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Data Visualization in Python by Examples: Setting Up and Getting Started with ggplot| packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2Hjke1L]. Introduce ggplot and setup your computer for creating visualizing with it. • Introduce ggplot • Install ggplot and dependencies • Verify setup For the latest Virtualization & Cloud tutorials, please visit http://bit.ly/2layAb4 Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 268 Packt Video
Greg Lamp - ggplot For Python
 
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PyData SV 2014 Making basic, good-looking plots in Python is tough. Matplotlib gives you great control, but at the expense of being very detailed. The rise of pandas has made Python the go-to language for data wrangling and munging but many people are still reluctant to leave R because of its outstanding data viz packages. ggplot is a port of the popular R package ggplot2. It provides a high level grammar that allow users to quickly and easily make good looking plots. So say good-bye to matplotlib, and hello to ggplot as your everyday Python plotting library! https://github.com/yhat/ggplot
Views: 3632 PyData
Basic Plotting with Jupyter and ggplot
 
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Making some simple plots with Jupyter and ggplot with R.
Views: 720 Jacob Koehler
Matplotlib Plotting Tutorials : 010 : Matplotlib Plot Styles
 
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All the programs and examples will be available in this public folder! https://www.dropbox.com/sh/okks00k2xufw9l3/AABkbbrfKetJPPsnfYa5BMSNa?dl=0
Views: 556 Fluidic Colours
Simple Monte Carlo Simulation of Stock Prices with Python
 
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In this tutorial, we will go over Monte Carlo simulations and how to apply them to generate randomized future prices within Python. My Website: http://programmingforfinance.com/ Code: #------------------------------------------------------------------------------------# import pandas_datareader.data as web import pandas as pd import datetime as dt import numpy as np import matplotlib.pyplot as plt from matplotlib import style style.use('ggplot') start = dt.datetime(2017, 01, 03) end = dt.datetime(2017, 11, 20) prices = web.DataReader('AAPL', 'google', start, end)['Close'] returns = prices.pct_change() last_price = prices[-1] #Number of Simulations num_simulations = 1000 num_days = 252 simulation_df = pd.DataFrame() for x in range(num_simulations): count = 0 daily_vol = returns.std() price_series = [] price = last_price * (1 + np.random.normal(0, daily_vol)) price_series.append(price) for y in range(num_days): if count == 251: break price = price_series[count] * (1 + np.random.normal(0, daily_vol)) price_series.append(price) count += 1 simulation_df[x] = price_series fig = plt.figure() fig.suptitle('Monte Carlo Simulation: AAPL') plt.plot(simulation_df) plt.axhline(y = last_price, color = 'r', linestyle = '-') plt.xlabel('Day') plt.ylabel('Price') plt.show() #------------------------------------------------------------------------------------#
Views: 14954 codebliss
Customizing plots with python matplotlib
 
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Learn how to make custom plots in Python with matplotlib: https://datacamp.com/courses/intermediate-python-for-data-science Creating a plot is one thing. Making the correct plot, that makes the message very clear, is the real challenge. For each visualization, you have many options. First of all, there are the different plot types. And for each plot, you can do an infinite number of customizations. You can change colors, shapes, labels, axes, and so on. The choice depends on, one, the data, and two, the story you want to tell with this data. Since there are a so many possible customizations, the best way to learn this, is by example. Let's start with the code in this script to build a simple line plot. It's similar to the line plot we've created in the first video, but this time the year and pop lists contain more data, including projections until the year 2100, forecasted by the United Nations. If we run this script, we already get a pretty nice plot: it shows that the population explosion that's going on, will have slowed down by the end of the century. But some things can be improved. First, it should be clearer which data we are displaying, especially to people who are seeing the graph for the first time. And second, the plot really needs to draw the attention to the population explosion. The first thing you always need to do is label your axes. Let's do this by adding the xlabel() and ylabel() functions. As inputs, we pass strings that should be placed alongside the axes. Make sure to call these functions before calling the show() method, otherwise your customizations will not be displayed. If we run the script again, this time the axes are annotated. We're also going to add a title to our plot, with the title function. We pass the actual title, 'World Population Projections', as an argument. And there's the title! So, using xlabel, ylabel and title, we can give the reader more information about the data on the plot: now they can at least tell what the plot is about. To put the population growth in perspective, I want to have the y-axis start from zero. You can do this with the yticks() function. The first input is a list, in this example with the numbers zero up to ten, with intervals of 2. If we run this, the plot will change: the curve shifts up. Now it's clear that already in 1950, there were already about 2.5 billion people on this planet. Next, to make it clear we're talking about billions, we can add a second argument to the yticks function, which is a list with the display names of the ticks. This list should have the same length as the first list. The tick 0 gets the name 0, the tick 2 gets the name 2B, the tick 4 gets the name 4B and so on. By the way, B stands for Billions here. If we run this version of the script, the labels will change accordingly, great. Finally, let's add some more historical data to accentuate the population explosion in the last 60 years. On wikipedia, I found the world population data for the years 1800, 1850 and 1900. I can write them in list form and append them to the pop and year lists with the plus sign. If I now run the script once more, three datapoints are added to the graph, giving a more complete picture. Now that's how you turn an average line plot into a visual that has a clear story to tell! Over to you now. Head over to the exercises, gradually customize the world development chart and become the next Hans Rosling!
Views: 20958 DataCamp
Rpy2 Tutorial: R plots in Jupyter Notebooks
 
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In this tutorial we learn how to run r in python and display plots in jupyter notebooks. We learn by plotting in two rpy2 examples in which we use R libraries (e.g., graphics and ggplot2). To display the R plots, using Python, in a Jupyter Notebook we use Image and display from IPython.display. For you who don't know; Rpy2 is a Python package that makes it very easy to call r from Python. If you are using anaconda Python distribution you can easily install r and rpy2 using conda (see example in video). Make sure you subscribe to the channel if you haven't: http://bit.ly/SUB2EM » A working Jupyter Notebook containing the code: http://bit.ly/RinPythonCode » Learn how to install R-packages from Python: https://youtu.be/GvmoOHkABNA » Another rpy2 tutorial (text): http://bit.ly/rpy2how-to » Rpy2 documentation: https://rpy2.readthedocs.io/ » Stackoverflow Questions: - How to Plot Inline: http://bit.ly/2wvgGpA - rpy2 and ggplot2: http://bit.ly/2P5xc7a
Views: 636 Erik Marsja
ggplot2 tutorial: ggplot2 Layers
 
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Learn more about ggplot2 layers in R: https://www.datacamp.com/courses/data-visualization-with-ggplot2-part-2 Now that we have some idea about the different grammatical elements of graphics, let's see how this works in practice. The grammar of graphic is implemented in R using the ggplot2 package, which was one of the first packages developed by the prolific statistician and R programmer Hadley Wickham. Essentially, we construct plots by layering grammatical elements on top of each other and use aesthetic mappings to define our visualisations. We are going to go through each grammatical element in depth in this and the next course. Here I'll introduce a data set which will be used throughout the videos and we'll go over some simple examples. The first layer is data. Obviously we need some data to plot. I'm going to use several different data sets in the course videos, one of which is the classic iris data set collected by Edgar Anderson in the 1930s and thereafter popularised by RA Fisher. The data set contains information on three iris species, setosa, virginica and versicolor. Four mearurements were taken from each plant - the petal length and with and the sepal length and width. You're probably familiar with petals, they're the colourful part of a flower. Sepals are the outter leaves of the flower, they are typicall green, but in this case they are colourful. There are 50 specimens of each species. The data is stored in an object called iris, there ar five variables: the species and one for each of the properties which were measured. The next layer is aesthetics, which tells us which scales we should map our data onto. This is where the second main component of the grammar of graphics comes into play. On top of layering the grammatical elements, it's here that we establish our aesthetic mappings. In this case we are going to make a scatter plot so we're going to map the Sepal.Length onto the X aesthetic and the Sepal.Width onto the Y aesthetic. The third essential layer is allows us to choose that geometry, that means how the plot will look. After we've established our three essential layers, we have enough instructions to make a basic scatter plot plot. It's pretty rough, so to get a more meaningful and cleaner visualisaiton, we'll have to use the other layers. The next layer we'll use is facets, which dictate how to split up our plot. In this case we want to make three separate plots one for each of three species under consideration. The statistics layer can be use to calculate and add many different parameters. For example, here we've chosen to add a linear model to each of the three subplots. Next comes the coordinate layer, which allows us to specify the precise dimensions of the plot. Here we've cleaned up the labelling and the scalling of both the x and x axes. And finally the theme layer controls all the non-data ink on our plot. Which allows us to get a nice looking, meaningful and publication quality plot directly in R. Let's explore these concepts further in the exercises.
Views: 9996 DataCamp
3D Plotting in Matplotlib for Python: 3D Scatter Plot
 
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Besides 3D wires, and planes, one of the most popular 3-dimensional graph types is 3D scatter plots. The idea of 3D scatter plots is that you can compare 3 characteristics of a data set instead of two. Sample code: http://pythonprogramming.net/matplotlib-3d-scatterplot-tutorial/ Link to the full playlist: http://www.youtube.com/playlist?list=PLQVvvaa0QuDfpEcGUM6ogsbrlWtqpS5-1 Sentdex.com Facebook.com/sentdex Twitter.com/sentdex How to generate interactive 3d scatterplots in Matplotlib and Python
Views: 73720 sentdex
Dash in 5 Minutes
 
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An overview of the Dash web application framework. Dash enables python data analysts to build and deploy rich web applications in nothing but pure python. Interested? Get in touch for early access: https://plot.ly/products/consulting-and-oem/
Views: 38852 Plotly
Data Science Tutorial | Data Science Training | Data Visualization Basics with GGPlot
 
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In this webinar, the speaker talks about the fundamentals of data visualization, types of visualization methods and the graphs. He has demonstrated some examples as well using GGPLOT. To know more about Data Science course with R (http://bit.ly/2yvsgR5) and Big data analytics course (http://bit.ly/2AziAFY). Techcanvass is a software development and training organization. We provide IT certifications training for mid-level professionals. We specialize in the following areas: a) Selenium v3.0 training (CP-SAT and Techcanvass Certification) b) IIBA Business Analysis certifications (all levels) c) Certified Agile Business Analyst Training d) Data Science Training ( R, Python and Big Data) Website: http://techcanvass.com Facebook Page: https://www.facebbook.com/Techcanvass Twitter Handle: @techcanvass Techcanvass is a software development and training organization. We provide IT certifications training for mid-level professionals. We specialize in the following areas: a) Selenium v3.0 training (CP-SAT and Techcanvass Certification) b) IIBA Business Analysis certifications (all levels) c) Certified Agile Business Analyst Training d) Data Science Training ( R, Python and Big Data) and Tableau Website: http://techcanvass.com Facebook Page: https://www.facebbook.com/Techcanvass Twitter Handle: @techcanvass
Views: 211 Techcanvass
PLOTCON 2016: Haley Jeppson, Visualizing Multivariate Categorical Data
 
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Visualizing multidimensional data can be cumbersome. There is a subtle complexity that arises from the hierarchical structure of the counts and proportions that are important for understanding the multivariate discrete distributions. In addition, the options of graphical methods for categorical variables are not well developed in comparison with what is available for numeric variables. One method of visualizing multidimensional data is through a mosaic plot. Mosaic plots can be an easy and powerful option for identifying relationships between multiple variables. However, while mosaic plots have been implemented in a variety of packages, the ordinary grammar of graphics does not support mosaic plots. With the R package ggmosaic, a custom ggplot2 geom designed for mosaic plots is implemented. In addition, ggmosaic creates plots that can be converted into interactive plotly graphs. In this talk, I'll present examples that highlight the versatility and ease of use of ggmosaic while demonstrating the practicality of mosaic plots. Haley is a PhD student in the Department of Statistics at Iowa State University. Her primary research interests are visual inference, statistical graphics, and statistical computing. Haley’s current work includes the R package ggmosaic which has the capability of creating interactive mosaic plots in the ggplot2 framework.
Views: 2326 Plotly
R stacked 100% bar chart
 
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learn how to create stacked 100% bar charts in R vs. Excel pivot tables download the code and data from here: http://bit.ly/2HziGRe if you are starting out with R, i really recommend this course for beginner R: https://bit.ly/2waBjqD
Views: 238 Excel2R
How to make 3D spinning scatter plots in R with RGL
 
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In this video I show you how to make one of my favorite scatterplots - the 3D spinning scatterplot. This is actually very simple to do and build in R through RStudio and it's literally just a few lines of code. Then you will have a standalone spinning graph that you can use to compare different attributes across the field and try and get some insight from it. New video on using multiple colors to differentiate the graphs and attributes: https://youtu.be/SEvPqesG_bc I show you in this video exactly how I use it in exploratory data analysis and data analysis projects. I also show you how to get insights on various fields from this 3-D scatterplot. Sometimes what you can't see in a two-dimensional graph you can see much better in a three-dimensional graph. You'll see in the examples in this video that you'll be able to clearly see the different seasons and their effect on sales for the data set that I use from the University of California at Irvine ' data science department. You also see the effect of different size plot points will have based on the size of the graph. So when you have a small scatterplot what looks to be too big as the points size might be just right or even to small for a full page size. Also some colors show better than others. The best part is it so simple to use this and then you can take a couple snapshots of it to show your boss or coworkers the insights that you take you have on the data. Again this is a scatterplot and you will need to use other graphs and plots to show a story. Feel free to look at all the other videos in my channel right show you all kinds of other graphs, plots, formulas, code, tips and tricks and more! Thank you for watching I hope you enjoy this video! Start making some awesome 3-D graphs and be sure and leave me a comment and let me know how it got how it's working for you! Please be sure and subscribe and like she can be notified of all the other great videos I have coming out! Also be sure and check out my channel for all the other great videos of ready put out there on all sorts a great stuff! Thanks again and God bless!
Views: 642 Tech Know How
Pandas with Python 2.7 Part 6 - Data visualization with Matplotlib
 
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One of the most powerful aspects of Pandas is it's easy inclusion into the Matplotlib module. Matplotlib is a popular and robust Python module that allows programmers to create graphs and charts from their data. Pandas makes loading your data into Matplotlib slightly easier, as well as handles almost all of the processing necessary to get it ready for Matplotlib. Pandas is basically created to do this in the most efficient way possible. Pandas is also quite remarkably good at working with data with dates and Matplotlib. Traditionally, working with data that is indexed by date is somewhat challenging with Matplotlib, but not when using Pandas! Sample code for the series: http://pythonprogramming.net/python-2-7-pandas-data-analysis/ Pandas tutorial series: https://www.youtube.com/playlist?list=PLQVvvaa0QuDfHt4XU7vTm22xDegR0v0fQ http://seaofbtc.com http://sentdex.com http://hkinsley.com https://twitter.com/sentdex Bitcoin donations: 1GV7srgR4NJx4vrk7avCmmVQQrqmv87ty6
Views: 32113 sentdex
Python 3 Programming Tutorial - Matplotlib plotting from a CSV
 
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In this Python 3 tutorial, we cover how to plot in Matplotlib from a CSV file. Sample code for this basics series: http://pythonprogramming.net/beginner-python-programming-tutorials/ Python 3 Programming tutorial Playlist: http://www.youtube.com/watch?v=oVp1vrfL_w4&feature=share&list=PLQVvvaa0QuDe8XSftW-RAxdo6OmaeL85M http://seaofbtc.com http://sentdex.com http://hkinsley.com https://twitter.com/sentdex Bitcoin donations: 1GV7srgR4NJx4vrk7avCmmVQQrqmv87ty6
Views: 48425 sentdex
Live Matplotlib Graph in Tkinter Window in Python 3 - Tkinter tutorial Python 3.4 p. 7
 
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Tkinter GUI TutorialPlaylist: http://www.youtube.com/playlist?list=PLQVvvaa0QuDclKx-QpC9wntnURXVJqLyk In this Tkinter tutorial, we take it a step further and show how we can have a live Matplotlib graph within our Tkinter GUI. How to put a matplotlib chart in a tkinter window has actually been one of the more popular requests that I've got, so I am happy to share this with you all finally! http://seaofbtc.com http://sentdex.com http://hkinsley.com https://twitter.com/sentdex Bitcoin donations: 1GV7srgR4NJx4vrk7avCmmVQQrqmv87ty6
Views: 44369 sentdex
Heatmaps using Matplotlib,  Seaborn, and Pandas
 
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Link to Code: https://github.com/mGalarnyk/Python_Tutorials/blob/master/Request/Heat%20Maps%20using%20Matplotlib%20and%20Seaborn.ipynb This tutorial goes over how to make Beautiful Heatmaps using Matplotlib, Seaborn, and Pandas (Python libraries). How to import data using pandas, utilizing groupby on data, an excel like pivot for transforming dataframes, and finally plotting using Matplotlib and Seaborn.
Views: 15412 Michael Galarnyk
3D Plane wire frame Graph Chart in Python
 
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In this tutorial, we cover how to make a wire frame / plane graph in Matplotlib. For this, we're just going to use the sample data provided by Matplotlib and leave it there. This type of graph is very specific in its application. If you happen to have your own data, feel free to substitute! Sample code: http://pythonprogramming.net/wireframe-graph-python/ Full Playlist: http://www.youtube.com/playlist?list=PLQVvvaa0QuDfpEcGUM6ogsbrlWtqpS5-1 Sentdex.com Facebook.com/sentdex Twitter.com/sentdex
Views: 49469 sentdex
Plot the Grand Mean - Data Analysis with R
 
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This video is part of an online course, Data Analysis with R. Check out the course here: https://www.udacity.com/course/ud651. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 3104 Udacity
Karla Fejfarová & Petr Šimeček: Python alternatives to R/Shiny
 
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Shiny is a web application framework for R that makes it easy to turn analyses into interactive applications. When a user changes input parameters using sliders, drop-down menus and text fields, the changes are propagated through a reactivity graph into outputs like plots, tables and summaries. While Python is generally more powerful and easier to use for a web development than R, it lack a comprehensive Shiny-like framework. Current tools are either too complex (Flask, Tornado) or too trivial and lacking Shiny-like graph interactivity (Bokeh, Dash, Spyre, Bowtie). We will demonstrate those tools on a web app that for a given face find look-alike actors / actresses and scientists. Then we will show how to implement Shiny-like reactivity in our Python module glossy. ----- Karla is a researcher at Institute of Biotechnology by day and a Python girl by night.
Views: 669 PyCon CZ
How to Modify Plots in R (R Tutorial 2.8)
 
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Learn how to modify plots produced in R to change from the default values. This tutorial shows how to use commands such as "plot", "cex", "pch", "axis", "par", "mfrow", "col" and many more. This video is a tutorial for programming in R Statistical Software for beginners. You can access and download the "LungCapData" dataset here: Excel format: https://bit.ly/LungCapDataxls Tab Delimited Text File: https://bit.ly/LungCapData Here is a quick overview of the topics addressed in this video: 0:00:31 how to access help menu in R for different plotting parameters using "par" command 0:01:00 how to change the size of the plotting characters in R using the "cex" argument 0:01:24 how to change the size of the title of a plot in R using the "cex.main" argument 0:01:36 how to change the size of the x and y labels of a plot in R using the "cex.lab" argument 0:01:51 how to change the size of the x and y axes values using "cex.axis" argument 0:02:16 how to change the fonts of the title of a plot in R using the "font.main" argument 0:02:45 how to change the font of the x or y labels of a plot in R using the "font.lab" argument 0:02:58 how to change the fonts of the x and y axis of a plot in R using the "font.axis" argument 0:03:21 how to change the colour of the plotting characters in R using the "col" argument 0:03:39 how to change the colour of the title of a plot in R using the "col.main" argument 0:03:48 how to change the colour of the x and y labels of a plot in R using the "col.lab" argument 0:03:56 how to change the colour of the x and y axis of a plot in R using the "col.axis" argument 0:04:26 how to change the plotting character in R using the "pch" argument 0:05:03 how to add a regression line to a plot in R using the "abline" command and change the line colour (using "col" argument), the line type (using "lty" argument) and the width of the line (using "lwd" argument) 0:05:45 how to identify different groups on the same plot (eg. male or female) using different plotting characters and colours 0:06:39 how to re-label x-axis and y-axis using the "xlab" and "ylab" arguments 0:07:05 how to add more points or observations to an existing plot using the "points" command 0:07:53 how to produce multiple plots on one screen in R using "mfrow" or "mfcol" arguments 0:10:10 how to re-label the axes of a plot in R 0:10:39 how to remove labels from x or the y-axis using the "axes" argument 0:11:47 how to re-label the x or y axis using the "side" argument and "axis" command 0:13:38 how to add a box around the plot in R using the "box" command 0:13:53 how to add values on top of the plot or on the right side of the plot in R using the "axis" command and "side" argument
Tiny Tutorial 5: Create Scatter Plots in Python with Matplotlib
 
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The Matplotlib package for Python provides tons of tools for creating line plots, image plots, and even some 3D plots. Today we are going to create a simple scatter plot. In this Tiny Tutorial, you will learn how to create a scatter plot, add axis labels, change the plot's color and point size, how to add a key, and how to add point labels. This Tiny Tutorial shows just a fraction of Matplotlib's capabilities. There is so much more you can do with this powerful tool! Be sure to press the Subscribe button below if you want to learn more. **See the the Intro to Plotting Tiny Tutorial here: https://youtu.be/ZFOGXZ0VEVg ** View Matplotlib's online documentation here: https://matplotlib.org **Learn more about Enthought's Python course offerings here: https://goo.gl/CzVQfA
Views: 3106 Enthought
Jupyter Notebook Tutorial: Introduction, Setup, and Walkthrough
 
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In this Python Tutorial, we will be learning how to install, setup, and use Jupyter Notebooks. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Let's get started. If you enjoy these videos and would like to support my channel, I would greatly appreciate any assistance through my Patreon account: https://www.patreon.com/coreyms Or a one-time contribution through PayPal: https://goo.gl/649HFY If you would like to see additional ways in which you can support the channel, you can check out my support page: http://coreyms.com/support/ Equipment I use and books I recommend: https://www.amazon.com/shop/coreyschafer You can find me on: My website - http://coreyms.com/ Facebook - https://www.facebook.com/CoreyMSchafer Twitter - https://twitter.com/CoreyMSchafer Google Plus - https://plus.google.com/+CoreySchafer44/posts Instagram - https://www.instagram.com/coreymschafer/ #Python
Views: 471466 Corey Schafer
Customizing embedded graph - Tkinter GUI development series p. 10
 
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In this Python 3 with Tkinter programming tutorial, we cover how to customize our embedded graph a bit. Tkinter GUI TutorialPlaylist: http://www.youtube.com/playlist?list=PLQVvvaa0QuDclKx-QpC9wntnURXVJqLyk http://seaofbtc.com http://sentdex.com http://hkinsley.com https://twitter.com/sentdex Bitcoin donations: 1GV7srgR4NJx4vrk7avCmmVQQrqmv87ty6
Views: 17633 sentdex
How to smooth graph and chart lines in Python and Matplotlib
 
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Full Playlist: http://www.youtube.com/playlist?list=PLQVvvaa0QuDfpEcGUM6ogsbrlWtqpS5-1 Sentdex.com Facebook.com/sentdex Twitter.com/sentdex
Views: 22577 sentdex
Python Tutorial: Anaconda - Installation and Using Conda
 
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In this Python Tutorial, we will be learning how to install Anaconda by Continuum Analytics. Anaconda is a data science platform that comes with a lot of useful features right out of the box. Many people find that installing Python through Anaconda is much easier than doing so manually. Also, we will look at Conda. Conda is Continuum's package, dependency and environment manager. Let's get started. Anaconda Download Page: https://www.anaconda.com/download/ If you enjoy these videos and would like to support my channel, I would greatly appreciate any assistance through my Patreon account: https://www.patreon.com/coreyms Or a one-time contribution through PayPal: https://goo.gl/649HFY If you would like to see additional ways in which you can support the channel, you can check out my support page: http://coreyms.com/support/ Equipment I use and books I recommend: https://www.amazon.com/shop/coreyschafer You can find me on: My website - http://coreyms.com/ Facebook - https://www.facebook.com/CoreyMSchafer Twitter - https://twitter.com/CoreyMSchafer Google Plus - https://plus.google.com/+CoreySchafer44/posts Instagram - https://www.instagram.com/coreymschafer/ #Python
Views: 517162 Corey Schafer
How We Designed Matplotlib's New Default Colormap (and You Can Too)
 
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BIDS Data Science Lecture Series | September 4, 2015 | 1:00-2:30 p.m. | 190 Doe Library, UC Berkeley Speaker: Nathaniel Smith, Computational Fellow, Berkeley Institute for Data Science Sponsors: Berkeley Institute for Data Science, Data, Society and Inference Seminar For many years, the default colormap in matplotlib—the most popular Python plotting library—has been the colorful rainbow map called "jet." Such rainbow maps are widely used despite being deficient in many ways: small changes in the data sometimes produce large perceptual differences and vice versa; their lightness gradient is non-monotonic, making visualizations unreadable when printed in black and white; and colorblind viewers find them difficult to read under any circumstances. To fix this, we designed a new colormap called "viridis," which has now been accepted as the new default in matplotlib and has already been ported by users to a variety of other plotting systems, including R/ggplot2, vispy, ParaView, and Matlab. In this talk, we'll present our new colormap and the theory, tools, data, and motivations behind its design together with a short and friendly tutorial on color theory and colormap design for the working scientist. Plus: an extended digression about guacamole.
Python Data Visualization with Matplotlib 2.x : The Course Overview | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2hVMRrE]. This video gives a glimpse of what this course offers you. For the latest Big Data and Business Intelligence tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 121 Packt Video
Saving multiple plots to PDF in R
 
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I show you how to save multiple plots to the same PDF file using R statistics.
Views: 13944 Jim Grange
PLOTCON 2016: David Robinson, gganimate: Animation within the grammar of graphics
 
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Animation is a powerful tool for creating engaging and dynamic data visualizations. Here I introduce the R package gganimate, which extends the popular ggplot2 package to allow the construction of animations within the layered grammar of graphics. The package’s philosophy treats time as a visual aesthetic that can be mapped to an underlying variable, just like position, color, or size. This offers both a high-level declarative grammar for describing animated visualizations and an intuitive software implementation, letting the user focus on visualization choices rather than programming logic. I show several examples of animations that can be concisely defined with this package, including explorations of Hans Rosling’s Gapminder dataset and educational illustrations of regression and smoothing methods. David Robinson is a Data Scientist at Stack Overflow. In May 2015 he received his PhD in Quantitative and Computational Biology from Princeton University, where he worked with John Storey on statistical genomics and experiment design. He is the author of the broom, fuzzyjoin and gganimate R packages, and writes about R, statistics, and education at his blog Variance Explained.
Views: 6045 Plotly
R Shiny app tutorial # 7 - how to plot using renderPlot() in shiny - Example of a reactive histogram
 
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This video tutorial demonstrates how to plot in shiny by making a reactive histogram based on iris dataset. It uses the renderPlot() function in the shiny server function and plotOutput in the shun ui to display the plot. Best viewed in full screen. Link to updated files for reference : https://github.com/aagarw30/shinyapps-histogram R Shiny You tube Video Tutorial Series: R Shiny app tutorial # 1 - How to make shiny apps - An introduction to Shiny R Shiny app tutorial # 2 - How to make shiny apps - My first shiny app, Hello Shiny!! how to run R shiny app examples that comes with the shiny package R Shiny app tutorial # 3 - how to use shiny widgets - textInput shiny widget R Shiny app tutorial # 4 - how to use shiny widgets - radioButtons shiny widget R Shiny app tutorial # 5 - how to use shiny widgets - sliderInput shiny widget R Shiny app tutorial # 6 - how to use shiny widgets - selectInput shiny widget R Shiny app tutorial # 7 - how to plot using renderPlot() in shiny - Example of a reactive histogram R Shiny app tutorial # 8 - how to use tabsets in shiny - part 1 R Shiny app tutorial # 8 - how to use tabsets in shiny - part 2 R Shiny app tutorial # 9 - how to use reactive() function in shiny R Shiny app tutorial # 10 - how to download base plot in shiny – downloadButton() and downloadHandler() functions Github: https://github.com/aagarw30/R-Shinyapp-Tutorial
Views: 29566 Abhinav Agrawal
Plotting in Python - Intro to Data Science
 
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This video is part of an online course, Intro to Data Science. Check out the course here: https://www.udacity.com/course/ud359. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 1017 Udacity
The Grammar of Graphics / plotnine (07a)
 
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Video Lecture from the course INST 414: Advanced Data Science at UMD's iSchool. Full course information here: http://www.umiacs.umd.edu/~jbg/teaching/INST_414/
Views: 391 Jordan Boyd-Graber
Interpreting Contour Plots
 
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A brief tutorial, covering a few examples of how to interpret contour plots. This video was created for Penn State's METEO 003 course (https://www.e-education.psu.edu/meteo003/) with the assistance of Steve Seman and the John A. Dutton e-Education Institute (https://www.e-education.psu.edu/)
Views: 4920 Dutton Institute
Panel Charts in Power BI with R
 
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This is a follow up from previous video on Panel Charts with Excel & R. Check that out too. https://www.youtube.com/watch?v=L72QFxKGRxM For full discussion and workbook, visit http://chandoo.org/wp/2017/08/11/power-bi-panel-charts/
Animation plot in R
 
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You can make Animation plot by using ggplt2 and plotly packages in R Data link https://raw.githubusercontent.com/OHI-Science/data-science-training/master/data/gapminder.csv
Views: 522 Data/ Fun
Getting Started with Python | Data Analysis and Visualization
 
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Uses yhat rodeo that has IDE similar to RStudio and matlab. Data file link: https://drive.google.com/open?id=1tHAdr3V1N8BzZShg0A5ZU6eAUN_9kZZf
Views: 857 Bharatendra Rai
12 Snapshot of Data Visualization: introduction, rug plot, histogram
 
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Lecture notes and other course materials are available on webpage of Dr. Waleed Yousef: http://fci.helwan.edu.eg/~wyousef/HTML/DataScience.html
Views: 516 FCIH OCW
Bryan Van De Ven - How to Create Interactive Browser Visualizations from Python with Bokeh
 
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View slides for this presentation here: http://www.slideshare.net/PyData/pydata-london-bokeh-tutorial-bryan-van-de-ven Download tutorial files here: http://cdn.pydata.org/BokehTutorial.tgz http://cdn.pydata.org/BokehTutorial.zip Bokeh is a Python interactive visualization library for large datasets that natively uses the latest web technologies. Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity over large data to thin clients. This tutorial will walk users through the steps to create different kinds of interactive plots using Bokeh. We will cover using Bokeh for static HTML output, the IPython notebook, and plot hosting and embedding using the Bokeh server.
Views: 11893 PyData
Python tutorial: Cumulative Distribution Functions
 
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Learn about empirical cumulative distribution functions: https://www.datacamp.com/courses/statistical-thinking-in-python-part-1 We saw in the last video the clarity of bee swarm plots. However, there is a limit to their efficacy. For example, imagine we wanted to plot the county-level voting data for all states east of the Mississippi River and all states west. We make the swarm plot as before, but using a DataFrame that contains all states, with each classified as being east or west of the Mississippi. The bee swarm plot has a real problem. The edges have overlapping data points, which was necessary in order to fit all points onto the plot. We are now obfuscating data. So, using a bee swarm plot here is not the best option. As an alternative, we can compute an empirical cumulative distribution function, or ECDF. Again, this is best explained by example. Here is a picture of an ECDF of the percentage of swing state votes that went to Obama. A x-value of an ECDF is the quantity you are measuring, in this case the percent of vote that sent to Obama. The y-value is the fraction of data points that have a value smaller than the corresponding x-value. For example, 20% of counties in swing states had 36% or less of its people vote for Obama. Similarly, 75% of counties in swing states had 50% or less of its people vote for Obama. Let's look at how to make one of these from our data. The x-axis is the sorted data. We need to generate it using the NumPy function sort, so we need to import Numpy, which we do using the alias np as is commonly done. The we can use np.sort() to generate our x-data. The y-axis is evenly spaced data points with a maximum of one, which we can generate using the np.arange() function and then dividing by the total number of data points. Once we specify the x and y values, we plot the points. By default, plt.plot() plots lines connecting the data points. To plot our ECDF, we just want points. To achieve this we pass the string '.' and the string 'none' to the keywords arguments marker and linestyle, respectively. As you remember from my forceful reminder in an earlier video, we label the axes. Finally, we use the plt.margins() function to make sure none of the data points run over the side of the plot area. Choosing a value of 0.02 gives a 2% buffer all around the plot. The result is the beautiful ECDF I just showed you. We can also easily plot multiple ECDFs on the same plot. For example, here are the ECDFs for the three swing states. We see that Ohio and Pennsylvania were similar, with Pennsylvania having slightly more Democratic counties. Florida, on the other hand, had a greater fraction of heavily Republican counties. In my workflow, I almost always plot the ECDF first. It shows all the data and gives a complete picture of how the data are distributed. But don't take my word for how great ECDFs are. You can see for yourself in the exercises!
Views: 32313 DataCamp
R Visuals in Power BI - 3D Scatter Plot
 
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In this video, I show you how to create a 3-dimensional representation of a scatter plot that you can even rotate via a slicer selection! R Visuals allow for a lot of flexibility over the built-in visuals while requiring just a little bit of code. To enroll in my introductory Power BI course: https://www.udemy.com/learn-power-bi-fast/?couponCode=CHEAPEST
Views: 1451 BI Elite
Data Visualization Projects in Python: Getting Started with Bokeh Python Library|packtpub.com
 
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This video tutorial has been taken from Data Visualization Projects in Python. You can learn more and buy the full video course here [http://bit.ly/2KqWJ8H] Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 28 Packt Video
R - Heatmap of crosstable
 
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Instructional video on creating a heat map of a cross table using R (studio) and the ggplot2 package. Companion website at http://PeterStatistics.com
Views: 594 stikpet
Visualizations in Databricks
 
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Learn how to visualize your data on the Databricks platform. Databricks can create detailed charts with a single click and also supports popular third-party libraries such as ggplot, d3, and matplotlib so that you can create your own custom visualizations.
Views: 3329 Databricks
Data Visualization in Python by Examples: The Course Overview| packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2Hjke1L]. This video gives glimpse of the entire course. For the latest Virtualization & Cloud tutorials, please visit http://bit.ly/2layAb4 Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 510 Packt Video
R Data Visualization - Word Clouds and 3D Plots : The Course Overview | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2rdofNF]. This video gives overview of the entire course. For the latest Application development video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 84 Packt Video

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