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1. Import data (CSV format) into JupyterImport Pandas as PDImport Matplotlib.pyplot as PltFilename= ' Data.csv 'Raw=pd.read_csv filenamePrint (Raw.shape)Raw.head () #打印前几行2. Remove null values for a columnKobe=raw[pd.notnull (raw[' Shot_made_flag ')]Print (Kobe.shape)3. Drawing with Matplotlibalpha=0.02# point transparency, the smaller the more transparentPlt.figure (figsize= (10,10))Plt.subplot (121) #一行两列
A total of 15 essays, mainly in order to record data analysis process of some small demo, share to other needs of netizens, more for the convenience of laterownView, 15 essays, each content is basically a sentence to add a piece of code, the way, Keep it simple and compact and look clear , altogether can be divided into three parts:The first part briefly describes the d
seconds, it takes several hours for R to run, and 8 GB of memory is fully occupied ).
In general, Python is a balanced language, which can be used in all aspects, while R is prominent in statistics. However, data analysis is not just about statistics, data collection, data
learning with Scikit-learnBooks:
"Ten minutes to Pandas" Chinese translation version: http://www.cnblogs.com/chaosimple/p/4153083.html
Founder of Pandas: Data analysis using Python (watercress) (recommend)
The collection of textbooks: Scipy lecture Notes (v
mining algorithms, data modeling, and so on, as long as it is more than m of data, R is very difficult to do, But Python is basically competent.
Add:
Python has a dedicated data analysis package
first, the initial knowledge of pandas
Pandas is a very useful library based on NumPy, which has two unique basic data Structures series (one-dimensional) and dataframe (two-dimensional) that make data operations simpler. Although pandas has two
own nested list (nested list structure) structure, which is also useful for representing matrices (matrix). It is said that NumPy Python is the equivalent of becoming a free, more powerful MATLAB system.It is fast and powerful, it can support linear algebra operation, Fourier transform, random number generation and so on all kinds of mathematical meta-calculation.Official website: http://www.numpy.org/4. PandasThe
','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.
First set up the basic environment, assuming there is already a Python operating environment. Then need to install some common basic library, such as NumPy, scipy for numerical calculation, pandas for data analysis, Matplotlib/bokeh/seaborn for data visualization. And then o
Python Data Analysis OverviewThe meaning and goal of data analysisStatistical analysis methodExtracting useful informationResearch, generalization, summaryPython and data analyticsPython:guido Van Rossum Christmas Holiday, 1989Fea
Summary of this section Basic EnvironmentIpython FoundationObjectiveThis is the first blog in 18, because boss for some of my job expectations, need to start doing some data analysis work, so began to write this series of blog. The main content of the classification is basically the landlord in view of the reading "Data anal
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
Python Data analysisWhy do you choose Python for data analysis?Python will inevitably be close to other open source and commercial domain-specific programming languages/tools such as R, MATLAB, SAS, Stata, etc. for
single language to process data has its benefits. If you've ever used C + + or Java before, Python should be easy for you.Data analysis can be implemented using Python, with enough Python libraries to support data
The data calculation functions of numpy and pandas are described earlier. But this data is constructed by ourselves manually. If data cannot be imported into Python automatically, then these calculations have little meaning. This chapter describes how
the required package again.4, after learning the introductory book, you need to learn how to use Python to do data analysis, recommend a book: using Python for data analysis, this book mainly introduces the
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 deve
Python for data analysis: Related Package installation, pythonpackage
1. Why use Python for data analysis?
Python has a huge and active scientific computing community with improved libr
Reprint: Learn to use yourselfA tool to learnPython languageRecommended to see Liaoche's Python3 tutorial.Data Analysis Python Basicssuch as List,tuple,dic,set and so on. My later blog will write.Two get dataPython crawlerRecommend a book: "Python Network data Collection" (Web scraping with
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
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