matlab数据文件到pandas DataFrame [英] matlab data file to pandas DataFrame

问题描述

是否存在将 matlab .mat(matlab格式的数据)文件转换为Panda DataFrame的标准方法?

Is there a standard way to convert matlab .mat (matlab formated data) files to Panda DataFrame?

我知道使用scipy.io可以解决问题，但是我想知道是否有直接的方法.

I am aware that a workaround is possible by using scipy.io but I am wondering whether there is a straightforward way to do it.

推荐答案

我发现了2种方法:scipy或mat4py.

I found 2 way: scipy or mat4py.

1. mat4py

从MAT文件加载数据

Load data from MAT-file

loadmat函数将存储在MAT文件中的所有变量加载到 简单的Python数据结构，仅使用Python的字典和列表 对象.数字和单元格数组转换为按行顺序嵌套 列表.压缩数组以消除仅包含一个元素的数组. 结果数据结构由以下几种简单类型组成: 与JSON格式兼容.

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.

示例:将MAT文件加载到Python数据结构中:

Example: Load a MAT-file into a Python data structure:

data = loadmat('datafile.mat')

发件人:

https://pypi.python.org/pypi/mat4py/0.1.0

1. Scipy:

示例:

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)

发件人:

http://poquitopicante.blogspot. fr/2014/05/loading-matlab-mat-file-into-pandas.html

1. 最后，您可以使用PyHogs，但仍然可以使用scipy:

读取复杂的.mat文件.

此笔记本显示了读取Matlab .mat文件的示例， 将数据转换为带有循环的简单字典，简单绘图 数据.

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.

http://pyhogs.github.io/reading-mat-files.html

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