Python for Data Analysis

Research Computing Services

Katia Oleinik (koleinik@bu.edu)

Tutorial Content

2

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

Python Libraries for Data Science

Many popular Python toolboxes/libraries:

• NumPy
• SciPy
• Pandas
• SciKit-Learn

​

Visualization libraries

• matplotlib
• Seaborn

​

and many more …

3

All these libraries are installed on the SCC

Python Libraries for Data Science

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

4

Link: http://www.numpy.org/

Python Libraries for Data Science

SciPy:

• collection of algorithms for linear algebra, differential equations, numerical integration, optimization, statistics and more

​

• part of SciPy Stack

​

• built on NumPy

5

Link: https://www.scipy.org/scipylib/

Python Libraries for Data Science

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

6

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

Python Libraries for Data Science

SciKit-Learn:

• provides machine learning algorithms: classification, regression, clustering, model validation etc.

​

• built on NumPy, SciPy and matplotlib

​

Link: http://scikit-learn.org/

7

Python Libraries for Data Science

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/

8

Python Libraries for Data Science

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/

9

Login to the Shared Computing Cluster

• Use your SCC login information if you have SCC account

​

• If you are using tutorial accounts see info on the blackboard

​

​

Note: Your password will not be displayed while you enter it.

10

Selecting Python Version on the SCC

# view available python versions on the SCC

[scc1 ~] module avail python

​

# load python 3 version

[scc1 ~] module load python/3.6.2

11

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

12

Start Jupyter nootebook

# On the Shared Computing Cluster

[scc1 ~] jupyter notebook

13

Loading Python Libraries

In [ ]:

14

#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

Reading data using pandas

In [ ]:

15

#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.

Exploring data frames

In [3]:

16

#List first 5 records

df.head()

Out[3]:

Hands-on exercises

17

• Try to read the first 10, 20, 50 records;
• Can you guess how to view the last few records; Hint:

Data Frame data types

18

Data Frame data types

In [4]:

19

#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

20

Python objects have attributes and methods.

Hands-on exercises

21

• 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

22

Unlike attributes, python methods have parenthesis.

All attributes and methods can be listed with a dir() function: dir(df)

Hands-on exercises

23

• 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.

24

Hands-on exercises

25

• 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

26

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()

Data Frames groupby method

27

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

Data Frames groupby method

28

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:

​

In [ ]:

#Calculate mean salary for each professor rank:

df.groupby(['rank'], sort=False)[['salary']].mean()

Data Frame: filtering

29

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:

​

In [ ]:

#Calculate mean salary for each professor rank:

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

30

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

31

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

32

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

33

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

34

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)

35

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]] #1^st and 6^th rows and 2^nd and 4^th columns

​

​

Data Frames: Sorting

36

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

37

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

38

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

39

There are a number of methods to deal with missing values in the data frame:

Missing Values

40

• 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

41

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

42

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

43

Graphics to explore the data

44

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

45

Basic statistical Analysis

46

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)

47