Proportion of Females

We aim to analyze the gender distribution across all categories of the awards, including Economic Sciences, Physics, Chemistry, Peace, Physiology or Medicine, and Literature, to identify which gender predominates in each field.

Taak

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Create a new column in the nobel DataFrame that identifies if a laureate is female.
Calculate the decade for each Nobel Prize award.
Compute the proportion of female winners for each decade and category.
Plot the proportion of female winners per decade by categories.

Oplossing

# Create a new column to identify if the laureate is female

nobel["female_winner"] = nobel["gender"] == "female"

# Calculate the decade for each Nobel Prize award

nobel["decade"] = nobel["awardYear"] // 10 * 10

# Compute the proportion of female winners for each decade and category

prop_female_winners = nobel.groupby(["decade", "category"], as_index=False)["female_winner"].mean()

# Plot the proportion of female winners per decade by categories

plt.figure(figsize=(10, 5))

sns.barplot(x="decade", y="female_winner", hue="category", data=prop_female_winners)

plt.title("The proportion of female winners per decade by categories")

plt.ylabel("Female winners ratio")

plt.xlabel("Decade")

plt.legend(loc="upper left")

plt.show()

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Sectie 1. Hoofdstuk 4

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