linear discriminant analysis python

storage = itemsize * Size b = Array ([1.J + 1, 2.J + 3]) imaginary numbersReal part B.imag imaginary part of B.real complex array The Flat property returns a Numpy.flatiter object that allows us to iterate over any multidimensional array like a one-dimensional array. In:b = Arange (4). Reshape (2,2) in:b out : Array ([[0, 1], [2, 3]]) in:f = B.flat in:f out: 2.12 Array Conversions The ToList function converts the numpy array into a python

Time Series Model Time Series Prediction Analysis is to use the characteristics of an event time over a period of time to predict the characteristics of the event in the future. This is a kind of relatively complex prediction modeling problem, and the regression analysis model is different from the prediction, time series model is dependent on the sequence of events, the same size of the value change Order

#-*-Coding:utf-8-*-# The Nineth chapter of Python for data analysis# Data aggregation and grouping operationsImport Pandas as PDImport NumPy as NPImport time# Group operation Process, Split-apply-combine# Split App MergeStart = Time.time ()Np.random.seed (10)# 1, GroupBy technology# 1.1, citationsDF = PD. DataFrame ({' Key1 ': [' A ', ' B ', ' A ', ' B ', ' a '],' Key2 ': [' one ', ' one ', ' one ', ' one '

Numpy (numerical Python) Foundation package for high performance scientific computing and data analysis; Ndarray, multi-dimensional Array (matrix), with vector computing ability, fast, save space; Matrix operations, without loops, can be done similar to MATLAB in the vector operation; Linear algebra, random send generation; Ndarray, n-di

One, NumPy: Array calculation1. NumPy is a basic package for high performance scientific computing and data analysis. It is the basis of various other tools such as pandas.2, the main functions of NumPy:# Ndarray, a multidimensional array structure, efficient and space-saving # mathematical functions that do not require a loop to perform fast operations on an entire set of data # * Tools to read and write disk data and tools for manipulating memory-ma

which one is best for use, so let's review it.Concat allows the user to append one or more dataframe (depending on how you define the axis) below or next to the table.Merge merges multiple dataframe to specify the same row as the primary key (key).Join, like merge, incorporates two dataframe. But it does not merge by a specified primary key, but is merged by the same column name or row name.Pandas ApplyApply is designed for the pandas series. If you're not familiar with series, you can think of

, the normal function can generate a sample array of 4*4: Samples = np.random.normal (size = (bis)) samplesout[]: Array ([[-1.22102285, 2.08688133, 1.15874399, 0.14342708], [-0.29772372, 0.36137871, 0.60243437, -0.09287792], [-0.49263459, 0.69445334, 1.02035894, -1.18263174], [-0.07184985,- 1.11834445, 0.89547984, 0.0585053]]) 3. ExampleRandom Walk 1000:nsteps = np.random.randint (0,2,size= Np.where (draws>0,1,-1= steps.cum