5 Best Ways to Convert Python CSV to MAT File

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💡 Problem Formulation: Users often need to convert CSV files, a common format for storing tabular data, into MAT files, which are a file format used by MATLAB. This conversion allows for the seamless transfer of data into the MATLAB environment for advanced analysis and visualization. For example, converting a CSV file containing sensor data readings into a MAT file to perform signal processing in MATLAB.

Method 1: Using SciPy and csv Module

This method involves using Python’s built-in csv module to read the CSV file contents and then employing the savemat function from the SciPy library to save the data to a .mat file. It is well-suited for straightforward conversion without needing MATLAB’s proprietary environment.

Here’s an example:

import csv
from scipy.io import savemat

csv_filename = 'data.csv'
mat_filename = 'data.mat'

with open(csv_filename, 'r') as csvfile:
    csvreader = csv.reader(csvfile)
    mat_data = {'data': [row for row in csvreader]}

savemat(mat_filename, mat_data)

Output: This code will produce a file named ‘data.mat’ with the data organized in a structure accessible by MATLAB.

This approach is straightforward and efficient, requiring only standard Python libraries. Users do not need to interact with MATLAB, making it an excellent choice for simple CSV to MAT conversions. However, it might not handle complex data structures efficiently.

Method 2: Using pandas and SciPy

Utilizing the power of pandas for data manipulation and the SciPy library for saving to MAT format, this method is ideal for data-heavy conversions. The pandas library simplifies data processing while SciPy’s savemat function ensures compatibility with MATLAB.

Here’s an example:

import pandas as pd
from scipy.io import savemat

csv_filename = 'data.csv'
mat_filename = 'data.mat'

dataframe = pd.read_csv(csv_filename)
mat_data = {'data': dataframe.values}

savemat(mat_filename, mat_data)

Output: ‘data.mat’ file containing data from the CSV in a MATLAB-readable format.

This method is very powerful due to pandas’ ability to efficiently handle large datasets and its numerous built-in functions for data manipulation. However, the need for pandas may be an overkill for simple datasets and adds an additional dependency.

Method 3: Direct Conversion Using scipy.io.loadmat

SciPy also offers a direct method to load CSV data and save it to MAT format using its loadmat function. This method eliminates the need for intermediate data parsing, which can sometimes streamline the process.

Here’s an example:

import numpy as np
from scipy.io import savemat, loadmat

csv_filename = 'data.csv'
mat_filename = 'data.mat'

data_array = np.loadtxt(csv_filename, delimiter=',')
mat_data = {'data': data_array}

savemat(mat_filename, mat_data)

Output: Generates ‘data.mat’ containing numerical data in an array format accessible in MATLAB.

By using numpy to load the CSV file, this method can be faster for numerical data and requires less code. However, it assumes the CSV file contains numeric data throughout, which might not always be the case.

Method 4: Using hdf5storage Package

For users requiring compatibility with newer versions of MATLAB’s mat file format (v7.3 and above), the hdf5storage package can be used to write data in the HDF5-based .mat format that MATLAB supports.

Here’s an example:

import pandas as pd
import hdf5storage

csv_filename = 'data.csv'
mat_filename = 'data_v73.mat'

dataframe = pd.read_csv(csv_filename)
mat_data = {'data': dataframe.to_dict('list')}

hdf5storage.writes(mat_data, filename=mat_filename, matlab_compatible=True)

Output: ‘data_v73.mat’ file that is compatible with MATLAB v7.3 or higher.

This method bridges the gap for users needing advanced features from newer .mat file formats. While it generally works well, it introduces an additional dependency on hdf5storage that is separate from the standard scientific stack in Python.

Bonus One-Liner Method 5: Using NumPy’s tofile

For a swift and minimalistic conversion of numerical CSV data to binary format compatible with MATLAB’s simple import functions, NumPy’s tofile method can be a quick choice.

Here’s an example:

import numpy as np

data = np.genfromtxt('data.csv', delimiter=',')
data.tofile('data.mat')

Output: A binary file ‘data.mat’ that can be loaded in MATLAB using load function.

This one-liner leverages NumPy’s capabilities to read and write data in binary format. It is incredibly concise but lacks the structure and metadata that come with the .mat file format, which may limit its applicability for more complex data.

Summary/Discussion

  • Method 1: Using SciPy and csv Module. It’s straightforward and does not require MATLAB. However, it’s best suited for simple data structures.
  • Method 2: Using pandas and SciPy. Ideal for complex datasets and data analysis, with the downside of additional dependency on pandas.
  • Method 3: Direct Conversion Using scipy.io.loadmat. A fast and succinct approach for numerical data, although it lacks the flexibility to work with non-numeric data.
  • Method 4: Using hdf5storage Package. Supports modern MATLAB formats but introduces dependency on hdf5storage, which is outside the standard Python scientific stack.
  • Bonus Method 5: Using NumPy’s tofile. Quick and easy but lacks .mat file features, making it impractical for complex data or when preserving structure is critical.