Everyone loves Lego (unless you ever stepped on one). Did you know by the way that "Lego" was derived from the Danish phrase leg godt, which means "play well"? Unless you speak Danish, probably not.
In this project, we will analyze a fascinating dataset on every single Lego block that has ever been built!
# Nothing to do here
2. Reading Data
A comprehensive database of lego blocks is provided by Rebrickable. The data is available as csv files and the schema is shown below.
Let us start by reading in the colors data to get a sense of the diversity of Lego sets!
# Import pandas # -- YOUR CODE FOR TASK 3 -- import pandas as pd # Read colors data colors=pd.read_csv('datasets/colors.csv') # Print the first few rows print(colors.head())
3. Exploring Colors
Now that we have read the
colors data, we can start exploring it! Let us start by understanding the number of colors available.
# How many distinct colors are available? # -- YOUR CODE FOR TASK 3 -- num_colors=len(colors) #.groupby('name')['rgb'].sum() # Print num_colors print(num_colors)
4. Transparent Colors in Lego Sets
colors data has a column named
is_trans that indicates whether a color is transparent or not. It would be interesting to explore the distribution of transparent vs. non-transparent colors.
# colors_summary: Distribution of colors based on transparency # -- YOUR CODE FOR TASK 4 -- colors_summary=colors.groupby('is_trans').count() print(colors_summary)
5. Explore Lego Sets
Another interesting dataset available in this database is the
sets data. It contains a comprehensive list of sets over the years and the number of parts that each of these sets contained.
Let us use this data to explore how the average number of parts in Lego sets has varied over the years.
%matplotlib inline from matplotlib import pyplot as plt # Read sets data as `sets` sets=pd.read_csv('datasets/sets.csv') # Create a summary of average number of parts by year: `parts_by_year` parts_by_year=sets.groupby('year')['num_parts'].count() #print(parts_by_year) # Plot trends in average number of parts by year parts_by_year.plot(kind='hist') plt.show()
# themes_by_year: Number of themes shipped by year # -- YOUR CODE HERE -- themes_by_year=pd.DataFrame(sets.groupby('year')['theme_id'].count()) print(themes_by_year.head())
7. Wrapping It All Up!
Lego blocks offer an unlimited amount of fun across ages. We explored some interesting trends around colors, parts, and themes. Before we wrap up, let's take a closer look at the
themes_by_year DataFrame you created in the previous step.
# Get the number of unique themes released in 1999 num_themes=sets['theme_id'][sets['year']==1999] # Print the number of unique themes released in 1999 num_themes