples. It summarizes 6 common types: line graphs show trends over time; bar charts compare categorical data with bars; pie charts illustrate proportional data with slices; histograms show distributions of continuous data with columns; scatter plots show relationships between two variables with x-y axes; and Venn charts visualize logical relationships between groups with overlapping circles. The document provides examples and descriptions of when each type would be useful.
This document provides summaries of various data visualization techniques in Python including:
Seaborn is a Python library for statistical graphics and is built on matplotlib. It supports NumPy and Pandas data structures.
FacetGrid allows plotting multiple axes showing the same relationship conditioned on different levels of variables from a Pandas DataFrame. It can condition on up to three variables.
Kdeplot fits and plots univariate or bivariate kernel density estimates and allows customizing colors, shading, and adding a colorbar.
Jointplot provides a wrapper for the JointGrid class to create scatter plots, regression plots, residual plots, and histograms of two variables jointly.
Heatmap plots a hierarchically clustered heatmap of a matrix dataset
Matplotlib is a popular Python library used for data visualization and creating plots from data. It allows creating different types of charts including line charts, bar charts, and pie charts. Line charts connect data points with straight lines and are used to show trends over time. Bar charts represent categorical data with rectangular bars of different heights. Pie charts show proportions between categories by dividing a circle into wedge-shaped slices. Matplotlib provides functions and methods to customize charts with options like colors, labels, titles and more.
Me 443 4 plotting curves Erdi Karaçal Mechanical Engineer University of Gaz...Erdi Karaçal
- The document discusses various plotting and graphics capabilities in Mathematica, including plotting functions, parametric plots, plotting lists of data, options for controlling plot appearance, and two-dimensional and three-dimensional graphics.
- It describes how to control aspects of plots like scales, sampling of functions, and colors using options. It also covers various graphics primitives and directives for customizing plots.
This short text will get you up to speed in no time on creating visualizations using R's ggplot2 package. It was developed as part of a training to those who had no prior experience in R and had limited knowledge on general programming concepts. It's a must have initial guide for those exploring the field of Data Science
The document discusses different types of graphs and charts used to visualize data, including their purposes and appropriate uses. It explains that line charts show trends over time, column charts compare items within categories, and pie charts show proportional relationships. Scatter plots and bubble charts illustrate correlations between two variables, while area charts emphasize changes between data points. Combination charts allow comparison of multiple categories. Proper visualization selection depends on the problem and data to be depicted.
A Subgraph Pattern Search over Graph DatabasesIJMER
The document discusses methods for continuous subgraph pattern searching over graph databases and graph streams. It proposes using Node-Neighbor Trees (NNTs) to represent local graph structures, and projecting NNTs to numerical vectors to enable approximate subgraph isomorphism checking. It also describes how to handle uncertain graph streams by computing probability upper bounds to filter out graph stream-query pairs that are unlikely to match. The overall approach conducts structural filtering followed by probability pruning to reduce the search space when capturing patterns over uncertain graph streams.
Similaire à Introduction to Matplotlib Library in Python.pptx (20)
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
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Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
2. INTRODUCTION TO MATPLOTLIB
● Matplotlib is a popular Python library used for creating visualizations,
charts, and plots.
● It helps in understanding data patterns, trends, and relationships
through graphical representation.
● Importing Matplotlib:
import matplotlib.pyplot as plt
● Matplotlib provides a wide range of plot types, from basic line plots
to complex 3D visualizations. This versatility makes it suitable for
various data analysis and presentation needs, catering to different
domains such as science, engineering, finance, and more.
● Matplotlib seamlessly integrates with other popular Python libraries
such as NumPy and Pandas. This integration allows you to visualize
data structures like arrays and data frames directly without extensive
data manipulation, enhancing productivity and workflow efficiency.
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3. CATEGORIZATION OF PLOTS
Matplotlib can be categorized into different types based on the number of
variables included in plots. Here are the categories:
1. Univariate Plots:
● These plots involve analyzing a single variable.
● Examples: Histograms, density plots, box plots, violin plots, bar
plots (for categorical data).
2. Bivariate Plots:
● These plots visualize relationships between two variables.
● Examples: Scatter plots, line plots (connecting two variables), bar
plots (comparing two variables), hexbin plots.
3. Multivariate Plots:
● These plots explore relationships involving more than two
variables.
● Examples: 3D scatter plots, bubble plots, pair plots (showing
relationships among multiple variables), parallel coordinate plots.
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4. CATEGORIZATION OF PLOTS
4. Categorical Plots:
● These plots focus on categorical data and its distribution.
● Examples: Bar plots (for categorical data), stacked bar plots,
grouped bar plots, mosaic plots, dendrogram plots.
5. Time Series Plots:
● These plots specifically deal with time-related data.
● Examples: Line plots (over time), area plots, candlestick plots,
seasonal decomposition plots.
6. Statistical Plots:
● These plots emphasize statistical relationships and summaries.
● Examples: Regression plots, distribution plots (showing
distributions and fit), QQ plots (quantile-quantile plots).
4
5. CATEGORIZATION OF PLOTS
7. Network Plots:
● These plots depict relationships within networks or graphs.
● Examples: Network graphs, directed graphs, node-link diagrams.
8. Interactive Plots:
● These plots allow user interaction for exploring data.
● Examples: Interactive scatter plots, interactive line plots,
interactive heat maps.
9. Composite Plots:
● These plots combine different types of plots to show complex
relationships.
● Examples: Facet grids (grid of subplots), composite heat maps
with annotations.
Matplotlib versatility makes it suitable for creating a wide range of plots,
catering to various data visualization needs.
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6. LINE PLOTS IN MATPLOTLIB
● A line plot is a fundamental type of visualization in Matplotlib that
displays data points connected by straight lines.
● It's often used to show the trend or progression of data over a
continuous interval.
● For example:
Suppose you have collected temperature data over a week. You can
create a line plot to visualize how the temperature changes each day.
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7. LINE PLOTS IN MATPLOTLIB
● In this example, days represent the x-axis values (days of the week), and
temperatures represent the y-axis values (temperature in degrees Celsius). The
marker, linestyle, and color arguments customize the appearance of the line plot.
● The output of the mentioned program is as follows:
7
8. BAR PLOTS IN MATPLOTLIB
● A bar plot is a visualization in Matplotlib that uses rectangular bars to
represent categorical data.
● It's commonly used to compare values across different categories or groups.
● For example:
Suppose you want to compare the sales of different products in a store. You
can create a bar plot to visualize the sales for each product
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9. BAR PLOTS IN MATPLOTLIB
● In this example, products represent the x-axis categories (product names), and sales
represent the heights of the bars (sales amounts). The color argument specifies the color
of the bars, and xticks(rotation=45) rotates the x-axis labels for better readability.
● The output of the mentioned program is as follows:
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10. HISTOGRAMS IN MATPLOTLIB
● A histogram is a graphical representation in Matplotlib that displays
the distribution of continuous data by dividing it into intervals (bins)
and showing the frequency of data points in each bin.
● For example:
Suppose you have a dataset of exam scores and want to visualize
their distribution. You can create a histogram to show how many
students scored within specific score ranges:
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11. HISTOGRAMS IN MATPLOTLIB
● In this example, exam_scores is the dataset of scores. The bins parameter specifies
the number of intervals to divide the data into. The histogram displays the
frequency of scores in each bin, helping you understand the distribution of exam
performance.
● The output of the mentioned program is as follows:
11
12. SCATTER PLOTS IN MATPLOTLIB
● A scatter plot is a visualization in Matplotlib that displays individual
data points as dots on a 2D plane.
● It's used to showcase relationships between two continuous
variables.
● For example:
Imagine you're analyzing the relationship between the study hours
and exam scores of a group of students. A scatter plot can help you
visualize whether there's a correlation between these variables
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13. SCATTER PLOTS IN MATPLOTLIB
● In this example, study_hours and exam_scores are the two continuous variables. Each
point on the scatter plot represents a student's study hours and their corresponding exam
score. The marker argument determines the shape of the data points.
● The output of the mentioned program is as follows:
13
14. SUBPLOTS IN MATPLOTLIB
● Subplots in Matplotlib allow you to create multiple plots within a
single figure, enabling side-by-side comparisons or visualizing related
data.
● For example:
Suppose you want to display two line plots comparing two sets of
data in separate subplots
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15. SUBPLOTS IN MATPLOTLIB
● In this example, subplot(1, 2, 1) creates the first subplot, and subplot(1, 2, 2) creates the
second. The tight_layout() call improves spacing between subplots for better presentation.
● The output of the mentioned program is as follows:
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