matlab怎么装换表格,如何在MATLAB中将熊猫数据框转换为表格？

我有MATLAB R2019a使用py包装器运行python脚本，该包装器返回pandasdataframe。这dataframe是一个字符串表。有没有办法将熊猫dataframe转换成MATLAB表？

目前，我正在将写入dataframea.csv并将其导入MATLAB中作为解决方法。

解决方案

这可能不是最好的方法，但是它可以给您一些新的想法：

functiontab=q57081181()% Import pandas:pd=py.importlib.import_module('pandas');% Create a dataframe:iris=pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv');% Convert to a table, going throgh dictionary and struct:st=struct(iris.to_dict());st2=structfun(@(x)py.list(x.values),st,'UniformOutput',false);tab=struct2table(importMixedData(st2));functionout=importMixedData(inStruct)% Import numpy:np=py.importlib.import_module('numpy');% Copy fieldnames:out=inStruct;% Convert every field separately:fields=fieldnames(inStruct);forf=1:numel(fields)fld=fields{f};try% this should work for numeric values:out.(fld)=double(np.array(inStruct.(fld))).';catch% this should work for text values:out.(fld)=string(cell(inStruct.(fld))).';endend

因此对于输入的情况：

iris =

Python DataFrame with properties:

T: [1×1 py.pandas.core.frame.DataFrame]

at: [1×1 py.pandas.core.indexing._AtIndexer]

axes: [1×2 py.list]

blocks: [1×1 py.dict]

columns: [1×1 py.pandas.core.indexes.base.Index]

dtypes: [1×1 py.pandas.core.series.Series]

empty: 0

ftypes: [1×1 py.pandas.core.series.Series]

iat: [1×1 py.pandas.core.indexing._iAtIndexer]

iloc: [1×1 py.pandas.core.indexing._iLocIndexer]

index: [1×1 py.pandas.core.indexes.range.RangeIndex]

ix: [1×1 py.pandas.core.indexing._IXIndexer]

loc: [1×1 py.pandas.core.indexing._LocIndexer]

ndim: [1×1 py.int]

plot: [1×1 py.pandas.plotting._core.FramePlotMethods]

shape: [1×2 py.tuple]

size: [1×1 py.numpy.int32]

style: [1×1 py.pandas.io.formats.style.Styler]

values: [1×1 py.numpy.ndarray]

is_copy: [1×1 py.NoneType]

sepal_length sepal_width petal_length petal_width species

0 5.1 3.5 1.4 0.2 setosa

1 4.9 3.0 1.4 0.2 setosa

2 4.7 3.2 1.3 0.2 setosa

3 4.6 3.1 1.5 0.2 setosa

4 5.0 3.6 1.4 0.2 setosa

5 5.4 3.9 1.7 0.4 setosa

6 4.6 3.4 1.4 0.3 setosa

7 5.0 3.4 1.5 0.2 setosa

8 4.4 2.9 1.4 0.2 setosa

...

我们得到：

tab =

150×5 table

sepal_length sepal_width petal_length petal_width species

____________ ___________ ____________ ___________ ____________

5.1 3.5 1.4 0.2 "setosa"

4.9 3 1.4 0.2 "setosa"

4.7 3.2 1.3 0.2 "setosa"

4.6 3.1 1.5 0.2 "setosa"

5 3.6 1.4 0.2 "setosa"

5.4 3.9 1.7 0.4 "setosa"

4.6 3.4 1.4 0.3 "setosa"

5 3.4 1.5 0.2 "setosa"

4.4 2.9 1.4 0.2 "setosa"

在R2019a上使用python 3.6进行了测试。