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© 2020 Minitab, LLC.
Welcome! In a few minutes we will begin the webinar.
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activated and your speakers turned up.
QUESTIONS:
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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
© 2020 Minitab, LLC.
Unleashing the Power of Python Using
the New Minitab/Python Integration Module
© 2020 Minitab, LLC.
• 20+ years of
experience
• Masters in Rocket
Science
• Masters in Statistical
Computing
Meet the Presenter:
Mikhail Golovnya
Senior Advisory Data Scientist
© 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
© 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
© 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
© 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
© 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
© 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
© 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
© 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
© 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
© 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
© 2020 Minitab, LLC.
Questions?
© 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
© 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
© 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
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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
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Start, track, manage
and execute
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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
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