Exploring the History of Lego

# Nothing to do here

# Import modules import pandas as pd # Read colors data colors = pd.read_csv('datasets/colors.csv') # Print the first few rows colors.head()

idint64

nameobject

-1

Unknown

Black

Blue

Green

Dark Turquoise

# How many distinct colors are available? # -- YOUR CODE FOR TASK 3 -- num_colors = colors.rgb.count() num_colors

# colors_summary: Distribution of colors based on transparency # -- YOUR CODE FOR TASK 4 -- colors_summary = colors.groupby('is_trans').count() print(colors_summary)

           id  name  rgb
is_trans                
f         107   107  107
t          28    28   28

%matplotlib inline # 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[['year', 'num_parts']].groupby('year').mean() # Plot trends in average number of parts by year parts_by_year

num_partsfloat64

10.142857142857142 - 300.1212765957447

1950

10.142857142857142

1953

16.5

1954

12.357142857142858

1955

36.857142857142854

1956

18.5

1957

42.61904761904762

1958

44.45238095238095

1959

16.25

1960

175.33333333333334

1961

70.58823529411765

# themes_by_year: Number of themes shipped by year # -- YOUR CODE HERE -- themes_by_year = sets[['year','theme_id']]\ .groupby('year', as_index=False)\ .agg({'theme_id':pd.Series.nunique}) themes_by_year.count()

# Nothing to do here