Python matlab 数据文件到 Pandas DataFrame
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matlab data file to pandas DataFrame
提问by Ramon Martinez
Is there a standard way to convert matlab.mat(matlab formated data) files to Panda DataFrame?
是否有将matlab.mat（matlab 格式化数据）文件转换为 Panda的标准方法DataFrame？
I am aware that a workaround is possible by using scipy.iobut I am wondering whether there is a straightforward way to do it.
我知道可以通过使用解决方法，scipy.io但我想知道是否有一种直接的方法来做到这一点。
回答by Destrif
I found 2 way: scipy or mat4py.
我找到了 2 种方式：scipy 或 mat4py。
- mat4py
- mat4py
Load data from MAT-file
The function loadmat loads all variables stored in the MAT-file into a simple Python data structure, using only Python's dict and list objects. Numeric and cell arrays are converted to row-ordered nested lists. Arrays are squeezed to eliminate arrays with only one element. The resulting data structure is composed of simple types that are compatible with the JSON format.
从 MAT 文件加载数据
函数 loadmat 将存储在 MAT 文件中的所有变量加载到一个简单的 Python 数据结构中，仅使用 Python 的 dict 和 list 对象。数值和元胞数组被转换为按行排序的嵌套列表。数组被压缩以消除只有一个元素的数组。生成的数据结构由与 JSON 格式兼容的简单类型组成。
Example: Load a MAT-file into a Python data structure:
示例：将 MAT 文件加载到 Python 数据结构中：
data = loadmat('datafile.mat')
From:
从：
https://pypi.python.org/pypi/mat4py/0.1.0
https://pypi.python.org/pypi/mat4py/0.1.0
- Scipy:
- 西皮：
Example:
例子：
import numpy as np
from scipy.io import loadmat # this is the SciPy module that loads mat-files
import matplotlib.pyplot as plt
from datetime import datetime, date, time
import pandas as pd
mat = loadmat('measured_data.mat') # load mat-file
mdata = mat['measuredData'] # variable in mat file
mdtype = mdata.dtype # dtypes of structures are "unsized objects"
# * SciPy reads in structures as structured NumPy arrays of dtype object
# * The size of the array is the size of the structure array, not the number
# elements in any particular field. The shape defaults to 2-dimensional.
# * For convenience make a dictionary of the data using the names from dtypes
# * Since the structure has only one element, but is 2-D, index it at [0, 0]
ndata = {n: mdata[n][0, 0] for n in mdtype.names}
# Reconstruct the columns of the data table from just the time series
# Use the number of intervals to test if a field is a column or metadata
columns = [n for n, v in ndata.iteritems() if v.size == ndata['numIntervals']]
# now make a data frame, setting the time stamps as the index
df = pd.DataFrame(np.concatenate([ndata[c] for c in columns], axis=1),
index=[datetime(*ts) for ts in ndata['timestamps']],
columns=columns)
From:
从：
http://poquitopicante.blogspot.fr/2014/05/loading-matlab-mat-file-into-pandas.html
http://poquitopicante.blogspot.fr/2014/05/loading-matlab-mat-file-into-pandas.html
- Finally you can use PyHogs but still use scipy:
- 最后你可以使用 PyHogs 但仍然使用 scipy：
Reading complex
.matfiles.This notebook shows an example of reading a Matlab .mat file, converting the data into a usable dictionary with loops, a simple plot of the data.
读取复杂的
.mat文件。该笔记本显示了读取 Matlab .mat 文件的示例，将数据转换为带有循环的可用字典，即数据的简单绘图。
回答by SerialDev
Ways to do this:
As you mentioned scipy
这样做的方法：
正如你提到的 scipy
import scipy.io as sio
test = sio.loadmat('test.mat')
Using the matlab engine:
使用matlab引擎：
import matlab.engine
eng = matlab.engine.start_matlab()
content = eng.load("example.mat",nargout=1)
