Data Visualization Skill

Chart selection guidance, Python visualization code patterns, design principles, and accessibility considerations for creating effective data visualizations.

Chart Selection Guide

Choose by Data Relationship

What You're Showing

Best Chart

Alternatives

Trend over time

Line chart

Area chart (if showing cumulative or composition)

Comparison across categories

Vertical bar chart

Horizontal bar (many categories), lollipop chart

Ranking

Horizontal bar chart

Dot plot, slope chart (comparing two periods)

Part-to-whole composition

Stacked bar chart

Treemap (hierarchical), waffle chart

Composition over time

Stacked area chart

100% stacked bar (for proportion focus)

Distribution

Histogram

Box plot (comparing groups), violin plot, strip plot

Correlation (2 variables)

Scatter plot

Bubble chart (add 3rd variable as size)

Correlation (many variables)

Heatmap (correlation matrix)

Pair plot

Geographic patterns

Choropleth map

Bubble map, hex map

Flow / process

Sankey diagram

Funnel chart (sequential stages)

Relationship network

Network graph

Chord diagram

Performance vs. target

Bullet chart

Gauge (single KPI only)

Multiple KPIs at once

Small multiples

Dashboard with separate charts

When NOT to Use Certain Charts

Pie charts

Avoid unless <6 categories and exact proportions matter less than rough comparison. Humans are bad at comparing angles. Use bar charts instead.

3D charts

Never. They distort perception and add no information.

Dual-axis charts

Use cautiously. They can mislead by implying correlation. Clearly label both axes if used.

Stacked bar (many categories)

Hard to compare middle segments. Use small multiples or grouped bars instead.

Donut charts

Slightly better than pie charts but same fundamental issues. Use for single KPI display at most. Python Visualization Code Patterns Setup and Style import matplotlib . pyplot as plt import matplotlib . ticker as mticker import seaborn as sns import pandas as pd import numpy as np

Professional style setup

plt . style . use ( 'seaborn-v0_8-whitegrid' ) plt . rcParams . update ( { 'figure.figsize' : ( 10 , 6 ) , 'figure.dpi' : 150 , 'font.size' : 11 , 'axes.titlesize' : 14 , 'axes.titleweight' : 'bold' , 'axes.labelsize' : 11 , 'xtick.labelsize' : 10 , 'ytick.labelsize' : 10 , 'legend.fontsize' : 10 , 'figure.titlesize' : 16 , } )

Colorblind-friendly palettes

PALETTE_CATEGORICAL

[ '#4C72B0' , '#DD8452' , '#55A868' , '#C44E52' , '#8172B3' , '#937860' ] PALETTE_SEQUENTIAL = 'YlOrRd' PALETTE_DIVERGING = 'RdBu_r' Line Chart (Time Series) fig , ax = plt . subplots ( figsize = ( 10 , 6 ) ) for label , group in df . groupby ( 'category' ) : ax . plot ( group [ 'date' ] , group [ 'value' ] , label = label , linewidth = 2 ) ax . set_title ( 'Metric Trend by Category' , fontweight = 'bold' ) ax . set_xlabel ( 'Date' ) ax . set_ylabel ( 'Value' ) ax . legend ( loc = 'upper left' , frameon = True ) ax . spines [ 'top' ] . set_visible ( False ) ax . spines [ 'right' ] . set_visible ( False )

Format dates on x-axis

fig . autofmt_xdate ( ) plt . tight_layout ( ) plt . savefig ( 'trend_chart.png' , dpi = 150 , bbox_inches = 'tight' ) Bar Chart (Comparison) fig , ax = plt . subplots ( figsize = ( 10 , 6 ) )

Sort by value for easy reading

df_sorted

df . sort_values ( 'metric' , ascending = True ) bars = ax . barh ( df_sorted [ 'category' ] , df_sorted [ 'metric' ] , color = PALETTE_CATEGORICAL [ 0 ] )

Add value labels

for bar in bars : width = bar . get_width ( ) ax . text ( width + 0.5 , bar . get_y ( ) + bar . get_height ( ) / 2 , f' { width : ,.0f } ' , ha = 'left' , va = 'center' , fontsize = 10 ) ax . set_title ( 'Metric by Category (Ranked)' , fontweight = 'bold' ) ax . set_xlabel ( 'Metric Value' ) ax . spines [ 'top' ] . set_visible ( False ) ax . spines [ 'right' ] . set_visible ( False ) plt . tight_layout ( ) plt . savefig ( 'bar_chart.png' , dpi = 150 , bbox_inches = 'tight' ) Histogram (Distribution) fig , ax = plt . subplots ( figsize = ( 10 , 6 ) ) ax . hist ( df [ 'value' ] , bins = 30 , color = PALETTE_CATEGORICAL [ 0 ] , edgecolor = 'white' , alpha = 0.8 )

Add mean and median lines

mean_val

df [ 'value' ] . mean ( ) median_val = df [ 'value' ] . median ( ) ax . axvline ( mean_val , color = 'red' , linestyle = '--' , linewidth = 1.5 , label = f'Mean: { mean_val : ,.1f } ' ) ax . axvline ( median_val , color = 'green' , linestyle = '--' , linewidth = 1.5 , label = f'Median: { median_val : ,.1f } ' ) ax . set_title ( 'Distribution of Values' , fontweight = 'bold' ) ax . set_xlabel ( 'Value' ) ax . set_ylabel ( 'Frequency' ) ax . legend ( ) ax . spines [ 'top' ] . set_visible ( False ) ax . spines [ 'right' ] . set_visible ( False ) plt . tight_layout ( ) plt . savefig ( 'histogram.png' , dpi = 150 , bbox_inches = 'tight' ) Heatmap fig , ax = plt . subplots ( figsize = ( 10 , 8 ) )

Pivot data for heatmap format

pivot

df . pivot_table ( index = 'row_dim' , columns = 'col_dim' , values = 'metric' , aggfunc = 'sum' ) sns . heatmap ( pivot , annot = True , fmt = ',.0f' , cmap = 'YlOrRd' , linewidths = 0.5 , ax = ax , cbar_kws = { 'label' : 'Metric Value' } ) ax . set_title ( 'Metric by Row Dimension and Column Dimension' , fontweight = 'bold' ) ax . set_xlabel ( 'Column Dimension' ) ax . set_ylabel ( 'Row Dimension' ) plt . tight_layout ( ) plt . savefig ( 'heatmap.png' , dpi = 150 , bbox_inches = 'tight' ) Small Multiples categories = df [ 'category' ] . unique ( ) n_cats = len ( categories ) n_cols = min ( 3 , n_cats ) n_rows = ( n_cats + n_cols - 1 ) // n_cols fig , axes = plt . subplots ( n_rows , n_cols , figsize = ( 5 * n_cols , 4 * n_rows ) , sharex = True , sharey = True ) axes = axes . flatten ( ) if n_cats

1 else [ axes ] for i , cat in enumerate ( categories ) : ax = axes [ i ] subset = df [ df [ 'category' ] == cat ] ax . plot ( subset [ 'date' ] , subset [ 'value' ] , color = PALETTE_CATEGORICAL [ i % len ( PALETTE_CATEGORICAL ) ] ) ax . set_title ( cat , fontsize = 12 ) ax . spines [ 'top' ] . set_visible ( False ) ax . spines [ 'right' ] . set_visible ( False )

Hide empty subplots

for j in range ( i + 1 , len ( axes ) ) : axes [ j ] . set_visible ( False ) fig . suptitle ( 'Trends by Category' , fontsize = 14 , fontweight = 'bold' , y = 1.02 ) plt . tight_layout ( ) plt . savefig ( 'small_multiples.png' , dpi = 150 , bbox_inches = 'tight' ) Number Formatting Helpers def format_number ( val , format_type = 'number' ) : """Format numbers for chart labels.""" if format_type == 'currency' : if abs ( val )

= 1e9 : return f'$ { val / 1e9 : .1f } B' elif abs ( val ) = 1e6 : return f'$ { val / 1e6 : .1f } M' elif abs ( val ) = 1e3 : return f'$ { val / 1e3 : .1f } K' else : return f'$ { val : ,.0f } ' elif format_type == 'percent' : return f' { val : .1f } %' elif format_type == 'number' : if abs ( val ) = 1e9 : return f' { val / 1e9 : .1f } B' elif abs ( val ) = 1e6 : return f' { val / 1e6 : .1f } M' elif abs ( val ) = 1e3 : return f' { val / 1e3 : .1f } K' else : return f' { val : ,.0f } ' return str ( val )

Usage with axis formatter

ax . yaxis . set_major_formatter ( mticker . FuncFormatter ( lambda x , p : format_number ( x , 'currency' ) ) ) Interactive Charts with Plotly import plotly . express as px import plotly . graph_objects as go

Simple interactive line chart

fig

px . line ( df , x = 'date' , y = 'value' , color = 'category' , title = 'Interactive Metric Trend' , labels = { 'value' : 'Metric Value' , 'date' : 'Date' } ) fig . update_layout ( hovermode = 'x unified' ) fig . write_html ( 'interactive_chart.html' ) fig . show ( )

Interactive scatter with hover data

fig

px

.

scatter

(

df

,

x

=

'metric_a'

,

y

=

'metric_b'

,

color

=

'category'

,

size

=

'size_metric'

,

hover_data

=

[

'name'

,

'detail_field'

]

,

title

=

'Correlation Analysis'

)

fig

.

show

(

)

Design Principles

Color

Use color purposefully

Color should encode data, not decorate

Highlight the story

Use a bright accent color for the key insight; grey everything else

Sequential data

Use a single-hue gradient (light to dark) for ordered values

Diverging data

Use a two-hue gradient with neutral midpoint for data with a meaningful center

Categorical data

Use distinct hues, maximum 6-8 before it gets confusing

Avoid red/green only

8% of men are red-green colorblind. Use blue/orange as primary pair

Typography

Title states the insight

"Revenue grew 23% YoY" beats "Revenue by Month"

Subtitle adds context

Date range, filters applied, data source

Axis labels are readable

Never rotated 90 degrees if avoidable. Shorten or wrap instead

Data labels add precision

Use on key points, not every single bar

Annotation highlights

Call out specific points with text annotations

Layout

Reduce chart junk

Remove gridlines, borders, backgrounds that don't carry information

Sort meaningfully

Categories sorted by value (not alphabetically) unless there's a natural order (months, stages)

Appropriate aspect ratio

Time series wider than tall (3:1 to 2:1); comparisons can be squarer

White space is good

Don't cram charts together. Give each visualization room to breathe

Accuracy

Bar charts start at zero

Always. A bar from 95 to 100 exaggerates a 5% difference

Line charts can have non-zero baselines

When the range of variation is meaningful

Consistent scales across panels

When comparing multiple charts, use the same axis range

Show uncertainty

Error bars, confidence intervals, or ranges when data is uncertain

Label your axes

Never make the reader guess what the numbers mean Accessibility Considerations Color Blindness Never rely on color alone to distinguish data series Add pattern fills, different line styles (solid, dashed, dotted), or direct labels Test with a colorblind simulator (e.g., Coblis, Sim Daltonism) Use the colorblind-friendly palette: sns.color_palette("colorblind") Screen Readers Include alt text describing the chart's key finding Provide a data table alternative alongside the visualization Use semantic titles and labels General Accessibility Sufficient contrast between data elements and background Text size minimum 10pt for labels, 12pt for titles Avoid conveying information only through spatial position (add labels) Consider printing: does the chart work in black and white? Accessibility Checklist Before sharing a visualization: Chart works without color (patterns, labels, or line styles differentiate series) Text is readable at standard zoom level Title describes the insight, not just the data Axes are labeled with units Legend is clear and positioned without obscuring data Data source and date range are noted