Python 如何将字符向量与平方矩阵连接起来

Python 如何将字符向量与平方矩阵连接起来,python,vector,concatenation,Python,Vector,Concatenation,我有一个距离矩阵,其形式为: 0 0.81556 0.87214 0.6861 0.81556 0 0.17909 0.33358 0.87214 0.17909 0 0.47373 0.6861 0.33358 0.47373 0 和字符串向量: A B C D 我想得到以下连接: A 0 0.81556 0.87214 0.6861 B 0.81556 0 0.17909 0.33358 C 0.87214 0.17909 0 0.47373 C

我有一个距离矩阵,其形式为:

0   0.81556 0.87214 0.6861
0.81556 0   0.17909 0.33358
0.87214 0.17909 0   0.47373
0.6861  0.33358 0.47373 0
和字符串向量:

A
B
C
D
我想得到以下连接:

A   0   0.81556 0.87214 0.6861
B   0.81556 0   0.17909 0.33358
C   0.87214 0.17909 0   0.47373
C   0.6861  0.33358 0.47373 0
这里的问题是,一旦字符串向量连接起来,整个矩阵就会转换成字符串,而不应该是这样

以下是我目前正在使用的函数:

"" Generates the random matrix based on the dimensions of the input matrix being compared"""
def generateMatrix(N): # N -> (x,y,z) or (x,y)

    labels =[]

    # creates random matrix with NxN dimmensions
    rn_matrix = np.random.uniform(0.0,5.0,N)  

    # computes Distance matrix 
    adist = squareform(pdist(rn_matrix)) 


    # Generates the labels vector for the random matrix 
    # it should generate the letters and insert them in the first column
    for i in range(N[0]):
        letter = random.choice("MLN")
        labels.append(letter)

    # random distance matrix with labels in it 
    # labels_df = pd.DataFrame({'arg1':np.array(labels)})
    # adist_df = pd.DataFrame(data=adist,index=list(range(15)),columns= list(range(15)))
    distance_matrix = np.c_[labels, adist]   
    # df = pd.concat(labels_df,adist_df) 
    print(distance_matrix)

    # filename assigned
    outfilename = "rn_distance_matrix.csv"

    # stores the distance matrix in a csv format
    # outfile = np.savetxt( outfilename, distance_matrix, delimiter=",")
    distance_matrix.to_csv(outfilename,index = False)
    # returns the name of the file where the distance matrix is stored
    return(outfilename)

到目前为止,您尝试了什么代码?您的数据是如何准确地存储在脚本中的?在脚本内部还是文件中?这些是numpy向量和矩阵还是vanilla python?如果您只需要进行处理,并且以后不会更新,那么您可以
zip(向量,矩阵)
获得您想要的结果。已经解决了!