pd dataframe

A KNN algorithm for recognizing handwritten numbers is written, as shown in. Refer to link http://blog.csdn.net/april_newnew/article/details/44176059.#-*-coding:utf-8-*-ImportNumPy as NPImportPandas as PDImportOSdefreadtxt (filename): Text=[] f= open (filename,'R', encoding='Utf-8') forLineinchf.readlines (): Text.append (line) txt=list (text) txt=np.array (txt,dtype='float') txt=txt.tolist ()returntxtdefReadData (rootfile): Data=[] Label= [] forRoot,dirs,filesinchOs.walk (rootfile): for

Ten Minutes to PandasConcatDF = PD. DataFrame (Np.random.randn (10, 4))Print(DF)#Break it into piecespieces = [Df[:3], Df[3:7], df[7:]]Print(Pd.concat (pieces))#0 1 2 3#0 0.879526-1.417311-1.309299 0.287933#1-1.194092 1.237536-0.375177-0.622846#2 1.449524 1.732103 1.866323 0.327194#3-0.028595 1.047751 0.629286-0.611354#4-1.237406 0.878287 1.407587-1.637072#5 0.536248 1.172208 0.405543 0.245162#6 0.166374 1.

). Strftime ('%y-%m-%d ') P2=get_price (Index[each] , start_date=p1_30d, end_date=p1_30d, frequency= ' daily ', fields= ' close ') if not p2.empty: Pass ELSE:DATE_STAMP2 = Datetime.strptime (p1_30d, '%y-%m-%d ') unix2=time.mktime (date_stamp 2.timetuple ()) Unix2_weekend=unix2-unix_weekend P2_weekend=datetime.fromtimestamp (unix2_weekend). Str Ftime ('%y-%m-%d ') P2=get_price (Index[each], start_date=p2_weekend, end_date=p2_weekend, frequency= ' Daily ', fields= ' close ')

Python functions(1) Another way to define the data frame is to put the data content (multidimensional array) directly into data, and then define columns and index. (Data frame. Columns is a column name,. Index is the row name, and the type that is taken is similar to the tuple, you can use [0],[1] ... Direct removal)DF = PD. DataFrame (data=[[34, ' null ', ' Mark '], [[a], ' null ', ' Mark '], [", ' null ',

%ba%e5%90%8d%e5%8d%95 "\" cardNum= "\" Iname= "+STR (name) + \" areaname= "\ "pn=" +str (i*10) + \ "rn=10" \ "ie=utf-8oe=utf-8format=json" Html=requests.get ( URL). Content html_json=json.loads (HTML) html_data=html_json[' data ' for each in html_data:k=each[' r Esult '] for each in K:print each[' iname '],each[' Cardnum '] iname.append (each[' iname ')) I Card.append (each[' cardnum ')) except:passif __name__ = = ' __main__ ': name= "Guo * *" person_executed (name) print Len ( Iname) ##########

. As the name implies, series is a sequence, similar to a one-dimensional array; The Data frame is the equivalent of a two-dimensional table, similar to a two-dimensional array, with each column being a Series. To locate the elements in the series, Pandas provides the Index object, each with a corresponding index, which is used to mark different elements, which are not necessarily numbers or letters, Chinese, and so on, similar to the primary key in SQL.Similarly, the Data frame is a combination

This is a short introduction to pandas and geared mainly for new users. Customarily, we import as follows In [1]: Import pandas as PD in [2]: Import NumPy as NP Object Creation The Data Structure Intro section Creating a Series by passing a list of values, letting pandas create a default integer index In [3]: s = PD. Series ([1,3,5,np.nan,6,8]) in [4]: S out[4]: 0 1 1 3 2 5 3 nan 4 6 5 8

Ten Minutes to Pandas This is a short introduction to pandas and geared mainly for new users. You can have a complex recipes in the cookbook Customarily, we import as follows In [1]: Import pandas as PD in [2]: Import NumPy as NP in [3]: Import Matplotlib.pyplot as Plt Object Creation The Data Structure Intro section Creating a Series by passing a list of values, letting pandas create a default integer index In [4]: s =

Here only the data analysis commonly used graphic drawing, as for the complex graphics is not in the scope of this discussion, a few of the graphics to meet the requirements of the data analysis process, as for reporting materials or other high-quality graphics, and then write another about the simple use of ggplot2.Python's drawing tools are mainly matplotlib, which is not complex to use, but simple to use. There are two ways to use matplotlib drawings:1.matplotlib drawing, specifying parameter

)#random reflow, column, and column rearrangement, since each operation randomly results in a different result, you can set the seed -n=0.8 -Train=data[:int (nlen (data)),:] -Test=data[int (nlen (data)):,:] in - #Modeling Data Grooming to #k=30 +m=100 -RECORD=PD. DataFrame (columns=['Acurrary_train','acurrary_test']) the forKinchRange (1,m+1): * #k feature expansion multiples, eigenvalues of 0-1, eac

Import NumPy as NP from pandas import Series, dataframe import pandas as PD df = dataframe {' key ': [' B ', ' B ', ' A ', ' C ', ' A ', ' B '], ' data1 ': Range (6)}) pd.get_dummies (df[' key ') print (DF) ' data1 key 0 0 B 1 1 B 2 2 a 3 3 C 4 4 A 5 5 B ' ' dummies = pd.get_dummies (df[' key '), prefix= ' key ') Df_with _dummy = df[[' data1 ']].join (dummie

1. In the dataframe of pandas, we often need to select the rows of a specified condition based on a property, at which point the Isin method is particularly effective. Import pandas as PD DF = PD. Dataframe ([[1,2,3],[1,3,4],[2,4,3]],index = [' One ', ' two ', ' three '],columns = [' A ', ' B ', ' C ']) print DF #

the writerows function.Read the csv file as DataFrame Code # Read the csv file DataFrameimport pandas as pddframe = pd. DataFrame. from_csv ('E:/iris.csv ') It can also be slightly tortuous: Import csvimport pandas as pdwith open ('E:/iris.csv ') as csvfile: reader = csv. dictReader (csvfile, fieldnames = None) # fieldnames is set to None by default. if the csv

Import NumPy as NP import pandas as PD Stack Rotate the row index to a column index and complete the hierarchy index. In the following example, first create a box of 5x2 dataframe. It is then stack, so the original row index becomes the outer index, and the original column index becomes an inner index. Df_obj = PD. Datafram