python pandas slice columns

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[Python] Slice the data with pandas

For example we have the dataframe like this: SPY AAPL IBM GOOG GLD2017-01-03 222.073914 114.311760 160.947433 786.140015 110.4700012017-01-04 223.395081 114.183815 162.940125 786.900024 110.8600012017-01-05 223.217606 114.764473 162.401047 794.020020 112.5800022017-01-06 224.016220 116.043915 163.200043 806.150024 111.7500002017-01-09 223.276779 117.106812 161.390244 806.650024 112.669998...Now we only we want to get highli

In python, pandas. DataFrame sums rows and columns and adds the new row and column sample code.

Pandas is the most famous data statistics package in the python environment, while DataFrame is translated as a data frame, which is a data organization method. This article mainly introduces pandas in python. dataFrame sums rows and columns and adds new rows and

Python uses pandas and xlrd to read Excel, feature filtering deletes columns with 0 values over 99%

Using XLRD to read ExcelFilter 0 columns with a value greater than 99% and removeImport XlrdWorkbook=xlrd.open_workbook (R "123.xlsx")Table = Workbook.sheet_by_name (' Sheet1 ')Nrows=table.nrowsNcols=table.ncolsDel_col=[]For j in Range (Ncols):sum = 0For Ai in table.col_values (j):if ai = = 0.0:Sum+=1if float (sum)/nrows>=0.99:Del_col.append (j)print Del_col Using Pandas to read Exc

Python uses pandas and xlrd to read excel files, feature filtering columns, and pandasxlrd

Python uses pandas and xlrd to read excel files, feature filtering columns, and pandasxlrd Use xlrd to read excelFilter and delete columns with 0 values over 99%.Import xlrdWorkbook = xlrd. open_workbook (R "123.xlsx ")Table = workbook. sheet_by_name ('Sheet1 ')Nrows = table. nrowsNcols = table. ncolsDel_col =

Python traversal pandas data method summary, python traversal pandas

Python traversal pandas data method summary, python traversal pandas Preface Pandas is a python data analysis package that provides a large number of functions and methods for fast and convenient data processing.

Pandas data processing based on filtering specified rows or columns

This article mainly introduces the pandas data processing basis to filter the specified row or the specified column of the relevant information, the need for friends can refer to the following The main two data structures of Pandas are: series (equivalent to one row or column of data bodies) and dataframe (a tabular data body equivalent to multiple rows and columns

"Data analysis using Python" reading notes--fifth Chapter pandas Introduction

pandas import Series,dataf The Rame#numpy element progression group method also applies to pandas object frame = DataFrame (Np.random.randn (4,3), columns = List (' abc '), index = [' Ut ', ' Oh ', ' Te ', ' Or ']) print frame# The following is the absolute value: #print Np.abs (frame) #另一种常见的做法是: Apply a function to a row or column, using the Apply method, like

Python Data Analysis-day2-pandas module

namePrint Food_info.columns #打印dataframe数据类型下的各列列名.5) Dataframe sample number and number of indicatorsPrint Food_info.shape #打印dataframe形状, a few rows of columns, where the number of rows is the number of samples, the number of columns is the number of indicators.6) Pandas fetch dataFetch data by sample (ROW):Pandas d

"Python Data Analysis" Note--pandas

is sometimes possible to replace missing data with 0, but this is not always the casePrint ("zero filled\n", Df.fillna (0))Pivot tablePivotTables can aggregate data from rows and columns specified in a flat file, which can be summed, averaged, and standard poor operationsSince the pandas API has provided us with the top-level pivot_table () function and the corresponding Dataframe method, you can let this

Python Data Analysis Package: Pandas basics

way, and filtering through a Boolean array.However, it is important to note that because the index of the Pandas object is not limited to integers, it is included at the end when using a non-integer as the tile index.>>> fooa 4.5b 7.2c -5.3d 3.6dtype:float64>>> bar0 4.51 7.22 -5.33 3.6dtype:float64>>> foo[:2]a 4.5b 7.2dtype:float64>>> bar[:2]0 4.51 7.2dtype:float64>>> foo[: ' C ']a 4.5b 7.2c -5.3dtype:float64

Python Data Analysis Library pandas basic operating methods _python

The following for you to share a Python data Analysis Library Pandas basic operation method, has a good reference value, I hope to help you. Come and see it together. What is Pandas? Is it it? 。。。。 Apparently pandas is not so cute as this guy .... Let's take a look at how Pandas's official website defines itself:

The dataframe of Python data processing learning Pandas

data (like select in SQL):DataFrame #从pandas库中引用DataFrameDf_obj = DataFrame () #创建DataFrame对象Df_obj.dtypes #查看各行的数据格式Df_obj.head () #查看前几行的数据, default first 5 rowsDf_obj.tail () #查看后几行的数据, default after 5 rowsDf_obj.index #查看索引Df_obj.columns #查看列名Df_obj.values #查看数据值Df_obj.describe #描述性统计Df_obj. T #转置Df_obj.sort (columns = ") #按列名进行排序Df_obj.sort_index (by=[","]) #多列排序, use the Times this function is obsole

How to use Python pandas framework to operate data in Excel files

This article mainly introduces how to use Python pandas framework to operate data in Excel files, including basic operations such as unit format conversion and classification and Summarization. For more information, see Introduction The purpose of this article is to show you how to use pandas to execute some common Excel tasks. Some examples are trivial, but I t

Python data processing: Pandas basics

the original value, which is different from ndarry, for example, the drop line after the call to the original object, found that there is no change      Drop column: Obj4.drop (' Nevada ', Axis=1)In the parameters of many functions of Python, the default is to consider row, so there is axis (axis) This parameter      Axis=1 is vertical, that is, the columnAxis=0 is a horizontal,  4.2 Select selection, slice

Preliminary study on pandas basic learning and spark python

Abstract:Pandas is a powerful Python data Analysis Toolkit, Pandas's two main data Structures series (one-dimensional) and dataframe (two-dimensional) deal with finance, statistics, most typical use case science in society, and many engineering fields. In Spark, the Python program can be easily modified, eliminating the need for Java and Scala packaging, and if you want to export files, you can convert the

Python Pandas simple introduction and use of __python

The pandas of Python is simply introduced and used Introduction of Pandas 1. The Python data analysis Library or pandas is a numpy based tool that is created to resolve data profiling tasks. Pandas incorporates a large number of

Use the Python Pandas framework to manipulate the data in Excel files tutorial _python

Introduction The purpose of this article is to show you how to use pandas to perform some common Excel tasks. Some examples are trivial, but I think showing these simple things is just as important as the complex functions you can find elsewhere. As an extra benefit, I'm going to do some fuzzy string matching to show some little tricks, and show how pandas uses the complete

Methods of dataframe type data manipulation functions in Python pandas

This article mainly introduced the Python pandas in the Dataframe type data operation function method, has certain reference value, now shares to everybody, has the need friend to refer to The Python data analysis tool pandas Dataframe and series as the primary data structures. This article is mainly about how to oper

Detailed analysis of cdn logs using the pandas library in Python

This article describes how to use the pandas library in Python to analyze cdn logs. It also describes the complete sample code of pandas for cdn log analysis, then we will introduce in detail the relevant content of the pandas library. if you need it, you can refer to it for reference. let's take a look at it. Preface

Python To Do data Analysis Pandas Library introduction of Dataframe basic operations

: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 an

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