Lecture 3 Homework - Duplicate

Homework 3: Pokemon Data Analysis

The following homework contains three questions, all of which require you to apply what you've learned about exploratory data analysis and visualizations. As always, please ask any of the mentors if you need help with this homework.

data = pd.read_csv("/work/Pokemon(1).csv") data.head()

What is the most common "Type 1" for all the Pokemon? Use the code cell below to figure this out.

(Hint: Use the function value_counts() to answer this question)

data['Type 1'].value_counts().idxmax()

Using data from every Psychic type (meaning Type 1 OR Type 2 is Psychic), create a scatterplot graph for the "Attack" and "Special Attack" columns. What kind of a relationship do you see? Is it a strong or weak relationship? Positive or negative?

pokemon_psychic = data[data["Type 1"] >= psychic] sns.scatterplot(x="Attack", y="Special Attack", pokemon=pokemon_psychic, color='coral')

Using histograms, compare the distributions of "HP" for Pokemon that are and aren't legendary. What do you see here? Talk about the skewness, outliers, mode(s), etc.

sns.histplot(pokemon=dara, x="Meta_score", kde=True, color="mediumpurple") plt.title("Distribution of HP") plt.xlabel("legendary") plt.ylabel("non-legendary");

.css-15w88e5{color:var(--chakra-colors-fg-neutral-primary);font-weight:inherit;letter-spacing:-0.09px;}Homework 3: Pokemon Data Analysis

Homework 3: Pokemon Data Analysis