python dataframe to numpy array

=true) # drop useless columns and create LABELSIDX = test.id.values.astype (int) test = Test.drop ([' id ', ' tube_assembly_id ', ' quote_date '), Axis = 1) labels = Train.cost.valuestrain = Train.drop ([' Quote_date ' , ' cost ', ' tube_assembly_id '], Axis = 1) # Convert data to NumPy Arraytrain = Np.array (train) test = Np.array (test)From:kaggle Copyright NOTICE: This article for Bo Master original article, without Bo Master permission not reprodu

Python numpy implements array merge instances (vstack and hstack) and numpyvstack Several Arrays can be combined along different axes for simple usage of vstack and hstack, >>> a = np.floor(10*np.random.random((2,2)))>>> aarray([[ 8., 8.], [ 0., 0.]])>>> b = np.floor(10*np.random.random((2,2)))>>> barray([[ 1., 8.], [ 0., 4.]])>>> np.vstack((a,b))

Loading a ndarray array from a file Loads the Ndarray array from the text file Np.loadtxt>>># textfile是文本文件 .npyloads the Ndarray array from or to the .npz file Np.loadReturns a single Ndarray array if it is a file that ends in. npyReturns a dictionary type object, {Filename:array}, if it is a file that en

Import NumPy as NPNp.arange (Dtype = float) #numpy中的arange与普通的range作用一样, range (start, stop, step)#arange可以通过dtype来指定创建的数组类型, arrays differ from tuples and lists, and the entire array must be of the same type.Np.linspace (Start, stop, number) #其中number指定了start到stop之间的个数 (contains both end point values)Of course, you can also generate random numbers to initialize

1 Convert list to arrayIf the array of arrays for the list is not structured, such asA = [[up], [3,4,5]]After a = Numpy.array (a)The type of a is ndarray, but the element in a a[i] is still a list.If a = [[up], [3,4]]After a = Numpy.array (a)The type of a is Ndarray, the element inside A[i] is also ndarray2 Flatten functionPython itself does not have a flatten function, and the array in

Attributes of Numpy.ndarray: Numpy.ndarray.shape:Dimensions (height, width, ...) Numpy.ndarray.ndim:No. of dimensions= len(shape) Numpy.ndarray.size:Total Number of elements Numpy.ndarray.dtype:Datatype ImportNumPy as NPdefArray (): a= Np.random.random ((5,4)) Print(A.shape[0])#Number of rows, 5 Print(A.shape[1])#Number of columns, 4 Print(Len (A.shape))#2 Print(a.size)#5*4=20 Print(A.dtype)#float64if __name__=="__main__": #gen_random ()

Numpy learning path (1) -- array creation, numpy path Array is the main object for Numpy operations and the main object for python data analysis. This series of articles is my note in Numpy