udemy python data analysis

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 book as possible, only to other places to find o

It 's written in front . After learning the Python basics, start with this section to formally learn about data structure and algorithm related content. This is a more complex topic, generally divided into the primary, advanced, and specialized algorithm analysis three stages to learn, so we also need to be gradual. This section is mainly familiar with the gener

Base LibraryA data Analysis library for Pandas:python (pip install pandas)Seaborn: Data visualization (pip install Seaborn)SCIPY: Numerical calculation library (pip install scipy) SciPy (pronounced "sigh Pie") is an open source mathematical, scientific, and engineering computing package. It is a convenient, easy-to-use, scientific and Engineered

I. Introduction of IPython IPython is an interactive Python interpreter, and it's more efficient. It differs from most traditional working modes (edit-and-compile-run),The working mode it uses is: Execute-and explore, and most of the code related to data analysis contains exploratory operations (such as trial and error methods and iterative methods), so IPython

Returns a Series that contains only non-empty data and index valuesRemove the missing field first: Cframe=frame[frame.a.notnull ()]Second, it calculates whether the rows are Windows based on the value of a, #np. The WHERE function is a vectorization ifelse functionOperating_system=np.where (cframe[' a '].str.contains (' windows '), ' windows ', ' no windows ')Next, the data is grouped according to the time

# mean averaging # std standard deviation # var asks for variance # min to find minimum # Max to find maximum value # argmin Minimum index # argmax Max indexXi. NumPy: Random number generationRandom number generation function within the Np.random sub-packageCommon functions: # Rand Given shape produces a random array (number between 0 and 1)# randint a given shape produces a random integer # Choice The given shape produces a random selection # Shuffle

May 15, 2017, the Python and r Data Mining analysis technology training starts in Shanghai.This training was attended by system architects, system analysts, senior programmers, senior developers, and heads of big data source units from various enterprises.650) this.width=650; "Src=" https://s5.51cto.com/wyfs02/M01/95/D

This paper illustrates the data transmission UDP implementation method of Python network programming. Share to everyone for your reference. The specific analysis is as follows: First, the question: Do you think that tools like msn,qq on the Web transmit data mysteriously between machines? You want to play a little bi

function can draw histograms directly.Call Mode: 1 N, bins, patches = plt.hist (arr, bins=10, normed=0, facecolor= ' black ', edgecolor= ' black ', alpha=1,histtype= ' bar) hist parameters are very many, but commonly used on these six, only the first one is necessary, the following four optionalArr: A one-dimensional array that needs to calculate the histogramBins: Histogram bar number, optional, default = 10Normed: Whether the resulting histogram vector is norma

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

I'm writing this article to show the basic ways to use Instagram programmatically. My approach can be used for data analysis, computer vision, and any cool projects you can think of. Instagram is the largest picture-sharing social media platform, with about 500 million active users per month, with 95 million of images and videos being uploaded to Instagram every day. Its

SummaryNumPy is the basis that must be mastered in data analysis using Python. is the foundation package for high-performance Scientific computing and data analysis. By using numpy, we can perform fast standard mathematical function calculation without loop, and can do linea

SummaryThe use of Python for data analysis, you need to install some common tools, such as numpy,pandas,scipy, etc., during the installation process, often encountered some installation details problems, such as version mismatch, need to rely on the package is not installed properly, etc. This article summarizes the next few necessary installation package install

RPY2 Environment Configuration　Recently, I want to integrate R into a web system with flask as a back-end framework, and perform data statistics analysis on the server side. The R language needs to be integrated into Python, and it is found that Rpy2 in Python can invoke the R language, so it took some time to configur

This article mainly introduces the real IP request Pandas for Python data analysis. in this article, we will introduce the example scheme in detail, I believe it has some reference value for everyone's learning or understanding. if you need it, you can refer to it. let's learn it together. Preface Pandas is a data

行一次测试frame4 = DataFrame ([[ Columns=[' A ', ' B ']) frame4.index.names = [' C ', ' d ']print frame4print frame4.reset_index (). Sort_index (axis = 1)Other topics related to pandas#-*-encoding:utf-8-*-import numpy as Npimport Osimport pandas as Pdfrom pandas import Series,dataframeimport matplotlib. Pyplot as Pltimport Pandas.io.data as web# here are some egg-ache problems: integer index and integer tag ser = Series (Np.arange (3.)) #print Ser[-1] #报错 because the ambiguity of the integer index

--pylabImport Pandasplot (Arange (10))The appearance of the tablet is the success:PS: often easy to appear during installation of Pandas error :' ASCII ' codec can ' t decode byte 0xd5 Workaround: Add in python/lib/site.py Import sysreload (SYS) sys.setdefaultencoding ('gbk')2. Install the Pycharm and install the pandas (you can also add a package such as NumPy, the same way)Download and install Pycharm, and then add Pandas in Pycharm: (The process

：import1 Import matplotlib.pyplot as Plt2 a=series (NP.RANDOM.RANDN (+), Index=pd.date_range (' 20100101 ', periods=1000)) 3 b= A.cumsum () 4 B.plot () 5 plt.show () #最后一定要加这个plt. Show (), or the graph will not appear.2.PNGYou can also use the following code to generate multiple time series diagrams:a=DataFrame(np.random.randn(1000，4),index=pd.date_range(‘20100101‘,periods=1000),columns=list(‘ABCD‘))b=a.cumsum()b.plot()plt.show()3.png 11, Import and Export filesWriting and reading Excel files

]])Mathematical and statistical methods:NumPy also provides a number of statistical functions to perform statistical operations on data, such as averaging, variance, and so on. Refer to the following table for detailsin [+]: arrOUT[35]:Array ([[0, 1, 2, 3, 4],[5, 6, 7, 8, 9],[10, 11, 12, 13, 14]])In [approx]: Np.mean (arr)OUT[36]: 7.0In [PNS]: np.std (arr)OUT[37]: 4.3204937989385739In []: Np.var (arr)OUT[38]: 18.666666666666668With these methods, we c

mature frame or wheel that can meet my needs? (Multi-threading, and can run at 7x24 hours, because the number of acquisitions is huge) In addition to ask, how to store the collected content (million to tens of millions), the data there are some digital data, the need for statistical analysis, with MySQL can it? Or is there any other more mature and simple wheels