Visualizing Synthetic Data with Pandas and Seaborn: A Step‑by‑Step Guide

Preparing Data and Required Libraries

Mainly using the Pandas and Seaborn libraries.

import pandas as pd
import numpy as np
import seaborn as sns
%matplotlib inline

Generate four datasets and convert them to a DataFrame data type.

xarray = np.linspace(0,10,100)  # generate 100 numbers from 0 to 10
yarray = xarray**3 + np.random.normal(0,100,100)  # y = x^3 + normal noise
zarray = -100 * xarray + np.random.normal(0,10,100)  # y = -100x + normal noise
warray = 200 * xarray**0.5 + np.random.normal(0,10,100)

Univariate Analysis

Histogram of Frequency Distribution

df.hist(bins=15, color='steelblue', edgecolor='black', linewidth=1.0,
        xlabelsize=8, ylabelsize=8, grid=False)

Probability Density Curve

sns.kdeplot(df['w'])

Box Plot

sns.boxplot(data=df)

Violin Plot

Using a kernel density plot to show grouped numeric data provides an effective way to depict the probability density at different values.

sns.violinplot(data=df)

Multivariate Analysis

Correlation Heatmap

sns.heatmap(round(df.corr(),2), annot=True, cmap="coolwarm", fmt='.2f', linewidths=.05)

The gradient in the heatmap varies with the strength of correlation, making it easy to spot attributes that are strongly related.

Pair Plot

sns.pairplot(data=df, diag_kind='kde')

Joint Probability Distribution

sns.jointplot(x='x', y='y', data=df, kind='kde')