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Python for Data Science


This tutorial is adapted from Web Age course  Applied Data Science with Python.

This tutorial provides  quick overview of Python modules and high-power features, NumPy library, pandas library, SciPy library, scikit-learn library, Jupyter notebooks and Anaconda distribution.

1.1 Importing Modules



from lib import x
from lib import x as y
Importing a single object from a module(lib) allows direct referencing of the object, e.g

x() or y()

Introduces potential variable name collision with other module imports

from lib import *
Not clean — potential variable name clobbering
import lib as alias You import the whole module giving it an alias; use the ‘.’ dot prefix to access objects in it:


That’s the preferred way to import modules

1.2 Listing Methods in a Module

  • Use the dir() function:

dir(module_alias), e.g. dir(np)
dir('module_name'), e.g. dir('sys')

1.3 Creating Your Own Modules

  • Create a Python file, e.g.

# my_utils.py
def foo(a, b):
   return a + b
  • Create a directory with the following structure:

├── my_library    	# directory
│ ├── __init__.py   	# empty file, without it, Python will not allow import from a directory
│ ├── my_utils.py		# your module (def …)
  • If my_library is in the current working directory of an interpreter, we can import my_utils in the following way:

from my_library import my_utils
my_utils.foo(4, 2)
  • When you import a module, its code gets automatically scanned and executed by Python

  • To create a runnable application, add the following (highlighted below) code:

def foo(a, b):
   return a + b
if __name__ == "__main__":
# Application entry point is here

1.4 Random Numbers

  • Python comes with a pseudo-random (deterministic) number generator

import random   # can alias the import  
random.random()    # a float in a range of [0, 1)
random.randint(x,y)  # an integer in a range of [x,y]
  • Note: NumPy offers additional functionality on top of this generator


Python uses the Mersenne Twister as the pseudo-random number generator. It produces 53-bit precision floats and has a generation cycle of 2**19937-1 (the cycle is 6002 digits long). The underlying implementation in C, which is both fast and threadsafe.

1.5 Zipping Lists

  • The zip() function allows you to iterate over two or more lists passed to it as parameters, for example:

a = [1,2,3,4,5]
b = [10,20,30,40,50]
[str(x) + ':' + str(y) for x, y in zip(a,b)]
['1:10', '2:20', '3:30', '4:40', '5:50']

1.6 List Comprehension

  • Comprehensions are constructs that allow sequences (e.g. lists) to be built from other sequences

    • Python 2 introduced list comprehensions and Python 3 extended this functionality to work with dictionaries and sets

y = 10 
[ i**2 + y for i in range (1, 20) if (i % 2 == 0) ]
[14, 26, 46, 74, 110, 154, 206, 266, 334]

1.7 Python Data Science-Centric Libraries

  • NumPy

  • SciPy

  • pandas

  • scikit-learn

  • Matplotlib

1.8 NumPy

  • Efficient numerical computing is not Python’s strong point

  • NumPy library (http://www.numpy.org/), first released in 2006, addresses Python’s shortcomings in this area

    • The project was a successful attempt to bring together a variety of projects in the space and unify the community around a single array package

    • NumPy is part of SciPy

    • It is open-source software

    • Modeled after Matlab

  • At its core, NumPy has an n-dimensional array called “ndarray” that may be shaped as an array or a matrix

  • NumPy also offers developers a large collection of functions to work on the ndarray structure

  • The ndarray structure replaces Python’s list object

1.9 NumPy Arrays

import numpy as np 
# Aliasing it as np – the accepted convention
# Simple arrays:
a1 = np.array ([1,2,3,4,5])  
# Takes in a Python list as input and outputs a numpy.ndarray object 
a2 = np.arange(5) 
# A numpy.ndarray object holding [0, 1, 2, 3, 4] 
# A matrix structure (note the nested brackets)
m = np.array ([[1,2,3,4], [5,6,7,8]])
Output: (2, 4)

1.10 Select NumPy Operations

  • Support for vectorization (no-loop ops, uses very fast C code):

10 * np.arange(5) + np.arange(5)
array([ 0, 11, 22, 33, 44])
np.cos( np.pi * np.array([0,1,2,3]))
array([ 1., -1.,  1., -1.])
  • Filtering:

a = np.array ([1,2,3,4,5])
a [a % 2 == 0]
array([2, 4])
  • Reshaping (in this case, an array into a 4×2 matrix):

m = np.array ([1,2,3,4,5,6,7,8]).reshape(4,2)
Output: a 4x2 matrix

1.11 SciPy


The SciPy module includes the following sub-packages:

constants: physical constants and conversion factors (since version 0.7.0[5])
cluster: hierarchical clustering, vector quantization, K-means
fftpack: Discrete Fourier Transform algorithms
integrate: numerical integration routines
interpolate: interpolation tools
io: data input and output
lib: Python wrappers to external libraries
linalg: linear algebra routines
misc: miscellaneous utilities (e.g. image reading/writing)
ndimage: various functions for multi-dimensional image processing
optimize: optimization algorithms including linear programming
signal: signal processing tools
sparse: sparse matrix and related algorithms
spatial: KD-trees, nearest neighbors, distance functions
special: special functions
stats: statistical functions
weave: tool for writing C/C++ code as Python multiline strings

1.12 pandas

  • pandas (https://pandas.pydata.org/) is an open source library that provides high-performance, memory-efficient, easy-to-use data structures, as well as support for data manipulation and analysis for Python

  • The core pandas’ data structure is the DataFrame object with integrated indexing similar to a relational table, Excel spreadsheet, and similar tabular data set containers

    • The major influence was R’s DataFrame object

  • Through dataframes, pandas offers compact and efficient interfaces for reading and writing data between its data structures and files stored in different formats: CSV, Microsoft Excel, SQL databases, and the fast HDF5 format

  • Dataframes offer integrated handling of missing data points and other mechanisms for patching data sets

  • Supported operations include: data set reshaping, grouping, aggregation, pivoting, joining, querying, and similar operations

1.13 Creating a pandas DataFrame

import pandas as pd
import numpy as np
# You build a DataFrame as a matrix (array of arrays)
m = np.array ([1,2,3,4,5,6,7,8]).reshape(4,2)
df = pd.DataFrame(m, columns = ["Col1", "Col2"])
# Now you have this structure:
	Col1	Col2
0	1	2
1	3	4
2	5	6
3	7	8

1.14 Fetching and Sorting Data

df.Col1 # or df['Col1']
# Col1 values with the their indexes as the left-most column
0    1
1    3
2    5
3    7
Name: Col1, dtype: int32
df.iloc[1, :] 
# Getting the second row via its index
Col1    3
Col2    4
Name: 1, dtype: int32 
# Sorting values in descending order (ascending order is default)
3    8
2    6
1    4
0    2
Name: Col2, dtype: int32

1.15 Scikit-learn

  • scikit-learn (http://scikit-learn.org/) is a Python module for machine learning built on top of SciPy

  • Supports algorithms in these areas:

    • Classification

    • Clustering

    • Data preprocessing (feature extraction and transformation)

    • Dimensionality reduction (deals with data multicollinearity and variance reduction)

    • Model selection (comparing, validating, and improving model accuracy)

    • Regression

1.16 Matplotlib

  • Matplotlib (https://matplotlib.org/) is a Python library for graphing and visualization

  • Depends on NumPy

  • With Matplotlib you can generate plots, histograms, bar charts,scatter plots, etc., with just a few lines of code

  • Matplotlib’s main focus is 2D plotting; 3D plotting is possible with the mplot3d package

1.17 Python Dev Tools and REPLs

  • In addition to the standard Python REPL, Python development is supported through these developer systems:

    • IPython

    • Jupyter with Python kernel (runtime)

    • Visual Studio Code’s Python plug-in

1.18 IPython

  • IPython (Interactive Python) is a command shell that, in addition to Python, supports other computing languages as well

  • Originally released in 2001

  • Offers code introspection with name auto-completion (on Tab) and command history

  • Supports in-line plotting

  • In addition to the primary single-user development on a user machine, it can also manage parallel computing clusters using asynchronous status callbacks and/or MPI

  • In 2014, the original author, Fernando Pérez, announced a spin-off project from IPython called Project Jupyter with IPython acting as Jupyter’s processing engine

1.19 Jupyter

  • Jupyter is a browser-based Python REPL serviced by an embedded web server

    • This Jupyter architecture allows for a remote access (that can be secured)

  • Depends on IPython and allows you to use multiple versions of Python (providing their runtimes are installed)

  • Supports other languages as well:

    • Julia, R, Haskell, and Ruby

  • Central to Jupyter development model is notebook that allows you enter, execute, and mark up code (for documentation and/or simple comments)

    • Notebook files are physical files with extension .ipynb automatically saved in your working directory served by the web server

    • You can have multiple Python notebook sessions running concurrently, each receiving its own Python interpreter sandbox

  • You start Jupyter by running this command:

jupyter notebook


The name Jupyter is an indirect acronym of the three core languages it was designed for: JUlia, PYThon, and R.

1.20 Jupyter Operation Modes

  • Developers use a Jupyter notebook in two modes:

    • Command mode (CM)

      • Visually indicated by a blue left-hand border line of the current cell

    • Edit mode (EM)

      • Visually indicated by a green left-hand border line of the current cell

  • When you start a notebook, it opens in EM, ready to accept your commands

  • To switch to CM, press Esc

  • To switch back to EM, click your mouse in a cell or press Enter

1.21 Jupyter Common Commands

  • Basic edit mode (EM) commands:

    • Shift+Enter – run code in the current cell and add a new cell below for the next command

    • Ctrl+Enter – run code in the current cell and switch to CM; if you have multiple selected cells (you can do it in CM), code in all the selected cells is executed

  • Basic CM commands:

    • a – add a cell above the current cell

    • b – add a cell below the current cell

    • c – copy a cell (Ctrl-v to paste it)

    • d – delete the current cell

  • If you need to re-execute commands in your notebook (All, All Above, or All Below) use the Cell menu option in the menu bar

  • Review Jupyter’s help (the Help menu option) to learn about available command shortcuts


You can preview and edit the command shortcuts by navigating to Help > Edit Keyboard Shortcuts using the menu bar. Unfortunately, for now, Jupyter does not support macros / scripting.

1.22 Anaconda

  • Anaconda is a distribution of Python along with its frequently used packages (NumPy, SciPy, pandas, scikit-learn, etc.)

  • Comes with its package manager called conda that helps you list, update and otherwise manage packages

  • Anaconda also includes Jupyter


The conda package manager supports the following commands:

clean Remove unused packages and caches
config Modify configuration values in .condarc. This is modeled after the git config command. Writes to the user .condarc file (C:\Users\Mikhail\.condarc) by default
Create a new conda environment from a list of specified packages
help Displays a list of available conda commands and their help strings
info Display information about current conda install
install Installs a list of packages into a specified conda environment
list List linked packages in a conda environment
package Low-level conda package utility. (EXPERIMENTAL)
remove Remove a list of packages from a specified conda environment
uninstall Alias for conda remove. See conda remove –help.
search Search for packages and display associated information. The input is a MatchSpec, a query language for conda packages.
update Updates conda packages to the latest compatible version. This command accepts a list of package names and updates them to the latest versions that are compatible with all other packages in the environment. Conda attempts to install the newest versions of the requested packages. To accomplish this, it may update some packages that are already installed, or install additional packages. To prevent existing packages from updating, use the –no-update-deps option. This may force conda to install older versions of the requested packages, and it does not prevent additional dependency packages from being installed. If you wish to skip dependency checking altogether, use the ‘–force’ option. This may result in an environment with incompatible packages, so this option must be used with great caution
upgrade Alias for conda update. See conda update –help

1.23 Summary

  • In this tutorial, we discussed the following topics:

    • Python module import considerations, zip and list comprehension commands

    • NumPy

    • pandas

    • SciPy

    • IPython

    • Jupyter notebooks

    • Anaconda Python distribution

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