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

Python Libraries for Data Science
Many popular Python toolboxes/libraries:
• NumPy
• SciPy
• Pandas
• SciKit-Learn
Visualization libraries
• matplotlib
• Seaborn
and many more …
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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/

Pandas:
 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
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Link: http://pandas.pydata.org/

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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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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Download tutorial notebook
# On the Shared Computing Cluster
[scc1 ~] cp /project/scv/examples/python/data_analysis/dataScience.ipynb .
# On a local computer save the link:
http://rcs.bu.edu/examples/python/data_analysis/dataScience.ipynb
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Start Jupyter nootebook
# On the Shared Computing Cluster
[scc1 ~] jupyter notebook
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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
Press Shift+Enter to execute the jupyter cell

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]:

Hands-on exercises
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 Try to read the first 10, 20, 50 records;
 Can you guess how to view the last few records; Hint:

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?

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)

Hands-on exercises
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 Give the summary for the numeric columns in the dataset
 Calculate standard deviation for all numeric columns;
 What are the mean values of the first 50 records in the dataset? Hint: use
head() method to subset the first 50 records and then calculate the mean

Selecting a column in a Data Frame
Method 1: Subset the data frame using column name:
df['sex']
Method 2: Use the column name as an attribute:
df.sex
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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Hands-on exercises
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 Calculate the basic statistics for the salary column;
 Find how many values in the salary column (use count method);
 Calculate the average salary;

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;

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
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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
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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
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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
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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
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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
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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
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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

Basic statistical Analysis
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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 is
more tailored for Machine Learning.
statsmodels:
• linear regressions
• ANOVA tests
• hypothesis testings
• many more ...
scikit-learn:
• kmeans
• support vector machines
• random forests
• many more ...
See examples in the Tutorial Notebook

Conclusion
Thank you for attending the tutorial.
Please fill the evaluation form:
http://scv.bu.edu/survey/tutorial_evaluation.html
Questions:
email: koleinik@bu.edu (Katia Oleinik)
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