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

问题描述

是否有一种标准的方法可以将 matlab .mat(matlab 格式的数据)文件转换为 Panda DataFrame?

我知道可以通过使用 scipy.io 来解决问题，但我想知道是否有一种直接的方法可以做到这一点.

解决方案

我找到了 2 种方式:scipy 或 mat4py.

1. mat4py

<块引用>

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，仅使用 Python 的 dict 和 list对象.数值和元胞数组转换为行序嵌套列表.数组被压缩以消除只有一个元素的数组.生成的数据结构由简单的类型组成，这些类型是兼容JSON格式.

示例:将 MAT 文件加载到 Python 数据结构中:

data = loadmat('datafile.mat')

来自:

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

2. Scipy:

示例:

将 numpy 导入为 npfrom scipy.io import loadmat # 这是加载 mat 文件的 SciPy 模块导入 matplotlib.pyplot 作为 plt从日期时间导入日期时间，日期，时间将熊猫导入为 pdmat = loadmat('measured_data.mat') # 加载 mat 文件mdata = mat['measuredData'] # mat 文件中的变量mdtype = mdata.dtype # 结构的 dtypes 是未确定大小的对象"# * SciPy 将结构读取为 dtype 对象的结构化 NumPy 数组# * 数组的大小是结构数组的大小，不是数字# 任何特定字段中的元素.形状默认为二维.# * 为方便起见，使用 dtypes 中的名称制作数据字典# * 由于结构只有一个元素，而且是二维的，因此将其索引在 [0, 0]ndata = {n: mdata[n][0, 0] for n in mdtype.names}# 仅从时间序列重建数据表的列# 使用区间数来测试一个字段是列还是元数据columns = [n for n, v in ndata.iteritems() if v.size == ndata['numIntervals']]# 现在制作一个数据框，将时间戳设置为索引df = pd.DataFrame(np.concatenate([ndata[c] for c in columns],axis=1),index=[datetime(*ts) for ts in ndata['timestamps']],列=列)

来自:

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

3. 最后，您可以使用 PyHogs 但仍然使用 scipy:

<块引用>

读取复杂的 .mat 文件.

本笔记本显示了读取 Matlab .mat 文件的示例，将数据转换为带有循环的可用字典，一个简单的图数据.

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

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

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.

解决方案

I found 2 way: scipy or mat4py.

1. 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.

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

data = loadmat('datafile.mat')

From:

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

2. Scipy:

Example:

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:

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

3. Finally you can use PyHogs but still use scipy:

Reading complex .mat files.

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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