今年研究生数模的时候用到了，113.xlsx 是325个样本数据，每个样本数据126个初步筛选的特征 

 文章目录

 按列读按行读

按列读 

import xlrd

import numpy as np

def excel2matrix(path):

    data = xlrd.open_workbook(path)

    table = data.sheets()[0]

    nrows = table.nrows  # 行数

    ncols = table.ncols  # 列数

    datamatrix = np.zeros((nrows, ncols))

    for i in range(ncols):

        cols = table.col_values(i)

        datamatrix[:, i] = cols

    return datamatrix

pathX = '113.xlsx'  #  113.xlsx 在当前文件夹下

x = excel2matrix(pathX)

print(x)

print(x.shape)

输出 

 [[0.01719892 0.86457238 0.01742387 … 1. 1. 0.9999603 ] [0.0319469 0.86909879 0.01730402 … 0.99351703 0.99445369 0.99997023] [0.02805216 0.87040528 0.01724861 … 0.99051946 0.99251572 0.99993467] … [0.63507299 0.03643383 0.99306191 … 0.03441377 0.01650907 0.00848835] [0.64464586 0.05750134 0.99653096 … 0.03317541 0.01337929 0.00424418] [0.64765852 0.09031475 1. … 0.03193705 0.01024951 0. ]]   (325, 126) 

简单，但是很有用的一个小东西 

按行读 

当然你可以按行读 

import xlrd

import numpy as np

def excel2matrix(path):

    data = xlrd.open_workbook(path)

    table = data.sheets()[0]

    nrows = table.nrows  # 行数

    ncols = table.ncols  # 列数

    datamatrix = np.zeros((nrows, ncols))

    for i in range(nrows):

        rows = table.row_values(i)

        datamatrix[i,:] = rows

    return datamatrix

pathX = '113.xlsx'  #  113.xlsx 在当前文件夹下

x = excel2matrix(pathX)

print(x)

print(x.shape)

输出 

 [[0.01719892 0.86457238 0.01742387 … 1. 1. 0.9999603 ] [0.0319469 0.86909879 0.01730402 … 0.99351703 0.99445369 0.99997023] [0.02805216 0.87040528 0.01724861 … 0.99051946 0.99251572 0.99993467] … [0.63507299 0.03643383 0.99306191 … 0.03441377 0.01650907 0.00848835] [0.64464586 0.05750134 0.99653096 … 0.03317541 0.01337929 0.00424418] [0.64765852 0.09031475 1. … 0.03193705 0.01024951 0. ]]   (325, 126)