About Dataset¶

This dataset focuses on the black-white wage gap in the United States. It provides insights into the disparities in hourly wages between black and white workers, as well as different gender and subgroup breakdowns.

The data is derived from the Economic Policy Institute’s State of Working America Data Library, a reputable source for socio-economic research and analysis.

This dataset contains information about the black-white wage gap in the USA at different levels, such as median, average. It includes data on houly wages for workers ages 16 and older, adjusted into 2022 dollars.

Dataset Link -Kaggle link

Data Dictionary -¶

Year - Year of the data collection
White_median- Median hourly wage for white workers.
White_average - Average hourly wage for white workers.
Black_median - Median hourly wage for black workers.
black_average - Average hourly wage for black workers.
white_men_median - Median hourly wage for white male workers
white_men_average - Average hourly wage for white male workers
black_men_median -Median hourly wage for black male workers.
black_men_average - Average hourly wage for black male workers.
white_women_median - Median hourly wage for white female workers.
white_women_average - Average hourly wage for white female workers.
black_women_median - Median hourly wage for black female workers.
black_women_average - Average hourly wage for black female workers.

Installing dependency¶

👉Ignore It if already installed

1. !pip install numpy
2. !pip install pandas
3. !pip install matplotlib
4. !pip install seaborn

step -1 Data Preprocessing and Cleaning¶

Importing Required library¶

In [1]:

# perform linear operations
import numpy as np

# Data manipulation
import pandas as pd

#Data Visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Remove warnings
import warnings
warnings.filterwarnings('ignore')

# Perfrom Stastical operation
from scipy.stats import ttest_ind

In [2]:

#Load the dataset
black_white = pd.read_csv(r"C:\Users\Lenovo\Downloads\content\Black-White Wage Gap Data Analysis\black_white_wage_gap.csv")


# Print top 5 rows
black_white.head()

Out[2]:

	year	white_median	white_average	black_median	black_average	white_men_median	white_men_average	black_men_median	black_men_average	white_women_median	white_women_average	black_women_median	black_women_average
0	2022	24.96	34.49	19.60	25.61	27.11	39.10	20.02	27.43	22.47	29.50	19.00	23.99
1	2021	25.40	34.50	19.45	25.40	27.76	38.78	20.08	26.88	22.76	29.90	18.85	24.13
2	2020	25.98	34.86	19.85	26.03	28.36	39.08	20.56	27.40	23.05	30.30	19.26	24.87
3	2019	24.39	32.79	18.45	24.09	27.39	36.84	19.31	25.18	22.01	28.41	18.08	23.17
4	2018	23.97	32.44	17.57	23.53	26.79	36.55	18.66	24.67	21.75	28.01	17.34	22.55

In [3]:

# Print last 5 rows
black_white.tail()

Out[3]:

	year	white_median	white_average	black_median	black_average	white_men_median	white_men_average	black_men_median	black_men_average	white_women_median	white_women_average	black_women_median	black_women_average
45	1977	20.00	23.38	16.23	18.93	24.94	27.66	18.70	20.84	15.33	17.57	14.06	16.91
46	1976	20.06	23.47	16.25	19.20	24.32	27.54	19.19	21.57	15.42	17.79	13.97	16.73
47	1975	19.96	23.30	16.15	18.46	24.68	27.37	19.15	20.60	15.32	17.45	13.41	16.14
48	1974	20.04	23.21	16.07	18.36	24.55	27.34	19.02	20.84	15.22	17.23	13.46	15.68
49	1973	20.53	23.72	15.96	18.61	24.98	27.93	19.29	21.09	15.36	17.57	13.38	15.83

In [4]:

black_white.shape

Out[4]:

(50, 13)

In [5]:

#Check info of each colummn
black_white.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50 entries, 0 to 49
Data columns (total 13 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   year                 50 non-null     int64  
 1   white_median         50 non-null     float64
 2   white_average        50 non-null     float64
 3   black_median         50 non-null     float64
 4   black_average        50 non-null     float64
 5   white_men_median     50 non-null     float64
 6   white_men_average    50 non-null     float64
 7   black_men_median     50 non-null     float64
 8   black_men_average    50 non-null     float64
 9   white_women_median   50 non-null     float64
 10  white_women_average  50 non-null     float64
 11  black_women_median   50 non-null     float64
 12  black_women_average  50 non-null     float64
dtypes: float64(12), int64(1)
memory usage: 5.2 KB

In [6]:

# check for duplicate
black_white.duplicated().sum()

Out[6]:

From above cell we see that there is no duplicate present in the data

Step -2 Data analysis¶

Let's ask some question from our data

Trend Analysis:¶

How has the overall median and average wage changed over the years for both white and black workers?¶

In [7]:

black_white['year'].unique()

Out[7]:

array([2022, 2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2013, 2012,
       2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004, 2003, 2002, 2001,
       2000, 1999, 1998, 1997, 1996, 1995, 1994, 1993, 1992, 1991, 1990,
       1989, 1988, 1987, 1986, 1985, 1984, 1983, 1982, 1981, 1980, 1979,
       1978, 1977, 1976, 1975, 1974, 1973], dtype=int64)

In [8]:

plt.figure(figsize=(12, 8))

# Overall median wages
plt.plot(black_white['year'], black_white['white_median'], label='White Median', linestyle='--', marker='o')
plt.plot(black_white['year'], black_white['black_median'], label='Black Median', linestyle='--', marker='o')

# Overall average wages
plt.plot(black_white['year'], black_white['white_average'], label='White Average', linestyle='-', marker='x')
plt.plot(black_white['year'], black_white['black_average'], label='Black Average', linestyle='-', marker='x')

plt.title('Overall Median and Average Wages Over Time')
plt.xlabel('Year')
plt.ylabel('Wage')
plt.legend()
plt.grid(True)
plt.show()

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Exploratory Data Analysis on Black - White Wage Gap in USA Data in Python