Visualization is one of the most important tools in data science.
A good plot helps reveal structure, compare groups, identify unusual values, and communicate findings clearly.
But visualization is not only about producing charts.
It is also about learning how to read evidence from patterns.
In this lesson, we focus on foundational plot types that appear in real analytical workflows.
We use the cleaned Iris dataset to practice both creating figures and interpreting what they show.
By the end of this lesson, you will be able to:
We use the cleaned dataset prepared in the previous lesson.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv("data/iris_clean.csv")
df.head()| sepal_length | sepal_width | petal_length | petal_width | species | |
|---|---|---|---|---|---|
| 0 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 | setosa |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 | setosa |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 | setosa |
Before plotting, confirm the structure of the dataset.
print("Shape:", df.shape)
print("\nColumns:", df.columns.tolist())
print("\nData types:")
print(df.dtypes)Shape: (149, 5)
Columns: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']
Data types:
sepal_length float64
sepal_width float64
petal_length float64
petal_width float64
species str
dtype: objectBefore making plots, ask:
For the Iris dataset, the numeric flower measurements and the categorical species variable support both distribution plots and grouped comparisons.
Histograms help us understand the distribution of a single numeric variable.
fig, ax = plt.subplots(figsize=(8, 5.5))
sns.histplot(
data=df,
x="sepal_length",
bins=12,
kde=True,
ax=ax
)
ax.set_title("Distribution of Sepal Length")
ax.set_xlabel("Sepal Length")
ax.set_ylabel("Count")
plt.show()
When reading a histogram, look for:
If a variable shows multiple peaks, this may suggest the presence of meaningful subgroups.
Boxplots summarize a distribution using the median, quartiles, and possible outliers.
fig, ax = plt.subplots(figsize=(8, 5.5))
sns.boxplot(
data=df,
x="species",
y="sepal_length",
ax=ax
)
ax.set_title("Sepal Length by Species")
ax.set_xlabel("Species")
ax.set_ylabel("Sepal Length")
plt.show()
A boxplot helps compare groups by showing:
If two groups overlap strongly, that variable alone may not separate them well.
Scatter plots show the relationship between two numeric variables.
fig, ax = plt.subplots(figsize=(8, 5.5))
sns.scatterplot(
data=df,
x="sepal_length",
y="petal_length",
hue="species",
s=70,
alpha=0.8,
ax=ax
)
ax.set_title("Sepal Length vs Petal Length")
ax.set_xlabel("Sepal Length")
ax.set_ylabel("Petal Length")
plt.show()
Scatter plots help you evaluate:
In many datasets, scatter plots are among the fastest ways to detect group structure.
A grouped histogram helps compare distributions across categories.
fig, ax = plt.subplots(figsize=(8, 5.5))
sns.histplot(
data=df,
x="petal_length",
hue="species",
bins=15,
kde=True,
ax=ax
)
ax.set_title("Petal Length Distribution by Species")
ax.set_xlabel("Petal Length")
ax.set_ylabel("Count")
plt.show()
This plot helps compare whether groups differ in:
If one group’s values occupy a clearly different range, that variable may be useful for distinguishing between groups.
Pairplots provide a compact view of multiple variable relationships at once.
g = sns.pairplot(
df,
hue="species",
corner=True,
plot_kws={"alpha": 0.7}
)
g.fig.suptitle("Iris — Pairplot by Species", y=1.02)
plt.show()
Pairplots help answer broader questions such as:
This is often one of the most useful first multivariate views of a dataset.
A plot is only useful if it is interpreted carefully.
When reading a figure, ask:
Visualization should support reasoning, not replace it.
Strong foundational plots share a few key qualities:
A histogram is useful for one-variable distributions.
A boxplot is useful for comparing distributions across groups.
A scatter plot is useful for relationships between two variables.
A pairplot is useful for quick multivariate exploration.
Plots can also function as validation tools.
Use them to check:
Visualization often reveals issues that summary tables alone can miss.
These are core visualization habits that carry forward into more advanced analysis.
Try the following:
sepal_width versus petal_widthpetal_widthpetal_width by speciesfig, ax = plt.subplots(figsize=(8, 5.5))
sns.scatterplot(
data=df,
x="sepal_width",
y="petal_width",
hue="species",
s=70,
alpha=0.8,
ax=ax
)
ax.set_title("Sepal Width vs Petal Width")
ax.set_xlabel("Sepal Width")
ax.set_ylabel("Petal Width")
plt.show()
fig, ax = plt.subplots(figsize=(8, 5.5))
sns.histplot(
data=df,
x="petal_width",
bins=12,
kde=True,
ax=ax
)
ax.set_title("Distribution of Petal Width")
ax.set_xlabel("Petal Width")
ax.set_ylabel("Count")
plt.show()
fig, ax = plt.subplots(figsize=(8, 5.5))
sns.boxplot(
data=df,
x="species",
y="petal_width",
ax=ax
)
ax.set_title("Petal Width by Species")
ax.set_xlabel("Species")
ax.set_ylabel("Petal Width")
plt.show()
print("Your interpretation:")
print("Petal-related features appear more useful than sepal-related features for separating species because their group differences are more distinct.")
Your interpretation:
Petal-related features appear more useful than sepal-related features for separating species because their group differences are more distinct.Visualization is not about producing more plots.
It is about choosing the right view of the data to support understanding.
A clear plot reduces uncertainty. A poor plot can introduce it.