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Matplotlib Cheat Sheet

Download our matplotlib cheat sheet for essential plotting commands, plus Seaborn and pandas commands for fast, customized visualizations.

Download cheat sheet PDF
← All cheat sheets

Matplotlib Cheat Sheet

Download our matplotlib cheat sheet for essential plotting commands, plus Seaborn and pandas commands for fast, customized visualizations.

Download cheat sheet PDF

Table of Contents

Importing Libraries

Syntax for

How to use

Explained

IMPORT

import matplotlib.pyplot as plt

Import the pyplot submodule using an alias

import seaborn as sns
sns.set_theme()

Import seaborn and set the default theme

import pandas as pd

Import pandas using its common alias

import matplotlib.style as style
style.use('fivethirtyeight')

Apply the fivethirtyeight predefined style

Basic Plotting with matplotlib

Syntax for

How to use

Explained

PLOT

plt.plot(x_values, y_values)
plt.show()

Plot a basic line graph

plt.plot(x_values1, y_values1)
plt.plot(x_values2, y_values2)
plt.show()

Plot multiple graphs on the same figure

plt.plot(x_values1, y_values1)
plt.show()
plt.plot(x_values2, y_values2)
plt.show()

Plot graphs in separate figures

plt.plot(x_values, y_values, color='blue', linestyle='--')
plt.show()

Customize line color and style

SCATTER

plt.scatter(x_values, y_values)
plt.show()

Create a scatter plot of points

plt.scatter(x_values, y_values, c='red', s=100)
plt.show()

Customize point color and size

BAR

plt.bar(x=x_values, height=heights)
plt.show()

Create a vertical bar chart

plt.bar(x, height, bottom=previous_heights)
plt.show()

Create a stacked bar chart

BARH

plt.barh(y=y_values, width=widths)
plt.show()

Create a horizontal bar chart

plt.barh(y, width, color='purple', edgecolor='black')
plt.show()

Customize bar colors and borders

HIST

plt.hist(data_column) 
plt.show()

Generate a histogram for a dataset

plt.hist(data_column, bins=30, color='orange')
plt.show()

Customize bin count and color

AREA

plt.fill_between(x, y1=lower, y2=upper)
plt.show()

Create an area plot shaded between y1 and y2

PIE

plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.show()

Create a pie chart with percentages

Customization

Syntax for

How to use

Explained

TITLE

plt.title('Title')

Add a title to the plot

plt.title('Custom Title', fontsize=16, color='green')

Customize title size and color

XLABEL

plt.xlabel('X-axis Label')

Add a label to the x-axis

YLABEL

plt.ylabel('Y-axis Label')

Add a label to the y-axis

LEGEND

plt.plot(x_values1, y_values1, label='Label 1')
plt.plot(x_values2, y_values2, label='Label 2')
plt.legend()
plt.show()

Add a legend to the plot

plt.legend(loc='upper right', fontsize=10) 
plt.show()

Customize legend position and font size

GRID

plt.grid(True)

Add gridlines to the plot

SET_XTICKS

plt.xticks(ticks=x_values, labels=labels)

Customize the tick labels on the x-axis

plt.xticks(rotation=45)

Rotate the x-axis tick labels

SET_YTICKS

plt.yticks(ticks=y_values, labels=labels)

Customize the tick labels on the y-axis

plt.yticks(rotation=30)

Rotate the y-axis tick labels

TICKLABEL
_FORMAT

plt.ticklabel_format(axis='both', style='plain')

Change scientific notation to plain text

COLORBAR

plt.scatter(x, y, c=values, cmap='viridis')
plt.colorbar()

Use a colormap in the scatter plot

plt.scatter(x, y, c=values, cmap='coolwarm')
plt.colorbar()

Use a different colormap

ANNOTATE

plt.annotate(
    'Text', xy=(x, y),
     xytext=(x_offset, y_offset),
     arrowprops=dict(facecolor='black', shrink=0.05))
plt.show()

Add text and an arrow annotation on the plot

Grid Charts

Syntax for

How to use

Explained

FIGURE

plt.figure(figsize=(8, 3))
plt.subplot(1, 2, 1)
plt.subplot(1, 2, 2)
plt.show()

Create two subplots in a 1-row, 2-column grid

SUBPLOT

plt.figure(figsize=(10, 12))
for i in range(1, 7):
    plt.subplot(3, 2, i)
    plt.plot(x_values, y_values)
plt.show()

Create a 3-row, 2-column grid of subplots

SUBPLOTS

fig, axes = plt.subplots(nrows=4, ncols=1)

Create a grid of 4 vertically stacked subplots

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 8))
axes[0, 0].plot(x_values1, y_values1)
axes[1, 1].plot(x_values2, y_values2)
plt.show()

Create a 2x2 grid of subplots and assign plots to specific axes

Advanced Plot Customization

Syntax for

How to use

Explained

SUBPLOTS

fig, ax = plt.subplots(figsize=(10, 8))
ax.bar(x, height)
plt.show()

Create a bar chart using the object-oriented approach

SPINES

for location in ['left', 'right', 'bottom', 'top']:
    ax.spines[location].set_visible(False)

Remove all borders (spines) from the plot

TICK_PARAMS

ax.tick_params(top=False, left=False)
ax.tick_params(axis='x', colors='grey')

Hide specific ticks and change tick colors

TICK_TOP

ax.xaxis.tick_top()

Move x-tick labels to the top of the plot

SET_XTICKS

ax.set_xticks([0, 150, 300])

Set custom tick locations on the x-axis

SET_XTICK LABELS

ax.set_xticklabels(['0', '150', '300'])

Set custom tick labels on the x-axis

TEXT

ax.text(x, y, 'Sample Text', fontsize=12, color='blue')

Add custom text to a specific position on the plot

AXVLINE

ax.axvline(x=5, color='red', linewidth=2)

Add a vertical line at a specified x-position with customization

AXHLINE

ax.axhline(y=3, color='green', linestyle='--')

Add a horizontal line at a specified y-position with customization

Pandas Visualization

Syntax for

How to use

Explained

LINE (SERIES)

Series.plot.line()
plt.show()

Create a line plot from a Series object

Series.plot.line(color='green', linestyle='--')
plt.show()

Create a line plot from a Series object

LINE (DATAFRAME)

DataFrame.plot.line(x='column1', y='column2')
plt.show()

Create a line plot from a DataFrame object

SCATTER (DATAFRAME)

DataFrame.plot.scatter(x='column1', y='column2')
plt.show()

Create a scatter plot from a DataFrame object

DataFrame.plot.scatter(x='col1', y='col2', color='red', s=50)
plt.show()

Customize scatter plot points

HIST (SERIES)

Series.plot.hist(bins=20)
plt.show()

Generate a histogram with custom bin count from a Pandas Series

Series.plot.hist(cumulative=True, bins=30) 
plt.show()

Create a cumulative histogram

BAR (SERIES)

Series.plot.bar()
plt.show()

Create a vertical bar chart from a Pandas Series

Series.plot.barh()
plt.show()

Create a horizontal bar chart from a Series object

Series.plot.barh(color='orange', edgecolor='black')
plt.show()

Customize bar colors and borders

BOXPLOT

DataFrame.plot.box()
plt.show()

Create a boxplot to visualize data distributions

Seaborn Visualizations

Syntax for

How to use

Explained

RELPLOT

sns.relplot(data=data, x='x_var', y='y_var', hue='hue_var', size='size_var', style='style_var')
plt.show()

Create a relational plot with multiple attributes

sns.relplot(data=data, x='x_var', y='y_var', hue='hue_var', col='col_var')
plt.show()

Create subplots for relational plots based on a column

HEATMAP

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

Create a heatmap with annotations

sns.heatmap(data, annot=True, linewidths=0.5, cmap='Blues')

Create a heatmap with line spacing and custom colors

PAIRPLOT

sns.pairplot(data)

Create pair plots for all combinations of features

VIOLINPLOT

sns.violinplot(x='x_var', y='y_var', data=data)

Create a violin plot to visualize data distribution

JOINTPLOT

sns.jointplot(x='x_var', y='y_var', data=data, kind='reg')

Create a joint plot to visualize bivariate data

Mike Levy

Written by

Mike Levy

Mike is a life-long learner who is passionate about mathematics, coding, and teaching. When he's not sitting at the keyboard, he can be found in his garden or at a natural hot spring.

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