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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-data-analysis-with-pandas-library/
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课程名称：Python Pandas库用于数据科学 课程概述：在处理表格数据（如数据存储在电子表格或数据库中）时，Pandas是一个理想的工具。Pandas能够帮助你探索、清理和处理数据。在Pandas中，数据表称为DataFrame。Pandas支持多种文件格式和数据源的集成（如csv、excel、sql、json、parquet等）。从各个数据源导入数据的函数以read_*为前缀，而to_*方法用于存储数据。 本课程将涵盖以下内容： - Pandas库的基础知识 - Pandas数据结构：Series和DataFrames - 操作DataFrames，选择DataFrame中的列和行 - DataFrames的子集选择 - df[start_index:end_index] - 索引 - DataFrames的合并与连接 Python编程已成为全球最受欢迎的编程语言之一，因其丰富的功能和高生产力。因此，能够在Python中使用Pandas，让你可以利用与Python结合的各种其他特性和库，比如NumPy、SciPy、MatPlotLib等。 学员评价： - “优良的课程” ~ Kwizerimana Amedee - “这是学习Pandas库的好课程。” ~ Kumesh Ranamuy - “非常好！” ~ Osvaldo Falabella 通过本课程，你将掌握使用Pandas进行数据科学的基础技能，为进一步的数据分析和处理打下扎实的基础。
Pandas Background:When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool for you. pandas will help you to explore, clean and process your data. In pandas, a data table is called a DataFrame. Pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,... ). Importing data from each of these data sources is provided by function with the prefix read_*. Similarly, the to_* methods are used to store data.Selecting or filtering specific rows and/or columns? Filtering the data on a condition? Methods for slicing, selecting, and extracting the data you need are available in pandas. There is no need to loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. Adding a column to a DataFrame based on existing data in other columns is straightforward.Pandas has great support for time series and has an extensive set of tools for working with dates, times, and timeindexed data. Data sets do not only contain numerical data. pandas provides a wide range of functions to cleaning textual data and extract useful information from it.In this course we cover:Basics of Pandas LibraryPandas Data structures - Series & DataframesPlaying with Dataframes, Selecting columns & rows from a dataframeSubsetting of dataframes - df[start_index:end_index]IndexingDataframes merging and concatenatingPython programming has become one of the most sought after programming languages in the world, with its extensive amount of features and the sheer amount of productivity it provides. Therefore, being able to code Pandas in Python, enables you to tap into the power of the various other features and libraries which will use with Python. Some of these libraries are NumPy, SciPy, MatPlotLib, etc.Testimonials:Excellent course ~ Kwizerimana AmedeeThis is one good Course for learning Pandas Lib. ~ Kumesh Ranamuy bueno! (Very Good) ~ Osvaldo Falabella
