Language:
English
繁體中文
Help
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
A Python Data Analyst’s Toolkit = Le...
~
Rajagopalan, Gayathri.
A Python Data Analyst’s Toolkit = Learn Python and Python-based Libraries with Applications in Data Analysis and Statistics /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
A Python Data Analyst’s Toolkit/ by Gayathri Rajagopalan.
Reminder of title:
Learn Python and Python-based Libraries with Applications in Data Analysis and Statistics /
Author:
Rajagopalan, Gayathri.
Description:
XX, 399 p. 169 illus.online resource. :
Contained By:
Springer Nature eBook
Subject:
Python (Computer program language). -
Online resource:
https://doi.org/10.1007/978-1-4842-6399-0
ISBN:
9781484263990
A Python Data Analyst’s Toolkit = Learn Python and Python-based Libraries with Applications in Data Analysis and Statistics /
Rajagopalan, Gayathri.
A Python Data Analyst’s Toolkit
Learn Python and Python-based Libraries with Applications in Data Analysis and Statistics /[electronic resource] :by Gayathri Rajagopalan. - 1st ed. 2021. - XX, 399 p. 169 illus.online resource.
Chapter 1: Introduction to Python -- Chapter 2: Exploring Containers, Classes & Objects, and Working with Files -- Chapter 3: Regular Expressions -- Chapter 4: Data Analysis Basics -- Chapter 5: Numpy Library -- Chapter 6: Data wrangling with Pandas -- Chapter 7: Data Visualization -- Chapter 8: Case Studies -- Chapter 9: Essentials of Statistics.
Explore the fundamentals of data analysis, and statistics with case studies using Python. This book will show you how to confidently write code in Python, and use various Python libraries and functions for analyzing any dataset. The code is presented in Jupyter notebooks that can further be adapted and extended. This book is divided into three parts – programming with Python, data analysis and visualization, and statistics. You'll start with an introduction to Python – the syntax, functions, conditional statements, data types, and different types of containers. You'll then review more advanced concepts like regular expressions, handling of files, and solving mathematical problems with Python. The second part of the book, will cover Python libraries used for data analysis. There will be an introductory chapter covering basic concepts and terminology, and one chapter each on NumPy(the scientific computation library), Pandas (the data wrangling library) and visualization libraries like Matplotlib and Seaborn. Case studies will be included as examples to help readers understand some real-world applications of data analysis. The final chapters of book focus on statistics, elucidating important principles in statistics that are relevant to data science. These topics include probability, Bayes theorem, permutations and combinations, and hypothesis testing (ANOVA, Chi-squared test, z-test, and t-test), and how the Scipy library enables simplification of tedious calculations involved in statistics. You will: Further your programming and analytical skills with Python Solve mathematical problems in calculus, and set theory and algebra with Python Work with various libraries in Python to structure, analyze, and visualize data Tackle real-life case studies using Python Review essential statistical concepts and use the Scipy library to solve problems in statistics .
ISBN: 9781484263990
Standard No.: 10.1007/978-1-4842-6399-0doiSubjects--Topical Terms:
1127623
Python (Computer program language).
LC Class. No.: QA76.73.P98
Dewey Class. No.: 005.133
A Python Data Analyst’s Toolkit = Learn Python and Python-based Libraries with Applications in Data Analysis and Statistics /
LDR
:03607nam a22003855i 4500
001
1050991
003
DE-He213
005
20210622010140.0
007
cr nn 008mamaa
008
220103s2021 xxu| s |||| 0|eng d
020
$a
9781484263990
$9
978-1-4842-6399-0
024
7
$a
10.1007/978-1-4842-6399-0
$2
doi
035
$a
978-1-4842-6399-0
050
4
$a
QA76.73.P98
072
7
$a
UMX
$2
bicssc
072
7
$a
COM051360
$2
bisacsh
072
7
$a
UMX
$2
thema
082
0 4
$a
005.133
$2
23
100
1
$a
Rajagopalan, Gayathri.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1355444
245
1 2
$a
A Python Data Analyst’s Toolkit
$h
[electronic resource] :
$b
Learn Python and Python-based Libraries with Applications in Data Analysis and Statistics /
$c
by Gayathri Rajagopalan.
250
$a
1st ed. 2021.
264
1
$a
Berkeley, CA :
$b
Apress :
$b
Imprint: Apress,
$c
2021.
300
$a
XX, 399 p. 169 illus.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
505
0
$a
Chapter 1: Introduction to Python -- Chapter 2: Exploring Containers, Classes & Objects, and Working with Files -- Chapter 3: Regular Expressions -- Chapter 4: Data Analysis Basics -- Chapter 5: Numpy Library -- Chapter 6: Data wrangling with Pandas -- Chapter 7: Data Visualization -- Chapter 8: Case Studies -- Chapter 9: Essentials of Statistics.
520
$a
Explore the fundamentals of data analysis, and statistics with case studies using Python. This book will show you how to confidently write code in Python, and use various Python libraries and functions for analyzing any dataset. The code is presented in Jupyter notebooks that can further be adapted and extended. This book is divided into three parts – programming with Python, data analysis and visualization, and statistics. You'll start with an introduction to Python – the syntax, functions, conditional statements, data types, and different types of containers. You'll then review more advanced concepts like regular expressions, handling of files, and solving mathematical problems with Python. The second part of the book, will cover Python libraries used for data analysis. There will be an introductory chapter covering basic concepts and terminology, and one chapter each on NumPy(the scientific computation library), Pandas (the data wrangling library) and visualization libraries like Matplotlib and Seaborn. Case studies will be included as examples to help readers understand some real-world applications of data analysis. The final chapters of book focus on statistics, elucidating important principles in statistics that are relevant to data science. These topics include probability, Bayes theorem, permutations and combinations, and hypothesis testing (ANOVA, Chi-squared test, z-test, and t-test), and how the Scipy library enables simplification of tedious calculations involved in statistics. You will: Further your programming and analytical skills with Python Solve mathematical problems in calculus, and set theory and algebra with Python Work with various libraries in Python to structure, analyze, and visualize data Tackle real-life case studies using Python Review essential statistical concepts and use the Scipy library to solve problems in statistics .
650
0
$a
Python (Computer program language).
$3
1127623
650
0
$a
Statistics .
$3
1253516
650
0
$a
Computer software.
$3
528062
650
1 4
$a
Python.
$3
1115944
650
2 4
$a
Statistics, general.
$3
671463
650
2 4
$a
Professional Computing.
$3
1115983
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9781484263983
776
0 8
$i
Printed edition:
$z
9781484264003
856
4 0
$u
https://doi.org/10.1007/978-1-4842-6399-0
912
$a
ZDB-2-CWD
912
$a
ZDB-2-SXPC
950
$a
Professional and Applied Computing (SpringerNature-12059)
950
$a
Professional and Applied Computing (R0) (SpringerNature-43716)
based on 0 review(s)
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login