DataMatrix

DataMatrix is an intuitive Python library for working with column-based, time-series, and multidimensional data. It's a light-weight and easy-to-use alternative to pandas.

Install Basic use Get support

Features

An intuitive syntax that makes your code easy to read
Mix tabular data with time series and multidimensional data in a single data structure
Support for large data by intelligent (and automatic) offloading of data to disk when memory is running low
Advanced memoization (caching)
Requires only the Python standard libraries (but you can use numpy to improve performance)
Compatible with your favorite data-science libraries:
- seaborn and matplotlib for plotting
- scipy, statsmodels, and pingouin for statistics
- mne for analysis of electroencephalographic (EEG) and magnetoencephalographic (MEG) data
- Convert to and from pandas.DataFrame
- Looks pretty inside a Jupyter Notebook

Ultra-short cheat sheet

from datamatrix import DataMatrix, io
# Read a DataMatrix from file
dm = io.readtxt('data.csv')
# Create a new DataMatrix
dm = DataMatrix(length=5)
# The first two rows
print(dm[:2])
# Create a new column and initialize it with the Fibonacci series
dm.fibonacci = 0, 1, 1, 2, 3
# You can also specify column names as if they are dict keys
dm['fibonacci'] = 0, 1, 1, 2, 3
# Remove 0 and 3 with a simple selection
dm = (dm.fibonacci > 0) & (dm.fibonacci < 3)
# Get a list of indices that match certain criteria
print(dm[(dm.fibonacci > 0) & (dm.fibonacci < 3)])
# Select 1, 1, and 2 by matching any of the values in a set
dm = dm.fibonacci == {1, 2}
# Select all odd numbers with a lambda expression
dm = dm.fibonacci == (lambda x: x % 2)
# Change all 1s to -1
dm.fibonacci[dm.fibonacci == 1] = -1
# The first two cells from the fibonacci column
print(dm.fibonacci[:2])
# Column mean
print('Mean: %s' % dm.fibonacci.mean)
# Multiply all fibonacci cells by 2
dm.fibonacci_times_two = dm.fibonacci * 2
# Loop through all rows
for row in dm:
    print(row.fibonacci) # get the fibonacci cell from the row
# Loop through all columns
for colname, col in dm.columns:
    for cell in col: # Loop through all cells in the column
        print(cell) # do something with the cell
# Or just see which columns exist
print(dm.column_names)