feature_pair_plot
load iris data
Thinking
Preparing to load data
I need to work in iterative notebook mode since the user is asking to load the iris dataset. First, I'll load and inspect the data, showing the shape, columns, and the head in one code block. Since the dataset isn’t loaded yet, I might use sklearn's datasets for seaborn.
I'll focus on displaying them sequentially: first the shape, then the columns, and lastly the head using a pandas DataFrame. I want to ensure I follow the rule of displaying one table or chart per step.Simplifying display approach
It's okay, I need to show code displays properly. The guideline says if multiple tables are needed, they should be shown sequentially with the display function. However, since I’m not using tables here, I can keep my code minimal. I’ll focus on displaying only what's necessary, ensuring clarity without overcomplicating things. I guess simplicity is key, and I want to ensure the user gets the most straightforward output possible!
Let’s start by loading the Iris dataset into a DataFrame and doing a quick inspection.
</>Load iris dataset
from sklearn.datasets import load_iris
import pandas as pd
from IPython.display import display
iris = load_iris(as_frame=True)
df = iris.frame.copy()
df["species"] = df["target"].map(dict(enumerate(iris.target_names)))
display(df.shape)
display(df.columns.to_list())
df.head()(150, 6)
['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)', 'target', 'species']
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | target | species | |
|---|---|---|---|---|---|---|
| 0 | 5.1 | 3.5 | 1.4 | 0.2 | 0 | setosa |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 | 0 | setosa |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 | 0 | setosa |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 | 0 | setosa |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 | 0 | setosa |
The Iris data is now in a pandas DataFrame named df.
From the notebook output:
- It has 150 rows and 6 columns
- The columns are:
- sepal length (cm)
- sepal width (cm)
- petal length (cm)
- petal width (cm)
- target
- species
The preview also shows the data loaded correctly, with numeric flower measurements plus:
- target: the numeric class label
- species: the mapped flower name such as setosa
So you now have the Iris dataset ready to use in the notebook as df.
create feature pair plot
Thinking
Analyzing feature pair plot
I need to provide a code block for creating a feature pair plot using seaborn or matplotlib. The user wants clarity, so I should focus on one chart per step. A pair plot usually shows multiple facets but may need to display everything in one chart. It seems I should exclude the target/species if it’s too cluttered. Instead, using hue='species' could effectively distinguish features colored by species. I’ll ensure it's concise and stays within one code block.
The data is ready, so the next step is a pair plot of the four numeric features, colored by species.
</>Plot feature pairplot
import seaborn as sns
import matplotlib.pyplot as plt
feature_cols = [
"sepal length (cm)",
"sepal width (cm)",
"petal length (cm)",
"petal width (cm)",
]
sns.pairplot(
df[feature_cols + ["species"]],
hue="species",
diag_kind="hist"
)
plt.show()
Thinking
Deciding on feature pair plot
I need to create a feature pair plot and decide between two paths: A or B. Given the latest outputs, which include both a chart figure and explicit numeric/text values, I’ll go with Path A. It fits the goal of visualizing data. In my final answer, I'll summarize insights from the observed outputs, explaining that the pair plot was created using four numeric features colored by species. I’ll also mention that the output aligns with the known chart patterns from the iris dataset.Summarizing the pair plot insights
I realized that the chart-only output doesn't actually display the chart but instead offers comparisons of pairwise relationships among sepal and petal measurements for setosa, versicolor, and virginica. From the data head, I see that petal dimensions are significantly smaller in the setosa examples. The initial rows highlight this with smaller petal length and width, illustrating the separation the pair plot aims to show. I’ll say: "The pair plot compares sepal and petal measurements, with each species shown in a different color."Clarifying the pair plot analysis
I noticed that the previewed rows already show setosa with shorter and narrower petals, indicating that the pair plot's petal length and width are likely the clearest features for distinguishing species, while sepal measurements typically overlap. While I inferred this from the domain, I need to be cautious about making claims based on limited observations. I’ll say that the pair plot uses four feature columns and colors by species, with diagonal histograms summarizing the distribution. The smaller petal measurements for setosa should help illustrate species differences across the feature pairs.
The feature pair plot has been created for the four Iris measurements:
- sepal length
- sepal width
- petal length
- petal width
It is colored by species, so each scatter panel compares a pair of features across the flower classes, and the diagonal panels show each feature’s distribution.
From the observed notebook state, this matches the Iris DataFrame structure correctly:
- the four numeric feature columns were used
- species was included as the hue grouping variable
The sample rows already show setosa with relatively small petal measurements, so this pair plot is the right visualization to inspect how well species separate across feature pairs, especially when comparing petal-related features.