导读:Python是目前数据科学领域的王者语言,众多科学家、工程师、分析师都使用它来完成数据相关的工作。由于Python具有简单易学、语法灵活的特点,很多需要处理数据的人士想要学习,主要有两大类:
- 财经类、统计类背景人员,他们的日常工作有大量数据需要处理、分析,但对于学习使用计算机领域的编程语言Python又感到无从下手。
- 一些想要学习Python的计算机人员,他们工作较忙,没有太多时间通过互联网去系统学习Python数据技术
针对这两类人员的需求,近期出版上市的《利用Python进行数据分析》第2版是很好的选择。下面我们结合本书内容,大致介绍下如何利用Python进行数据分析。
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01 Python数据分析流程及学习路径
数据分析的流程概括起来主要是:读写、处理计算、分析建模和可视化四个部分。在不同的步骤中会用到不同的Python工具。每一步的主题也包含众多内容。
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根据每个部分需要用到的工具,Python数据分析的学习路径如下:
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02 利用Python读写数据
Python读写数据,主要包括以下内容:
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我们以一小段代码来看:
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可见,仅需简短的两三行代码即可实现Python读入EXCEL文件。
03 利用Python处理和计算数据
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在第一步和第二步,我们主要使用的是Python的工具库NumPy和pandas。其中,NumPy主要用于矢量化的科学计算,pandas主要用于表型数据处理。
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▲NumPy
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▲pandas
04 利用Python分析建模
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在分析和建模方面,本书主要介绍了Statsmdels和Scikit-learn两个库。
.Statsmodels允许用户浏览数据,估计统计模型和执行统计测试。可以为不同类型的数据和每个估算器提供广泛的描述性统计,统计测试,绘图函数和结果统计列表。
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▲.Statsmodels
Scikit-leran则是著名的机器学习库,可以迅速使用各类机器学习算法。
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▲Scikit-leran
05 利用Python数据可视化
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数据可视化是数据工作中的一项重要内容,它可以辅助分析也可以展示结果。本书主要介绍了Python众多可视化库中最为主流的Matplotlib:
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