Unleashing the Power of Python Using the New Minitab/Python Integration Module Webinar Presentation
0 likes320 views
Join our Senior Data Scientist, Mikhail Golovnya, for this webinar where we will introduce a new and exciting feature of Minitab - Python integration module. We will explain how to set up the Python connector in a series of simple intuitive steps. Next, we will introduce the core elements of the new functionality: first, how to send Minitab data, such as individual constants and data columns, to Python; second, how to send the results of Python computing back to Minitab and conveniently report them in textual, graphical, and tabular forms. For more details, please go here: https://hubs.ly/H0r5XTz0
![© 2020 Minitab, LLC.
Passing Columns Around
► # Loading MTB connector
► from mtbpy import mtbpy
► # Receive columns from MTB side as Python lists
► var1 = mtbpy.mtb_instance().get_constant("K1")
► var2 = mtbpy.mtb_instance().get_constant("K2")
► list1 = mtbpy.mtb_instance().get_column(var1)
► list2 = mtbpy.mtb_instance().get_column(var2)
► # Write messages back to MTB
► mtbpy.mtb_instance().add_message("Received " + str(var1)
+ ": " + ", ".join([str(i) for i in list1]))
► mtbpy.mtb_instance().add_message("Received " + str(var2)
+ ": " + ", ".join([str(i) for i in list2]))
8
Minitab
Python](https://image.slidesharecdn.com/unleashingthepowerofpythonpowerpointapac-final-200604073043/85/Unleashing-the-Power-of-Python-Using-the-New-Minitab-Python-Integration-Module-Webinar-Presentation-8-320.jpg)
![© 2020 Minitab, LLC.
Manipulating Datasets
► # Loading libraries
► import pandas as pd
► from mtbpy import mtbpy
► # Receive parameters from Minitab and user feedback
► …
► # Loading dataset for analysis
► dataset = pd.read_csv(input_file)
► # Creating a new variable
► dataset[new_var] = dataset[var]*multiplier
► # Saving new dataset
► dataset.to_csv(output_file, index = False, header=True)
► # Writing message to MTB
► mtbpy.mtb_instance().add_message("Finished processing.")
9
Minitab
Python](https://image.slidesharecdn.com/unleashingthepowerofpythonpowerpointapac-final-200604073043/85/Unleashing-the-Power-of-Python-Using-the-New-Minitab-Python-Integration-Module-Webinar-Presentation-9-320.jpg)
![© 2020 Minitab, LLC.
Inserting Tables
► # Loading MTB connector
► from mtbpy import mtbpy
► # Initialize the arrays
► words = ['one', 'two', 'three', 'four']
► freqs = [10, 20, 30, 40]
► # Send the table to the Minitab output
► mtbpy.mtb_instance().add_table(columns=[words, freqs],
headers=["Word", "Frequency"], title="Simple Table",
footnote="Footnote here")
11
Minitab
Python](https://image.slidesharecdn.com/unleashingthepowerofpythonpowerpointapac-final-200604073043/85/Unleashing-the-Power-of-Python-Using-the-New-Minitab-Python-Integration-Module-Webinar-Presentation-11-320.jpg)
![© 2020 Minitab, LLC.
Explore and Have Fun!
► …
► # Initialize the arrays to store words and their frequencies
► words = []
► freqs = []
► # Loop to count the words in the word cloud frequency dictionary
► for word in cloud.words_:
words.append(word)
freqs.append(cloud.words_[word])
► # Send the table to the Minitab output
► mtbpy.mtb_instance().add_table(columns=[words, freqs],
headers=["Word", "Frequency"], title="Word Cloud Data",
footnote="{0} rows are in this table".format(nWords))
13
Minitab
Python](https://image.slidesharecdn.com/unleashingthepowerofpythonpowerpointapac-final-200604073043/85/Unleashing-the-Power-of-Python-Using-the-New-Minitab-Python-Integration-Module-Webinar-Presentation-13-320.jpg)
Ad
More Related Content
What's hot (20)
Similar to Unleashing the Power of Python Using the New Minitab/Python Integration Module Webinar Presentation (20)
Ad
More from Minitab, LLC (20)
Ad
Recently uploaded (20)
Unleashing the Power of Python Using the New Minitab/Python Integration Module Webinar Presentation
- 1. © 2020 Minitab, LLC. Welcome! In a few minutes we will begin the webinar. AUDIO WEB: Please make sure you have your computer audio system activated and your speakers turned up. QUESTIONS: Please write your questions in the questions pane at any time. Mikhail Golovnya Sr. Advisory Data Scientist Abbie Wong Regional Marketing ManagerUnleashing the Power of Python Using the New Minitab/Python Integration Module
- 2. © 2020 Minitab, LLC. Unleashing the Power of Python Using the New Minitab/Python Integration Module
- 3. © 2020 Minitab, LLC. • 20+ years of experience • Masters in Rocket Science • Masters in Statistical Computing Meet the Presenter: Mikhail Golovnya Senior Advisory Data Scientist
- 4. © 2020 Minitab, LLC. Why Python? ► Python is the main working environment of a vibrant community of data scientists centered around open source philosophy ► Python provides a convenient scripting language to accommodate different data processing tasks ► Python offers huge diversity of various packages to serve the ever growing needs of data scientists ► But, implementing all data analysis steps in Python is not for everyone You have to be a programmer You have to figure things out You have to cope with possible errors ► Python/Minitab connector allows you to preserve the simplicity and user conveniences of Minitab while getting access to the unique extensions offered on the Python side! 4
- 5. © 2020 Minitab, LLC. Installing Python/Minitab Connector ► You need to install the latest version of Python 3 We recommend Anaconda distribution available here https://www.anaconda.com/products/individual The easiest option is to add Python to the system Path variable during the Python installation (you can ignore more elaborate options like Anaconda Navigator, etc.) ► You need to make sure that Python is visible Use the following commands to check that this is the case and that you have the right version of Python python –V python –version ► Install “mtbpy” package using the wheel file (a binary file) supplied by Minitab The easiest way is to use the following command pip install mtbpy-1.0.0rc1-py3-none-any.whl here mtbpy-1.0.0rc1-py3-none-any.whl is the wheel file 5
- 6. © 2020 Minitab, LLC. Testing Python/Minitab Connector ► Use File – Options: General menu to indicate the Default file location ► Place the supplied “test.py” script in the default location ► Execute the PYSC command in Minitab Command Line ► Confirm that Minitab sees Python 6
- 7. © 2020 Minitab, LLC. Passing Constants Around # Loading MTB connector from mtbpy import mtbpy # Receive constants from MTB side K1 = mtbpy.mtb_instance().get_constant("K1") K2 = mtbpy.mtb_instance().get_constant("K2") # Write messages back to MTB mtbpy.mtb_instance().add_message("Received K1: " + str(K1)) mtbpy.mtb_instance().add_message("Received K2: " + K2) 7 Minitab Python
- 8. © 2020 Minitab, LLC. Passing Columns Around ► # Loading MTB connector ► from mtbpy import mtbpy ► # Receive columns from MTB side as Python lists ► var1 = mtbpy.mtb_instance().get_constant("K1") ► var2 = mtbpy.mtb_instance().get_constant("K2") ► list1 = mtbpy.mtb_instance().get_column(var1) ► list2 = mtbpy.mtb_instance().get_column(var2) ► # Write messages back to MTB ► mtbpy.mtb_instance().add_message("Received " + str(var1) + ": " + ", ".join([str(i) for i in list1])) ► mtbpy.mtb_instance().add_message("Received " + str(var2) + ": " + ", ".join([str(i) for i in list2])) 8 Minitab Python
- 9. © 2020 Minitab, LLC. Manipulating Datasets ► # Loading libraries ► import pandas as pd ► from mtbpy import mtbpy ► # Receive parameters from Minitab and user feedback ► … ► # Loading dataset for analysis ► dataset = pd.read_csv(input_file) ► # Creating a new variable ► dataset[new_var] = dataset[var]*multiplier ► # Saving new dataset ► dataset.to_csv(output_file, index = False, header=True) ► # Writing message to MTB ► mtbpy.mtb_instance().add_message("Finished processing.") 9 Minitab Python
- 10. © 2020 Minitab, LLC. Inserting Images ► # Loading MTB connector ► from mtbpy import mtbpy ► # Transfering parameters from MTB ► input_file = mtbpy.mtb_instance().get_constant("K1") ► # User feedback ► mtbpy.mtb_instance().add_message("Input dataset: " + input_file) ► # Send the png file to the Minitab output ► mtbpy.mtb_instance().add_image(input_file) 10 Minitab Python
- 11. © 2020 Minitab, LLC. Inserting Tables ► # Loading MTB connector ► from mtbpy import mtbpy ► # Initialize the arrays ► words = ['one', 'two', 'three', 'four'] ► freqs = [10, 20, 30, 40] ► # Send the table to the Minitab output ► mtbpy.mtb_instance().add_table(columns=[words, freqs], headers=["Word", "Frequency"], title="Simple Table", footnote="Footnote here") 11 Minitab Python
- 12. © 2020 Minitab, LLC. Explore and Have Fun! ► from wordcloud import WordCloud, STOPWORDS ► import numpy as np ► from PIL import Image ► from mtbpy import mtbpy ► # Generate word cloud ► nWords = 150 ► dataset = open("coffee_reviews.txt", "r", encoding="utf8").read() ► dataset = dataset.lower() ► maskArray = np.array(Image.open("cloud.png")) ► cloud = WordCloud(background_color = "white", max_words = nWords, mask = maskArray, stopwords = set(STOPWORDS)) ► cloud.generate(dataset) ► cloud.to_file("word_cloud.png") ► mtbpy.mtb_instance().add_image("word_cloud.png") 12 Minitab Python
- 13. © 2020 Minitab, LLC. Explore and Have Fun! ► … ► # Initialize the arrays to store words and their frequencies ► words = [] ► freqs = [] ► # Loop to count the words in the word cloud frequency dictionary ► for word in cloud.words_: words.append(word) freqs.append(cloud.words_[word]) ► # Send the table to the Minitab output ► mtbpy.mtb_instance().add_table(columns=[words, freqs], headers=["Word", "Frequency"], title="Word Cloud Data", footnote="{0} rows are in this table".format(nWords)) 13 Minitab Python
- 14. © 2020 Minitab, LLC. Questions?
- 15. © 2020 Minitab, LLC. Upcoming Webinars Machine Learning Series A three-part series where we explore multiple regression, binary logistic regression and Classification and Regression Trees (Cart ®) in Minitab. Dates Wednesday 17 June Tuesday 30 June Wednesday 15 July Time 12pm AEST (10am HKT/ 2pm NZST) Get the details here https://hubs.ly/H0r5TZw0
- 16. © 2020 Minitab, LLC. Upcoming Training Virtual Training Take our Minitab Signature Trainings virtually now from anywhere and learn directly from our expert statisticians. Upcoming training include: • Predictive Analytics: Machine Learning: June 18-19 See all the details at: https://hubs.ly/H0r5XRL0
- 17. © 2020 Minitab, LLC. Our Approach: More Than Business Analytics… Solutions Analytics Software Services Training Learn first-hand by attending public or customized trainings in your facilities according to your requirements. Statistical Consulting Personalized help with statistical challenges from collecting the right data to interpreting analysis more. Support Assistance with installation, implementation, version updates and license management. Master statistics and Minitab anywhere with online training Machine learning and predictive analytics software Start, track, manage and execute improvement projects with real-time dashboards Powerful statistical software everyone can use. Data Analysis Predictive Modeling Visual Business Tools Project Oversight Visual tools to process and product excellence. Online Training Solutions analytics is our integrated approach to providing software and services that enable organizations to make better decisions that drive business excellence.
- 18. © 2020 Minitab, LLC. Thank you