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Today, I will talk to you about how to use Python thermal map to visualize the missing data in the table. maybe many people don't know much about it. in order to make you understand better, the editor has summarized the following content for you. I hope you can get something according to this article.

Python thermal map to find missing data

Have you ever encountered a situation where it is difficult to find missing data in each column of a large csv table, or it is very slow to process it?

? Of course, if your excel level is very high, these may not be your problem. But what if you want to visualize the distribution and amount of missing data in each column? At this point, you need to use Python to draw the thermal map!

1. Install the required third-party Python libraries

Before we begin, we need to install the following Python package (library), open your CMD (Windows system) / Terminal (macOS system) and enter the following instructions:

Pip install seaborn

Pip install pandas

Pip install matplotlib

Among them, pandas is used for data operation and processing, matplotlib and seaborn are mainly used for Python data visualization, that is, to draw the thermal map we need.

2.Python load data

All right, enough nonsense, let's start loading data using panda right now:

Yes, the use of pandas is as simple as that. You can use the read_csv function directly to read csv files. You can use the csv files you need, or you can use the Python Twitter robot classification dataset:

Https://pythondict.com/download/python-twitter-bot-classify-dataset-download/

You only need to move the training_data.csv to the folder of the current code.

3.Python tectonic thermal map

Use Python to construct a thermal map to identify the missing data in the table:

Python thermal map to find missing data

Seaborn.heatmap is used to generate heat maps, which checks each cell in data. If empty, it is marked yellow, cmap is a color map, and viridis is blue-green-yellow. In addition, matplotlib.pyplot is mainly used to display pictures. If you need to know more about the seaborn parameter documentation, you can read this article:

Https://seaborn.pydata.org/api.html

The overall code is as follows (only 7 lines are used)

After reading the above, do you have any further understanding of how to use Python thermal maps to visualize the missing data in the table? If you want to know more knowledge or related content, please follow the industry information channel, thank you for your support.

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