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008200831s2020 enka ob 000 0 eng d
019 ▼a 1148884727
020 ▼a 178953562X
020 ▼a 9781789535624 ▼q (electronic bk.)
020 ▼z 9781789537253
035 ▼a 2411474 ▼b (N$T)
035 ▼a (OCoLC)1191844268 ▼z (OCoLC)1148884727
037 ▼a CL0501000138 ▼b Safari Books Online
040 ▼a UMI ▼b eng ▼e rda ▼e pn ▼c UMI ▼d OCLCF ▼d EBLCP ▼d N$T ▼d 248032
049 ▼a MAIN
050 4 ▼a QA76.73.P98
08204 ▼a 001.420285 ▼2 23
1001 ▼a Mukhiya, Suresh Kumar, ▼e author.
24510 ▼a Hands-on exploratory data analysis with Python : ▼b perform EDA techniques to understand, summarize, and investigate your data / ▼c Suresh Kumar Mukhiya, Usman Ahmed.
260 ▼a Birmingham, UK : ▼b Packt Publishing, ▼c 2020.
300 ▼a 1 online resource (1 volume) : ▼b illustrations
336 ▼a text ▼b txt ▼2 rdacontent
337 ▼a computer ▼b c ▼2 rdamedia
338 ▼a online resource ▼b cr ▼2 rdacarrier
504 ▼a Includes bibliographical references.
5050 ▼a Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: The Fundamentals of EDA -- Chapter 01: Exploratory Data Analysis Fundamentals -- Understanding data science -- The significance of EDA -- Steps in EDA -- Making sense of data -- Numerical data -- Discrete data -- Continuous data -- Categorical data -- Measurement scales -- Nominal -- Ordinal -- Interval -- Ratio -- Comparing EDA with classical and Bayesian analysis -- Software tools available for EDA -- Getting started with EDA -- NumPy -- Pandas -- SciPy -- Matplotlib
5058 ▼a Applying descriptive statistics -- Data refactoring -- Dropping columns -- Refactoring timezones -- Data analysis -- Number of emails -- Time of day -- Average emails per day and hour -- Number of emails per day -- Most frequently used words -- Summary -- Further reading -- Chapter 04: Data Transformation -- Technical requirements -- Background -- Merging database-style dataframes -- Concatenating along with an axis -- Using df.merge with an inner join -- Using the pd.merge() method with a left join -- Using the pd.merge() method with a right join -- Using pd.merge() methods with outer join
5058 ▼a Merging on index -- Reshaping and pivoting -- Transformation techniques -- Performing data deduplication -- Replacing values -- Handling missing data -- NaN values in pandas objects -- Dropping missing values -- Dropping by rows -- Dropping by columns -- Mathematical operations with NaN -- Filling missing values -- Backward and forward filling -- Interpolating missing values -- Renaming axis indexes -- Discretization and binning -- Outlier detection and filtering -- Permutation and random sampling -- Random sampling without replacement -- Random sampling with replacement
5058 ▼a Computing indicators/dummy variables -- String manipulation -- Benefits of data transformation -- Challenges -- Summary -- Further reading -- Section 2: Descriptive Statistics -- Chapter 05: Descriptive Statistics -- Technical requirements -- Understanding statistics -- Distribution function -- Uniform distribution -- Normal distribution -- Exponential distribution -- Binomial distribution -- Cumulative distribution function -- Descriptive statistics -- Measures of central tendency -- Mean/average -- Median -- Mode -- Measures of dispersion -- Standard deviation -- Variance -- Skewness
520 ▼a Further reading -- Chapter 02: Visual Aids for EDA -- Technical requirements -- Line chart -- Steps involved -- Bar charts -- Scatter plot -- Bubble chart -- Scatter plot using seaborn -- Area plot and stacked plot -- Pie chart -- Table chart -- Polar chart -- Histogram -- Lollipop chart -- Choosing the best chart -- Other libraries to explore -- Summary -- Further reading -- Chapter 03: EDA with Personal Email -- Technical requirements -- Loading the dataset -- Data transformation -- Data cleansing -- Loading the CSV file -- Converting the date -- Removing NaN values
520 ▼a This book provides practical knowledge about the main pillars of EDA including data cleaning, data preparation, data exploration, and data visualization. You can leverage the power of Python to understand, summarize and investigate your data in the best way possible. The book presents a unique approach to exploring hidden features in your data.
588 ▼a Description based on online resource; title from title page (Safari, viewed August 28, 2020).
590 ▼a Added to collection customer.56279.3
650 0 ▼a Python (Computer program language)
650 0 ▼a Data mining.
650 0 ▼a Electronic data processing ▼x Distributed processing.
650 0 ▼a Information visualization.
650 7 ▼a Data mining ▼2 fast ▼0 (OCoLC)fst00887946
650 7 ▼a Electronic data processing ▼x Distributed processing ▼2 fast ▼0 (OCoLC)fst00906987
650 7 ▼a Information visualization ▼2 fast ▼0 (OCoLC)fst00973185
650 7 ▼a Python (Computer program language) ▼2 fast ▼0 (OCoLC)fst01084736
655 4 ▼a Electronic books.
7001 ▼a Ahmed, Usman, ▼e author.
77608 ▼i Print version: ▼a Mukhiya, Suresh Kumar. ▼t Hands-On Exploratory Data Analysis with Python : Perform EDA Techniques to Understand, Summarize, and Investigate Your Data. ▼d Birmingham : Packt Publishing, Limited, 짤2020
85640 ▼3 EBSCOhost ▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2411474
938 ▼a Askews and Holts Library Services ▼b ASKH ▼n AH37351390
938 ▼a ProQuest Ebook Central ▼b EBLB ▼n EBL6151526
938 ▼a EBSCOhost ▼b EBSC ▼n 2411474
990 ▼a 관리자
994 ▼a 92 ▼b N$T