data analysis in python with pandas pdf

One, NumPy: Array calculation1. NumPy is a basic package for high performance scientific computing and data analysis. It is the basis of various other tools such as pandas.2, the main functions of NumPy:# Ndarray, a multidimensional array structure, efficient and space-saving # mathematical functions that do not require a loop to perform fast operations on an ent

said that the interactive way right-click and hold the date will be dynamically expanded or shrunk, actually do it, no effect ...plt.show ()>>>AA AAPL GE IBM JNJ MSFT PEP SPX XOM1990-02-01 4.98 7.86 2.87 16.79 4.27 0.51 6.04 328.79 6.121990-02-02 5.04 8.00 2.87 16.89 4.37 0.51 6.09 330.92 6.241990-02-05 5.07 8.18 2.87 17.32 4.34 0.51 6.05 331.85 6.251990-02-06 5.01 8.12 2.88 17.56 4.32 0.51 6.15 329.66 6.231990-02-07 5.04 7.77 2.91 17.93 4.38 0.51 6.17 333.75 6.33AAPL MSFT XOM1990-02-01 7.86 0

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

(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

When we are dealing with a lot of data, we have to use the concept of time. such as timestamps, fixed periods, or time intervals. Pandas provides a standard set of time-series processing tools and data algorithms. The datetime.datetime module is the most used module in Python. Using datetime.datetime.now () , for exa

last=20 multiplier=0.1station_id=1 All station [id=1 name=de Bilt, Id=2name=utrecht] All sensor [id=1 last=20 multiplier=0.1station_id=1] Query sensor by station Id=1 last=20multiplier=0.1 station_id=1 Read_sql All station ID name 0 1 De bilt 1 2 Utrecht [Winerror 32] Another program is using this file and the process is inaccessible. : ' Demo.db ' 4 Pony ORM The ORM package written in Python Database, db_session to_sqlsm # Create SQLite db = Dat

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

First, Brief introduction matplotlib1. Matplotlib is a powerful toolkit for Python drawing and data visualization2. Installation method: Pip Install Matplotlib3. Citation method: Import Matplotlib.pyplot as Plt4. Drawing function: Plt.plot ()5. Display Image: Plt.show () --Linetype LineStyle (-,-.,--,.. ) -colour color (b,g,r,y,k,w,... 2, plot function draw multiple curves 3,

This article records some of the knowledge that appears in the book, convenient to use when the query. Implied volatility rate The implied volatility is the value of those fluctuations in the price of different options and the market quotations measured on the maturity date under other conditions unchanged.In this case, the implied volatility is not the input parameter of the model/formula, but the result of a digital optimization process of the Formula 4.1 basic

NumPy modules can efficiently process data, provide array support, and many modules rely on him, such as: Pandas, SciPy, matplotlibInstalling NumPyFirst to the website: https://www.lfd.uci.edu/~gohlke/pythonlibs/Find NUMPY+MKL My Python version is 3.6.1, the system is 64-bit So the package that corresponds to the download is: After downloading the package, go t

areas of the drawing method (one is using the above column chart that way Fig,ax = Plt.subplots), the other is the following, this can be customized to occupy the number of spaces)Fig = plt.figure () Ax1 = Plt.subplot2grid ((2,3), (0,0)) Ax1.bar (data_bar.index,data_bar.values) fig.set_size_inches ( 12,6) Ax2 = Plt.subplot2grid ((2,3), (0,1), colspan=2) #占据几个空额, can also be rowspan, one is horizontal, one is vertical ax2.scatter (data[' Tip '],

This time will be the next issue of SHUANGSE Qiu number forecast, think of a little excitement ah. The code uses the linear regression algorithm, which uses this algorithm to predict the effect, and you can consider using other algorithms to try the results. Before discovering a lot of code is repetitive work, in order to make the code look more elegant, define the function, to call, suddenly tall #!/usr/bin/python #-*-Coding:utf-8-*-#导入需要的包 Import

In computer science, algorithmic analysis (analyst ofalgorithm) is the process of analyzing the amount of computing resources (such as compute time, memory usage, etc.) that are consumed by executing a given algorithm. The efficiency or complexity of an algorithm is theoretically represented as a function. The defined field is the length of the input data, which is usually the number of steps (time complexi

Pandas Foundation Stream Processing Stream processing, sounds very tall, ah, in fact, is the block read. There are so many cases, there is a very large number of G files, no way to deal with, then the batch processing, processing 1 million lines at a time, and then deal with the next 1 million lines, slowly always can be processed. # using a similar iterator approach data=pd.read_csv (file, chunksize=1000

First, Ipython basic functions1. Ipython is an interactive python command line2. Installation and use1 # Installation: Pip install Ipython 2 # use: Ipython is consistent with Python interpreter usageNote: Readers who want to learn machine learning are strongly advised to install Anaconda (including NumPy, pandas, etc.)Second, Ipython advanced features1. Basic use

This article is all from my (wheat) "Big Data Public" course handout, including three Python and numpy data analysis package related tutorials, Excel and SPSS data Analysis tutorial, etc., the author is wheat and Yi Wen classmate,

The procedure of the fourth chapter of data analysis using Python introduces the basic use method of NumPy. (chapter III is the basic use of Ipython)Scientific calculations, common functions, array processing, linear algebra operations, random modules ...#-*-Coding:utf-8-*-# Python for

Convert Timestamp to PeriodBy using the to_period method, the Series and DataFrame objects indexed by the timestamp can be converted to a time -indexedRng=pd.date_range (' 1/1/2000 ', periods=3,freq= ' M ')Ts=series (RANDN (3), index=rng)Print (TS)Pts2=ts.to_period (freq= ' M ')Print (PTS2)The results are as follows:TS is the date of the last day of each month,pts2 is the day of the month cycle2000-01-31 on 0.9900972000-02-29 on 0.4397612000-03-31-3.395317Freq:m, Dtype:float642000-01 0.990097200

Za003-python data analysis and machine learning Combat (Tang Yudi)The beginning of the new year, learning to be early, drip records, learning is progress!Do not look everywhere, seize the promotion of their own.For learning difficulties do not know how to improve themselves can be added: 1225462853 get information.Za003-pytho