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You're reading from  The Data Analysis Workshop

Product typeBook
Published inJul 2020
Reading LevelIntermediate
PublisherPackt
ISBN-139781839211386
Edition1st Edition
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Authors (3):
Gururajan Govindan
Gururajan Govindan
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Gururajan Govindan

Gururajan Govindan is a data scientist, intrapreneur, and trainer with more than seven years of experience working across domains such as finance and insurance. He is also an author of The Data Analysis Workshop, a book focusing on data analytics. He is well known for his expertise in data-driven decision-making and machine learning with Python.
Read more about Gururajan Govindan

Shubhangi Hora
Shubhangi Hora
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Shubhangi Hora

Shubhangi Hora is a data scientist, Python developer, and published writer. With a background in computer science and psychology, she is particularly passionate about healthcare-related AI, including mental health. Shubhangi is also a trained musician.
Read more about Shubhangi Hora

Konstantin Palagachev
Konstantin Palagachev
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Konstantin Palagachev

Konstantin Palagachev holds a Ph.D. in applied mathematics and optimization, with an interest in operations research and data analysis. He is recognized for his passion for delivering data-driven solutions and expertise in the area of urban mobility, autonomous driving, insurance, and finance. He is also a devoted coach and mentor, dedicated to sharing his knowledge and passion for data science.
Read more about Konstantin Palagachev

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9. Analysis of the Energy Consumed by Appliances

Activity 9.01: Analyzing the Appliances Energy Consumption

  1. Using seaborn, plot a boxplot for the a_energy column:
    app_box = sns.boxplot(new_data.a_energy)

    The output will be as follows:

    Figure 9.28: Box plot of a_energy

    Figure 9.28: Box plot of a_energy

  2. Use .sum() to determine the total number of instances wherein the value of the energy consumed by appliances is above 200 Wh:
    out = (new_data['a_energy'] > 200).sum()
    out

    The output will be as follows:

    1916
  3. Calculate the percentage of the number of instances wherein the value of the energy consumed by appliances is above 200 Wh:
    (out/19735) * 100

    The output will be as follows:

    9.708639473017481
  4. Use .sum() to check the total number of instances wherein the value of the energy consumed by appliances is above 950 Wh:
    out_e = (new_data['a_energy'] > 950).sum()
    out_e

    The output will be as follows:

    2
  5. Calculate the percentage of the number of instances wherein the value of the energy consumed...
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You have been reading a chapter from
The Data Analysis Workshop
Published in: Jul 2020Publisher: PacktISBN-13: 9781839211386

Authors (3)

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Gururajan Govindan

Gururajan Govindan is a data scientist, intrapreneur, and trainer with more than seven years of experience working across domains such as finance and insurance. He is also an author of The Data Analysis Workshop, a book focusing on data analytics. He is well known for his expertise in data-driven decision-making and machine learning with Python.
Read more about Gururajan Govindan

author image
Shubhangi Hora

Shubhangi Hora is a data scientist, Python developer, and published writer. With a background in computer science and psychology, she is particularly passionate about healthcare-related AI, including mental health. Shubhangi is also a trained musician.
Read more about Shubhangi Hora

author image
Konstantin Palagachev

Konstantin Palagachev holds a Ph.D. in applied mathematics and optimization, with an interest in operations research and data analysis. He is recognized for his passion for delivering data-driven solutions and expertise in the area of urban mobility, autonomous driving, insurance, and finance. He is also a devoted coach and mentor, dedicated to sharing his knowledge and passion for data science.
Read more about Konstantin Palagachev