登录
首页 » Python » python数据分析 韩波

python数据分析 韩波

于 2018-09-08 发布 文件大小:171KB
0 327
下载积分: 1 下载次数: 8

代码说明:

  一本python数据分析的优秀资料 《python数据分析》(python data analysis),作者【印尼】Ivan Idris,翻译:韩波。 本人制作的PDF图书,带目录和书签。 作为一种高级程序设计语言,Python凭借其简洁、易读及可扩展性日渐成为程序设计领域备受推崇的语言。同时,Python语言的数据分析功能也逐渐为大众所认可。, 本书是一本介绍如何用Python进行数据分析的学习指南。全书共12章,从Python程序库入门、NumPy数组、matplotlib和pandas开始,陆续介绍了数据加工、数据处理和数据可视化等内容。同时,本书还介绍了信号处理、数据库、文本分析、机器学习、互操作性和性能优化等高级主题。在本书的结尾,还采用3个附录的形式为读者补充了一些重要概念、常用函数以及在线资源等重要内容。, 本书示例丰富、简单易懂,非常适合对Python语言感兴趣或者想要使用Python语言进行数据分析的读者参考阅读。(python data analysis)

文件列表:

3358OS_Code, 0 , 2014-10-24
3358OS_Code\3358OS_01_Code, 0 , 2014-10-24
3358OS_Code\3358OS_01_Code\code1, 0 , 2014-10-24
3358OS_Code\3358OS_01_Code\code1\vectorsum.py, 1148 , 2014-05-04
3358OS_Code\3358OS_02_Code, 0 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code, 0 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code\code2, 0 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code\code2\arrayattributes.py, 587 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code\code2\arrayattributes2.py, 2016 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code\code2\arrayconversion.py, 1264 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code\code2\boolean_indexing.py, 545 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code\code2\broadcasting.py, 731 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code\code2\charcodes.py, 399 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code\code2\copy_view.py, 386 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code\code2\dtypeattributes.py, 344 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code\code2\dtypeattributes2.py, 340 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code\code2\dtypeconstructors.py, 488 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code\code2\elementselection.py, 396 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code\code2\fancy.py, 503 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code\code2\ix.py, 479 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code\code2\numericaltypes.py, 772 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code\code2\shapemanipulation.py, 1350 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code\code2\slicing1d.py, 599 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code\code2\splitting.py, 1707 , 2014-10-24
3358OS_Code\3358OS_02_Code\3358OS_02_Code\code2\stacking.py, 2817 , 2014-10-24
3358OS_Code\3358OS_03_Code, 0 , 2014-10-24
3358OS_Code\3358OS_03_Code\3358OS_03_Code, 0 , 2014-10-24
3358OS_Code\3358OS_03_Code\3358OS_03_Code\basic_stats.py, 517 , 2014-05-10
3358OS_Code\3358OS_03_Code\3358OS_03_Code\eigenvalues.py, 384 , 2014-05-11
3358OS_Code\3358OS_03_Code\3358OS_03_Code\goog_flutrends.csv, 7549 , 2014-05-11
3358OS_Code\3358OS_03_Code\3358OS_03_Code\headortail.py, 441 , 2014-09-09
3358OS_Code\3358OS_03_Code\3358OS_03_Code\inversion.py, 201 , 2014-09-09
3358OS_Code\3358OS_03_Code\3358OS_03_Code\masked.py, 589 , 2014-10-15
3358OS_Code\3358OS_03_Code\3358OS_03_Code\masked_funcs.py, 778 , 2014-09-09
3358OS_Code\3358OS_03_Code\3358OS_03_Code\mdrtb_2012.csv, 3337 , 2014-05-10
3358OS_Code\3358OS_03_Code\3358OS_03_Code\MLB2008.csv, 7136 , 2014-05-11
3358OS_Code\3358OS_03_Code\3358OS_03_Code\normaldist.py, 304 , 2014-05-11
3358OS_Code\3358OS_03_Code\3358OS_03_Code\normality_test.py, 806 , 2014-05-11
3358OS_Code\3358OS_03_Code\3358OS_03_Code\pkg_check.py, 839 , 2014-05-11
3358OS_Code\3358OS_03_Code\3358OS_03_Code\solution.py, 190 , 2014-05-11
3358OS_Code\3358OS_04_Code, 0 , 2014-10-24
3358OS_Code\3358OS_04_Code\code4, 0 , 2014-10-24
3358OS_Code\3358OS_04_Code\code4\data_aggregation.py, 843 , 2014-05-19
3358OS_Code\3358OS_04_Code\code4\date_handling.py, 757 , 2014-05-30
3358OS_Code\3358OS_04_Code\code4\dest.csv, 47 , 2014-05-19
3358OS_Code\3358OS_04_Code\code4\df_demo.py, 261 , 2014-05-18
3358OS_Code\3358OS_04_Code\code4\join_demo.py, 913 , 2014-05-20
3358OS_Code\3358OS_04_Code\code4\missing_values.py, 498 , 2014-05-21
3358OS_Code\3358OS_04_Code\code4\pivot_demo.py, 493 , 2014-06-11
3358OS_Code\3358OS_04_Code\code4\pkg_check.py, 664 , 2014-06-10
3358OS_Code\3358OS_04_Code\code4\price_straddle.py, 677 , 2014-06-10
3358OS_Code\3358OS_04_Code\code4\query_demo.py, 704 , 2014-05-19
3358OS_Code\3358OS_04_Code\code4\series_demo.py, 621 , 2014-05-18
3358OS_Code\3358OS_04_Code\code4\stats_demo.py, 553 , 2014-05-19
3358OS_Code\3358OS_04_Code\code4\tips.csv, 28 , 2014-05-19
3358OS_Code\3358OS_04_Code\code4\WHO_first9cols.csv, 7776 , 2014-05-18
3358OS_Code\3358OS_05_Code, 0 , 2014-10-24
3358OS_Code\3358OS_05_Code\code5, 0 , 2014-10-24
3358OS_Code\3358OS_05_Code\code5\binary_formats.py, 563 , 2014-05-31
3358OS_Code\3358OS_05_Code\code5\hf5storage.py, 491 , 2014-06-01
3358OS_Code\3358OS_05_Code\code5\json_demo.py, 381 , 2014-06-02
3358OS_Code\3358OS_05_Code\code5\loremIpsum.html, 3623 , 2014-06-07
3358OS_Code\3358OS_05_Code\code5\pd_hdf.py, 597 , 2014-06-01
3358OS_Code\3358OS_05_Code\code5\pd_json.py, 410 , 2014-06-03
3358OS_Code\3358OS_05_Code\code5\pd_xls.py, 320 , 2014-06-01
3358OS_Code\3358OS_05_Code\code5\rss.py, 239 , 2014-06-03
3358OS_Code\3358OS_05_Code\code5\soup_request.py, 1056 , 2014-06-07
3358OS_Code\3358OS_05_Code\code5\writing_csv.py, 277 , 2014-05-31
3358OS_Code\3358OS_06_Code, 0 , 2014-10-24
3358OS_Code\3358OS_06_Code\code6, 0 , 2014-10-24
3358OS_Code\3358OS_06_Code\code6\autocorr_plot.py, 450 , 2014-06-16
3358OS_Code\3358OS_06_Code\code6\basic_plot.py, 137 , 2014-06-14
3358OS_Code\3358OS_06_Code\code6\gpu_transcount.csv, 483 , 2014-06-15
3358OS_Code\3358OS_06_Code\code6\lag_plot.py, 426 , 2014-06-16
3358OS_Code\3358OS_06_Code\code6\legend_annotations.py, 1097 , 2014-06-15
3358OS_Code\3358OS_06_Code\code6\log_plots.py, 373 , 2014-06-14
3358OS_Code\3358OS_06_Code\code6\pd_plotting.py, 478 , 2014-06-15
3358OS_Code\3358OS_06_Code\code6\pkg_check.py, 679 , 2014-06-14
3358OS_Code\3358OS_06_Code\code6\plot_ly.py, 714 , 2014-06-16
3358OS_Code\3358OS_06_Code\code6\scatter_plot.py, 579 , 2014-06-15
3358OS_Code\3358OS_06_Code\code6\three_dimensional.py, 735 , 2014-06-15
3358OS_Code\3358OS_06_Code\code6\transcount.csv, 1123 , 2014-06-14
3358OS_Code\3358OS_06_Code\__MACOSX, 0 , 2014-10-24
3358OS_Code\3358OS_06_Code\__MACOSX\code6, 0 , 2014-10-24
3358OS_Code\3358OS_06_Code\__MACOSX\code6\._gpu_transcount.csv, 120 , 2014-06-15
3358OS_Code\3358OS_06_Code\__MACOSX\code6\._transcount.csv, 120 , 2014-06-14
3358OS_Code\3358OS_07_Code, 0 , 2014-10-24
3358OS_Code\3358OS_07_Code\code7, 0 , 2014-10-24
3358OS_Code\3358OS_07_Code\code7\ar.py, 1350 , 2014-07-05
3358OS_Code\3358OS_07_Code\code7\arma.py, 506 , 2014-07-05
3358OS_Code\3358OS_07_Code\code7\autocorrelation.py, 540 , 2014-07-05
3358OS_Code\3358OS_07_Code\code7\cointegration.py, 695 , 2014-07-04
3358OS_Code\3358OS_07_Code\code7\filtering.py, 609 , 2014-07-06
3358OS_Code\3358OS_07_Code\code7\fourier.py, 898 , 2014-07-06
3358OS_Code\3358OS_07_Code\code7\iterate.dat, 3525 , 2014-07-05
3358OS_Code\3358OS_07_Code\code7\moving_average.py, 461 , 2014-07-04
3358OS_Code\3358OS_07_Code\code7\periodic.py, 1479 , 2014-07-05
3358OS_Code\3358OS_07_Code\code7\pkg_check.py, 678 , 2014-07-03
3358OS_Code\3358OS_07_Code\code7\spectrum.py, 547 , 2014-07-06
3358OS_Code\3358OS_07_Code\code7\window_functions.py, 551 , 2014-07-04

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • 最近邻分类代码
    在linux 下C语言实现最近邻聚类算法,工程已经使用(near K neighbor cluster)
    2017-12-21 16:45:51下载
    积分:1
  • 用matlab 实现了kmeans算法
    用matlab 实现了kmeans算法还附有评价指标计算(Matlab to achieve kmeans algorithm also attached to the evaluation index calculation)
    2020-06-19 04:40:01下载
    积分:1
  • guanlianguize
    说明:  r语言中关联规则代码实现 运用arulesViz包和arules包中的apriori函数(Code Implementation of Association Rule)
    2019-01-24 15:39:51下载
    积分:1
  • 710776
    用C++实现各种排序算法:如冒泡排序,选择排序,插入排序,希尔排序,快速排序,归并排序,基数排序和堆排序,并带有源代码说明()
    2018-05-11 20:06:44下载
    积分:1
  • kasterenDataset
    主要针对行为识别,常用数据处理方法及分类,可视化 包含数据集(Mainly for behavior recognition, commonly used data processing methods and classification, visualization Contain data sets)
    2021-03-30 10:29:10下载
    积分:1
  • 技术在公安犯罪行为分析中的应用研究
    数据挖掘在经侦项目中的应用,本文用到python中的社区划分算法(In the application of data mining in economic investigation projects, this paper uses community partition algorithm in Python.)
    2020-07-03 08:00:02下载
    积分:1
  • boxcox
    boxcox函数的python实现,引用该函数可将偏态分布调整为正态分布(Python implementation of box Cox function)
    2020-06-17 09:40:01下载
    积分:1
  • 煤炭价格多元时序预测
    说明:  内附源数据、代码及word。代码包括:平稳性检验、协整检验、滞后阶数的确定、VAR 模型的拟合、脉冲响应分析、VAR 模型的预测(Stationarity test, co integration test, determination of lag order, VAR model fitting, impulse response analysis, VAR model prediction)
    2021-03-30 19:09:09下载
    积分:1
  • edge
    工程算法 这是一个很有用的工程数值算法集锦(Engineering algorithm this is a useful collection of engineering numerical algorithms.)
    2018-09-05 06:04:58下载
    积分:1
  • mlno
    一个833分酒问题的求解,C++编写,简单易读,输出最佳路径解,()
    2018-02-04 15:22:16下载
    积分:1
  • 696516资源总数
  • 106637会员总数
  • 8今日下载