Activity - Data Visualization

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_theme(style="darkgrid")

We will use the breast cancer dataset for this activity. The main goal is to create visualization for finding the attributes that could predict or classify the type of tumor.

Attribute	Domain
Sample code number	id number
Clump Thickness	1 - 10
Uniformity of Cell Size	1 - 10
Uniformity of Cell Shape	1 - 10
Marginal Adhesion	1 - 10
Single Epithelial Cell Size	1 - 10
Bare Nuclei	1 - 10
Bland Chromatin	1 - 10
Normal Nucleoli	1 - 10
Mitoses	1 - 10
Class	(2 for benign, 4 for malignant)

More details here.

breast_cancer_data = pd.read_csv(
    "https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data",
    names=[
        "code",
        "clump_thickness",
        "uniformity_cell_size",
        "uniformity_cell_shape",
        "marginal_adhesion",
        "single_epithelial_cell_size",
        "bare_nuclei",
        "bland_chromatin",
        "normal_cucleoli",
        "mitoses",
        "class",
    ],
    index_col=0
)
breast_cancer_data.head()

	clump_thickness	uniformity_cell_size	uniformity_cell_shape	marginal_adhesion	single_epithelial_cell_size	bare_nuclei	bland_chromatin	normal_cucleoli	mitoses	class
code
1000025	5	1	1	1	2	1	3	1	1	2
1002945	5	4	4	5	7	10	3	2	1	2
1015425	3	1	1	1	2	2	3	1	1	2
1016277	6	8	8	1	3	4	3	7	1	2
1017023	4	1	1	3	2	1	3	1	1	2

Just for this time we will drop the bare_nublei column.

breast_cancer_data.dtypes

clump_thickness                 int64
uniformity_cell_size            int64
uniformity_cell_shape           int64
marginal_adhesion               int64
single_epithelial_cell_size     int64
bare_nuclei                    object
bland_chromatin                 int64
normal_cucleoli                 int64
mitoses                         int64
class                           int64
dtype: object

breast_cancer_data.drop(columns="bare_nuclei", inplace=True)

breast_cancer_data.dtypes

clump_thickness                int64
uniformity_cell_size           int64
uniformity_cell_shape          int64
marginal_adhesion              int64
single_epithelial_cell_size    int64
bland_chromatin                int64
normal_cucleoli                int64
mitoses                        int64
class                          int64
dtype: object

And let’s add a categorical column with the type of tumor.

class_dict = {2: "benign", 4: "malignant"}
breast_cancer_data["class_name"] = breast_cancer_data["class"].map(class_dict)

breast_cancer_data.head()

	clump_thickness	uniformity_cell_size	uniformity_cell_shape	marginal_adhesion	single_epithelial_cell_size	bland_chromatin	normal_cucleoli	mitoses	class	class_name
code
1000025	5	1	1	1	2	3	1	1	2	benign
1002945	5	4	4	5	7	3	2	1	2	benign
1015425	3	1	1	1	2	3	1	1	2	benign
1016277	6	8	8	1	3	3	7	1	2	benign
1017023	4	1	1	3	2	3	1	1	2	benign

For example,

sns.scatterplot(
    data=breast_cancer_data,
    x="clump_thickness",
    y="uniformity_cell_size",
    hue="class_name"
)

<Axes: xlabel='clump_thickness', ylabel='uniformity_cell_size'>

Your turn!