語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Thinking in Pandas = How to Use the ...
~
Stepanek, Hannah.
Thinking in Pandas = How to Use the Python Data Analysis Library the Right Way /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Thinking in Pandas/ by Hannah Stepanek.
其他題名:
How to Use the Python Data Analysis Library the Right Way /
作者:
Stepanek, Hannah.
面頁冊數:
XI, 186 p. 27 illus.online resource. :
Contained By:
Springer Nature eBook
標題:
Big Data. -
電子資源:
https://doi.org/10.1007/978-1-4842-5839-2
ISBN:
9781484258392
Thinking in Pandas = How to Use the Python Data Analysis Library the Right Way /
Stepanek, Hannah.
Thinking in Pandas
How to Use the Python Data Analysis Library the Right Way /[electronic resource] :by Hannah Stepanek. - 1st ed. 2020. - XI, 186 p. 27 illus.online resource.
Chapter 1: Introduction -- Chapter 2: Basic Data Access and Merging -- Chapter 3: How Pandas Works Under the Hood -- Chapter 4: Loading and Normalizing Data in pandas -- Chapter 5: Basic Data Transformation in pandas -- Chapter 6: The Apply Method -- Chapter 7: Groupby -- Chapter 8: Performance Improvements Beyond pandas -- Chapter 9: The Future of Pandas -- Appendix.-.
Understand and implement big data analysis solutions in pandas with an emphasis on performance. This book strengthens your intuition for working with pandas, the Python data analysis library, by exploring its underlying implementation and data structures. Thinking in Pandas introduces the topic of big data and demonstrates concepts by looking at exciting and impactful projects that pandas helped to solve. From there, you will learn to assess your own projects by size and type to see if pandas is the appropriate library for your needs. Author Hannah Stepanek explains how to load and normalize data in pandas efficiently, and reviews some of the most commonly used loaders and several of their most powerful options. You will then learn how to access and transform data efficiently, what methods to avoid, and when to employ more advanced performance techniques. You will also go over basic data access and munging in pandas and the intuitive dictionary syntax. Choosing the right DataFrame format, working with multi-level DataFrames, and how pandas might be improved upon in the future are also covered. By the end of the book, you will have a solid understanding of how the pandas library works under the hood. Get ready to make confident decisions in your own projects by utilizing pandas—the right way. You will: Understand the underlying data structure of pandas and why it performs the way it does under certain circumstances Discover how to use pandas to extract, transform, and load data correctly with an emphasis on performance Choose the right DataFrame so that the data analysis is simple and efficient. Improve performance of pandas operations with other Python libraries.
ISBN: 9781484258392
Standard No.: 10.1007/978-1-4842-5839-2doiSubjects--Topical Terms:
1017136
Big Data.
LC Class. No.: QA76.73.P98
Dewey Class. No.: 005.133
Thinking in Pandas = How to Use the Python Data Analysis Library the Right Way /
LDR
:03382nam a22003855i 4500
001
1028491
003
DE-He213
005
20200629164349.0
007
cr nn 008mamaa
008
210318s2020 xxu| s |||| 0|eng d
020
$a
9781484258392
$9
978-1-4842-5839-2
024
7
$a
10.1007/978-1-4842-5839-2
$2
doi
035
$a
978-1-4842-5839-2
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
Stepanek, Hannah.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1325056
245
1 0
$a
Thinking in Pandas
$h
[electronic resource] :
$b
How to Use the Python Data Analysis Library the Right Way /
$c
by Hannah Stepanek.
250
$a
1st ed. 2020.
264
1
$a
Berkeley, CA :
$b
Apress :
$b
Imprint: Apress,
$c
2020.
300
$a
XI, 186 p. 27 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 -- Chapter 2: Basic Data Access and Merging -- Chapter 3: How Pandas Works Under the Hood -- Chapter 4: Loading and Normalizing Data in pandas -- Chapter 5: Basic Data Transformation in pandas -- Chapter 6: The Apply Method -- Chapter 7: Groupby -- Chapter 8: Performance Improvements Beyond pandas -- Chapter 9: The Future of Pandas -- Appendix.-.
520
$a
Understand and implement big data analysis solutions in pandas with an emphasis on performance. This book strengthens your intuition for working with pandas, the Python data analysis library, by exploring its underlying implementation and data structures. Thinking in Pandas introduces the topic of big data and demonstrates concepts by looking at exciting and impactful projects that pandas helped to solve. From there, you will learn to assess your own projects by size and type to see if pandas is the appropriate library for your needs. Author Hannah Stepanek explains how to load and normalize data in pandas efficiently, and reviews some of the most commonly used loaders and several of their most powerful options. You will then learn how to access and transform data efficiently, what methods to avoid, and when to employ more advanced performance techniques. You will also go over basic data access and munging in pandas and the intuitive dictionary syntax. Choosing the right DataFrame format, working with multi-level DataFrames, and how pandas might be improved upon in the future are also covered. By the end of the book, you will have a solid understanding of how the pandas library works under the hood. Get ready to make confident decisions in your own projects by utilizing pandas—the right way. You will: Understand the underlying data structure of pandas and why it performs the way it does under certain circumstances Discover how to use pandas to extract, transform, and load data correctly with an emphasis on performance Choose the right DataFrame so that the data analysis is simple and efficient. Improve performance of pandas operations with other Python libraries.
650
2 4
$a
Big Data.
$3
1017136
650
2 4
$a
Machine Learning.
$3
1137723
650
2 4
$a
Open Source.
$3
1113081
650
1 4
$a
Python.
$3
1115944
650
0
$a
Big data.
$3
981821
650
0
$a
Machine learning.
$3
561253
650
0
$a
Computer programming.
$3
527822
650
0
$a
Open source software.
$3
561177
650
0
$a
Python (Computer program language).
$3
1127623
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9781484258385
776
0 8
$i
Printed edition:
$z
9781484258408
856
4 0
$u
https://doi.org/10.1007/978-1-4842-5839-2
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)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入