دسترسی نامحدود
برای کاربرانی که ثبت نام کرده اند
دانلود کتاب Python: End-to-end Data Analysis : Leverage the Power of Python to Clean, Scrape, Analyze, and Visualize Your Data : a Course in Three Modules
دانلود کتاب پایتون: تجزیه و تحلیل داده‌های سرتاسر: از قدرت پایتون برای تمیز کردن، خراشیدن، تجزیه و تحلیل و تجسم داده‌های خود استفاده کنید: دوره آموزشی در سه ماژول
مشخصات کتاب
Python: End-to-end Data Analysis : Leverage the Power of Python to Clean, Scrape, Analyze, and Visualize Your Data : a Course in Three Modules
ویرایش: نویسندگان: Phuong Vo. T. H, Phuong Vothihong سری: ISBN (شابک) : 9781788394697, 1788394690 ناشر: سال نشر: 2016 تعداد صفحات: 931 [911] زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 22 Mb
قیمت کتاب (تومان) : 33,000
در صورت تبدیل فایل کتاب Python: End-to-end Data Analysis : Leverage the Power of Python to Clean, Scrape, Analyze, and Visualize Your Data : a Course in Three Modules به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب پایتون: تجزیه و تحلیل داده‌های سرتاسر: از قدرت پایتون برای تمیز کردن، خراشیدن، تجزیه و تحلیل و تجسم داده‌های خود استفاده کنید: دوره آموزشی در سه ماژول نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
توضیحاتی درمورد کتاب به خارجی
فهرست مطالب
Cover Copyright Credits Preface Module 1 & 2: Table of content Module 3: Table of content Module 1: Getting Started with Python Data Analysis Chapter 1: Introducing Data Analysis and Libraries Data analysis and processing An overview of the libraries in data analysis Python libraries in data analysis NumPy Pandas Matplotlib PyMongo The scikit-learn library Summary Chapter 2: NumPy Arrays and Vectorized Computation NumPy arrays Data types Array creation Indexing and slicing Fancy indexing Numerical operations on arrays Array functions Data processing using arrays Loading and saving data Saving an array Loading an array Linear algebra with NumPy NumPy random numbers Summary Chapter 3: Data Analysis with Pandas An overview of the Pandas package The Pandas data structure Series The DataFrame The essential basic functionality Reindexing and altering labels Head and tail Binary operations Functional statistics Function application Sorting Indexing and selecting data Computational tools Working with missing data Advanced uses of Pandas for data analysis Hierarchical indexing The Panel data Summary Chapter 4: Data Visualization The matplotlib API primer Line properties Figures and subplots Exploring plot types Scatter plots Bar plots Contour plots Histogram plots Legends and annotations Plotting functions with Pandas Additional Python data visualization tools Bokeh MayaVi Summary Chapter 5: Time Series Time series primer Working with date and time objects Resampling time series Downsampling time series data Upsampling time series data Time zone handling Timedeltas Time series plotting Summary Chapter 6: Interacting with Databases Interacting with data in text format Reading data from text format Writing data to text format Interacting with data in binary format HDF5 Interacting with data in MongoDB Interacting with data in Redis The simple value List Set Ordered set Summary Chapter 7: Data Analysis Application Examples Data munging Cleaning data Filtering Merging data Reshaping data Data aggregation Grouping data Summary Chapter 8: Machine Learning Models with scikit-learn An overview of machine learning models The scikit-learn modules for different models Data representation in scikit-learn Supervised learning – classification and regression Unsupervised learning – clustering and dimensionality reduction Measuring prediction performance Summary Module 2: Python Data Analysis Cookbook Chapter 1: Laying the Foundation for Reproducible Data Analysis Introduction Setting up Anaconda Getting ready How to do it... There's more... See also Installing the Data Science Toolbox Getting ready How to do it... How it works... See also Creating a virtual environment with virtualenv and virtualenvwrapper Getting ready How to do it... See also Sandboxing Python applications with Docker images Getting ready How to do it... How it works... See also Keeping track of package versions and history in IPython Notebook Getting ready How to do it... How it works... See also Configuring IPython Getting ready How to do it... See also Learning to log for robust error checking Getting ready How to do it... How it works... See also Unit testing your code Getting ready How to do it... How it works... See also Configuring pandas Getting ready How to do it... Configuring matplotlib Getting ready How to do it... How it works... See also Seeding random number generators and NumPy print options Getting ready How to do it... See also Standardizing reports, code style, and data access Getting ready How to do it... See also Chapter 2: Creating Attractive Data Visualizations Introduction Graphing Anscombe's quartet How to do it... See also Choosing seaborn color palettes How to do it... See also Choosing matplotlib color maps How to do it... See also Interacting with IPython Notebook widgets How to do it... See also Viewing a matrix of scatterplots How to do it... Visualizing with d3.js via mpld3 Getting ready How to do it... Creating heatmaps Getting ready How to do it... See also Combining box plots and kernel density plots with violin plots How to do it... See also Visualizing network graphs with hive plots Getting ready How to do it... Displaying geographical maps Getting ready How to do it... Using ggplot2-like plots Getting ready How to do it... Highlighting data points with influence plots How to do it... See also Chapter 3: Statistical Data Analysis and Probability Introduction Fitting data to the exponential distribution How to do it... How it works… See also Fitting aggregated data to the gamma distribution How to do it... See also Fitting aggregated counts to the Poisson distribution How to do it... See also Determining bias How to do it... See also Estimating kernel density How to do it... See also Determining confidence intervals for mean, variance, and standard deviation How to do it... See also Sampling with probability weights How to do it... See also Exploring extreme values How to do it... See also Correlating variables with Pearson's correlation How to do it... See also Correlating variables with the Spearman rank correlation How to do it... See also Correlating a binary and a continuous variable with the point biserial correlation How to do it... See also Evaluating relations between variables with ANOVA How to do it... See also Chapter 4: Dealing with Data and Numerical Issues Introduction Clipping and filtering outliers How to do it... See also Winsorizing data How to do it... See also Measuring central tendency of noisy data How to do it... See also Normalizing with the Box-Cox transformation How to do it... How it works See also Transforming data with the power ladder How to do it... Transforming data with logarithms How to do it... Rebinning data How to do it... Applying logit() to transform proportions How to do it... Fitting a robust linear model How to do it... See also Taking variance into account with weighted least squares How to do it... See also Using arbitrary precision for optimization Getting ready How to do it... See also Using arbitrary precision for linear algebra Getting ready How to do it... See also Chapter 5: Web Mining, Databases, and Big Data Introduction Simulating web browsing Getting ready How to do it… See also Scraping the Web Getting ready How to do it… Dealing with non-ASCII text and HTML entities Getting ready How to do it… See also Implementing association tables Getting ready How to do it… Setting up database migration scripts Getting ready How to do it… See also Adding a table column to an existing table Getting ready How to do it… Adding indices after table creation Getting ready How to do it… How it works… See also Setting up a test web server Getting ready How to do it… Implementing a star schema with fact and dimension tables How to do it… See also Using HDFS Getting ready How to do it… See also Setting up Spark Getting ready How to do it… See also Clustering data with Spark Getting ready How to do it… How it works… There's more… See also Chapter 6: Signal Processing and Timeseries Introduction Spectral analysis with periodograms How to do it... See also Estimating power spectral density with the Welch method How to do it... See also Analyzing peaks How to do it... See also Measuring phase synchronization How to do it... See also Exponential smoothing How to do it... See also Evaluating smoothing How to do it... See also Using the Lomb-Scargle periodogram How to do it... See also Analyzing the frequency spectrum of audio How to do it... See also Analyzing signals with the discrete cosine transform How to do it... See also Block bootstrapping time series data How to do it... See also Moving block bootstrapping time series data How to do it... See also Applying the discrete wavelet transform Getting started How to do it... See also Chapter 7: Selecting Stocks with Financial Data Analysis Introduction Computing simple and log returns How to do it... See also Ranking stocks with the Sharpe ratio and liquidity How to do it... See also Ranking stocks with the Calmar and Sortino ratios How to do it... See also Analyzing returns statistics How to do it... Correlating individual stocks with the broader market How to do it... Exploring risk and return How to do it... See also Examining the market with the non-parametric runs test How to do it... See also Testing for random walks How to do it... See also Determining market efficiency with autoregressive models How to do it... See also Creating tables for a stock prices database How to do it... Populating the stock prices database How to do it... Optimizing an equal weights two-asset portfolio How to do it... See also Chapter 8: Text Mining and Social Network Analysis Introduction Creating a categorized corpus Getting ready How to do it... See also Tokenizing news articles in sentences and words Getting ready How to do it... See also Stemming, lemmatizing, filtering, and TF-IDF scores Getting ready How to do it... How it works See also Recognizing named entities Getting ready How to do it... How it works See also Extracting topics with non-negative matrix factorization How to do it... How it works See also Implementing a basic terms database How to do it... How it works See also Computing social network density Getting ready How to do it... See also Calculating social network closeness centrality Getting ready How to do it... See also Determining the betweenness centrality Getting ready How to do it... See also Estimating the average clustering coefficient Getting ready How to do it... See also Calculating the assortativity coefficient of a graph Getting ready How to do it... See also Getting the clique number of a graph Getting ready How to do it... See also Creating a document graph with cosine similarity How to do it... See also Chapter 9: Ensemble Learning and Dimensionality Reduction Introduction Recursively eliminating features How to do it... How it works See also Applying principal component analysis for dimension reduction How to do it... See also Applying linear discriminant analysis for dimension reduction How to do it... See also Stacking and majority voting for multiple models How to do it... See also Learning with random forests How to do it... There's more… See also Fitting noisy data with the RANSAC algorithm How to do it... See also Bagging to improve results How to do it... See also Boosting for better learning How to do it... See also Nesting cross-validation How to do it... See also Reusing models with joblib How to do it... See also Hierarchically clustering data How to do it... See also Taking a Theano tour Getting ready How to do it... See also Chapter 10: Evaluating Classifiers, Regressors, and Clusters Introduction Getting classification straight with the confusion matrix How to do it... How it works See also Computing precision, recall, and F1-score How to do it... See also Examining a receiver operating characteristic and the area under a curve How to do it... See also Visualizing the goodness of fit How to do it... See also Computing MSE and median absolute error How to do it... See also Evaluating clusters with the mean silhouette coefficient How to do it... See also Comparing results with a dummy classifier How to do it... See also Determining MAPE and MPE How to do it... See also Comparing with a dummy regressor How to do it... See also Calculating the mean absolute error and the residual sum of squares How to do it... See also Examining the kappa of classification How to do it... How it works See also Taking a look at the Matthews correlation coefficient How to do it... See also Chapter 11: Analyzing Images Introduction Setting up OpenCV Getting ready How to do it... How it works There's more Applying Scale-Invariant Feature Transform (SIFT) Getting ready How to do it... See also Detecting features with SURF Getting ready How to do it... See also Quantizing colors Getting ready How to do it... See also Denoising images Getting ready How to do it... See also Extracting patches from an image Getting ready How to do it... See also Detecting faces with Haar cascades Getting ready How to do it... See also Searching for bright stars Getting ready How to do it... See also Extracting metadata from images Getting ready How to do it... See also Extracting texture features from images Getting ready How to do it... See also Applying hierarchical clustering on images How to do it... See also Segmenting images with spectral clustering How to do it... See also Chapter 12: Parallelism and Performance Introduction Just-in-time compiling with Numba Getting ready How to do it... How it works See also Speeding up numerical expressions with Numexpr How to do it... How it works See also Running multiple threads with the threading module How to do it... See also Launching multiple tasks with the concurrent.futures module How to do it... See also Accessing resources asynchronously with the asyncio module How to do it... See also Distributed processing with execnet Getting ready How to do it... See also Profiling memory usage Getting ready How to do it... See also Calculating the mean, variance, skewness, and kurtosis on the fly Getting ready How to do it... See also Caching with a least recently used cache Getting ready How to do it... See also Caching HTTP requests Getting ready How to do it... See also Streaming counting with the Count-min sketch How to do it... See also Harnessing the power of the GPU with OpenCL Getting ready How to do it... See also Appendix A: Glossary Appendix B: Function Reference IPython Matplotlib NumPy pandas Scikit-learn SciPy Seaborn Statsmodels Appendix C: Online Resources IPython notebooks and open data Mathematics and statistics Presentations Appendix D: Tips and Tricks for Command-Line and Miscellaneous Tools IPython notebooks Command-line tools The alias command Command-line history Reproducible sessions Docker tips Module 3: Mastering Python Data Analysis Chapter 1: Tools of the Trade Chapter 2: Exploring Data Chapter 3: Learning About Models Chapter 4: Regression Chapter 5: Clustering Chapter 6: Bayesian Methods Chapter 7: Supervised and UnsupervisedLearning Chapter 8: Time Series Analysis Appendix: More on Jupyter Notebook andmatplotlib Styles Bibliography Index Thanks Page Blank Page Untitled
نظرات کاربران
کتاب های تصادفی
دانلود کتاب Communication Strategies for Managing Conflict: A Guide for Academic Leader.
دانلود کتاب W. E. B. Dubois (Bloom's Modern Critical Views)
دانلود کتاب The Foreign Encounter in Myth and Religion: Modes of Foreign Relations and Political Economy, Volume 2
دانلود کتاب Vozes do Bolsa Familia. Autonomia, dinheiro e cidadania
