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Plotting overview

Data visualization is essential for exploring, understanding, and communicating insights from data. This guide covers common plot types, their purposes, and when to use them.

1. Plot selection guide

1.1. By analysis goal

Goal Recommended plot types
Understand distribution of single variable Histogram, Box plot, Violin plot, Density plot
Compare groups Box plot, Violin plot, Bar chart, Strip plot
Find relationships between variables Scatter plot, Line plot, Regression plot, Heatmap
Show composition Pie chart, Stacked bar chart, Area plot
Analyze time series Line plot, Area plot, Seasonal plot
Detect outliers Box plot, Scatter plot, Strip plot
Explore multivariate data Pair plot, Correlation heatmap, Facet grid
Check statistical assumptions Q-Q plot, Residual plot, Histogram
Show uncertainty Error bars, Confidence bands, Violin plots

1.2. By data type combination

X Variable Y Variable Z Variable (optional) Recommended Plots
Categorical None None Bar chart, Pie chart, Count plot
Continuous None Box plot, Violin plot, Bar chart, Strip plot
Categorical None Heatmap, Stacked bar chart, Grouped bar chart
Continuous None None Histogram, Box plot, Violin plot, Density plot
Continuous None Scatter plot, Line plot, Hexbin, 2D density
Continuous 3D scatter, Bubble chart, Contour plot
Categorical Scatter with hue, Facet grid

2. Best practices

2.1. General guidelines

  1. Choose the right plot for your data type and message
  2. Match plot type to data structure (categorical vs continuous)

  3. Consider what you want to communicate

  4. Keep it simple

  5. Avoid unnecessary 3D effects
  6. Remove chart junk (excessive gridlines, decorations)

  7. Use appropriate aspect ratios

  8. Use color effectively

  9. Use color to encode information, not just for decoration
  10. Ensure accessibility (colorblind-friendly palettes)

  11. Maintain consistency across related plots

  12. Label clearly

  13. Always include axis labels with units
  14. Add informative titles
  15. Include legends when needed

  16. Annotate important points

  17. Consider your audience

  18. Technical vs general audience
  19. Level of detail appropriate for context
  20. Medium of presentation (paper, screen, presentation)

2.2. Common mistakes to avoid

Mistake Problem Solution
Starting y-axis at non-zero Exaggerates differences Start at zero for bar charts; flexible for line plots
Too many categories Cluttered, hard to read Limit to 7-10 categories; consider grouping or filtering
3D when 2D suffices Distorts perception, hard to read Use 2D alternatives with color or size
Pie charts with many slices Hard to compare angles Use bar chart instead
Dual y-axes Can be misleading Use separate plots or normalize scales
Missing error bars Unclear uncertainty Add error bars or confidence intervals
Poor color choices Not colorblind-safe, poor contrast Use established palettes (ColorBrewer, Seaborn)
Overplotting Too many overlapping points Use transparency, jitter, hexbin, or sample data

3. Common plot types by data type and purpose

3.1. Univariate plots (single variable)

Plot Type Example Data Type Purpose Key Features Python Implementation
Histogram Histogram Continuous Show distribution and frequency of values Bins group continuous data; bar heights show frequency plt.hist(data, bins=30) or sns.histplot(data)
Box Plot (Box-and-Whisker) Box Plot Continuous Display distribution summary (quartiles, median, outliers) Shows Q1, median, Q3, whiskers (1.5×IQR), and outliers as points plt.boxplot(data) or sns.boxplot(data)
Violin Plot Violin Plot Continuous Combination of box plot and density plot Wider sections indicate higher density; includes median marker sns.violinplot(x=data)
Density Plot (KDE) Density Plot Continuous Smooth estimate of probability density function Smooth curve showing probability density sns.kdeplot(data) or data.plot(kind='kde')
Bar Chart Bar Chart Categorical Compare frequencies or values across categories Each bar represents a category; height shows value/count plt.bar(categories, values) or sns.barplot(x, y)
Count Plot Count Plot Categorical Show frequency of categorical values Specialized bar chart for counts sns.countplot(x=category)
Pie Chart Pie Chart Categorical Show proportions of a whole Circular chart divided into slices; each slice represents proportion plt.pie(values, labels=labels)
Strip Plot Strip Plot Continuous (grouped by category) Show individual data points along one axis Points plotted along axis; shows each individual value sns.stripplot(x=category, y=values)
Swarm Plot Swarm Plot Continuous (grouped by category) Like strip plot but points don't overlap Non-overlapping points; good for seeing density sns.swarmplot(x=category, y=values)

3.2. Bivariate plots (two variables)

Plot Type Example X-axis Type Y-axis Type Purpose Key Features Python Implementation
Scatter Plot Scatter Plot Continuous Continuous Show relationship between two continuous variables Each point represents one observation plt.scatter(x, y) or sns.scatterplot(x, y)
Line Plot Line Plot Continuous/Time Continuous Show trends over continuous variable (often time) Points connected by lines plt.plot(x, y) or data.plot()
Bar Chart Bar Chart Categorical Continuous Compare continuous values across categories Bars represent values for each category plt.bar(categories, values) or sns.barplot(x, y)
Box Plot (Grouped) Grouped Box Plot Categorical Continuous Compare distributions across categories Multiple box plots side by side sns.boxplot(x=category, y=values)
Violin Plot (Grouped) Grouped Violin Plot Categorical Continuous Compare distribution shapes across categories Multiple violin plots side by side sns.violinplot(x=category, y=values)
Heatmap Heatmap Categorical/Discrete Categorical/Discrete Show magnitude of values in two-dimensional space Color intensity represents value magnitude sns.heatmap(data, annot=True)
Hexbin Plot Hexbin Plot Continuous Continuous Show density of points in 2D space Hexagonal bins; color shows point density plt.hexbin(x, y, gridsize=30)
2D Density Plot 2D Density Plot Continuous Continuous Show probability density in 2D space Contour lines or color gradients show density sns.kdeplot(x=x, y=y)
Joint Plot Joint Plot Continuous Continuous Combine scatter plot with marginal distributions Central scatter with histograms/KDEs on margins sns.jointplot(x=x, y=y)

3.3. Multivariate plots (three or more variables)

Plot Type Example Purpose Key Features Python Implementation
Pair Plot (Scatter Matrix) Pair Plot Show all pairwise relationships in dataset Grid of scatter plots; diagonal shows distributions sns.pairplot(dataframe)
3D Scatter Plot 3D Scatter Plot Show relationship between three continuous variables Points plotted in 3D space from mpl_toolkits.mplot3d import Axes3D then ax.scatter3D(x, y, z)
Bubble Chart Bubble Chart Show three continuous variables (x, y, size) Like scatter plot but point size represents third variable plt.scatter(x, y, s=sizes)
Facet Grid (Small Multiples) Facet Grid Show subsets of data in separate subplots Multiple plots arranged in grid sns.FacetGrid(data, col='category').map(plt.scatter, 'x', 'y')
Parallel Coordinates Parallel Coordinates Compare multiple variables across observations Lines connect values across parallel axes pd.plotting.parallel_coordinates(df, 'class_column')
Correlation Heatmap Correlation Heatmap Show correlation between all variable pairs Color-coded correlation matrix sns.heatmap(df.corr(), annot=True)

3.4. Specialized statistical plots

Plot Type Example Purpose Key Features Python Implementation
Q-Q Plot (Quantile-Quantile) Q-Q Plot Test if data follows theoretical distribution Points should follow diagonal line if normally distributed stats.probplot(data, dist="norm", plot=plt)
Residual Plot Residual Plot Diagnose regression model fit Plot residuals vs fitted values; should show random pattern sns.residplot(x=predictions, y=residuals)
ROC Curve ROC Curve Evaluate binary classifier performance Plots True Positive Rate vs False Positive Rate from sklearn.metrics import roc_curve then plt.plot(fpr, tpr)
Confusion Matrix Confusion Matrix Show classification results Matrix showing predicted vs actual classes sns.heatmap(confusion_matrix, annot=True, fmt='d')
Error Bars Error Bars Show uncertainty or variability Bars extend from points to show range plt.errorbar(x, y, yerr=errors)
Regression Plot Regression Plot Show linear relationship and confidence interval Scatter plot with fitted line and confidence band sns.regplot(x=x, y=y)

3.5. Time series plots

Plot Type Example Purpose Key Features Python Implementation
Line Plot Time Series Line Plot Show values changing over time X-axis is time; y-axis is value plt.plot(dates, values) or data.plot()
Area Plot Area Plot Show cumulative totals over time Filled area under line(s) data.plot.area() or plt.fill_between(x, y)
Stacked Area Plot Stacked Area Plot Show multiple time series composition Multiple series stacked on top of each other data.plot.area(stacked=True)
Seasonal Plot Seasonal Plot Show patterns that repeat over time Multiple lines for each season/cycle Manually create with groupby and plot
Autocorrelation Plot Autocorrelation Plot Show correlation of series with lagged versions Correlation at different lag values pd.plotting.autocorrelation_plot(data)
Lag Plot Lag Plot Check for randomness in time series Current value vs lagged value pd.plotting.lag_plot(data)

4. Additional resources

4.1. Python visualization libraries

Library Strengths Best For Import Statement
Matplotlib Highly customizable, fine control Publication-quality plots, custom visualizations import matplotlib.pyplot as plt
Seaborn Beautiful defaults, statistical plots Exploratory data analysis, statistical visualization import seaborn as sns
Pandas Integrated with DataFrames Quick exploration, simple plots Built-in: df.plot()
Plotly Interactive plots, 3D support Dashboards, web applications, interactive exploration import plotly.express as px
Bokeh Interactive web-ready plots Interactive dashboards, streaming data from bokeh.plotting import figure

4.3. Python plotting resources

4.4. Color palettes

# Colorblind-friendly palettes
sns.color_palette("colorblind")
sns.color_palette("Set2")

# Diverging (for correlation matrices)
sns.color_palette("RdBu_r", as_cmap=True)
sns.color_palette("coolwarm", as_cmap=True)

# Sequential (for heatmaps)
sns.color_palette("YlOrRd", as_cmap=True)
sns.color_palette("viridis", as_cmap=True)