best python book for data analysis

1. Read and write data in text formatPandas provides some functions for reading tabular data as dataframe objects.File import, using Read_csv to import data into a dataframedf= pd.read_csv ('b:/test/ch06/ex1.csv') dfout[142]: a B c D message0 1 2 3 4 hello1 5 6 7 8 world2 9 ten foo Read_table, just need to make a delimiterDF = pd.read_table (

('key1'). STD () # also has count (), sum (), mean (), median () Std,var, Min,max,prod,first,last#可以自定义函数Df.groupby (' Key1 '). Agg ([Lambda X:x.max ()-x.min (), NP.MEAN,NP.STD])# You can customize the function df.groupby ('key1'). Agg ([' Custom Function ', Lambda X:x.max ()-x.min ()), (' mean ', Np.mean), (' standard deviation ') , NP.STD)])#不同列做不同的动作, one takes the maximum value, one takes the minimum valueDf.groupby (' Key1 '). Agg ({' data1 ': Np.max, ' data2 ': np.min})Df.groupby (' Key

resample: resampling function that can increase or decrease the sampling frequency by time, Fill_method can use different filling methods.Freq parameter enumeration for Pandas.data_range: Alias Description B Business Day Frequency C Custom Business Day Frequency D Calendar Day Frequency W Weekly frequency M Month End Frequency Sm Semi-month End Frequency (1

','a','b','a'],'data1': Range (6)}) DF2=PD. DataFrame ({'Key':['a','a','C','b','D'],'data2': Range (5)}) Pd.merge (Df1,df2,on='Key', how=' Right') back to key data1 data20B0.0 31B2.0 32B4.0 33C1.0 24A3.0 05A5.0 06A3.0 17A5.0 18D NaN4Many-to-many merges produce a Cartesian product of rows, that is, DF1 has 2 a,df2 with 2 A, and rallies produce 4 aWhen you need to merge from multiple keys, simply pass in a list of column names.When merging operations, you need to handle dup

developers, data scientists, and statisticians. There are many tools to assist in big data analysis, but the most popular one is Python. Why Python? Python is easy to use. This language has an intuitive syntax and is also a power

[Python Data Analysis] Python3 multi-thread concurrent web crawler-taking Douban library Top250 as an example, python3top250 Based on the work of the last two articles [Python Data Analysis] Python3 Excel operation-Take Douban lib

packages are written by the R language, LaTeX, Java, and the most commonly used C language and Fortran. The version of the executable that you download will be accompanied by a batch of core features, and there are thousands of different packages based on the Cran record. Several of them are more commonly used, such as economic metrology, financial analysis, humanities research, and artificial intelligence. The common features of

I usually like reading, so I made a catalogue of books, and recorded the list of books I read:This is a XSLX file. The following code, query each of the above books, and download the book cover. What needs to be stated are:1. Query the platform of the book is a watercress reading2. The Chinese name of the book is embedded directly in the request link, because it

the internal relationship of data. The interactive GUI is a good choice for interactive support.MayaviThis is a 3D graphics toolkit based on the open source C + + graphics library VTK. can be integrated into Ipython for interactive use.Other librariesOther libraries or applications include: PYQWT, Veusz, Gnuplotpy, Biggles, and so on, and large libraries are developing to web-based technologies and moving away from desktop graphics technology.The fut

It is no exaggeration to say that big data has become an integral part of any business communication. Desktop and mobile search provides data to marketers and companies around the world at an unprecedented scale, and with the advent of the internet of things, large amounts of data for consumption will grow exponentially. This consumer

Download address: Network disk download Introduction to the content More than 10 data mining senior experts and researchers, more than 10 years of large data mining consulting and implementation experience crystallization. From the application of data mining, based on the real cases of power, aviation, medical, Internet, manufacturing and public service, th

Recently, analysis and programming joined Planet Python. As the first of its special blogs, I'm here to share how to start data analysis through Python. The specific contents are as follows: Data importImport a local or web-side

A lot of programming in data analysis and modeling is used for data preparation: onboarding, cleanup, transformation, and remodeling. Sometimes, the data stored in a file or database does not meet the requirements of your data processing application. Many people choose to sp

More recently, analysis with programming joined Planet Python. As the first special blog of the site, I'll share how to start data analysis with Python. The specific contents are as follows: Data importImport a local or web-side

Download address: Network disk download Book Introduction the data analysis tools from the Pandas Library start using high-performance tools to load, clean, transform, merge, and reshape data, using matpiotlib to create scatter graphs and static or interactive visualization results Using Pandas's groupby funct

Where is the Python address book and foxmail address book? 1 #! /Usr/bin/python 2 # coding = UTF-8 3 # FileName: address. py 4 # Python Address Book 5 import cPickle as p; 6 import OS; 7 import sys; 8 class Address: 9 '''

1 Content IntroductionFirst, through the crawler to collect all the online housing data of Nanjing, and the data collected to clean; then, after the cleaning of the data for visual analysis, explore hidden in a large number of data behind the law; Finally, a clustering algor

Reference book "Technical analysis using Python: Python for Data analyses"The official upgraded the EPD (https://www.enthought.com/products/canopy/package-index/) to Canopy (https://www.enthought.com/products/ canopy/package-index/), in order to be as consistent with the

Introduction: Python is a popular scripting language that provides a science and technology stack for fast and easy data analysis, and this series focuses on how to use the Python-based technology stack to build a collection of tools for data

│?? │?? ├ class 162. Data reading and preprocessing. flv_d.flv│?? │?? ├ class 163. Data segmentation module. flv_d.flv│?? │?? ├ lesson 164. Visual analysis of missing values. flv_d.flv│?? │?? ├ class 165. Feature visualization display. flv_d.flv│?? │?? ├ class 166. Analysis of relationships among multiple features. flv