Python project 3

  1. The Script

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns


# Load dataset

df = pd.read_csv("gapminder.csv")


# Convert variables to numeric

vars_of_interest = ["incomeperperson", "internetuserate", "lifeexpectancy"]

df[vars_of_interest] = df[vars_of_interest].apply(pd.to_numeric, errors="coerce")


# Data management: group variables

df["income_group"] = pd.cut(df["incomeperperson"],

                            bins=[0, 5000, 20000, 100000],

                            labels=["Low Income", "Middle Income", "High Income"])

df["internet_group"] = pd.cut(df["internetuserate"],

                              bins=[0, 30, 70, 100],

                              labels=["Low Internet Use", "Medium Internet Use", "High Internet Use"])

df["lifeexp_group"] = pd.cut(df["lifeexpectancy"],

                             bins=[0, 60, 75, 90],

                             labels=["Low Life Expectancy", "Medium Life Expectancy", "High Life Expectancy"])


# --- Univariate graphs ---

sns.countplot(x="income_group", data=df)

plt.title("Distribution of Income Groups")

plt.show()


sns.countplot(x="internet_group", data=df)

plt.title("Distribution of Internet Use Groups")

plt.show()


sns.countplot(x="lifeexp_group", data=df)

plt.title("Distribution of Life Expectancy Groups")

plt.show()

# --- Bivariate graph ---

sns.scatterplot(x="incomeperperson", y="internetuserate", data=df)

plt.title("Association between Income per Person and Internet Use Rate")

plt.xlabel("Income per Person (US$)")

plt.ylabel("Internet Use Rate (%)")

plt.show()



2. The Output








3. Summary of Frequency Distributions

  • Income Groups (Univariate): Most countries fall into the Low Income category, with fewer in Middle Income and only a small number in High Income. This shows global inequality in economic development.

  • Internet Use Groups (Univariate): The majority of countries are in Low and Medium Internet Use, while relatively few countries reach High Internet Use. This highlights the persistence of the digital divide.

  • Life Expectancy Groups (Univariate): Most countries cluster in Medium or High Life Expectancy, while some still fall into the Low Life Expectancy category, suggesting disparities in health outcomes.

  • Income vs Internet Use (Bivariate): The scatter plot reveals a clear positive relationship: countries with higher income per person also tend to have higher internet usage. Low-income countries cluster in the bottom-left, while high-income countries are in the top-right, strongly supporting the hypothesis that income is associated with internet access.


Comments

Post a Comment

Popular posts from this blog

Exploring the Relationship Between Economic Prosperity, Health, and Internet Adoption Across Countries

  1. Project Title: Exploring the Relationship Between Economic Prosperity, Health, and Internet Adoption Across Countries 2.  Research Question How are a country’s income per person and life expectancy associated with its internet use rate ? Specifically, does higher income and longer life expectancy predict greater internet adoption across nations? 3.  Motivation / Rationale Access to the internet has become a key indicator of technological advancement, educational access, and economic opportunity in the modern world. However, large disparities still exist globally. Understanding what drives these differences—especially whether economic well-being and population health influence digital inclusion —can reveal important insights into global inequality and development. By using publicly available Gapminder data, I aim to identify the strongest predictors of internet use and examine how economic and health indicators interact to shape technological access across...
Read more

Python project 2

  1. The Script import pandas as pd # Load dataset df = pd.read_csv("gapminder.csv") # Select variables vars_of_interest = ["incomeperperson", "internetuserate", "lifeexpectancy"] # Convert to numeric, handle missing/invalid df[vars_of_interest] = df[vars_of_interest].apply(pd.to_numeric, errors="coerce") # --- Data Management Decisions --- # 1. Drop rows where all three variables are missing df = df.dropna(subset=vars_of_interest, how="all") # 2. Create categorical bins df["income_group"] = pd.cut(df["incomeperperson"],                             bins=[0, 5000, 20000, 100000],                             labels=["Low Income", "Middle Income", "High Income"]) df["internet_group"] = pd.cut(df["internetuserate"],                               bins=[0, 30, 70, 100],  ...
Read more

Simple Linear Regression

 1. Program/Script import pandas as pd import statsmodels.api as sm # Load dataset df = pd.read_csv("gapminder.csv") # Convert variables to numeric df["incomeperperson"] = pd.to_numeric(df["incomeperperson"], errors="coerce") df["internetuserate"] = pd.to_numeric(df["internetuserate"], errors="coerce") # Drop missing values df_clean = df.dropna(subset=["incomeperperson", "internetuserate"]) # Center the explanatory variable (income per person) df_clean["income_centered"] = df_clean["incomeperperson"] - df_clean["incomeperperson"].mean() # Check the mean after centering print("Mean of centered income:", df_clean["income_centered"].mean()) # Add constant and run regression X = sm.add_constant(df_clean["income_centered"]) y = df_clean["internetuserate"] model = sm.OLS(y, X).fit() print(model.summary()) 2. Output Mean of centered in...
Read more