某省传统技艺类非遗资源数据集

在筛选涉及艺术创作类和技术创新类的项目时，设定了特定的筛选条件，为名称中包含 ' 制作技艺 ' 且包含 ' 花灯 | 图案 | 雕刻 | 绘画 ' 等艺术相关词汇，根据对艺术创作的理解，通过关键词匹配来筛选出可能符合要求的项目。
1、使用value_counts函数统计不同类别的数量。该函数内部实现了计数算法，它遍历数据列中的每个元素，对相同元素进行计数，最终返回每个元素及其对应的数量。例如df['项目类别'].value_counts()会统计项目类别列中每个类别出现的次数。
2、通过str.contains函数结合布尔索引进行条件筛选。str.contains函数基于字符串匹配算法，在每个项目名称字符串中查找是否包含指定的关键词。布尔索引则根据匹配结果（True 或 False）来筛选出符合条件的行。例如df[df['项目名称'].str.contains('制作技艺') & (df['项目名称'].str.contains('花灯|图案|雕刻|绘画'))]，先分别判断每个项目名称是否包含 ' 制作技艺 ' 和艺术相关词汇，然后通过逻辑与（&）运算确定最终符合条件的行。

When screening projects related to artistic creation and technological innovation, specific screening criteria were established: projects whose names contain both the phrase "craftsmanship techniques" and art-related keywords including "lantern", "pattern", "carving", and "painting". Eligible projects are screened through keyword matching based on the understanding of artistic creation.

1. Count the number of distinct categories using the `value_counts` function. This function implements a counting algorithm internally: it iterates over each element in the data column, counts identical elements, and finally returns each element along with its corresponding count. For example, `df["project_category"].value_counts()` counts the occurrences of each category in the project category column.

2. Perform conditional filtering using the `str.contains` function combined with boolean indexing. The `str.contains` function relies on a string matching algorithm to check if a specified keyword exists in each project name string. Boolean indexing then filters out rows that meet the criteria based on the matching results (True or False). For example, `df[df["project_name"].str.contains("craftsmanship techniques") & (df["project_name"].str.contains("lantern|pattern|carving|painting"))]` first determines whether each project name contains "craftsmanship techniques" and art-related keywords respectively, then uses a logical AND (&) operation to confirm the final qualifying rows.