Applying XND on your data analysis workflow

This document show some ways to apply XND on a data analysis workflow.

Basically, a data analysis workflow has 5 main task groups:

  • domain acknolodge
  • data storage
  • data cleaning
  • data processing
  • data visualization

This document focus on the last 3 task groups: data cleaning, processing and visualization.

Some examples will be discussed using xnd with gumath integrated with other libraries


First we need to import some libraries

In [1]:
import sys
In [2]:
from importnb import Notebook
from IPython.display import display
import random 
from xnd import xnd
In [3]:
xnd_gumath = Notebook.load('utils/math_utils.ipynb', main=True).xnd_gumath

Data Process

The data processing tasks aim to work on raw/cleaning data and get information from that. Digital Signal Processing, Machine Learning, Math, Statistics, Scientific Computing, etc are some possible approaches that someone want to work with data. This document shows some operations with: Math

Math operations

XND math operations could be done using gumath. The follow examples use a class that combine xnd and gumath libraries (@ xnd_gumath). libgumath is an C library that supports a general dispatch mechanism for xnd containers as well as a composable, generalized function concept.

Binary operations:

In [4]:
# using arrays from a dictionary
v = xnd_gumath({'x': [1, 2, 3], 'y': [4, 5, 6]})
# add
v['x'] + v['y']
xnd([5, 7, 9], type='3 * int64')
In [5]:
# assign from dictionary
x = v['x']
y = v['y']
# subtract
x - y
xnd([-3, -3, -3], type='3 * int64')
In [6]:
# multiplication
x * y
xnd([4, 10, 18], type='3 * int64')
In [7]:
# division
xnd_gumath(10, type='int32') / 2
xnd(5, type='int32')
In [8]:
# or
20 / xnd_gumath(10, type='int32')
xnd(2, type='int32')
In [9]:
x = xnd_gumath(1.0)
x.sin(), x.cos()
(xnd(0.8414709848078965, type='float64'),
 xnd(0.5403023058681398, type='float64'))

Processing using SciPy

SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering (

This section shows examples using some methods from SciPy library.

In [10]:
import scipy
In [11]:
x = xnd([random.uniform(0, 100) for x in range(100)])
y = xnd([random.uniform(0, 100) for x in range(100)])

x.type, y.type
(ndt("100 * float64"), ndt("100 * float64"))
In [12]:, y)

Statstics functions

SciPy could be used to process some statistical functions on xnd arrays:

In [13]:
print('Mean of x =', scipy.mean(x))
Mean of x = 51.10352114212457
In [14]:
print('Median of y =', scipy.median(y))
Median of y = 42.117034694883316
In [15]:
print('Standard Deviation of x =', scipy.std(x))
Standard Deviation of x = 27.69640514378039
In [16]:
print('Variance of y =', scipy.var(y))
Variance of y = 869.348316652364

Signal Processing functions

In [17]:
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy.signal import savgol_filter
In [18]:
n_samples = 700

raw_data = xnd_gumath([v/100.0 for v in range(n_samples)]).sin()
raw_data += xnd_gumath([random.uniform(0, 1) for x in range(n_samples)]) 

In [19]:
filtered_data = savgol_filter(raw_data, window_length=n_samples//3, polyorder=2)

/mnt/sda1/storage/miniconda/envs/xnd-notebooks/lib/python3.6/site-packages/scipy/signal/ FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  b = a[a_slice]

Processing using scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy (

The follow example shows PCA working with xnd array.

In [20]:
from sklearn.decomposition import PCA
/mnt/sda1/storage/miniconda/envs/xnd-notebooks/lib/python3.6/importlib/ RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
  return f(*args, **kwds)
/mnt/sda1/storage/miniconda/envs/xnd-notebooks/lib/python3.6/importlib/ RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
  return f(*args, **kwds)
In [21]:
X = xnd([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])

pca = PCA(n_components=2)

print('explained_variance_ratio:', pca.explained_variance_ratio_)  
print('singular_values:', pca.singular_values_)  
explained_variance_ratio: [0.99244289 0.00755711]
singular_values: [6.30061232 0.54980396]

Data Visualization

Communication is a very important task on a data analysis workflow.

This section shows XND working with matplotlib.

Plotting with matplotlib

In [22]:
import matplotlib as mpl
import matplotlib.pyplot as plt
In [23]:
x = xnd_gumath([v/100.0 for v in range(1000)])
In [24]:
In [25]:
# Install version_information if not installed
#!pip install version_information
%load_ext version_information
%version_information matplotlib, xnd, gumath, scipy
Python3.6.6 64bit [GCC 4.8.2 20140120 (Red Hat 4.8.2-15)]
OSLinux 4.15.0 29 generic x86_64 with debian buster sid
Fri Aug 17 16:46:05 2018 -04