Analytical work does not begin with modeling.
It begins with looking carefully at the data.
Before fitting models or drawing conclusions, we need to understand:
In CDI, this stage is not a formality. It is where analytical judgment begins.
In this lesson, we work with the classic Iris dataset (Fisher 1936), which contains measurements of flower characteristics across three species.
You will:
A dataset can appear clean at first glance and still contain issues that affect later analysis.
Early exploration helps answer practical questions:
This is where defensible analysis begins.
We will use the Iris dataset from scikit-learn, convert it to a pandas DataFrame, and standardize the column names.
import pandas as pd
from sklearn.datasets import load_iris
iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df["species"] = pd.Categorical.from_codes(iris.target, iris.target_names)
df.columns = ["sepal_length", "sepal_width", "petal_length", "petal_width", "species"]
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 |
iris.data contains the numeric measurementsiris.target contains the species labels encoded as integerspd.Categorical.from_codes() converts those encoded values into readable species namessnake_case so later code stays clean and consistentWe will keep reusable datasets inside a local data/ folder.
from pathlib import Path
Path("data").mkdir(exist_ok=True)
df.to_csv("data/iris.csv", index=False)
print("Saved dataset to: data/iris.csv")Saved dataset to: data/iris.csvSaving the dataset at this point helps later lessons stay reproducible and consistent.
Before plotting or summarizing, inspect the shape and variable types.
print("Columns:")
print(df.columns.tolist())
print("\nShape (rows, columns):")
print(df.shape)
print("\nData types:")
print(df.dtypes)Columns:
['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']
Shape (rows, columns):
(150, 5)
Data types:
sepal_length float64
sepal_width float64
petal_length float64
petal_width float64
species category
dtype: objectAt this stage, you should notice:
species is a categorical grouping variableThat already tells us this is a small, tidy dataset that is well suited for learning exploratory analysis.
df.sample(8, random_state=42)| sepal_length | sepal_width | petal_length | petal_width | species | |
|---|---|---|---|---|---|
| 73 | 6.1 | 2.8 | 4.7 | 1.2 | versicolor |
| 18 | 5.7 | 3.8 | 1.7 | 0.3 | setosa |
| 118 | 7.7 | 2.6 | 6.9 | 2.3 | virginica |
| 78 | 6.0 | 2.9 | 4.5 | 1.5 | versicolor |
| 76 | 6.8 | 2.8 | 4.8 | 1.4 | versicolor |
| 31 | 5.4 | 3.4 | 1.5 | 0.4 | setosa |
| 64 | 5.6 | 2.9 | 3.6 | 1.3 | versicolor |
| 141 | 6.9 | 3.1 | 5.1 | 2.3 | virginica |
Looking at a few random rows is often more informative than only checking the first five rows.
It helps confirm that values look realistic across the dataset instead of only at the top.
df.describe()| sepal_length | sepal_width | petal_length | petal_width | |
|---|---|---|---|---|
| count | 150.000000 | 150.000000 | 150.000000 | 150.000000 |
| mean | 5.843333 | 3.057333 | 3.758000 | 1.199333 |
| std | 0.828066 | 0.435866 | 1.765298 | 0.762238 |
| min | 4.300000 | 2.000000 | 1.000000 | 0.100000 |
| 25% | 5.100000 | 2.800000 | 1.600000 | 0.300000 |
| 50% | 5.800000 | 3.000000 | 4.350000 | 1.300000 |
| 75% | 6.400000 | 3.300000 | 5.100000 | 1.800000 |
| max | 7.900000 | 4.400000 | 6.900000 | 2.500000 |
These summary statistics help answer early questions:
For Iris, petal measurements often show stronger separation across species than sepal measurements. We will confirm that visually next.
Before comparing groups, verify that each group is represented.
df["species"].value_counts().sort_index()species
setosa 50
versicolor 50
virginica 50
Name: count, dtype: int64This dataset is balanced across species, which makes visual comparison easier.
We will use modern plotting with seaborn and matplotlib, without CDI theme dependencies.
These plots are not yet final presentation graphics. Their purpose is to help us inspect the data and notice patterns worth explaining.
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="whitegrid", context="notebook")
df_long = df.melt(
value_vars=["sepal_length", "sepal_width", "petal_length", "petal_width"],
var_name="feature",
value_name="value"
)
g = sns.displot(
data=df_long,
x="value",
col="feature",
col_wrap=2,
bins=12,
height=3.6,
aspect=1.15,
facet_kws={"sharex": False, "sharey": False}
)
g.set_titles("{col_name}")
g.set_axis_labels("Value", "Count")
g.fig.suptitle("Iris — Distribution of Numeric Features", y=1.03)
plt.show()
These histograms help us see:
A single histogram does not separate species, but it gives a first impression of the measurement ranges.
A boxplot gives a compact summary of group differences using the median, quartiles, and overall spread.
fig, ax = plt.subplots(figsize=(8, 5.5))
sns.boxplot(
data=df,
x="species",
y="petal_length",
width=0.5,
fliersize=0,
ax=ax
)
ax.set_title("Petal Length by Species")
ax.set_xlabel("Species")
ax.set_ylabel("Petal Length")
plt.show()
This plot makes it easy to compare:
However, a boxplot hides the individual observations.
That means we cannot directly see how densely values are clustered or how points are distributed within each group.
To make the distribution more visible, we can overlay the individual observations.
fig, ax = plt.subplots(figsize=(8, 5.5))
sns.boxplot(
data=df,
x="species",
y="petal_length",
width=0.5,
fliersize=0,
ax=ax
)
sns.stripplot(
data=df,
x="species",
y="petal_length",
color="black",
alpha=0.6,
size=4,
jitter=0.22,
ax=ax
)
ax.set_title("Petal Length by Species")
ax.set_xlabel("Species")
ax.set_ylabel("Petal Length")
plt.show()
Adding the observed points makes the plot more informative.
Now we can see:
This provides a fuller view than the boxplot alone.
Petal length differs strongly by species.
This makes it a strong candidate for distinguishing between groups in later analysis.
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 us inspect relationships between two variables.
Here, the species begin to separate into visible clusters, especially when petal length is involved.
This is a strong exploratory signal that some measurements carry more discriminatory information than others.
g = sns.pairplot(
df,
hue="species",
corner=True,
diag_kind="hist",
plot_kws={"alpha": 0.7, "s": 45}
)
g.fig.suptitle("Iris — Pairwise Relationships by Species", y=1.02)
plt.show()
The pairplot provides a broader view of the dataset:
Feature creation is often part of exploration.
Here, we define a new variable, petal_area, as a simple combination of petal length and petal width.
df["petal_area"] = df["petal_length"] * df["petal_width"]
df.head()| sepal_length | sepal_width | petal_length | petal_width | species | petal_area | |
|---|---|---|---|---|---|---|
| 0 | 5.1 | 3.5 | 1.4 | 0.2 | setosa | 0.28 |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 | setosa | 0.28 |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 | setosa | 0.26 |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 | setosa | 0.30 |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 | setosa | 0.28 |
This does not mean the new feature is automatically better.
It illustrates how exploratory analysis can lead to new candidate variables.
Try the following:
petal_area for each species.sepal_width versus petal_width.print("Last 5 rows:")
print(df.tail())
print("\nMean petal_area by species:")
print(df.groupby("species", observed=False)["petal_area"].mean())
fig, 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()Last 5 rows:
sepal_length sepal_width petal_length petal_width species \
145 6.7 3.0 5.2 2.3 virginica
146 6.3 2.5 5.0 1.9 virginica
147 6.5 3.0 5.2 2.0 virginica
148 6.2 3.4 5.4 2.3 virginica
149 5.9 3.0 5.1 1.8 virginica
petal_area
145 11.96
146 9.50
147 10.40
148 12.42
149 9.18
Mean petal_area by species:
species
setosa 0.3656
versicolor 5.7204
virginica 11.2962
Name: petal_area, dtype: float64
Interpretation:
Petal-based features provide clearer separation between species than sepal-based features.
Exploration is not just about making plots.
It is about understanding how the dataset behaves before it is used to support conclusions.
A responsible analyst does not move directly from data loading to modeling.
They pause, inspect, compare, and question — and only then move forward.
That habit is a foundation of reliable analysis.
In this lesson, you:
data/iris.csvNext, we clean and prepare the dataset for analysis.