Meetup Junio Data Analysis with python 2018

Introduction to Data Analysis
techniques using Python
First steps into insight discovery using Python and specialized libraries
Alex Chalini - sentoul@Hotmail.com

About me
• Computer Systems Engineer, Master of Computer Science (…)
• Actively working in Business Solutions development since
2001
• My areas of specialty are Business Intelligence, Data Analysis,
Data Visualization, DB modeling and optimization.
• I am also interested in Data Science path for engineering.
2
Alex Chalini

Agenda
• What is Data Analysis?
• Python Libraries for Data Analysis and Data Science
• Hands-on data analysis workflow using Python
• Statistical Analysis & ML overview
• Big Data & Data Analytics working together
• Applicatons in Pharma industry
3

Question:
The process of systematically applying
techniques to evaluate data is known as ?
A. Data Munging
B. Data Analysis
C. Data Science
D. Data Bases
0A B C D
4

Data Analysis:
•What is it?
•Apply logical
techniques to
•Describe, condense,
recap and evaluate
Data and
•Illustrate Information
•Goals of Data Analysis:
1. Discover useful
information
2. Provide insights
3. Suggest conclusions
4. Support Decision
Making
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Phyton Data Analysis Basics
• Series
• DataFrame
• Creating a DataFrame from a dict
• Select columns, Select rows with Boolean indexing
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Essential Concepts
• A Series is a named Python list (dict with list as value).
{ ‘grades’ : [50,90,100,45] }
• A DataFrame is a dictionary of Series (dict of series):
{ { ‘names’ : [‘bob’,’ken’,’art’,’joe’]}
{ ‘grades’ : [50,90,100,45] }
}
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Python Libraries for Data Analysis and Data
Science
Many popular Python toolboxes/libraries:
• NumPy
• SciPy
• Pandas
• SciKit-Learn
Visualization libraries
• matplotlib
• Seaborn
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All these libraries are
free to download and
use

AnalyticsWorkflow
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Overview of Python Libraries for Data
Scientists
Reading Data; Selecting and Filtering the Data; Data manipulation,
sorting, grouping, rearranging
Plotting the data
Descriptive statistics
Inferential statistics

NumPy:
 introduces objects for multidimensional arrays and matrices, as well as
functions that allow to easily perform advanced mathematical and statistical
operations on those objects
 provides vectorization of mathematical operations on arrays and matrices
which significantly improves the performance
 many other python libraries are built on NumPy
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Link: http://www.numpy.org/

SciPy:
 collection of algorithms for linear algebra, differential equations, numerical
integration, optimization, statistics and more
 part of SciPy Stack
 built on NumPy
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Link: https://www.scipy.org/scipylib/

• It Provides built-in data structures which simplify the manipulation and analysis of data sets.
• Pandas is easy to use and powerful, but “with great power comes great responsibility”
• adds data structures and tools designed to work with table-like data (similar to Series and Data
Frames in R)
• provides tools for data manipulation: reshaping, merging, sorting, slicing, aggregation etc.
• allows handling missing data
I cannot teach you all things Pandas, we must focus on how it works, so you can figure out the rest
on your own.
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Link: http://pandas.pydata.org/
Pandas is Python package for data analysis.

Link: http://scikit-learn.org/
SciKit-Learn:
 provides machine learning algorithms: classification, regression, clustering,
model validation etc.
 built on NumPy, SciPy and matplotlib
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Link: https://www.tensorflow.org/
TensorFlow™ is an open source software library for high performance numerical
computation.
It comes with strong support for machine learning and deep learning and the
flexible numerical computation core is used across many other scientific domains
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matplotlib:
 python 2D plotting library which produces publication quality figures in a
variety of hardcopy formats
 a set of functionalities similar to those of MATLAB
 line plots, scatter plots, barcharts, histograms, pie charts etc.
 relatively low-level; some effort needed to create advanced visualization
Link: https://matplotlib.org/
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Seaborn:
 based on matplotlib
 provides high level interface for drawing attractive statistical graphics
 Similar (in style) to the popular ggplot2 library in R
Link: https://seaborn.pydata.org/
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Hands-on workflow using Python
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In [ ]:
Loading Python Libraries
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#Import Python Libraries
import numpy as np
import scipy as sp
import pandas as pd
import matplotlib as mpl
import seaborn as sns

In [ ]:
Reading data using pandas
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#Read csv file
df = pd.read_csv("http://rcs.bu.edu/examples/python/data_analysis/Salaries.csv")
There is a number of pandas commands to read other data formats:
pd.read_excel('myfile.xlsx',sheet_name='Sheet1', index_col=None, na_values=['NA'])
pd.read_stata('myfile.dta')
pd.read_sas('myfile.sas7bdat')
pd.read_hdf('myfile.h5','df')
Note: The above command has many optional arguments to fine-tune the data import process.

In [3]:
Exploring data frames
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#List first 5 records
df.head()
Out[3]:

Data Frame data types
Pandas Type Native Python Type Description
object string The most general dtype. Will be
assigned to your column if column
has mixed types (numbers and
strings).
int64 int Numeric characters. 64 refers to
the memory allocated to hold this
character.
float64 float Numeric characters with decimals.
If a column contains numbers and
NaNs(see below), pandas will
default to float64, in case your
missing value has a decimal.
datetime64, timedelta[ns] N/A (but see the datetime module
in Python’s standard library)
Values meant to hold time data.
Look into these for time series
experiments.
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In [4]:
Data Frame data types
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#Check a particular column type
df['salary'].dtype
Out[4]: dtype('int64')
In [5]: #Check types for all the columns
df.dtypes
Out[4]: rank
discipline
phd
service
sex
salary
dtype: object
object
object
int64
int64
object
int64

Data Frames attributes
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Python objects have attributes and methods.
df.attribute description
dtypes list the types of the columns
columns list the column names
axes list the row labels and column names
ndim number of dimensions
size number of elements
shape return a tuple representing the dimensionality
values numpy representation of the data

Hands-on exercises
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 Find how many records this data frame has;
 How many elements are there?
 What are the column names?
 What types of columns we have in this data frame?
In [5]: df.shape
Out[5]: (4, 3)
>>> df.count()
Person 4
Age 4
Single 5
dtype: int64
list(my_dataframe.columns.values)
Also you can simply use:
list(my_dataframe)
>>> df2.dtypes

Series or DataFrame?
Match the code to the
result. One result is a Series,
the other a DataFrame
1.df[‘Quarter’]
2.df[ [‘Quarter’] ]
A. Series B. Data Frame
0A B
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Data Frames methods
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df.method() description
head( [n] ), tail( [n] ) first/last n rows
describe() generate descriptive statistics (for numeric columns only)
max(), min() return max/min values for all numeric columns
mean(), median() return mean/median values for all numeric columns
std() standard deviation
sample([n]) returns a random sample of the data frame
dropna() drop all the records with missing values
Unlike attributes, python methods have parenthesis.
All attributes and methods can be listed with a dir() function: dir(df)

Selecting a column in a Data Frame
Method 1: Subset the data frame using column name:
df[‘gender']
Method 2: Use the column name as an attribute:
df.gender
Note: there is an attribute rank for pandas data frames, so to select a column with a name
"rank" we should use method 1.
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Data Frames groupby method
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Using "group by" method we can:
• Split the data into groups based on some criteria
• Calculate statistics (or apply a function) to each group
• Similar to dplyr() function in R
In [ ]: #Group data using rank
df_rank = df.groupby(['rank'])
In [ ]: #Calculate mean value for each numeric column per each group
df_rank.mean()

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Once groupby object is create we can calculate various statistics for each group:
In [ ]: #Calculate mean salary for each professor rank:
df.groupby('rank')[['salary']].mean()
Note: If single brackets are used to specify the column (e.g. salary), then the output is Pandas Series object.
When double brackets are used the output is a Data Frame

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groupby performance notes:
- no grouping/splitting occurs until it's needed. Creating the groupby object
only verifies that you have passed a valid mapping
- by default the group keys are sorted during the groupby operation. You may
want to pass sort=False for potential speedup:
df.groupby(['rank'], sort=False)[['salary']].mean()

Data Frame: filtering
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To subset the data we can apply Boolean indexing. This indexing is commonly
known as a filter. For example if we want to subset the rows in which the salary
value is greater than $120K:
df_sub = df[ df['salary'] > 120000 ]
In [ ]: #Select only those rows that contain female professors:
df_f = df[ df['sex'] == 'Female' ]
Any Boolean operator can be used to subset the data:
> greater; >= greater or equal;
< less; <= less or equal;
== equal; != not equal;

Boolean filtering
Which rows are included in this
Boolean index?
df[ df[‘Sold’] < 110 ]
A. 0, 1, 2
B. 1, 2, 3
C. 0, 1
D. 0, 3
0A B C D
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Data Frames: Slicing
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There are a number of ways to subset the Data Frame:
• one or more columns
• one or more rows
• a subset of rows and columns
Rows and columns can be selected by their position or label

Data Frames: Slicing
34
When selecting one column, it is possible to use single set of brackets, but the
resulting object will be a Series (not a DataFrame):
In [ ]: #Select column salary:
df['salary']
When we need to select more than one column and/or make the output to be a
DataFrame, we should use double brackets:
In [ ]: #Select column salary:
df[['rank','salary']]

Data Frames: Selecting rows
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If we need to select a range of rows, we can specify the range using ":"
In [ ]: #Select rows by their position:
df[10:20]
Notice that the first row has a position 0, and the last value in the range is omitted:
So for 0:10 range the first 10 rows are returned with the positions starting with 0
and ending with 9

Data Frames: method loc
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If we need to select a range of rows, using their labels we can use method loc:
In [ ]: #Select rows by their labels:
df_sub.loc[10:20,['rank','sex','salary']]
Out[ ]:

Data Frames: method iloc
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If we need to select a range of rows and/or columns, using their positions we can
use method iloc:
In [ ]: #Select rows by their labels:
df_sub.iloc[10:20,[0, 3, 4, 5]]
Out[ ]:

Data Frames: method iloc (summary)
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df.iloc[0] # First row of a data frame
df.iloc[i] #(i+1)th row
df.iloc[-1] # Last row
df.iloc[:, 0] # First column
df.iloc[:, -1] # Last column
df.iloc[0:7] #First 7 rows
df.iloc[:, 0:2] #First 2 columns
df.iloc[1:3, 0:2] #Second through third rows and first 2 columns
df.iloc[[0,5], [1,3]] #1st and 6th rows and 2nd and 4th columns

Data Frames: Sorting
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We can sort the data by a value in the column. By default the sorting will occur in
ascending order and a new data frame is return.
In [ ]: # Create a new data frame from the original sorted by the column Salary
df_sorted = df.sort_values( by ='service')
df_sorted.head()
Out[ ]:

Data Frames: Sorting
40
We can sort the data using 2 or more columns:
In [ ]: df_sorted = df.sort_values( by =['service', 'salary'], ascending = [True, False])
df_sorted.head(10)
Out[ ]:

Missing Values
41
Missing values are marked as NaN
In [ ]: # Read a dataset with missing values
flights = pd.read_csv("http://rcs.bu.edu/examples/python/data_analysis/flights.csv")
In [ ]: # Select the rows that have at least one missing value
flights[flights.isnull().any(axis=1)].head()
Out[ ]:

Missing Values
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There are a number of methods to deal with missing values in the data frame:
dropna() Drop missing observations
dropna(how='all') Drop observations where all cells is NA
dropna(axis=1, how='all') Drop column if all the values are missing
dropna(thresh = 5) Drop rows that contain less than 5 non-missing values
fillna(0) Replace missing values with zeros
isnull() returns True if the value is missing
notnull() Returns True for non-missing values

Missing Values
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• When summing the data, missing values will be treated as zero
• If all values are missing, the sum will be equal to NaN
• cumsum() and cumprod() methods ignore missing values but preserve them in
the resulting arrays
• Missing values in GroupBy method are excluded (just like in R)
• Many descriptive statistics methods have skipna option to control if missing
data should be excluded . This value is set to True by default (unlike R)

Aggregation Functions in Pandas
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Aggregation - computing a summary statistic about each group, i.e.
• compute group sums or means
• compute group sizes/counts
Common aggregation functions:
min, max
count, sum, prod
mean, median, mode, mad
std, var

Aggregation Functions in Pandas
45
agg() method are useful when multiple statistics are computed per column:
In [ ]: flights[['dep_delay','arr_delay']].agg(['min','mean','max'])
Out[ ]:

Basic Descriptive Statistics
46
describe Basic statistics (count, mean, std, min, quantiles, max)
min, max Minimum and maximum values
mean, median, mode Arithmetic average, median and mode
var, std Variance and standard deviation
sem Standard error of mean
skew Sample skewness
kurt kurtosis

Graphics to explore the data
47
To show graphs within Python notebook include inline directive:
In [ ]: %matplotlib inline
Seaborn package is built on matplotlib but provides high level
interface for drawing attractive statistical graphics, similar to ggplot2
library in R. It specifically targets statistical data visualization

Graphics
48
description
distplot histogram
barplot estimate of central tendency for a numeric variable
violinplot similar to boxplot, also shows the probability density of the
data
jointplot Scatterplot
regplot Regression plot
pairplot Pairplot
boxplot boxplot
swarmplot categorical scatterplot
factorplot General categorical plot

Statistical Analysis & ML overview
50
statsmodel and scikit-learn - both have a number of function for statistical analysis
The first one is mostly used for regular analysis using R style formulas, while scikit-learn and Tensorflow are more
tailored for Machine Learning.
statsmodels:
• linear regressions
• ANOVA tests
• hypothesis testings
• many more ...
scikit-learn:
• kmeans
• support vector machines
• random forests
• many more ...
Tensorflow:
• Image Recognition
• Neural Networks
• Linear Models
• TensorFlow Wide & Deep Learning
• etc...

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Big Data & Data Analytics working together

WORKING WITH BIG DATA: MAP-REDUCE
• When working with large datasets, it’s often useful to utilize MapReduce.
MapReduce is a method when working with big data which allows you to
first map the data using a particular attribute, filter or grouping and then
reduce those using a transformation or aggregation mechanism. For
example, if I had a collection of cats, I could first map them by what color
they are and then reduce by summing those groups. At the end of the
MapReduce process, I would have a list of all the cat colors and the sum of
the cats in each of those color groupings.
• Almost every data science library has some MapReduce functionality built
in. There are also numerous larger libraries you can use to manage the data
and MapReduce over a series of computers (or a cluster / grouping of
computers). Python can speak to these services and software and extract
the results for further reporting, visualization or alerting.
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Hadoop
• If the most popular libraries for MapReduce with large datasets is Apache’s Hadoop. Hadoop
uses cluster computing to allow for faster data processing of large datasets. There are many
Python libraries you can use to send your data or jobs to Hadoop and which one you choose
should be a mixture of what’s easiest and most simple to set up with your infastructure, and
also what seems like the most clear library for your use case.
Spark
• If you have large data which might work better in streaming form (real-time data, log data,
API data), then Apache’s Spark is a great tool. PySpark, the Python Spark API, allows you to
quickly get up and running and start mapping and reducing your dataset. It’s also incredibly
popular with machine learning problems, as it has some built-in algorithms.
• There are several other large scale data and job libraries you can use with Python, but for now
we can move along to looking at data with Python.
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Apache Spark is written in Scala programming language. To
support Python with Spark, Apache Spark community released
a tool, PySpark. Using PySpark, you can work with RDDs in
Python programming language also.
BigQuery is Google's serverless, highly scalable, low cost enterprise data
warehouse.
BigQuery allows organizations to capture and analyze data in real-time
using its powerful streaming ingestion capability so that your insights are
always current.

Industries using Real-Time Big Data-Analytics
• e-Commerce
• Social Networks
• Healthcare
• Fraud Detection
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OPTIMIZE CUSTOMER SERVICE PROCESS IN A FLOW OF
CONTINUOUS DATA, MAKING LIFE SAVING DECISIONS IN
A SAFE ENVIRONMENT TO RUN THE BUSINESS.

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Applications in Pharma Industry

Applications in the Pharma Industry
57

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