Skip to main content
HomePython

Project

Data-Driven Product Management: Conducting a Market Analysis

BasicSkill Level
4.8+
698 reviews
Updated 05/2024
Explore local and global fitness trends to identify product niches. Investigate online interest in gyms, workouts, digital services, and web apps.
Start Project

Included withPremium or Teams

PythonData Manipulation1 hr1 Task1,500 XP12,929

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
Group

Training 2 or more people?

Try DataCamp for Business

Loved by learners at thousands of companies

Project Description

Data-Driven Product Management: Conducting a Market Analysis

In this project, you'll explore local and international markets to find opportunities for your fitness products. You'll use your data manipulation skills to examine data about online interest in home gyms, gym workouts, home workouts, and fitness products, and create visualizations to help guide your product decisions.

Data-Driven Product Management: Conducting a Market Analysis

Explore local and global fitness trends to identify product niches. Investigate online interest in gyms, workouts, digital services, and web apps.
Start Project
  • 1

    Load data on global interest in fitness

Don’t just take our word for it

*4.8
from 698 reviews
82%
17%
0%
0%
0%
  • Alvaro
    5 hours ago

    Might be useful to indicate the scale of indexes, higher is better? or lower is better (first in ranking is usually better but for top_county answer that is not the case.

  • Sandip
    7 hours ago

  • Rodrigo
    21 hours ago

  • Gabriel
    2 days ago

  • Gernot
    2 days ago

  • Amy
    2 days ago

    1. Define a list variable containing the columns to be compared.
    2. Use max() on the selected columns to get the maximum value of each column. This returns a pandas Series where the index is the column name and the value is the maximum value of that column.
    3. Use idxmax() on the above Series to get the index corresponding to the largest maximum value
    4. Use melt() to convert data with multiple columns and one row into a single column with multiple rows. 5.Use df.loc[df.[column name].idxmax(),column name] to get the value of a specific column in the row with the maximum value across multiple rows

"Might be useful to indicate the scale of indexes, higher is better? or lower is better (first in ranking is usually better but for top_county answer that is not the case."

Alvaro

Sandip

Rodrigo

FAQs

    Join over 18 million learners and start Data-Driven Product Management: Conducting a Market Analysis today!

    Create Your Free Account

    or

    By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.