python data analysis coursera

The python language has been increasingly liked and used by program stakeholders in recent years, as it is not only easy to learn and master, but also has a wealth of third-party libraries and appropriate management tools; from the command line script to the GUI program, from B/S to C, from graphic technology to scientific computing, Software development to automated testing, from cloud computing to virtualization, all these areas have

graphs, but the results can be further processed to obtain more detailed results. Each data also has an agent value, that is, the browser's user_agent information, through this information to know the operating system used,so the statistical results generated in the previous step can also be differentiated by operating system differences. Agent value: v. To distinguish a bar chart from an operating system (windows/non-Windows) Not all

is not only easy to learn and master, but also has a wealth of third-party libraries and appropriate management tools; from the command line script to the GUI program, from B/S to C, from graphic technology to scientific computing, Software development to automated testing, from cloud computing to virtualization, all these areas have python, Python has gone deep into all areas of program development, and w

Python data analysis-two-color ball-based linear regression algorithm to predict the next winning results example, python winning results This article describes how to use a two-color ball in Python data

Since 2005, Python has been used more and more in the financial industry, thanks to increasingly sophisticated libraries (numpy and pandas) and a wealth of experienced programmers. Many organizations find that Python is not only a great fit for an interactive analysis environment, but also a very useful system for developing files, which takes much less time than

','W') as F:writer= Csv.writer (F,lineterminator ='\ n') Writer.writerow (' One',' Both','three')) Writer.writerow ('1','2','3'))JSON dataIn addition to the null value null and some other nuances (such as the absence of extra commas at the end of the list), JSON is very close to the valid Python code. Basic data types have objects (dictionaries), arrays (lists), strings, numeric values, Booleans, and null.

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 ({'

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

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;

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

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.

=[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