Demographic Descriptive Statistics using Python

Author

Mahesh Gurumoorthi
August 03, 2025

They’re statistical summaries used to describe the characteristics of a population or sample, focusing on aspects like age, gender, income, education, ethnicity, and more.

Common Demographic Variables

  • Age: Usually presented as mean, median, mode, or grouped into age ranges (e.g., 18–24, 25–34).

  • Gender: Shown as counts or percentages (e.g., 55% female, 45% male).

  • Ethnicity or Race: Reported by category and frequency (e.g., 40% White, 30% Black, 20% Asian, etc.).

  • Education Level: Percentages of participants with high school, undergraduate, graduate degrees, etc.

  • Income: Mean, median, and ranges or income brackets.

  • Employment Status: Percentages employed, unemployed, retired, and students.

  • Geographic Location: By region, country, city, rural vs. urban.

Common Statistical Measure

  • Frequencies: How often each category appears.

  • Percentages: Proportion of the sample in each category.

  • Mean: Average value (e.g., average age).

  • Median: Middle value when data is ranked.

  • Standard Deviation: Variation around the mean.

Step 1: Required Packages Importing from the library

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

from scipy import stats
from sklearn.linear_model import LinearRegression
import warnings
warnings.filterwarnings("ignore")

Step 2 : Exploratory Data Analysis

population_data = pd.read_csv('/Users/Sample Datasets Kaggle/city_population.csv')
population_data.shape
(64, 10)
population_data.columns

Index(['Name', 'Abbr.', 'Division', 'Established', 'Native', 'Area (km2)',
       'Population_1991', 'Population_2001', 'Population_2011',
       'Population_2022'],
      dtype='object')
population_data.head(n=10)
Name	Abbr.	Division	Established	Native	Area (km2)	Population_1991	Population_2001	Population_2011	Population_2022
0	Barguna	BRG	Barisal	1984	বরগুনা জেলা	1831	805000	887376	927889	1035596
1	Barishal	BRS	Barisal	1797	বরিশাল জেলা	2785	2299000	2465249	2414729	2634203
2	Bhola	BHO	Barisal	1984	ভোলা জেলা	3403	1532000	1781043	1846351	1980452
3	Jhalokati	JHA	Barisal	1984	ঝালকাঠি জেলা	707	694000	726182	709914	677559
4	Patuakhali	PAT	Barisal	1969	পটুয়াখালী জেলা	3221	1323000	1527628	1596223	1770096
5	Pirojpur	PIR	Barisal	1984	পিরোজপুর জেলা	1278	1104000	1161548	1157215	1227915
6	Bandarban	BAN	Chattogram	1981	বান্দরবান জেলা	4496	246000	311741	404091	


population_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 64 entries, 0 to 63
Data columns (total 10 columns):
 #   Column           Non-Null Count  Dtype 
---  ------           --------------  ----- 
 0   Name             64 non-null     object
 1   Abbr.            64 non-null     object
 2   Division         64 non-null     object
 3   Established      64 non-null     int64 
 4   Native           64 non-null     object
 5   Area (km2)       64 non-null     int64 
 6   Population_1991  64 non-null     int64 
 7   Population_2001  64 non-null     int64 
 8   Population_2011  64 non-null     int64 
 9   Population_2022  64 non-null     int64 
dtypes: int64(6), object(4)
memory usage: 5.1+ KB

Step 3 : Checking Missing Values

population_data.isna().sum()
Name               0
Abbr.              0
Division           0
Established        0
Native             0
Area (km2)         0
Population_1991    0
Population_2001    0
Population_2011    0
Population_2022    0
dtype: int64
population_data.isnull().sum()
Name               0
Abbr.              0
Division           0
Established        0
Native             0
Area (km2)         0
Population_1991    0
Population_2001    0
Population_2011    0
Population_2022    0
dtype: int64
population_data.describe()
Established	Area (km2)	Population_1991	Population_2001	Population_2011	Population_2022
count	64.000000	64.000000	6.400000e+01	6.400000e+01	6.400000e+01	6.400000e+01
mean	1937.703125	2306.000000	1.741500e+06	2.039416e+06	2.340193e+06	2.653577e+06
std	84.372884	1184.492899	1.086475e+06	1.415458e+06	1.811774e+06	2.203057e+06
min	1666.000000	684.000000	2.460000e+05	3.117410e+05	4.040910e+05	4.952520e+05
25%	1963.500000	1379.250000	1.102250e+06	1.213935e+06	1.283516e+06	1.430418e+06
50%	1984.000000	2084.000000	1.545500e+06	1.829092e+06	2.008954e+06	2.215752e+06
75%	1984.000000	2960.750000	2.160750e+06	2.474170e+06	2.745524e+06	3.020196e+06
max	1984.000000	6116.000000	6.164000e+06	9.151343e+06	1.251736e+07	1.521085e+07
<class 'pandas.core.frame.DataFrame'>
population_data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 64 entries, 0 to 63
Data columns (total 10 columns):
 #   Column           Non-Null Count  Dtype 
---  ------           --------------  ----- 
 0   Name             64 non-null     object
 1   Abbr.            64 non-null     object
 2   Division         64 non-null     object
 3   Established      64 non-null     int64 
 4   Native           64 non-null     object
 5   Area (km2)       64 non-null     int64 
 6   Population_1991  64 non-null     int64 
 7   Population_2001  64 non-null     int64 
 8   Population_2011  64 non-null     int64 
 9   Population_2022  64 non-null     int64 
dtypes: int64(6), object(4)
memory usage: 5.1+ KB
population_data.describe(include='object')

Name	Abbr.	Division	Native
count	64	64	64	64
unique	64	64	8	64
top	Barguna	BRG	Dhaka	বরগুনা জেলা
freq	1	1	13	1

Step 4: Perform the Central Tendency Theorem 

mean_population = population_data['Population_1991'].mean()
print(f"Mean Population in 1991: {mean_population}")

Mean Population in 1991: 1741500.0

mean_population = population_data['Population_2001'].mean()
print(f"Mean Population in 2001: {mean_population}")

Mean Population in 2001: 2039415.59375
mean_population = population_data['Population_2011'].mean()
print(f"Mean Population in 2011: {mean_population}")

Mean Population in 2011: 2340193.0

mean_population = population_data['Population_2022'].mean()
print(f"Mean Population in 2022: {mean_population}")

Mean Population in 2022: 2653576.765625

median_population = population_data['Population_1991'].median()
print(f"Median Population in 1991: {median_population}") 

Median Population in 1991: 1545500.0

median_population = population_data['Population_2001'].median()
print(f"Median Population in 2001: {median_population}")

Median Population in 2001: 1829092.5

median_population = population_data['Population_2011'].median()
print(f"Median Population in 2011: {median_population}")

Median Population in 2011: 2008954.5

median_population = population_data['Population_2022'].median()
print(f"Median Population in 2022: {median_population}")

Median Population in 2022: 2215751.5

mode_population = population_data['Population_1991'].mode()
print(f"Mode Population in 1991: {mode_population[0]}")

Mode Population in 1991: 246000

mode_population = population_data['Population_2001'].mode()[0]
print(f"Mode Population in 2001: {mode_population}")

Mode Population in 2001: 311741

mode_population = population_data['Population_2011'].mode()[0]
print(f"Mode Population in 2011: {mode_population}")

Mode Population in 2011: 404091

mode_population = population_data['Population_2022'].mode()[0]
print(f"Mode Population in 2022: {mode_population}")

Mode Population in 2022: 495252

Step 5: Histogram View 

fig, axes = plt.subplots(2, 2, figsize=(12, 10))
years = ['Population_1991', 'Population_2001', 'Population_2011', 'Population_2022']
for ax, year in zip(axes.flatten(), years):
    sns.histplot(population_data[year], bins=15, kde=True, ax=ax)
    ax.set_title(f'Histogram of {year}')
    ax.set_xlabel('Population')
    ax.set_ylabel('Frequency')
plt.tight_layout()
plt.show()