categorical data analysis using sas

In the previous section, we crawled nearly 70 thousand pieces of second-hand house data using crawler tools. This section pre-processes the data, that is, the so-called ETL (extract-transform-load) I. Necessity of ETL tools Data cleansing is a prerequisite for data

Case: rfm analysis of member customer transaction data using Excel Background: A Member Service Enterprise has about 1200 member customers in the past year. As the company wants to activate promotions for different categories of inactive customers, it also plans to launch a series of promotions for key customers to retain these customers and maintain their activi

Data Loading storage and file format for data analysis using python, Before learning, we need to install the pandas module. Since the python version I installed is 2.7Https://pypi.python.org/pypi/pandas/0.16.2/#downloadsDownload version 0.16.2 from this website, decompress it, and use the DOS command to open the corres

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 Java or C + +. Python is also a very good glue layer that makes it very easy to build Pyt

','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. All keys in an object must be strings (very i

-------------------------------------------------------------------------------------------------- After the preceding operations, save the published data packet to Cognos connection and view the report again. Then, users with different roles can log on and view the data of different departments, this article sets permissions for dimension tables, so all fact tables associated with this dimension will pl

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

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

. Because R2 >0.99, so this is a very obvious experimental model of linear characteristics, that is, the fitting line can be explained by more than 99.99%, covering the measured data, has a good general, can be used as a standard work curve for other unknown concentration solution measurement. To further use more metrics to describe this model, we use the "regression" tool in data

= connection.cursor () Cursor.execute ( "" "Select Unix_timestamp (Date_format (CTime, "%%y-%%m-01")) *1000, COUNT (ID) from repository_trouble WHERE processer_id=%s GROUP by Date_format (CTime, "%%y-%%m") "" ", [Row.nid]) result = Cursor.fetchall () temp = { ' name ': Row.username, ' data ': Result } reponse.append (temp) import JSON return HttpResponse ( Json.dumps (repons

','a','b','a'],'data1': Range (6)}) DF2=PD. DataFrame ({'Key':['a','a','C','b','D'],'data2': Range (5)}) Pd.merge (Df1,df2,on='Key', how=' Right') back to key data1 data20B0.0 31B2.0 32B4.0 33C1.0 24A3.0 05A5.0 06A3.0 17A5.0 18D NaN4Many-to-many merges produce a Cartesian product of rows, that is, DF1 has 2 a,df2 with 2 A, and rallies produce 4 aWhen you need to merge from multiple keys, simply pass in a list of column names.When merging operations, you need to handle dup

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

written in front of the words: All of the data in the instance is downloaded from the GitHub and packaged for download.The address is: Http://github.com/pydata/pydata-book there are certain to be explained: I'm using Python2.7, the code in the book has some bugs, and I use my 2.7 version to tune in. # Coding:utf-8 from pandas import Series, dataframe import pandas as PD import NumPy as NP df =dataframe ({'

written in front of the words: All of the data in the instance is downloaded from the GitHub and packaged for download.The address is: Http://github.com/pydata/pydata-book there are certain to be explained: I'm using Python2.7, the code in the book has some bugs, and I use my 2.7 version to tune in. # Coding:utf-8 from pandas import Series, dataframe import pandas as PD import NumPy as NP df = pd.read_csv

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