# 数据分析-实验 ## Te-s-r-act，是 TensorAct，还是Tesseract ? {% content-ref url="../../../bfm-urs-wei-mian-jian-meng-lian-he-ti-chu-bei-xi-tong-qu-kuai-lian-jia-mi-huo-bi-hong-guan-tiao-kong/the-tzolkin-calendar/zhuo-er-jin-sheng-ji-5-shen-jing-wang-luo" %} [zhuo-er-jin-sheng-ji-5-shen-jing-wang-luo](https://www.bfm-unity.org/bfm-urs-wei-mian-jian-meng-lian-he-ti-chu-bei-xi-tong-qu-kuai-lian-jia-mi-huo-bi-hong-guan-tiao-kong/the-tzolkin-calendar/zhuo-er-jin-sheng-ji-5-shen-jing-wang-luo) {% endcontent-ref %} {% hint style="info" %} #### 问题描述 #### #### 使用工具 [Anaconda](https://www.anaconda.com/) (Jupyter Notebook，Jupyter Lab，Numpy，Pandas，SciPy，TensorFlow，Keras)还有matplotlib，差点忘了 [**TensorFlow**](https://www.tensorflow.org/) **|** [**TensorBoard**](https://www.tensorflow.org/tensorboard?hl=zh-cn) **|** [**TensorBoard.dev**](https://tensorboard.dev/) **|** [**Google Colab**](https://colab.research.google.com/notebooks/intro.ipynb)\*\*\*\* [**https://playground.tensorflow.org/**](https://playground.tensorflow.org/)\*\*\*\* [**https://nbviewer.jupyter.org/**](https://nbviewer.jupyter.org/) **数据来源** [MVRV](https://www.qkl123.com/data/mvrv/btc) | [S2F](https://www.qkl123.com/data/s2f/btc) | [NVT](https://www.qkl123.com/data/nvt/btc) {% endhint %} ![](https://content.gitbook.com/content/b7kgec2aOBgFdNaAaH2R/blobs/e5g0ij8FArbbpy6VbqNI/A4_3.png) [22 款神经网络的设计和可视化工具](https://www.huaweicloud.com/articles/d90130bfd852a55e505155e381ebfab1.html) [Mac版Excel如何添加数据分析功能？](https://answers.microsoft.com/zh-hans/msoffice/forum/all/mac%E7%89%88excel%E5%A6%82%E4%BD%95%E6%B7%BB/4f9c7fcf-ba8a-4f1a-8f00-bc35a43cb480) [机器学习-回归问题(Regression)](https://zhuanlan.zhihu.com/p/127972563) [标准化、归一化、正规化](https://zhuanlan.zhihu.com/p/73080065) [一文读懂机器学习分类算法（附图文详解）](https://zhuanlan.zhihu.com/p/82114104) [Q\&A: 机器学习与金融行业](http://www.fintechgl.com/articles/59) ## 开发环境 ![](https://content.gitbook.com/content/b7kgec2aOBgFdNaAaH2R/blobs/1gddJho6qeSiamxlHa86/%E5%B1%8F%E5%B9%95%E5%BF%AB%E7%85%A7%202021-07-09%20%E4%B8%8A%E5%8D%887.54.06.png) ![](https://content.gitbook.com/content/b7kgec2aOBgFdNaAaH2R/blobs/5Bf6LCRDNzNyTvNafwvA/QQ20210625-2.jpg) **pandas**是一个数据处理的包，本身提供了许多读取文件的函数，像read\_csv（读取csv文件），read\_excel（读取excel文件）等，只需一行代码就能实现文件的读取。 ## 规整化算法 第一步：去指数转线性，y = LN(x/x的几何平均数)，\ 第二步：Logistic 模式 ，y = 1/(1+e^(-x))\ 第三步：z-score 标准化，y=(x-μ)/σ 只要经过这三步处理，不管之前是怎样分布的数据，都会转化为标准正态分布。 ## 研发进度 ### 2021.7.9 安装了Anaconda，借助Anaconda 安装了\ Jupyter Notebook，Jupyter Lab，Numpy，Pandas，SciPy，TensorFlow，Keras\ 还有matplotlib，差点忘了 课程？[在看了在看了](https://www.bilibili.com/video/BV1rz4y117p1)。 ```python from tensorflow import keras import numpy as np import pandas as pd import matplotlib.pyplot as plt ``` 找到了 [TensorFlow学习笔记](https://www.zhihu.com/column/c_1377756991213998081) ，发现逻辑回归分类问题应该用**交叉熵损失函数，而不是均方误差MSE损失函数** \*\*\*\*[交叉熵](https://baike.baidu.com/item/%E4%BA%A4%E5%8F%89%E7%86%B5/8983241) | \*\*\*\* [损失函数](https://baike.baidu.com/item/%E6%8D%9F%E5%A4%B1%E5%87%BD%E6%95%B0/1783236) | [归一化指数函数](https://baike.baidu.com/item/%E5%BD%92%E4%B8%80%E5%8C%96%E6%8C%87%E6%95%B0%E5%87%BD%E6%95%B0)(Softmax函数) [matplotlib-tutorial](https://www.runoob.com/w3cnote/matplotlib-tutorial.html) 学会复制粘贴了，这是一种进步 [python-tutorial](https://www.runoob.com/python/python-tutorial.html) 找到了python基础教程，重新回去学python 不学了，要吐了 再想想 找到了一本书：利用Python进行数据分析：[![](https://gblobscdn.gitbook.com/spaces%2F-LF_s_ql-GHXb31QEE8_%2Favatar.png?alt=media)README](https://seancheney.gitbook.io/python-for-data-analysis-2nd/) 找到了英文版 找到了中文版 * [Chapter 4: NumPy Basics: Arrays and Vectorized Computation（NumPy基础：数组和向量化计算）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/tree/master/Chapter-04/) * [4.1 The NumPy ndarray: A Multidimensional Array Object（ndarray: 多维数组对象)](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/blob/master/Chapter-04/4.1%20The%20NumPy%20ndarray%EF%BC%88%E5%A4%9A%E7%BB%B4%E6%95%B0%E7%BB%84%E5%AF%B9%E8%B1%A1%EF%BC%89.ipynb) * [4.2 Universal Functions: Fast Element-Wise Array Functions（通用函数：快速点对点数组函数）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/blob/master/Chapter-04/4.2%20Universal%20Functions%20\(%E9%80%9A%E7%94%A8%E5%87%BD%E6%95%B0\).ipynb) * [4.3 Array-Oriented Programming with Arrays（数组导向编程）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/blob/master/Chapter-04/4.3%20Array-Oriented%20Programming%20with%20Arrays%EF%BC%88%E6%95%B0%E7%BB%84%E5%AF%BC%E5%90%91%E7%BC%96%E7%A8%8B%EF%BC%89.ipynb) * [Chapter 5: Getting Started with pandas（开始使用pandas）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/tree/master/Chapter-05/) * [5.1 Introduction to pandas Data Structures（pandas的数据结构）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/blob/master/Chapter-05/5.1%20Introduction%20to%20pandas%20Data%20Structures%EF%BC%88pandas%E7%9A%84%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%EF%BC%89.ipynb) * [5.2 Essential Functionality（主要功能）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/blob/master/Chapter-05/5.2%20Essential%20Functionality%EF%BC%88%E4%B8%BB%E8%A6%81%E5%8A%9F%E8%83%BD%EF%BC%89.ipynb) * [5.3 Summarizing and Computing Descriptive Statistics（汇总和描述性统计）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/blob/master/Chapter-05/5.3%20Summarizing%20and%20Computing%20Descriptive%20Statistics%EF%BC%88%E6%80%BB%E7%BB%93%E5%92%8C%E6%8F%8F%E8%BF%B0%E6%80%A7%E7%BB%9F%E8%AE%A1%EF%BC%89.ipynb) * [Chapter 7: Data Cleaning and Preparation（数据清洗和准备）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/tree/master/Chapter-07/) * [7.1 Handling Missing Data（处理缺失数据）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/blob/master/Chapter-07/7.1%20Handling%20Missing%20Data%EF%BC%88%E5%A4%84%E7%90%86%E7%BC%BA%E5%A4%B1%E6%95%B0%E6%8D%AE%EF%BC%89.ipynb) * [7.2 Data Transformation（数据变换）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/blob/master/Chapter-07/7.2%20Data%20Transformation%EF%BC%88%E6%95%B0%E6%8D%AE%E5%8F%98%E6%8D%A2%EF%BC%89.ipynb) * [7.3 String Manipulation（字符串处理）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/blob/master/Chapter-07/7.3%20String%20Manipulation%EF%BC%88%E5%AD%97%E7%AC%A6%E4%B8%B2%E5%A4%84%E7%90%86%EF%BC%89.ipynb) * [Chapter 11: Time Series（时间序列）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/tree/master/Chapter-11/) * [11.1 Date and Time Data Types and Tools（日期和时间数据类型及其工具）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/blob/master/Chapter-11/11.1%20Date%20and%20Time%20Data%20Types%20and%20Tools%EF%BC%88%E6%97%A5%E6%9C%9F%E5%92%8C%E6%97%B6%E9%97%B4%E6%95%B0%E6%8D%AE%E7%B1%BB%E5%9E%8B%E5%8F%8A%E5%85%B6%E5%B7%A5%E5%85%B7%EF%BC%89.ipynb) * [11.2 Time Series Basics（时间序列基础）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/blob/master/Chapter-11/11.2%20Time%20Series%20Basics%EF%BC%88%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97%E5%9F%BA%E7%A1%80%EF%BC%89.ipynb) * [11.3 Date Ranges, Frequencies, and Shifting（日期范围，频度，和位移）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/blob/master/Chapter-11/11.3%20Date%20Ranges%2C%20Frequencies%2C%20and%20Shifting%EF%BC%88%E6%97%A5%E6%9C%9F%E8%8C%83%E5%9B%B4%EF%BC%8C%E9%A2%91%E5%BA%A6%EF%BC%8C%E5%92%8C%E4%BD%8D%E7%A7%BB%EF%BC%89.ipynb) * [Chapter 12: Advanced pandas（高级pandas用法）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/tree/master/Chapter-12/) * [12.1 Categorical Data（类别数据）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/blob/master/Chapter-12/12.1%20Categorical%20Data%EF%BC%88%E7%B1%BB%E5%88%AB%E6%95%B0%E6%8D%AE%EF%BC%89.ipynb) * [Chapter 14: Data Analysis Examples（数据分析实例）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/tree/master/Chapter-14/) * [14.1 USA.gov Data from Bitly（USA.gov数据集）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/blob/master/Chapter-14/14.1%20USA.gov%20Data%20from%20Bitly%EF%BC%88USA.gov%E6%95%B0%E6%8D%AE%E9%9B%86%EF%BC%89.ipynb) * [14.2 MovieLens 1M Dataset（MovieLens 1M数据集）](http://nbviewer.jupyter.org/github/LearnXu/pydata-notebook/blob/master/Chapter-14/14.2%20MovieLens%201M%20Dataset%EF%BC%88MovieLens%201M%E6%95%B0%E6%8D%AE%E9%9B%86%EF%BC%89.ipynb) * [14.3 US Baby Names 1880–2010（1880年至2010年美国婴儿姓名](http://nbviewer.jupyter.org/github/BrambleXu/pydata-notebook/blob/master/Chapter-14/14.3%20US%20Baby%20Names%201880%E2%80%932010%EF%BC%881880%E5%B9%B4%E8%87%B32010%E5%B9%B4%E7%BE%8E%E5%9B%BD%E5%A9%B4%E5%84%BF%E5%A7%93%E5%90%8D%EF%BC%89.ipynb)\*\*\*\* **pandas**是一个数据处理的包，本身提供了许多读取文件的函数，像read\_csv（读取csv文件），read\_excel（读取excel文件）等，只需一行代码就能实现文件的读取。 ### 2021.7.10 [python pandas read\_html快读读取中国商情网的A股，港股以及新三板股票](https://www.bilibili.com/video/BV1aQ4y1A7fh) [pandas.read\_csv 详细介绍](https://www.gairuo.com/p/pandas-read-csv) [pandas.read\_excel 详细介绍](https://www.gairuo.com/p/pandas-read-excel) ### 教程 [30分钟带你入门数据分析工具 Pandas](https://oicebot.github.io/2018/09/05/30-mins-into-pandas-for-data-science.html) [十分钟入门 Pandas](https://www.pypandas.cn/docs/getting_started/10min.html) 🚩 ### 读取写入数据 * CSV * [写入 CSV 文件 (opens new window)](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-store-in-csv)。 ``` In [143]: df.to_csv('foo.csv') ``` * * 读取 CSV 文件数据： ``` In [144]: pd.read_csv('foo.csv') ``` * Excel * 详见 [Excel (opens new window)](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-excel)文档。 * 写入 Excel 文件： ``` In [147]: df.to_excel('foo.xlsx', sheet_name='Sheet1') ``` * * 读取 Excel 文件： ``` In [148]: pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA']) ``` ### Pandas 数据结构 #### 1. Series Series 是一种一维数组，和 NumPy 里的数组很相似。事实上，Series 基本上就是基于 NumPy 的数组对象来的。和 NumPy 的数组不同，Series 能为数据自定义标签，也就是索引（index），然后通过索引来访问数组中的数据。 ``` import numpy as np import pandas as pd ``` 创建一个 Series 的基本语法如下： ``` my_series = pd.Series(data, index) ``` #### 2. DataFrames Pandas 的 DataFrame（数据表）是一种 2 维数据结构，数据以表格的形式存储，分成若干行和列。通过 DataFrame，你能很方便地处理数据。常见的操作比如选取、替换行或列的数据，还能重组数据表、修改索引、多重筛选等。 构建一个 DataFrame 对象的基本语法如下： ``` pd.DataFrame(data,index) ``` ``` df = pd.DataFrame(data,index) ``` ### 可视化 详见[可视化 (opens new window)](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html#visualization)文档。 ``` In [135]: ts = pd.Series(np.random.randn(1000), .....: index=pd.date_range('1/1/2000', periods=1000)) .....: In [136]: ts = ts.cumsum() In [137]: ts.plot() Out[137]: ``` ![可视化](https://static.pypandas.cn/public/static/images/series_plot_basic.png) DataFrame 的 [plot() (opens new window)](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html#visualization)方法可以快速绘制所有带标签的列： ``` In [138]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, .....: columns=['A', 'B', 'C', 'D']) .....: In [139]: df = df.cumsum() In [140]: plt.figure() Out[140]:
In [141]: df.plot() Out[141]: In [142]: plt.legend(loc='best') Out[142]: ``` ![可视化2](https://static.pypandas.cn/public/static/images/frame_plot_basic.png) ## [【美国微软大神的数据分析课】Pandas vs Excel](https://www.bilibili.com/video/BV1rt411y7PY) 🚩 ## [python3带你玩转excel--工作党福利--（评论区附有课件和代码）](https://www.bilibili.com/video/BV1W7411G7wP)🚩 ## [Excel动态仪表盘制作流程，非常详细【完结】](https://www.bilibili.com/video/BV1ov41147UG)🚩 ## [python实现统计学包Scipy所有语法](https://www.bilibili.com/read/cv12026943) 🚩 ## [python 正态分布\_python绘制正态分布及三大抽样分布的概率密度图像](https://blog.csdn.net/weixin_39725403/article/details/111293843) ## [太玄的工具库0X04 NumPy SciPy](https://www.bilibili.com/read/cv115510) 🚩 ## [【2021新版】Python入门编程100例，用小例子提升实战技能！](https://www.bilibili.com/video/BV1of4y1a7pz) [python3带你玩转excel--工作党福利--（评论区附有课件和代码）](https://www.bilibili.com/video/BV1W7411G7wP)\\ | | | | | -------------------- | ---------------------------- | -------------------------------------------------------------------------------------------------------------- | | numpy |

np.array
# ndarray

| NumPy 提供了很多[统计函数](https://www.runoob.com/numpy/numpy-statistical-functions.html)，用于从数组中查找最小元素，最大元素，百分位标准差和方差等。 | | scipy | | scipy.stats | | pandas | Series,DataFrame | excel,csv | | matplotlib | | | | tensorflow | | keras | | | | | | IPython | | | | Spyder | | | | Jupyter Notebook/Lab | | | | PyCharm | | | [SciPy - Stats\_学习Scipy|WIKI教程](https://iowiki.com/scipy/scipy_stats.html) [Scipy统计函数- Scipy教程™ - 易百教程](https://www.yiibai.com/scipy/scipy_stats.html) 🚩 [NumPy Ndarray 对象| 菜鸟教程](https://www.runoob.com/numpy/numpy-ndarray-object.html) [Scipy统计函数- Scipy教程教学| 程式教程网- 亿聚网](https://www.1ju.org/scipy/scipy-stats) ## [1.5 ScSciPy Lecture Notes 中文版 🚩](https://wizardforcel.gitbooks.io/scipy-lecture-notes/content/4.html) * [scipy：高级科学计算| SciPy Lecture Notes 中文版 🚩](https://wizardforcel.gitbooks.io/scipy-lecture-notes/content/4.html) ## 1.5 Scipy：高级科学计算 作者：Adrien Chauve, Andre Espaze, Emmanuelle Gouillart, Gaël Varoquaux, Ralf Gommers **Scipy** `scipy`包包含许多专注于科学计算中的常见问题的工具箱。它的子模块对应于不同的应用，比如插值、积分、优化、图像处理、统计和特殊功能等。 `scipy`可以与其他标准科学计算包相对比，比如GSL (C和C++的GNU科学计算包), 或者Matlab的工具箱。`scipy`是Python中科学程序的核心程序包；这意味着有效的操作`numpy`数组，因此，numpy和scipy可以一起工作。 在实现一个程序前，有必要确认一下需要的数据处理时候已经在scipy中实现。作为非专业程序员，科学家通常倾向于**重新发明轮子**，这产生了小玩具、不优化、很难分享以及不可以维护的代码。相反，scipy的程序是优化并且测试过的，因此应该尽可能使用。 **警告** 这个教程根本不是数值计算的介绍。因为列举scipy的不同子模块和功能将会是非常枯燥的，相反我们将聚焦于列出一些例子，给出如何用scipy进行科学计算的大概思路。 scipy是由针对特定任务的子模块组成的： | | | | --------------------------------------------------------------------------------------------------- | ------------- | | [`scipy.cluster`](http://docs.scipy.org/doc/scipy/reference/cluster.html#scipy.cluster) | 向量计算 / Kmeans | | [`scipy.constants`](http://docs.scipy.org/doc/scipy/reference/constants.html#scipy.constants) | 物理和数学常量 | | [`scipy.fftpack`](http://docs.scipy.org/doc/scipy/reference/fftpack.html#scipy.fftpack) | 傅里叶变换 | | [`scipy.integrate`](http://docs.scipy.org/doc/scipy/reference/integrate.html#scipy.integrate) | 积分程序 | | [`scipy.interpolate`](http://docs.scipy.org/doc/scipy/reference/interpolate.html#scipy.interpolate) | 插值 | | [`scipy.io`](http://docs.scipy.org/doc/scipy/reference/io.html#scipy.io) | 数据输入和输出 | | [`scipy.linalg`](http://docs.scipy.org/doc/scipy/reference/linalg.html#scipy.linalg) | 线性代数程序 | | [`scipy.ndimage`](http://docs.scipy.org/doc/scipy/reference/ndimage.html#scipy.ndimage) | n-维图像包 | | [`scipy.odr`](http://docs.scipy.org/doc/scipy/reference/odr.html#scipy.odr) | 正交距离回归 | | [`scipy.optimize`](http://docs.scipy.org/doc/scipy/reference/optimize.html#scipy.optimize) | 优化 | | [`scipy.signal`](http://docs.scipy.org/doc/scipy/reference/signal.html#scipy.signal) | 信号处理 | | [`scipy.sparse`](http://docs.scipy.org/doc/scipy/reference/sparse.html#scipy.sparse) | 稀疏矩阵 | | [`scipy.spatial`](http://docs.scipy.org/doc/scipy/reference/spatial.html#scipy.spatial) | 空间数据结构和算法 | | [`scipy.special`](http://docs.scipy.org/doc/scipy/reference/special.html#scipy.special) | 一些特殊数学函数 | | [`scipy.stats`](http://docs.scipy.org/doc/scipy/reference/stats.html#scipy.stats) | 统计 | 他们全都依赖于[numpy](http://docs.scipy.org/doc/numpy/reference/index.html#numpy), 但是大多数是彼此独立的。导入Numpy和Scipy的标准方式： In \[1]: ``` import numpy as np from scipy import stats # 其他的子模块类似 ``` `scipy`的主要命名空间通常包含的函数其实是numpy（试一下`scipy.cos`其实是`np.cos`) 。这些函数的暴露只是因为历史原因；通常没有必要在你的代码中使用`import scipy`。 ![](https://content.gitbook.com/content/b7kgec2aOBgFdNaAaH2R/blobs/8iwnYYaLV1HtEYecyo5G/A3.png) ## [如何系统地学习Python 中 matplotlib, numpy, scipy, pandas？](https://www.zhihu.com/question/37180159/answer/304720890) {% hint style="info" %} * [【美国微软大神的数据分析课】Pandas vs Excel](https://www.bilibili.com/video/BV1rt411y7PY) 🚩 * [python3带你玩转excel--工作党福利--（评论区附有课件和代码）](https://www.bilibili.com/video/BV1W7411G7wP)🚩 * [如何系统地学习Python 中 matplotlib, numpy, scipy, pandas？](https://www.zhihu.com/question/37180159/answer/304720890)🚩 * \*\*\*\*[**机器学习之数据的偏态分布和数据的标准化**](https://blog.csdn.net/m0_37870649/article/details/80638179)**🚩** * [人工智能实践：Tensorflow笔记](https://www.icourse163.org/course/PKU-1002536002?tid=1452937471)🚩 * 源码下载链接： 提取码：mocm {% endhint %} ## 2021.7.13 ### columns 列 ### 连接 pandas.concat() pandas.concat()通常用来连接DataFrame对象。\ [pandas中concat()的用法](https://zhuanlan.zhihu.com/p/69224745) #### .reset\_index(drop=True) ### 索引 pandas (loc、iloc、ix) [pandas (loc、iloc、ix)的区别](https://www.cnblogs.com/keye/p/7825280.html)\ \*\*loc：\*\*通过行标签索引数据\ \*\*iloc：\*\*通过行号索引行数据\ \*\*ix：\*\*通过行标签或行号索引数据（基于loc和iloc的混合） ### 删除 pandas.drop(columns=\_\_\_\_\_) ### 插入 pandas.insert(1,columns=\_\_\_\_\_,value=\_\_\_\_\_) ### 改名 pandas.rename(字典) ### line 行 ### 追加.append #### .reset\_index(drop=True) ### .at ### 删除行.index ### .concat(),.iloc(),.drop(),.insert(),.rename(),.append(),.reset\_index(),.at(),.index() [原创 PANDAS 数据合并与重塑（concat篇）](https://blog.csdn.net/stevenkwong/article/details/52528616) [原创 PANDAS 数据合并与重塑（join/merge篇）](https://blog.csdn.net/stevenkwong/article/details/52540605) [原创 pandas笔记 基础／groupby／concat／merge／join](https://blog.csdn.net/sinat_27400761/article/details/79647448) [原创 Series 创建、索引、删除、数学运算 归纳总结](https://blog.csdn.net/Krinys/article/details/105668376) [数据分析之Pandas（四）DataFrame运算](https://blog.csdn.net/qq_42642945/article/details/88636151) [pandas笔记（7）DataFrame数学运算](https://blog.csdn.net/weixin_44830542/article/details/108248919) ## 三维散点图 [Python绘制三维散点图](https://blog.csdn.net/qq_29066329/article/details/89916973) [matplotlib绘制三维散点图](https://blog.csdn.net/qq_41149269/article/details/81774026) ## 二维散点图（高级） [厉害了，Matplotlib还能这样画散点图！](https://cloud.tencent.com/developer/article/1708487) ## 简单图形绘制 [Python Pandas 图形绘制（一）：折线图，柱状图](https://blog.csdn.net/qq_42067550/article/details/106366432) [matplotlib绘制直方图、条形图和饼图](https://blog.csdn.net/hohaizx/article/details/79101322) [68–95–99.7原则](https://zh.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7%E5%8E%9F%E5%89%87) [Python求均值，方差，标准差](https://blog.csdn.net/qq_38826019/article/details/82875407) [pandas数据合并之一文弄懂pd.concat（）](https://zhuanlan.zhihu.com/p/132593960) [pandas-DataFrame列移动](https://blog.csdn.net/sinat_41701878/article/details/80945861) [Python-pandas的fillna()方法-填充空值](https://blog.csdn.net/qq_17753903/article/details/89892631) [Pandas中把dataframe和np.array的相互转换](https://blog.csdn.net/weixin_39223665/article/details/79935467) [将Pandas中的DataFrame转换成Numpy中数组](https://blog.csdn.net/weixin_42263508/article/details/90487483) [pandas将字段中的字符类型转化为时间类型，并设置为索引](https://blog.csdn.net/onemorepoint/article/details/86174624) [pandas设置index索引的标签名](https://blog.csdn.net/RogerFedereYY/article/details/109121721) [高效的5个pandas函数，你都用过吗？](https://zhuanlan.zhihu.com/p/258699675) ## [Pandas 窗口函数](https://geek-docs.com/pandas/pandas-tutorials/pandas-window-function.html) [PythonPandas-根据给定的窗口并从某个值开始计算某一列的和。](https://cloud.tencent.com/developer/ask/189430) [如何更快计算Dataframe里某一列中，上下两值的差值](https://blog.csdn.net/C10997/article/details/109526567) [pandas应用——滚动计算、筛选、判断空值、列移动、行列删除](https://blog.csdn.net/lamusique/article/details/95099965) [python数据处理——同一行或同一列的错位相减法](https://blog.csdn.net/m0_37876745/article/details/85163883) [【python】pandas库常用函数之shift详解](https://blog.csdn.net/brucewong0516/article/details/80157639) [pandas选择单元格，选择行列](https://www.cnblogs.com/guxh/p/8627251.html) 🚩 [数据可视化—使用matplotlib绘制常用类型图表](https://zhuanlan.zhihu.com/p/357477796) [matplotlib画3D图形时设置z轴尺寸](https://blog.csdn.net/qq_41009742/article/details/106292396) [np.log 和math.log的底数是什么，默认都是e](https://www.cnblogs.com/cgmcoding/p/13640281.html) ## 已经实现部分代码并开源 {% embed url="" %} {% embed url="" %} {% content-ref url="" %} [](https://www.bfm-unity.org/bfm-rins.-wei-mian-jian-meng-ke-xue-yan-jiu-yuan/shu-ju-zhi-neng-bu/lv6-xing-mo-fang-fen-lei) {% endcontent-ref %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://www.bfm-unity.org/bfm-rins.-wei-mian-jian-meng-ke-xue-yan-jiu-yuan/shu-ju-zhi-neng-bu/lv6-xing-mo-fang-fen-lei/yu-zhou-mo-fang-hui-gui-fen-xi.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.