IQ scores

Comparing IQ scores among different test groups using a box plot of the Seaborn library and quickly change the style and context of a Figure using the pre-configurations supplied by Seaborn.

Once a suitable DataFrame exists, the plotting is achieved with a single function. Column names are automatically used for labeling the axis. Even categorical variables are supported out of the box.

Importing libraries and packages

# Warnings
import warnings

# Mathematical operations and data manipulation
import pandas as pd

# Plotting
import matplotlib.pyplot as plt
import seaborn as sns

warnings.filterwarnings("ignore")

Set paths

# Path to datasets directory
data_path = "./datasets"
# Path to assets directory (for saving results to)
assets_path = "./assets"

Loading dataset

dataset = pd.read_csv(f"{data_path}/iq_scores.csv")

Exploring dataset

# Shape of the dataset
print("Shape of the dataset: ", dataset.shape)
# View
dataset

Shape of the dataset:  (100, 4)

	group_a	group_b	group_c	group_d
0	118	126	108	93
1	103	89	89	99
2	125	90	114	91
3	107	101	116	110
4	111	102	126	80
...	...	...	...	...
95	88	91	102	105
96	122	112	69	74
97	125	102	96	85
98	93	112	101	85
99	78	75	90	95

100 rows × 4 columns

Preprocessing

group_a = dataset[dataset.columns[0]].tolist()
group_b = dataset[dataset.columns[1]].tolist()
group_c = dataset[dataset.columns[2]].tolist()
group_d = dataset[dataset.columns[3]].tolist()

# Testing
print(group_a)
print(group_b)
print(group_c)
print(group_d)

[118, 103, 125, 107, 111, 96, 104, 97, 96, 114, 96, 75, 114, 107, 87, 117, 117, 114, 117, 112, 107, 133, 94, 91, 118, 110, 117, 86, 143, 83, 106, 86, 98, 126, 109, 91, 112, 120, 108, 111, 107, 98, 89, 113, 117, 81, 113, 112, 84, 115, 96, 93, 128, 115, 138, 121, 87, 112, 110, 79, 100, 84, 115, 93, 108, 130, 107, 106, 106, 101, 117, 93, 94, 103, 112, 98, 103, 70, 139, 94, 110, 105, 122, 94, 94, 105, 129, 110, 112, 97, 109, 121, 106, 118, 131, 88, 122, 125, 93, 78]
[126, 89, 90, 101, 102, 74, 93, 101, 66, 120, 108, 97, 98, 105, 119, 92, 113, 81, 104, 108, 83, 102, 105, 111, 102, 107, 103, 89, 89, 110, 71, 110, 120, 85, 111, 83, 122, 120, 102, 84, 118, 100, 100, 114, 81, 109, 69, 97, 95, 106, 116, 109, 114, 98, 90, 92, 98, 91, 81, 85, 86, 102, 93, 112, 76, 89, 110, 75, 110, 90, 96, 94, 107, 108, 95, 96, 96, 114, 93, 95, 117, 141, 115, 95, 86, 100, 121, 103, 66, 99, 96, 111, 110, 105, 110, 91, 112, 102, 112, 75]
[108, 89, 114, 116, 126, 104, 113, 96, 69, 121, 109, 102, 107, 122, 104, 107, 108, 137, 107, 116, 98, 132, 108, 114, 82, 93, 89, 90, 86, 91, 99, 98, 83, 93, 114, 96, 95, 113, 103, 81, 107, 85, 116, 85, 107, 125, 126, 123, 122, 124, 115, 114, 93, 93, 114, 107, 107, 84, 131, 91, 108, 127, 112, 106, 115, 82, 90, 117, 108, 115, 113, 108, 104, 103, 90, 110, 114, 92, 101, 72, 109, 94, 122, 90, 102, 86, 119, 103, 110, 96, 90, 110, 96, 69, 85, 102, 69, 96, 101, 90]
[93, 99, 91, 110, 80, 113, 111, 115, 98, 74, 96, 80, 83, 102, 60, 91, 82, 90, 97, 101, 89, 89, 117, 91, 104, 104, 102, 128, 106, 111, 79, 92, 97, 101, 106, 110, 93, 93, 106, 108, 85, 83, 108, 94, 79, 87, 113, 112, 111, 111, 79, 116, 104, 84, 116, 111, 103, 103, 112, 68, 54, 80, 86, 119, 81, 84, 91, 96, 116, 125, 99, 58, 102, 77, 98, 100, 90, 106, 109, 114, 102, 102, 112, 103, 98, 96, 85, 97, 110, 131, 92, 79, 115, 122, 95, 105, 74, 85, 85, 95]

data = pd.DataFrame(
    {
        "Groups": ["Group A"] * len(group_a)
        + ["Group B"] * len(group_b)
        + ["Group C"] * len(group_c)
        + ["Group D"] * len(group_d),
        "IQ score": group_a + group_b + group_c + group_d,
    }
)

print(data.columns)
data

Index(['Groups', 'IQ score'], dtype='object')

	Groups	IQ score
0	Group A	118
1	Group A	103
2	Group A	125
3	Group A	107
4	Group A	111
...	...	...
395	Group D	105
396	Group D	74
397	Group D	85
398	Group D	85
399	Group D	95

400 rows × 2 columns

Visualisation

Box plot

plt.figure(dpi=300)
# Set style to 'whitegrid'
sns.set_style("whitegrid")
# Set context to 'talk' to use the data visualization in a slide/talk
sns.set_context("talk")
# Create boxplot
sns.boxplot("Groups", "IQ score", data=data)
# Despine removes spines from the plot by default (without passing any
# arguments to the function)
sns.despine(left=True, right=True, top=True)
# Add title
plt.title("IQ scores for different test groups")
# Show plot
plt.show()

Violin plot

plt.figure(dpi=150)
# Set style
sns.set_style("whitegrid")
# Create boxplot
sns.violinplot("Groups", "IQ score", data=data)
# Despine
sns.despine(left=True, right=True, top=True)
# Add title
plt.title("IQ scores for different test groups")
# Show plot
plt.show()