tankaraco

[tankaraco]

My first program

```python

import pandas as pd

Load data

mydata = pd.read_csv('ool_pds.csv', low_memory=False)

print(f'📊 Number of observations: {len(mydata)}') print(f'🧩 Number of variables: {len(mydata.columns)}')

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🟩 Race/Ethnicity (PPETHM)

race_labels = { 1: 'White, Non-Hispanic', 2: 'Black, Non-Hispanic', 3: 'Other, Non-Hispanic', 4: 'Hispanic', 5: '2+ Races, Non-Hispanic' }

race_counts = mydata['PPETHM'].value_counts(sort=False) race_percentages = mydata['PPETHM'].value_counts(sort=False, normalize=True)

race_counts.index = race_counts.index.map(race_labels) race_percentages.index = race_percentages.index.map(race_labels)

print('\n🟩 Race/Ethnicity - Counts:') print(race_counts)

print('\n🟩 Race/Ethnicity - Percentages:') print(race_percentages)

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🟥 Political Identification (W1_C1)

political_labels = { 1: 'Republicans', 2: 'Democrats', 3: 'Independents', 4: 'Something else', 5: 'Refused' }

party_counts = mydata['W1_C1'].value_counts(sort=False) party_percentages = mydata['W1_C1'].value_counts(sort=False, normalize=True)

party_counts.index = party_counts.index.map(political_labels) party_percentages.index = party_percentages.index.map(political_labels)

print('\n🟥 Party Identification - Counts:') print(party_counts)

print('\n🟥 Party Identification - Percentages:') print(party_percentages)

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🟪 Age Category (PPAGECAT)

age_labels = { 1: '18–24', 2: '25–34', 3: '35–44', 4: '45–54', 5: '55–64', 6: '65–74', 7: '75+' }

age_filtered = mydata[mydata['PPAGECAT'] != 99] # Exclude under 18 age_counts = age_filtered['PPAGECAT'].value_counts(sort=False) age_percentages = age_filtered['PPAGECAT'].value_counts(sort=False, normalize=True)

age_counts.index = age_counts.index.map(age_labels) age_percentages.index = age_percentages.index.map(age_labels)

print('\n🟪 Age Group (18+) - Counts:') print(age_counts)

print('\n🟪 Age Group (18+) - Percentages:') print(age_percentages) ```

Output:

📊 Number of observations: 2294 🧩 Number of variables: 436

🟩 Race/Ethnicity - Counts: PPETHM Hispanic 123 Black, Non-Hispanic 1278 White, Non-Hispanic 814 Other, Non-Hispanic 46 2+ Races, Non-Hispanic 33 Name: count, dtype: int64

🟩 Race/Ethnicity - Percentages: PPETHM Hispanic 0.053618 Black, Non-Hispanic 0.557105 White, Non-Hispanic 0.354839 Other, Non-Hispanic 0.020052 2+ Races, Non-Hispanic 0.014385 Name: proportion, dtype: float64

🟥 Party Identification - Counts: W1_C1 Republicans 331 Democrats 1251 Independents 555 Something else 108 NaN 49 Name: count, dtype: int64

🟥 Party Identification - Percentages: W1_C1 Republicans 0.144289 Democrats 0.545336 Independents 0.241935 Something else 0.047079 NaN 0.021360 Name: proportion, dtype: float64

🟪 Age Group (18+) - Counts: PPAGECAT 55–64 514 35–44 360 65–74 329 18–24 237 45–54 457 25–34 289 75+ 108 Name: count, dtype: int64

🟪 Age Group (18+) - Percentages: PPAGECAT 55–64 0.224063 35–44 0.156931 65–74 0.143418 18–24 0.103313 45–54 0.199215 25–34 0.125981 75+ 0.047079 Name: proportion, dtype: float64

Description:

The sample includes 2,294 observations and 436 variables. Among the participants, Black, Non-Hispanic individuals make up the largest group (56%), followed by White, Non-Hispanic (35%). Only a small portion identified as Hispanic (5%) or multiracial (1.4%).

In terms of political identity, a majority identified as Democrats (54.5%), while Republicans and Independents represented 14% and 24%, respectively. About 2% of the data is missing for this question.

Regarding age, most respondents are between 35 and 74 years old, with the largest single group being ages 55–64. Very few are over 75, and those under 18 were excluded from the analysis.

[tankaraco]

Race & Political ID

Is party identification associated with race in the US? I will use the Outlook for Life Survey data set to answer this question.

In the United States, party identification is significantly associated with race, with minority racial groups (African American, Hispanic, and Asian American) more likely to identify as Democrats, while white Americans, particularly those without a college degree, are more likely to identify as Republicans.

Variables Considered

Race/Ethnicity: African American, Hispanic/Latino, Asian American, and White (Abramowitz & Webster, 2018).

Education Level: College-educated vs. non-college-educated voters (Pew Research Center, 2024).

Socioeconomic Status: Income levels and economic mobility influence political affiliation (Tesler, 2016).

Geographic Location: Urban voters lean Democratic, while rural voters lean Republican (Hopkins, 2018).

Religious Affiliation: Evangelical Christians strongly support Republicans, while non-religious groups lean Democratic (Putnam & Campbell, 2012).

Immigration Status: First-generation immigrants show variation in party alignment based on country of origin and policy concerns (Wong et al., 2011).

Patterns of Findings

African Americans: Over 80% identify as Democratic, largely due to civil rights policies and historical realignment (Dawson, 1994).

Hispanic/Latino Voters: Lean Democratic (~60%), with variations among subgroups (e.g., Cuban Americans lean Republican) (Abrajano & Alvarez, 2010).

Asian Americans: Increasing Democratic alignment (~65%), with significant differences by national origin (Ramakrishnan et al., 2017).

White Voters: White non-college voters lean Republican, while white college-educated voters are shifting Democratic (Pew Research Center, 2020).

Education Divide: College-educated voters trend Democratic, while non-college-educated voters trend Republican (Sides et al., 2018).

Religious Influence: Evangelicals remain a key Republican base, while Jewish and Muslim voters lean Democratic (Putnam & Campbell, 2012).