10 Essential Plotting Techniques to Make Your Data Visualizations Stand Out

I must be honest: when I started learning data science I severely underestimated the importance of plotting. I learned Python, algorithms, and the underlying mathematics, but my visualization skills were poor.

We often rely on standard charts like pairplots, distplots, and qqplots, copying and pasting code without thinking about interpretability.

In many projects the deliverable is a model, and after extensive data cleaning and feature engineering we may achieve a good score, but the visualizations are often ignored because the author assumes they are only for themselves.

Neglecting interpretability and explainability is a major failure in data science. If you cannot explain how a model arrived at its conclusions, the results are meaningless to stakeholders.

For example, at a fraud‑prevention company, telling a client that X% of transactions were blocked because the model said so, without understanding why, is unconvincing; the same applies to sensitive domains like healthcare.

Understanding the algorithm’s inner workings helps you explain outputs, coordinate with analysts, and satisfy legal or business requirements.

Therefore, personalized, explanatory charts are crucial.

10 Plotting Tools and Techniques

Below are the Python libraries I use:

import seaborn as sns
import matplotlib.pyplot as plt

You can also set a style, e.g.:

plt.style.use('fivethirtyEight')
%config InlineBackend.figure_format='retina'
%matplotlib inline

Subplots

Use plt.subplots to create a figure with multiple axes:

fig, ax = plt.subplots(ncols=number_of_cols, nrows=number_of_rows, figsize=(x, y))

Example with two rows:

sns.scatterplot(x=horizontal_data_1, y=vertical_data_1, ax=ax[0])
sns.scatterplot(x=horizontal_data_2, y=vertical_data_2, ax=ax[1])

Axis Labels

Set clear axis labels to avoid confusion:

ax[0].set(xlabel='My X Label', ylabel='My Y Label')
ax[1].set(xlabel='My Second X Label', ylabel='My Second and Very Creative Y Label')

Titles

Add descriptive titles to each subplot:

ax[0].title.set_text('This title has to be very clear and explicative')
ax[1].title.set_text('And this title has to explain what’s different in this chart')

Annotations

Annotate bar heights directly on the plot:

for p in ax[0].patches:
    ax[0].annotate("%.2f" % p.get_height(),
                   (p.get_x() + p.get_width() / 2., p.get_height()),
                   ha='center', va='center', fontsize=12, color='white',
                   xytext=(0, -10), textcoords='offset points')

Color Hue

Use the hue parameter to differentiate categories by color:

weight = [5,4,8,2,6,2]
month = ['febrero','enero','abril','junio','marzo','mayo']
animal_type = ['dog','cat','cat','dog','dog','dog']
hue = ['blue','red','red','blue','blue','blue']
sns.scatterplot(x=month, y=weight, hue=hue)

Point Size

Encode an additional variable with point size:

size = [2,3,5,1,4,1]
sns.scatterplot(x=month, y=weight, hue=hue, size=size, sizes=(50,300))

Threshold Line

Draw a vertical line to indicate a threshold: ax[0].axvline(32, 0, c='r') Or without subplots:

g = sns.scatterplot(x=month, y=weight, hue=hue, legend=False)
g.axvline(2, c='r')
plt.show()

Multiple Y‑Axes

Create a secondary Y‑axis:

ax2 = ax[0].twinx()
sns.lineplot(x=month, y=average_animal_weight, ax=ax2)

Without subplots:

g = sns.scatterplot(x=month, y=weight, hue=hue, legend=False)
ax2 = g.twinx()
sns.lineplot(x=month, y=average_animal_weight, ax=ax2, c='y')
plt.show()

Overlapping Plots

Plot multiple lines on the same axes:

a = [1,2,3,4,5]
b = [4,5,6,2,2]
c = [2,5,6,2,1]
sns.lineplot(x=a, y=b, color='r')
sns.lineplot(x=a, y=c, color='b')
plt.show()

Bar‑Plot Order

Specify a custom order for categorical bars:

a = ['second','first','third']
b = [15,10,20]
sns.barplot(x=a, y=b, order=['first','second','third'])

Practicing these tools will improve your data‑science workflow and help you communicate results effectively.