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Python data visualization normal distribution simple analysis and implementation code, python Visualization
Python is simple but not simple, especially when combined with high numbers...
Normaldistribution, also known as "Normal Distribution", also known as Gaussiandistribut
functions of read_csv and read_table are as follows:Read a text file by blockWhen working with very large files, or finding the set of parameters in a large file for subsequent processing, you only need to read a small part of the file or iterate over the file by block.Reading a few lines requires setting the nrows parameter, where the nrows subscript is starting from 0. So nrows=2 represents the first 3 lines. in [+]: result=pd.read_csv ('/home/zhf/1.csv ', nrows=2)in [+]: ResultOUT[20]:1 2 3
#-*-Coding:utf-8-*-# The Nineth chapter of Python for data analysis# Data aggregation and grouping operationsImport Pandas as PDImport NumPy as NPImport time# Group operation Process, Split-apply-combine# Split App MergeStart = Time.time ()Np.random.seed (10)# 1, GroupBy technology# 1.1, citationsDF = PD. DataFrame ({'
DirectoryPreface 1Chapter 1th Preparation of work 5Main contents of this book 5Why use Python for data analysis 6Important Python Library 7Setup and Setup 10Communities and Seminars 16Using this book 16Acknowledgements 18Chapter 2nd Introduction 201.usa.gov data from bit.ly
(Np.mean (A)) -7.5Wuyi Print(Np.average (A)) the7.5 - Print(A.mean ()) Wu7.5# cumsum Iteration Add the A -Out[24]: inArray ([[[2, 3, 4, 5], the[6, 7, 8, 9], the[10, 11, 12, 13]])Bayi Print(A.cumsum ()) the[2 5 9 14 20 27 35 44 54 65 77 90] the A -Out[27]: -Array ([[[2, 3, 4, 5], the[6, 7, 8, 9], the[10, 11, 12, 13]])# Clip (A, a_min, A_max) will determine the data in the Ndarray, the value of less than A_min is assigned to A_min, is greater than the
Using Python for data analysis (12) pandas basics: data merging and pythonpandas Pandas provides three main methods to merge data:
Pandas. merge () method: database-style merge;
Pandas. concat () method: axial join, that is, stacking multiple objects along one axis;
SummaryIntroductionResearch background and research status of the projectBackground and purpose of the project Research status meaning Main work Project arrangement Development tools and their development environmentDemand Analysis and Design Functional AnalysisCrawler page CrawlCrawler page ProcessingCrawler function implementationCrawler SummaryPython Programming Course report the application of Python te
Using Python for data analysis (10) pandas basics: processing missing data, pythonpandasIncomplete Data is common in data analysis. Pandas uses the floating-point value NaN to indicate
A lightweight web framework for the Flask:python system.1. Web Crawler toolset
Scrapy
Recommended Daniel Pluskid an early article: "Scrapy easy to customize web crawler"
Beautiful Soup
Objectively speaking, Beautifu soup is not entirely a set of crawler tools, need to cooperate with urllib use, but a set of html/xml data analysis, cleaning and acquisition tools.
values appearDf.boxplot (column= ' label 1 ', by = ' Label 2 ')Plt.show ()The data under label 1 can then be plotted in a numerical distribution according to label 2As indicated below, it has been classified according to the level of education, high-level wage extremes, and other conclusions can be obtainedNote: When you want to paint, the individual input drawing instructions can not display graphics, then you need to enter Plt.show () on another li
=[np.sum]) pd.pivot_tabl E (data = Pokemon, index= ' Type 1 ', columns= ' Type 2 ', values=[' HP ', ' Total '],aggfunc=[np.sum,np.mean])Interaction table:Calculation frequency:Pd.crosstab (index = pokemon[' type 1 '],columns= pokemon[' Type 2 ']) pd.crosstab (index = pokemon[' type 1 '],columns= Pokemon [' Type 2 '], margins=true) # margins Show Total frequencyDummy variablesNo meaningful category, no data
RT reply: I strongly recommend the python course at rice University. The course is well designed and the teacher is very responsible.
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Answer questions by phone last night. Update the questions today;
There are a total of three courses at Rice University, which now seems to have been divided into six. Each course lasts for 8 weeks in a simple order.
The first course is the basics of
[Python Data analysis notes-data loading and finishinghttps://mp.weixin.qq.com/s?__biz=MjM5MDM3Nzg0NA==mid=2651588899idx=4sn= bf74cbf3cd26f434b73a581b6b96d9acchksm= bdbd1b388aca922ee87842d4444e8b6364de4f5e173cb805195a54f9ee073c6f5cb17724c363mpshare=1scene=1 srcid=0214nftjpp2oedvrgrjis3mxpass_ticket=fm74de5nrjn2tpc44mn3
Using Python for data analysis (13) pandas basics: Data remodeling/axial rotation, pythonpandas Remodeling DefinitionRemodeling refers to re-arranging data, also called axial rotation.DataFrame provides two methods:
Stack: rotate the column of
-----15:18 2016/10/14-----1.Import NumPy as Np;import pandas as Pdvalues = PD. Series (Np.random.normal (0,1,size=2000))#Series可看作一个定长的有序字典.The probability density function corresponding to the Gaussian distribution corresponds to the numpy:Np.random.normal (Loc=mu, Scale=sigma, Size=non) standard normal distribution (mu=0,sigma=1) np.random.normal (loc=0, scale=1, Size=non) Values.hist (bins=100, alpha=0.3, color= ' K ', normed= True) #bins interval number alpha Transparency normed=true paramet
3. Data Conversion After the reflow of the data is introduced, the following describes the filtering, cleanup, and other conversion work for the data.
Go heavy
#-*-encoding:utf-8-*-ImportNumPy as NPImportPandas as PDImportMatplotlib.pyplot as Plt fromPandasImportSeries,dataframe#Dataframe to Heavydata = DataFrame ({'K1':[' One']*3 + [' Both'] * 4,
data conversion refers to filtering, cleaning, and other conversion operations on the data. Remove Duplicate data Repeating rows often appear in the Dataframe, Dataframe provides a duplicated () method to detect whether rows are duplicated, and another drop_duplicates () method to discard duplicate rows:Duplicated () and Drop_duplicates () methods defaultJudgi
Python is a common tool for data processing, can handle the order of magnitude from a few k to several T data, with high development efficiency and maintainability, but also has a strong commonality and cross-platform, here for you to share a few good data analysis tools, th
Rt
Reply content:I highly recommend the Python class at Rice University, which is very well designed and the teacher is very responsible.
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Last night mobile phone answer, updated today;
Rice University has a total of 3 courses, now seemingly dismantled into 6 doors, 8 weeks per course, according to the order of the more-than-digest.
The first course is the
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 (
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