Pandas——value_counts&index&to_dict

最新推荐文章于 2025-07-11 00:15:00 发布

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最新推荐文章于 2025-07-11 00:15:00 发布 · 1w 阅读

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本文利用Pandas分析大学专业与就业数据，涉及all-ages.csv和recent-grads.csv两个CSV文件。主要内容包括计算Major Categories的就读人数、低薪工作学生占比，以及对比两个数据集的就业率变化。

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本文数据是大学专业和就业的信息。有两个csv文件all-ages.csv和recent-grads.csv

主要的属性如下：

Rank - The numerical rank of the major by post-graduation median earnings.
Major_code - The numerical code of the major.
Major - The description of the major.
Major_category - The category of the major.
Total - The total number of people who studied the major.
Men - The number of men who studied the major.
Women - The number of women who studied the major.
ShareWomen - The share of women (from 0 to 1) who studied the major.
Employed - The number of people who studied the major and were employed post-graduation.

recent-grads.csv
all-ages.csv和这个类似，只是某些列的值不同

Summarizing Major Categories

计算两个数据集中每个Major Categories（每个Major Categories包含多个Major
）的就读的人数。

Series.value_counts返回的是该Series对象中独一无二的元素的个数（Returns object containing counts of unique values.）是个Series对象。

print(all_ages['Major_category'].value_counts())
'''
Engineering                            29
Education                              16
Humanities & Liberal Arts              15
Biol