Descriptive Analytics
Understanding historical data through statistical analysis and visualization
What is Descriptive Analytics?
Descriptive analytics is the foundation of data analysis, focusing on summarizing historical data to derive meaningful insights. It answers the question "What happened?" through various statistical methods and visualizations.
Common Descriptive Metrics
Key Measures
- Mean, Median, Mode
- Standard Deviation
- Variance
- Percentiles
- Range
Visualization Techniques
Distribution Plots
Histograms, box plots, and density plots help visualize data distributions
Relationship Plots
Scatter plots, correlation matrices, and pair plots show relationships between variables
Implementation Example
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Load and prepare data
df = pd.read_csv('sales_data.csv')
# Basic descriptive statistics
print(df.describe())
# Create visualizations
plt.figure(figsize=(12, 6))
# Distribution plot
sns.histplot(data=df, x='sales', kde=True)
plt.title('Sales Distribution')
# Box plot
plt.figure(figsize=(10, 6))
sns.boxplot(data=df, x='category', y='sales')
plt.title('Sales by Category')
# Correlation heatmap
plt.figure(figsize=(8, 8))
sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
plt.title('Correlation Matrix')
plt.show()Best Practices
Data Preparation
- • Clean missing values
- • Handle outliers appropriately
- • Ensure data consistency
- • Document assumptions
Visualization Tips
- • Choose appropriate chart types
- • Use clear labels and titles
- • Consider color accessibility
- • Include context and explanations
Recommended Tools
Python Libraries
- • Pandas
- • NumPy
- • Matplotlib
- • Seaborn
BI Tools
- • Tableau
- • Power BI
- • Looker
- • Qlik
Statistical Software
- • R
- • SPSS
- • SAS
- • Stata