Python for Finance: Analyze Big Financial Data

(Elle) #1

Class


date-time information support in, pandas–pandas


development of, Financial Time Series


error tolerance in, Basic Analytics


groupby operations, GroupBy Operations


hierarchically indexed data sets and, Implied Volatilities


input-output operations


data as CSV file, Data as CSV File


data as Excel file, Data as Excel File


from SQL to pandas, From SQL to pandas


SQL databases, SQL Database


reading/writing spreadsheets with, Using pandas for Reading and Writing


Series class, Series Class


working with missing data, First Steps with DataFrame Class


wrapper for matplotlib library, Basic Analytics


parallel computing, Parallel Computing–Performance Comparison


Monte Carlo algorithm, The Monte Carlo Algorithm


parallel calculation, The Parallel Calculation


performance comparison, Performance Comparison


sequential calculation, The Sequential Calculation


PEP (Python Enhancement Proposal) 20, What Is Python?


PEP (Python Enhancement Proposal) 8, Python Syntax


performance computing


benefits of Python for, Ensuring high performance


dynamic compiling, Dynamic Compiling–Binomial Option Pricing


memory layout and, Memory Layout and Performance


multiprocessing module, multiprocessing


parallel computing, Parallel Computing–Performance Comparison


Python paradigms and, Python Paradigms and Performance


random number generation on GPUs, Generation of Random Numbers on GPUs


static compiling with Cython, Static Compiling with Cython


petascale processing, Input/Output Operations


pickle module, Writing Objects to Disk


plot function, One-Dimensional Data Set

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