我知道以前有人问过这种事情，但我没有尝试过任何一种方法。
我为一个项目从GitHub借用了一个函数，但它是用matlab编写的，而我的项目是用python编写的，所以我想将输出数据另存为csv，这样我就可以把它输入回python了。
我只用了大约4个小时，所以这可能是个愚蠢的问题……
我尝试了一系列不同的保存为CSV的方法，但都没有奏效。但它们不会抛出错误，只是文件不会出现。
这是我的最新尝试：

clear;clc;

fileName = 'AlzScSK.csv';
csvData = importdata(fileName);

rawData = csvData.data;
dataMV = rawData;
scaledMV = dataMV

K = 6;

function [dataImputed dataImputedWeighted] = NSkNNDataHM(dataMV)
% Function to impute missing values in a dataset using NSkNN. scaledMV is 
% the autoscale dataset with the missing values and K is the # of nearest 
% neighbors to use to impute the data. filteredMV is the missing value 
% dataset after filtering, but before autoscaling.
% NSkNNData_HM does not skip neighbors that have NaN values in the same 
% location as the metabolite being imputed. Instead, it replaces these NaN 
% values with the half minimum value of that metabolite.

numCol = size(scaledMV,2);

for col = 1:numCol
    rowMV{col} = find(isnan(scaledMV(:,col))); % Finds the row # of every missing value in each column
end

counter = 1;
for targetCol = 1:numCol % i is the target sample
    for neighborCol = 1:numCol % Calculate the Euclidean distances between the target sample (i) and the other samples (j)
        MVRowsRemoved = scaledMV;
        rowsToRemove = union(rowMV{targetCol},rowMV{neighborCol}); % Ignore NaNs when calculating distances
        MVRowsRemoved(rowsToRemove,:) = []; % Remove rows in target sample that have missing values
        numMetInCalc = size(MVRowsRemoved,1); % # of metabolites used in calculation of metabolites in order to weight distance
        % Divide by numMetInCalc to avoid scenarios where distances 
        % calculated with only a few metabolites are weighted more heavily 
        % over distances that are close to the same distance, but used more 
        % metabolites in the calculation.
        distance = pdist2(MVRowsRemoved(:,targetCol)',MVRowsRemoved(:,neighborCol)')/sqrt(numMetInCalc);  
        %distance = pdist2(MVRowsRemoved(:,targetCol)',MVRowsRemoved(:,neighborCol)');
        distIdx(counter,:) = [targetCol distance neighborCol];
        counter = counter+1;
    end
end

% Remove rows that calculated the Euclidean distance between a sample and
% itself.
sameSample = find(distIdx(:,1)==distIdx(:,3));
distIdx(sameSample,:) = [];
distIdxSorted = sortrows(distIdx);

minValperRow = min(scaledMV,[],2); % Finds minimum of each metabolite

% Implement NSkNN with half minimum replacement
dataImputed = scaledMV;
dataImputedWeighted = scaledMV;
for targetCol = 1:numCol
    numMV = size(rowMV{targetCol},1); % # of missing values in a column
    firstNNIdx = (targetCol-1)*(numCol-1)+1; % Column index of the first nearest neighbor of target sample (i) in distIdxSorted
    for MVidx = 1:numMV % For each missing value in the target sample...
        tempDataMV = scaledMV;
        NN = distIdxSorted(firstNNIdx:firstNNIdx+K-1,3); % Column #s of the k nearest neighbors
        DistanceNN = distIdxSorted(firstNNIdx:firstNNIdx+K-1,2); % Distances of k nearest neighbors
        idxNaNinCol = find(isnan(tempDataMV(rowMV{targetCol}(MVidx),NN))); % Finds missing values that are the same metabolite as the target metabolite to be imputed
        if isempty(idxNaNinCol)~=1 % If NaN values found...
            % If there are NaN values in the nearest neighbor
            % metabolite that is the same as the target metabolite to
            % be imputed, replace with half min value of the target
            % metabolite

            tempDataMV(rowMV{targetCol}(MVidx),NN(idxNaNinCol)) = (minValperRow(rowMV{targetCol}(MVidx)) - avgMV(rowMV{targetCol}(MVidx))/stddevMV(rowMV{targetCol}(MVidx)))/2;           
        end
        % Imputed data is weighted by the inverse of the distance
        WeightMultiplier = (1./DistanceNN')/sum(1./DistanceNN);
        dataImputedWeighted(rowMV{targetCol}(MVidx),targetCol) = sum(tempDataMV(rowMV{targetCol}(MVidx),NN).*WeightMultiplier);
        % Not weighted
        dataImputed(rowMV{targetCol}(MVidx),targetCol) = mean(tempDataMV(rowMV{targetCol}(MVidx),NN));
        writematrix(dataImputed,['M.csv'])
    end
end
end

我不确定我是否引用了错误的东西，或者我是否将writematrix(dataImputed,['M.csv'])放错了位置。
我也尝试过以下几种路线：

% Convert cell to a table and use first row as variable names
T = cell2table(c(2:end,:),'VariableNames',c(1,:))
 
% Write the table to a CSV file
writetable(T,'myDataFile.csv')

dlmwrite(filename,M)

csvwrite('filename.csv',variable2,0,2)

This is currently what my workspace looks like