Pandas is an open-source library commonly used in data science. It is primarily used for data analysis, data manipulation, and data cleaning. Pandas allow for simple data modeling and data analysis operations without needing to write a lot of code. As stated on their website, pandas is a ...
Beautiful Soup is a valuable library for web scraping and data extraction tasks in Python. Its ease of use and versatility make it a popular choice for those who need to access and analyze data from the web. When combined with other libraries and tools, it can be a powerful asset for dat...
convtools is a specialized Python library for dynamic, declarative data transformations with automatic code generation - westandskif/convtools
Openly sharing data with sensitive attributes and privacy restrictions is a challenging task. In this document we present the implementation ofpyCANON, a Python library and command line interface (CLI) to check and assess the level of anonymity of a dataset through some of the most common anonymi...
Polars is a high-performance DataFrame library, designed to provide fast and efficient data processing capabilities. Inspired by the reigning pandas library, Polars takes things to another level, offering a seamless experience for working with large datasets that might not fit into memory. In this ...
df=pd.read_csv('student_data.csv') 在加载数据后,我们可以使用pandas提供的方法对数据进行分类汇总。例如,我们可以按照学生的性别进行分组,并计算每个性别的学生人数: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 gender_count=df.groupby('Gender')['Name'].count()print(gender_count) ...
Google Finance:谷歌金融 Enigma:Enigma是一个公共数据搜索的提供商 St.Louis FED (FRED):圣路易斯联邦储备银行 Kenneth French’s data library:肯尼斯弗兰奇资料库 World Bank:世界银行 OECD:经合组织 Eurostat:欧盟统计局 Thrift Savings Plan:美国联邦政府管理离退休的组织import...
NumPy is a Python library for numerical computing. It provides high-level math functions along with data manipulations on large arrays and matrices. The library helps in enhancing computation speed and performance. The different use-cases of NumPy are shown in the image below....
What are the benefits of using a decoupled data processing system? How do you write reusable queries for a variety of backend data platforms? This week on the show, Phillip Cloud, the lead maintainer of Ibis, will discuss this portable Python dataframe library. Play EpisodeEpisode...
flat_map(func)Mapsfuncto each element, then merges the result to one flat sequence.funcmust return an iterabletransformation group_by(func)Groups sequence into(key, value)pairs wherekey=func(element)andvalueis from the original sequencetransformation ...