Matplotlib/Seaborn

Matplotlib is a popular Python library for creating static, animated, and interactive visualizations. It provides an interface similar to MATLAB and is useful for plotting various graphs, such as line charts, bar charts, histograms, scatter plots, and more.

You can install Matplotlib using pip:

pip install matplotlib

A Matplotlib figure consists of:

• Figure: The overall container holding the plot.
• Axes: The plotting area (can be multiple in a figure).
• Axis: The x and y coordinate system.
• Ticks & Labels: Markings along the axes.

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 25, 30, 40]

plt.plot(x, y)
plt.show()

pyplot is a module that provides a simple interface for creating figures. figure is used to create a blank canvas for plotting multiple subplots.

plt.figure(figsize=(10, 5))  # Width=10, Height=5

fig, ax = plt.subplots(2, 2)  # 2x2 grid of plots

plt.xlabel("X-axis Label")
plt.ylabel("Y-axis Label")
plt.title("Plot Title")

plt.show() displays the plot on the screen, while plt.savefig("plot.png") saves it as an image file.

plt.style.use('ggplot')  # Apply the ggplot style

plt.bar(x, y, color='blue')

plt.plot(x, y, label="Line 1")
plt.plot(x, [y_i + 5 for y_i in y], label="Line 2")
plt.legend()

A scatter plot shows individual points, while a line plot connects them.

plt.hist(data, bins=10, color='green')

plt.plot(x, y, linestyle='--', marker='o', color='red')

plt.xticks([0, 2, 4, 6])
plt.yticks([10, 20, 30])

fig, ax1 = plt.subplots()
ax2 = ax1.twinx()

import pandas as pd
data = pd.date_range(start="2023-01-01", periods=10, freq="D")
plt.plot(data, y)

plt.savefig("plot.png", dpi=300)

• Use scatter() instead of plot() for large datasets.
• Reduce transparency with alpha=0.5.
• Optimize with downsampling techniques.

Seaborn is a higher-level visualization library built on Matplotlib that provides better defaults and advanced statistical plotting.

import seaborn as sns
sns.scatterplot(x="column1", y="column2", data=df)

It groups data points by color.

sns.scatterplot(x="x", y="y", hue="category", data=df)

sns.violinplot(x="category", y="value", data=df)

sns.heatmap(data.corr(), annot=True, cmap="coolwarm")

sns.regplot(x="x", y="y", data=df)

g = sns.FacetGrid(df, col="category")
g.map(sns.histplot, "value")

sns.despine()

Use sample(), agg() for aggregation, or FacetGrid for breaking data into smaller plots.

• ImportError: Ensure libraries are installed.
• ValueError: Check data types before plotting.

import numpy as np
x = np.arange(3)
values1 = [5, 7, 9]
values2 = [6, 8, 10]

plt.bar(x - 0.2, values1, width=0.4, label='Group 1')
plt.bar(x + 0.2, values2, width=0.4, label='Group 2')
plt.xticks(x, ['A', 'B', 'C'])
plt.legend()
plt.show()

plt.text(2, 15, "Annotation Here", fontsize=12, color='red')

alpha controls the transparency of a plot.

plt.scatter(x, y, alpha=0.5)

plt.grid(True, linestyle='--', linewidth=0.5)

plt.bar(x, values1, label="Category 1")
plt.bar(x, values2, bottom=values1, label="Category 2")
plt.legend()

plt.xscale("log")
plt.yscale("log")

plt.legend(loc='upper left', bbox_to_anchor=(1,1))

labels = ['A', 'B', 'C']
sizes = [40, 35, 25]
plt.pie(sizes, labels=labels, autopct='%1.1f%%')

plt.title("Title", fontsize=14, fontweight='bold')

Use bar() with cumulative sums and a baseline shift.

sns.boxplot(x="category", y="value", data=df)

• It sets the color scheme for the plots.

sns.scatterplot(x="x", y="y", data=df, palette="coolwarm")

sns.kdeplot(df["column"], shade=True)

sns.pairplot(df)

• stripplot() places points randomly along the x-axis.
• swarmplot() avoids overlapping points.

sns.swarmplot(x="category", y="value", data=df)

plt.figure(figsize=(10, 5))

sns.heatmap(df.corr(), annot=True, cmap="Blues")

A FacetGrid allows multiple plots to be displayed side by side for different subsets of the data.

sns.jointplot(x="x", y="y", data=df, kind="scatter")

fig, ax = plt.subplots(2, 2)

plt.scatter(x, y, c=y, cmap="viridis")

Seaborn provides better aesthetics and statistical plots.

%matplotlib notebook

sns.set_theme(style="darkgrid")

sns.set_theme() provides better control over aesthetics.

for index, value in enumerate(y):
    plt.text(index, value, str(value))

sns.heatmap(df.corr(), annot=True)

plt.violinplot(data)

• Large datasets slow down rendering.
• Incompatible versions cause unexpected behavior.
• Matplotlib styles can sometimes override Seaborn themes.