Master the key techniques for data visualization, Matplotlib and Seaborn can present data efficiently. 1. Select the appropriate chart type: line chart displays trends, bar chart/bar chart is used for classification comparison, scatter chart observes variable relationships, heat map displays correlation, box chart view distribution and outliers, Seaborn high-level interface automatically matches the types. 2. Improve readability: add clear labels, adjust font size, reasonable color matching, set legend position, and use grid lines to assist in reading. 3. Combining the advantages of both: use Seaborn to quickly draw, Matplotlib adjusts details such as style, layout, export HD pictures and avoid overlap. 4. Pay attention to common problems: manually set the axis range, solve Chinese garbled code, control the drawing order, and avoid excessive beautification and interference with information transmission.

It is actually not difficult to use Python's Matplotlib and Seaborn to visualize data, but to make clear and persuasive charts, you still have to master some skills. These two libraries are powerful, but how to choose and use them lies in understanding your data and the information you want to express.

1. Select the appropriate chart type

Not all charts are suitable for all data. For example, if you want to show trends, a line chart is the most suitable; if you want to look at the distribution, a histogram or box chart is more suitable; and when comparing classified data, a histogram or bar chart is more intuitive.

• Line chart: data suitable for time series or continuous changes
• Bar chart/bar chart: suitable for classification comparison
• Scatter plot: used to observe the relationship between two variables
• Thermal graph: Shows the correlation or density of matrix data
• Box chart: Look at the distribution and outliers

Seaborn is well encapsulated in this regard. High-level interfaces such as sns.catplot() or sns.relplot() can automatically help you match appropriate chart types.

2. A few tips to improve readability

No matter how beautiful the chart is, it is useless if others can't understand it. To improve readability, you can start from these aspects:

• Add tags : write the x-axis, y-axis, and titles clearly, don't be afraid of being too verbose
• Adjust the font size : The default font size is too small, especially in reports or PPTs.
• Reasonable color matching : avoid using too many colors, you can use Seaborn's built-in color palette (such as sns.color_palette("Set2") )
• The legend is suitable : Sometimes the legend blocks the data, you can try loc='upper right' or put it outside
• Grid line assisted reading : Adding plt.grid(True) can make the values ??easier to align

For example, if you draw a bar chart and find that the color distinction between different categories is not obvious enough, changing a color palette can improve a lot.

3. Combine Matplotlib and Seaborn to maximize results

Although Seaborn is based on Matplotlib, the two work better together. You can use Seaborn to quickly generate charts and then use Matplotlib to adjust details.

for example:

• Use sns.set_style() to set the overall style
• Use plt.subplots() to control multiple subgraph layouts
• Use plt.tight_layout() to avoid overlapping tags
• Export high-definition pictures with fig.savefig('output.png')

A small trick is that Seaborn's default DPI is not high. It is recommended to add the parameter dpi=300 when saving pictures, so that it is clearer when printing or inserting documents.

4. Pay attention to common pitfalls

• The axis range is not suitable : Sometimes the default axis range will enlarge or narrow the difference. Remember to manually set it with plt.xlim() and plt.ylim()
• Chinese garbled code : Matplotlib does not support Chinese by default, you can use plt.rcParams['font.sans-serif'] = ['SimHei'] to solve it
• Drawing order affects vision : If you superimpose multiple graphic elements, pay attention to who draws first and who then draws, which will affect the final display effect.
• Don't over-beautify : The core of the chart is to convey information, not show off skills. Some 3D pictures look cool, but they interfere with the judgment.

Basically that's it. Master the basic chart types and commonly used adjustment methods, and practice them a few times based on actual data, and you will be able to get started soon.

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