This cheat sheet—part of our Complete Guide to NumPy, pandas, and Data Visualization—provides a quick reference for essential plotting functions in matplotlib, helping you create and customize various types of visualizations. It covers fundamental plot types—from line and scatter plots to histograms and bar charts—and includes advanced customization options like subplots, color mapping, and annotations.
You’ll also find guidance on using pandas Series and DataFrame objects to quickly generate visualizations, perfect for exploring and understanding your data at a glance. These functions are ideal for developing a data cleaning strategy and identifying key trends in your dataset early on.
Designed to help you present data clearly and effectively, this guide ensures you can easily leverage matplotlib’s powerful features to gain insights and make data-driven decisions.
IMPORT
PLOT, SCATTER, BAR, BARH, HIST, AREA, PIE
TITLE, XLABEL, YLABEL, LEGEND, GRID, SET_XTICKS, SET_YTICKS, TICKLABEL_FORMAT, COLORBAR, ANNOTATE
FIGURE, SUBPLOT, SUBPLOTS
SUBPLOTS, SPINES, TICK_PARAMS, TICK_TOP, SET_XTICKS, SET_XTICKLABELS, TEXT, AXVLINE, AXHLINE
LINE (SERIES), LINE (DATAFRAME), SCATTER (DATAFRAME), HIST (SERIES), BAR (SERIES), BARH (SERIES), BOXPLOT
RELPLOT, HEATMAP, PAIRPLOT, VIOLINPLOT, JOINTPLOT
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IMPORT
import matplotlib.pyplot as pltImport the pyplot submodule using an alias
import seaborn as sns
sns.set_theme()Import seaborn and set the default theme
import pandas as pdImport pandas using its common alias
import matplotlib.style as style
style.use('fivethirtyeight')Apply the fivethirtyeight predefined style
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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
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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
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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
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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
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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
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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