Practice GroupBy operations with Laptops spec data
Practice GroupBy operations with Laptops spec data Data Science Project
Data Wrangling with Pandas

Practice GroupBy operations with Laptops spec data

Sharpen your data analysis skills with the Laptop Prices dataset. Dive into the world of laptops and master aggregation and grouping techniques to unveil valuable insights. Explore price variations, identify top laptop brands, and analyze the impact of specifications on pricing. Gain a deeper understanding of the laptop market through data-driven analysis and uncover key trends in this dynamic industry.
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Practice GroupBy operations with Laptops spec dataPractice GroupBy operations with Laptops spec data
Project Created by

Anurag Verma

Project Activities

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

All our activities include solutions with explanations on how they work and why we chose them.

codevalidated

Calculate the average price for each `processor_brand` and `brand`.

Store the resulting series in the variable brand_avg_price.

codevalidated

Calculate the total number of laptops for each `ram_type` and `warranty`.

Store the resulting series in the variable ram_type_warranty_counts.

codevalidated

Calculate the average rating for each `os`.

Store the resulting dataframe in the variable os_avg_rating.

Your result should look similar to this dataframe:

activity3-answer

codevalidated

Calculate the minimum laptop price for each `warranty` plan.

Store the resulting dataframe in the variable warranty_min_price.

Your result should look similar to this dataframe:

activity4-answer

codevalidated

Calculate the average `number of reviews` & `price` for laptops per `touchscreen option` and `processor generation`.

Store the resulting dataframe in the variable touchscreen_proc_gnr_avg_rating.

Your result should look similar to this dataframe:

activity5-answer

codevalidated

Calculate the maximum and minimum price for each `SSD` and `HDD` combination.

Store the resulting dataframe in the variable ssd_hdd_prices.

Your result should look similar to this dataframe:

activity6-answer

codevalidated

Calculate the total `number of reviews` & average `price` for each `weight`, `os` , and `os_bit` combination.

Store the resulting dataframe in the variable weight_os_bit_avg_price_tot_reviews.

Your result should look similar to this dataframe:

activity7-answer

codevalidated

Calculate the mean price for each brand and subtract it from the individual laptop prices to obtain the price deviations.

Store the resulting series in the variable brand_price_deviations.

codevalidated

Calculate the weighted average of laptop prices based on the number of ratings for each `brand`.

Store the resulting series in the variable weighted_avg_price.

codevalidated

Calculate the percentage of laptops with `16 GB` RAM for each `brand` and `ram_type` combination.

Store the resulting series in the variable brand_ram_percentage.

codevalidated

Calculate minimum and maximum ratings and their difference for each processor brand & name combination

In this task, you are asked to calculate the minimum, maximum, and the difference between these ratings for each laptop's processor brand and name combination. Store this information in an rating_stats named dataframe for easy reference and visualization. Your output should be similar to this provided example:

Your result should look similar to this dataframe:

activity11-answer

codevalidated

Calculate the total `number of ratings` for each `brand` & `processor_name` combination for laptops that have `No warranty`.

Store the resulting series in the variable highest_avg_rating_without_warranty.

codevalidated

Calculate the percentage of each laptop's price compared to the maximum price within its `brand` and `processor_name`.

Store the resulting column with the name Price_Percentage in the original dataframe laptops_df.

Practice GroupBy operations with Laptops spec dataPractice GroupBy operations with Laptops spec data
Project Created by

Anurag Verma

What's up, friends! 👋 I'm a computer science student about to finish my last year of college. 🎓 I LOVE writing code! ❤️ It makes me so happy! 😄 Whether I'm goofing in notebooks 📓 or coding in Python 🐍, writing programs is a blast! 💥

What's up, friends! 👋 I'm a computer science student about to finish my last year of college. 🎓 I LOVE writing code! ❤️ It makes me so happy! 😄 Whether I'm goofing in notebooks 📓 or coding in Python 🐍, writing programs is a blast! 💥

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Data Wrangling with Pandas

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