Data Visualization

Overview

Data visualization transforms complex data into clear, compelling visual representations that reveal patterns, trends, and insights for storytelling and decision-making.

When to Use

Exploratory data analysis and pattern discovery

Communicating insights to stakeholders

Comparing distributions and relationships

Presenting findings in reports and dashboards

Identifying outliers and anomalies visually

Creating publication-ready charts and graphs

Visualization Types

Distributions

Histograms, KDE, violin plots

Relationships

Scatter plots, line plots, heatmaps

Comparisons

Bar charts, box plots, ridge plots

Compositions

Pie charts, stacked bars, treemaps

Temporal

Line plots, area charts, time series

Multivariate

Pair plots, correlation heatmaps Design Principles Choose appropriate chart type for data Minimize ink-to-data ratio Use color purposefully Label clearly and completely Maintain consistent scales Consider accessibility Implementation with Python import pandas as pd import numpy as np import matplotlib . pyplot as plt import seaborn as sns from matplotlib . gridspec import GridSpec

Set style

sns . set_style ( "whitegrid" ) plt . rcParams [ 'figure.figsize' ] = ( 12 , 6 )

Generate sample data

np . random . seed ( 42 ) n = 500 data = pd . DataFrame ( { 'age' : np . random . uniform ( 20 , 70 , n ) , 'income' : np . random . exponential ( 50000 , n ) , 'education_years' : np . random . uniform ( 12 , 20 , n ) , 'category' : np . random . choice ( [ 'A' , 'B' , 'C' ] , n ) , 'region' : np . random . choice ( [ 'North' , 'South' , 'East' , 'West' ] , n ) , 'satisfaction' : np . random . uniform ( 1 , 5 , n ) , 'purchased' : np . random . choice ( [ 0 , 1 ] , n ) , } ) print ( data . head ( ) )

1. Distribution Plots

fig , axes = plt . subplots ( 2 , 2 , figsize = ( 12 , 8 ) )

Histogram

axes [ 0 , 0 ] . hist ( data [ 'age' ] , bins = 30 , color = 'skyblue' , edgecolor = 'black' ) axes [ 0 , 0 ] . set_title ( 'Age Distribution (Histogram)' ) axes [ 0 , 0 ] . set_xlabel ( 'Age' ) axes [ 0 , 0 ] . set_ylabel ( 'Frequency' )

KDE plot

data [ 'income' ] . plot ( kind = 'kde' , ax = axes [ 0 , 1 ] , color = 'green' , linewidth = 2 ) axes [ 0 , 1 ] . set_title ( 'Income Distribution (KDE)' ) axes [ 0 , 1 ] . set_xlabel ( 'Income' )

Box plot

sns . boxplot ( data = data , y = 'satisfaction' , x = 'category' , ax = axes [ 1 , 0 ] , palette = 'Set2' ) axes [ 1 , 0 ] . set_title ( 'Satisfaction by Category (Box Plot)' )

Violin plot

sns . violinplot ( data = data , y = 'age' , x = 'category' , ax = axes [ 1 , 1 ] , palette = 'Set2' ) axes [ 1 , 1 ] . set_title ( 'Age by Category (Violin Plot)' ) plt . tight_layout ( ) plt . show ( )

2. Relationship Plots

fig , axes = plt . subplots ( 2 , 2 , figsize = ( 12 , 8 ) )

Scatter plot

axes [ 0 , 0 ] . scatter ( data [ 'age' ] , data [ 'income' ] , alpha = 0.5 , s = 30 ) axes [ 0 , 0 ] . set_title ( 'Age vs Income (Scatter Plot)' ) axes [ 0 , 0 ] . set_xlabel ( 'Age' ) axes [ 0 , 0 ] . set_ylabel ( 'Income' )

Scatter with regression line

sns . regplot ( x = 'age' , y = 'income' , data = data , ax = axes [ 0 , 1 ] , scatter_kws = { 'alpha' : 0.5 } ) axes [ 0 , 1 ] . set_title ( 'Age vs Income (with Regression Line)' )

Joint plot alternative

ax_hex

axes [ 1 , 0 ] hexbin = ax_hex . hexbin ( data [ 'age' ] , data [ 'income' ] , gridsize = 15 , cmap = 'YlOrRd' ) ax_hex . set_title ( 'Age vs Income (Hex Bin)' ) ax_hex . set_xlabel ( 'Age' ) ax_hex . set_ylabel ( 'Income' )

Bubble plot

scatter

axes [ 1 , 1 ] . scatter ( data [ 'age' ] , data [ 'income' ] , s = data [ 'satisfaction' ] * 50 , c = data [ 'satisfaction' ] , cmap = 'viridis' , alpha = 0.6 , edgecolors = 'black' ) axes [ 1 , 1 ] . set_title ( 'Age vs Income (Bubble Plot)' ) axes [ 1 , 1 ] . set_xlabel ( 'Age' ) axes [ 1 , 1 ] . set_ylabel ( 'Income' ) plt . colorbar ( scatter , ax = axes [ 1 , 1 ] , label = 'Satisfaction' ) plt . tight_layout ( ) plt . show ( )

3. Comparison Plots

fig , axes = plt . subplots ( 2 , 2 , figsize = ( 12 , 8 ) )

Bar plot

category_counts

data [ 'category' ] . value_counts ( ) axes [ 0 , 0 ] . bar ( category_counts . index , category_counts . values , color = 'skyblue' , edgecolor = 'black' ) axes [ 0 , 0 ] . set_title ( 'Category Distribution (Bar Chart)' ) axes [ 0 , 0 ] . set_ylabel ( 'Count' )

Grouped bar plot

grouped_data

data . groupby ( [ 'category' , 'region' ] ) . size ( ) . unstack ( ) grouped_data . plot ( kind = 'bar' , ax = axes [ 0 , 1 ] , edgecolor = 'black' ) axes [ 0 , 1 ] . set_title ( 'Category by Region (Grouped Bar)' ) axes [ 0 , 1 ] . set_ylabel ( 'Count' ) axes [ 0 , 1 ] . legend ( title = 'Region' )

Stacked bar plot

grouped_data . plot ( kind = 'bar' , stacked = True , ax = axes [ 1 , 0 ] , edgecolor = 'black' ) axes [ 1 , 0 ] . set_title ( 'Category by Region (Stacked Bar)' ) axes [ 1 , 0 ] . set_ylabel ( 'Count' )

Horizontal bar plot

region_counts

data [ 'region' ] . value_counts ( ) axes [ 1 , 1 ] . barh ( region_counts . index , region_counts . values , color = 'lightcoral' , edgecolor = 'black' ) axes [ 1 , 1 ] . set_title ( 'Region Distribution (Horizontal Bar)' ) axes [ 1 , 1 ] . set_xlabel ( 'Count' ) plt . tight_layout ( ) plt . show ( )

4. Correlation and Heatmaps

numeric_cols

data [ [ 'age' , 'income' , 'education_years' , 'satisfaction' ] ] . corr ( ) fig , axes = plt . subplots ( 1 , 2 , figsize = ( 14 , 5 ) )

Correlation heatmap

sns . heatmap ( numeric_cols , annot = True , fmt = '.2f' , cmap = 'coolwarm' , center = 0 , square = True , ax = axes [ 0 ] , cbar_kws = { 'label' : 'Correlation' } ) axes [ 0 ] . set_title ( 'Correlation Matrix Heatmap' )

Clustermap alternative

from scipy . cluster . hierarchy import dendrogram , linkage from scipy . spatial . distance import pdist , squareform

Create a simpler heatmap for category averages

category_avg

data . groupby ( 'category' ) [ [ 'age' , 'income' , 'education_years' , 'satisfaction' ] ] . mean ( ) sns . heatmap ( category_avg . T , annot = True , fmt = '.1f' , cmap = 'YlGnBu' , ax = axes [ 1 ] , cbar_kws = { 'label' : 'Average Value' } ) axes [ 1 ] . set_title ( 'Average Values by Category' ) plt . tight_layout ( ) plt . show ( )

5. Pair Plot

pair_cols

[ 'age' , 'income' , 'education_years' , 'satisfaction' ] plt . figure ( figsize = ( 12 , 10 ) ) pair_plot = sns . pairplot ( data [ pair_cols ] , diag_kind = 'hist' , corner = False ) pair_plot . fig . suptitle ( 'Pair Plot Matrix' , y = 1.00 ) plt . show ( )

6. Multi-dimensional Visualization

fig

plt . figure ( figsize = ( 14 , 6 ) ) gs = GridSpec ( 2 , 3 , figure = fig )

Subplots with different aspects

ax1

fig . add_subplot ( gs [ 0 , 0 ] ) ax1 . scatter ( data [ 'age' ] , data [ 'income' ] , c = data [ 'satisfaction' ] , cmap = 'viridis' , alpha = 0.6 ) ax1 . set_title ( 'Age vs Income (colored by Satisfaction)' ) ax1 . set_xlabel ( 'Age' ) ax1 . set_ylabel ( 'Income' ) ax2 = fig . add_subplot ( gs [ 0 , 1 ] ) for cat in data [ 'category' ] . unique ( ) : subset = data [ data [ 'category' ] == cat ] ax2 . scatter ( subset [ 'age' ] , subset [ 'income' ] , label = cat , alpha = 0.6 ) ax2 . set_title ( 'Age vs Income (by Category)' ) ax2 . set_xlabel ( 'Age' ) ax2 . set_ylabel ( 'Income' ) ax2 . legend ( ) ax3 = fig . add_subplot ( gs [ 0 , 2 ] ) sns . boxplot ( data = data , x = 'region' , y = 'income' , ax = ax3 , palette = 'Set2' ) ax3 . set_title ( 'Income Distribution by Region' ) ax4 = fig . add_subplot ( gs [ 1 , 0 ] ) data . groupby ( 'category' ) [ 'satisfaction' ] . mean ( ) . plot ( kind = 'bar' , ax = ax4 , color = 'skyblue' , edgecolor = 'black' ) ax4 . set_title ( 'Average Satisfaction by Category' ) ax4 . set_ylabel ( 'Satisfaction' ) ax4 . set_xlabel ( 'Category' ) ax5 = fig . add_subplot ( gs [ 1 , 1 : ] ) region_category = pd . crosstab ( data [ 'region' ] , data [ 'category' ] ) region_category . plot ( kind = 'bar' , ax = ax5 , edgecolor = 'black' ) ax5 . set_title ( 'Region vs Category Distribution' ) ax5 . set_ylabel ( 'Count' ) ax5 . set_xlabel ( 'Region' ) ax5 . legend ( title = 'Category' ) plt . tight_layout ( ) plt . show ( )

7. Time Series Visualization (if temporal data)

dates

pd . date_range ( '2023-01-01' , periods = len ( data ) ) data [ 'date' ] = dates data [ 'cumulative_income' ] = data [ 'income' ] . cumsum ( ) fig , axes = plt . subplots ( 2 , 1 , figsize = ( 12 , 8 ) )

Line plot

axes [ 0 ] . plot ( data [ 'date' ] , data [ 'income' ] , linewidth = 1 , alpha = 0.7 , label = 'Income' ) axes [ 0 ] . fill_between ( data [ 'date' ] , data [ 'income' ] , alpha = 0.3 ) axes [ 0 ] . set_title ( 'Income Over Time' ) axes [ 0 ] . set_ylabel ( 'Income' ) axes [ 0 ] . grid ( True , alpha = 0.3 ) axes [ 0 ] . legend ( )

Area plot

axes [ 1 ] . plot ( data [ 'date' ] , data [ 'cumulative_income' ] , linewidth = 2 , color = 'green' ) axes [ 1 ] . fill_between ( data [ 'date' ] , data [ 'cumulative_income' ] , alpha = 0.3 , color = 'green' ) axes [ 1 ] . set_title ( 'Cumulative Income Over Time' ) axes [ 1 ] . set_ylabel ( 'Cumulative Income' ) axes [ 1 ] . set_xlabel ( 'Date' ) axes [ 1 ] . grid ( True , alpha = 0.3 ) plt . tight_layout ( ) plt . show ( )

8. Composition Visualization

fig , axes = plt . subplots ( 1 , 2 , figsize = ( 12 , 5 ) )

Pie chart

category_counts

data [ 'category' ] . value_counts ( ) colors = [ '#ff9999' , '#66b3ff' , '#99ff99' ] axes [ 0 ] . pie ( category_counts . values , labels = category_counts . index , autopct = '%1.1f%%' , colors = colors , startangle = 90 ) axes [ 0 ] . set_title ( 'Category Distribution (Pie Chart)' )

Donut chart

axes [ 1 ] . pie ( category_counts . values , labels = category_counts . index , autopct = '%1.1f%%' , colors = colors , startangle = 90 , wedgeprops = dict ( width = 0.5 , edgecolor = 'white' ) ) axes [ 1 ] . set_title ( 'Category Distribution (Donut Chart)' ) plt . tight_layout ( ) plt . show ( )

9. Dashboard-style Visualization

fig

plt . figure ( figsize = ( 16 , 10 ) ) gs = GridSpec ( 3 , 3 , figure = fig , hspace = 0.3 , wspace = 0.3 )

Key metrics

ax_metric

fig . add_subplot ( gs [ 0 , : ] ) ax_metric . axis ( 'off' ) metrics_text = f""" Average Age: { data [ 'age' ] . mean ( ) : .1f } | Average Income: $ { data [ 'income' ] . mean ( ) : .0f } | Average Satisfaction: { data [ 'satisfaction' ] . mean ( ) : .2f } | Purchase Rate: { ( data [ 'purchased' ] . mean ( ) * 100 ) : .1f } % """ ax_metric . text ( 0.5 , 0.5 , metrics_text , ha = 'center' , va = 'center' , fontsize = 12 , bbox = dict ( boxstyle = 'round' , facecolor = 'lightblue' , alpha = 0.7 ) )

Subplots

ax1

fig

.

add_subplot

(

gs

[

1

,

0

]

)

data

[

'age'

]

.

hist

(

bins

=

20

,

ax

=

ax1

,

color

=

'skyblue'

,

edgecolor

=

'black'

)

ax1

.

set_title

(

'Age Distribution'

)

ax2

=

fig

.

add_subplot

(

gs

[

1

,

1

]

)

category_counts

.

plot

(

kind

=

'bar'

,

ax

=

ax2

,

color

=

'lightcoral'

,

edgecolor

=

'black'

)

ax2

.

set_title

(

'Category Counts'

)

ax3

=

fig

.

add_subplot

(

gs

[

1

,

2

]

)

data

.

groupby

(

'category'

)

[

'satisfaction'

]

.

mean

(

)

.

plot

(

kind

=

'bar'

,

ax

=

ax3

,

color

=

'lightgreen'

,

edgecolor

=

'black'

)

ax3

.

set_title

(

'Avg Satisfaction by Category'

)

ax4

=

fig

.

add_subplot

(

gs

[

2

,

:

2

]

)

sns

.

boxplot

(

data

=

data

,

x

=

'region'

,

y

=

'income'

,

ax

=

ax4

,

palette

=

'Set2'

)

ax4

.

set_title

(

'Income by Region'

)

ax5

=

fig

.

add_subplot

(

gs

[

2

,

2

]

)

data

[

'satisfaction'

]

.

value_counts

(

)

.

sort_index

(

)

.

plot

(

kind

=

'bar'

,

ax

=

ax5

,

color

=

'orange'

,

edgecolor

=

'black'

)

ax5

.

set_title

(

'Satisfaction Scores'

)

plt

.

suptitle

(

'Data Analytics Dashboard'

,

fontsize

=

16

,

fontweight

=

'bold'

,

y

=

0.995

)

plt

.

show

(

)

print

(

"Visualization examples completed!"

)

Visualization Best Practices

Choose chart type based on data type and question

Use consistent color schemes

Label axes clearly with units

Include title and legend

Avoid 3D charts when 2D suffices

Make fonts large and readable

Consider colorblind-friendly palettes

Common Chart Types

Bar charts

Categorical comparisons

Line plots

Trends over time

Scatter plots

Relationships between variables

Histograms

Distributions

Heatmaps

Matrix data

Box plots

Distribution with quartiles Deliverables Exploratory visualizations Publication-ready charts Interactive dashboard mockups Statistical plots with annotations Trend analysis visualizations Comparative analysis charts Summary infographics