LEADER 05909nam 2200793 450 001 9910797265203321 005 20151005070350.0 035 $a(CKB)3710000000417249 035 $a(EBL)2057551 035 $a(OCoLC)910282285 035 $a(SSID)ssj0001539164 035 $a(PQKBManifestationID)11878928 035 $a(PQKBTitleCode)TC0001539164 035 $a(PQKBWorkID)11529120 035 $a(PQKB)10955772 035 $a(MiAaPQ)EBC2057551 035 $a(CaSebORM)9781783985104 035 $a(PPN)228048583 035 $a(EXLCZ)993710000000417249 100 $a20150609h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMastering pandas for finance $emaster pandas, an open source Python Data Analysis Library, for financial data analysis /$fMichael Heydt ; reviewers, James Beveridge [and four others] ; commissioning editor, Kartikey Pandey ; content development editor, Merwyn D'souza ; technical editor, Shashank Desai ; copy editor, Sarang Chari ; project coordinator, Neha Bhatnagar ; proofreaders, Stephen Copestake, Safis Editing ; indexer, Mariammal Chettiyar ; graphics, Sheetal Aute, Disha Haria ; production coordinator, Conidon Miranda ; cover work, Conidon Miranda 205 $a1st edition 210 1$aBirmingham, England ;$aMumbai, [India] :$cPackt Publishing,$d2015. 210 4$dİ2015 215 $a1 online resource (298 p.) 225 1 $aCommunity experience distilled 300 $a"Community experience distilled"--Cover. 300 $aIncludes index. 311 $a1-78398-511-9 311 $a1-78398-510-0 327 $a""Cover""; ""Copyright""; ""Credits""; ""About the Author""; ""About the Reviewers""; ""www.PacktPub.com""; ""Table of Contents""; ""Preface""; ""Chapter 1: Getting Started with pandas Using Wakari.io""; ""What is Wakari?""; ""Creating a Wakari cloud account""; ""Updating existing packages""; ""Installing new packages""; ""Installing the samples in Wakari""; ""Summary""; ""Chapter 2: Introducing the Series and DataFrame""; ""Notebook setup""; ""The main pandas data structures a??? Series and DataFrame""; ""The Series""; ""The DataFrame""; ""The basics of the Series and DataFrame objects"" 327 $a""Creating a Series and accessing elements""""Size, shape, uniqueness, and counts of values""; ""Alignment via index labels""; ""Creating a DataFrame""; ""Example data""; ""Selecting columns of a DataFrame""; ""Selecting rows of a DataFrame using the index""; ""Slicing using the [] operator""; ""Selecting rows by the index label and location a??? .loc[] and .iloc[]""; ""Selecting rows by the index label and/or location a??? .ix[]""; ""Scalar lookup by label or location using .at[] and .iat[]""; ""Selecting rows using the Boolean selection""; ""Arithmetic on a DataFrame"" 327 $a""Reindexing the Series and DataFrame objects""""Summary""; ""Chapter 3: Reshaping, Reorganizing, and Aggregating""; ""Notebook setup""; ""Loading historical stock data""; ""Organizing the data for the examples""; ""Reorganizing and reshaping data""; ""Concatenating multiple DataFrame objects""; ""Merging DataFrame objects""; ""Pivoting""; ""Stacking and unstacking""; ""Melting""; ""Grouping and aggregating""; ""Splitting""; ""Aggregating""; ""Summary""; ""Chapter 4: Time-series""; ""Notebook setup""; ""Time-series data and the DatetimeIndex"" 327 $a""Creating time-series with specific frequencies""""Representing intervals of time using periods""; ""Shifting and lagging time-series data""; ""Frequency conversion of time-series data""; ""Resampling of time-series""; ""Summary""; ""Chapter 5: Time-series Stock Data""; ""Notebook setup""; ""Obtaining historical stock and index data""; ""Fetching historical stock data from Yahoo!""; ""Fetching index data from Yahoo!""; ""Visualizing financial time-series data""; ""Plotting closing prices""; ""Plotting volume-series data""; ""Combined price and volumes""; ""Plotting candlesticks"" 327 $a""Fundamental financial calculations""""Calculating simple daily percentage change""; ""Calculating simple daily cumulative returns""; ""Analyzing the distribution of returns""; ""Histograms""; ""Q-Q plots""; ""Box-and-whisker plots""; ""Comparison of daily percentage change between stocks""; ""Moving windows""; ""Volatility calculation""; ""Rolling correlation of returns""; ""Least-squares regression of returns""; ""Comparing stocks to the S&P 500""; ""Summary""; ""Chapter 6: Trading Using Google Trends""; ""Notebook setup"" 327 $a""A brief on Quantifying Trading Behavior in Financial Markets Using Google Trends"" 330 $aIf you are interested in quantitative finance, financial modeling, and trading, or simply want to learn how Python and pandas can be applied to finance, then this book is ideal for you. Some knowledge of Python and pandas is assumed. Interest in financial concepts is helpful, but no prior knowledge is expected. 606 $aFinance$xMathematical models 606 $aPython (Computer program language) 606 $aData mining 615 0$aFinance$xMathematical models. 615 0$aPython (Computer program language) 615 0$aData mining. 676 $a332 700 $aHeydt$b Michael$01535389 702 $aBeveridge$b James 702 $aPandey$b Kartikey 702 $aD'souza$b Merwyn 702 $aDesai$b Shashank 702 $aChari$b Sarang 702 $aBhatnagar$b Neha 702 $aCopestake$b Stephen 702 $aChettiyar$b Mariammal 702 $aAute$b Sheetal 702 $aHaria$b Disha 702 $aMiranda$b Conidon 712 02$aSafis Editing, 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910797265203321 996 $aMastering pandas for finance$93783553 997 $aUNINA