Table 3447. Plot cheatsheet.
| Application |
Details |
ax.set_xlim(0, 6) # Set plot limits
ax.set_ylim(0, 4) # Set plot limits
# Set axes limits explicitly
x_min, x_max = min(x), max(x)
y_min, y_max = min(y), max(y)
plt.xlim(x_min - 0.1 * (x_max - x_min), x_max + 0.1 * (x_max - x_min))
plt.ylim(y_min, y_max * 1.1) # This will extend the y-axis to add space at the top |
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| ax.axis('off') # Turn off axes |
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plt.title('Workflow', fontsize=16, color='darkblue') |
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| draw_arrow(3.3, 2, 0.4, 1, 'Group By Key', text_color='red') |
Change text color |
| Adjusting space around the plot with dummy categories |
df.loc[-1] = ['', np.nan]
df.loc[len(df)] = ['', np.nan] |
| .xticks() and .yticks() |
labels = ['Liao', 'Global', 'Sino', 'Best']
plt.xticks(x, labels, rotation ='vertical') |
| Layout of plotting multiple images by hiding frame/border |
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| Plot tables |
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| plt.savefig() |
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| Modify x-tick labels |
Remove the "feature_" from the x-tick labels |
| Plot from dictionary |
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| Plot images from dataframe from CSVs |
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| Plot image with text label on image |
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| Plot histogram with seaborn, pie, histogram |
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| Transparency of marker |
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| Second x-axis tick label (dual label) |
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| Arrow annotation (.annotate()) |
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| Box and whisker plots |
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| Select columns to plot, plot from dataframe |
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| Plot with specific (e.g. first and remaining) columns of DataFrame for x- and y-axis |
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| Waterfall plot |
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| ax.axis('on') |
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| Plot curves |
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| Arrow annotation: .annotate('Point A', xy=(a, a), xytext=(a+4, a) |
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| Avoid plot mixture: plt.close() |
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| Plot with x- and y- intensity of image |
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| fig.add_trace(go.Bar(x=[3, 5, 10], y=[20, 7, 10])) |
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| Plot from dictionary |
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| plt.margins(0.2), white area on image frame/edge: plt.savefig(imageFile, bbox_inches='tight') |
# Pad margins so that markers don't get clipped by the axes
plt.margins(x=0.1, y=0.1) |
| plt.subplots_adjust(bottom = 0.25), plt.subplots_adjust(left=None, bottom=None, right=None, top=None,
wspace=None, hspace=None), or plt.subplots_adjust(wspace=0, hspace=0) |
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| plt.xticks(x, labels, rotation ='vertical') |
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| matplotlib.pyplot axis/text color (xticks, rotation, xlabel, ylabel, title, fontsize, grid(), legend(), show()) |
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| Partial (e.g. first portion) of headers of a DataFrame |
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| Font size of tick labels |
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| dot size color: plt.scatter(X, Y2, color='black', label='', s=300) |
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| Set logarithmic scale (exponential) for y-axis |
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| Crop images: image[y:h, x:w] |
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| Figure size in plotted image |
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| Highlight the plotted dots uncer certain condition |
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| Generating heatmaps for grouped data occurrences |
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| Heatmap |
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| Plot a heatmap with three columns of data |
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| Fit and smooth plotted curves |
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| Least squares fit |
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| Colored scatter plot |
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| Merge two dataframes with two columns from each dataframe, and then plot the data |
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| plt.xticks(range(min(x), max(x) + 1, 3)) |
Generates a sequence of numbers starting from the minimum value in the list x, ending at the maximum value in x (inclusive because of the + 1), with a step size of 3, for setting the x-axis ticks at intervals of 3. |
plt.bar(my_list, new_y_values, label=f'Data from DataFrame {idx}')
|
Bar plot |
plt.plot(my_list, new_y_values, marker='o', linestyle='-', label=f'Data from DataFrame {idx}')
|
Line-dot plot |
| plot with x- and y-list ; plot from list , plot from list, plot from list |
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