Python videos

# 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`.

## 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
# 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)
``````