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دانلود کتاب Python Data Analysis, Third Edition
دانلود کتاب تجزیه و تحلیل داده های پایتون، ویرایش سوم
مشخصات کتاب
Python Data Analysis, Third Edition
ویرایش: [3 ed.] نویسندگان: Navlani. Avinash, Fandango. Armando, Idris. Ivan سری: ISBN (شابک) : 9781789955248 ناشر: Packt Publishing سال نشر: 2021 تعداد صفحات: زبان: English فرمت فایل : AZW3 (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 8 Mb
قیمت کتاب (تومان) : 39,000
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توجه داشته باشید کتاب تجزیه و تحلیل داده های پایتون، ویرایش سوم 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 در مورد کتاب تجزیه و تحلیل داده های پایتون، ویرایش سوم
توضیحاتی درمورد کتاب به خارجی
Understand data analysis pipelines using machine learning algorithms and techniques with this practical guideKey Features* Prepare and clean your data to use it for exploratory analysis, data manipulation, and data wrangling* Discover supervised, unsupervised, probabilistic, and Bayesian machine learning methods* Get to grips with graph processing and sentiment analysisBook DescriptionData analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you\'ll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines.Starting with the essential statistical and data analysis fundamentals using Python, you\'ll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You\'ll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you\'ll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you\'ll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask.By the end of this data analysis book, you\'ll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.What you will learn* Explore data science and its various process models* Perform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing values* Create interactive visualizations using Matplotlib, Seaborn, and Bokeh* Retrieve, process, and store data in a wide range of formats* Understand data preprocessing and feature engineering using pandas and scikit-learn* Perform time series analysis and signal processing using sunspot cycle data* Analyze textual data and image data to perform advanced analysis* Get up to speed with parallel computing using DaskWho this book is forThis book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands-on approach. A basic understanding of math and working knowledge of the Python programming language will help you get started with this book.
فهرست مطالب
Cover......Page 1
Title Page......Page 2
Copyright and Credits......Page 3
About Packt......Page 4
Contributors......Page 5
Table of Contents......Page 7
Preface......Page 15
Section 1: Foundation for Data Analysis......Page 20
Chapter 1: Getting Started with Python Libraries......Page 21
Understanding data analysis......Page 22
The standard process of data analysis......Page 23
The KDD process......Page 24
SEMMA ......Page 25
CRISP-DM......Page 26
The roles of data analysts and data scientists......Page 28
The skillsets of data analysts and data scientists......Page 29
Python installation and setup on Windows......Page 31
Python installation and setup on Mac OS X with brew......Page 32
Software used in this book......Page 33
Using IPython as a shell......Page 34
Reading manual pages......Page 37
Using JupyterLab......Page 38
Using Jupyter Notebooks......Page 40
Advanced features of Jupyter Notebooks......Page 41
Keyboard shortcuts......Page 42
Installing other kernels......Page 43
Extensions for Notebook......Page 44
Summary......Page 50
Chapter 2: NumPy and pandas......Page 51
Understanding NumPy arrays......Page 52
Array features......Page 55
Selecting array elements......Page 56
NumPy array numerical data types......Page 57
dtype objects......Page 59
Data type character codes......Page 60
dtype attributes......Page 61
Manipulating array shapes......Page 62
The stacking of NumPy arrays......Page 64
Partitioning NumPy arrays......Page 67
Changing the data type of NumPy arrays......Page 69
Creating NumPy views and copies......Page 70
Slicing NumPy arrays......Page 72
Boolean and fancy indexing......Page 74
Broadcasting arrays......Page 75
Creating pandas DataFrames......Page 77
Understanding pandas Series......Page 79
Reading and querying the Quandl data......Page 82
Describing pandas DataFrames......Page 86
Grouping and joining pandas DataFrame......Page 89
Working with missing values......Page 93
Creating pivot tables......Page 95
Dealing with dates......Page 97
References......Page 99
Chapter 3: Statistics......Page 100
Types of attributes......Page 101
Mean......Page 103
Mode......Page 104
Measuring dispersion......Page 105
Skewness and kurtosis......Page 109
Understanding relationships using covariance and correlation coefficients......Page 110
Pearson's correlation coefficient......Page 111
Central limit theorem......Page 112
Collecting samples......Page 114
Performing parametric tests......Page 115
Performing non-parametric tests ......Page 121
Summary......Page 127
Chapter 4: Linear Algebra......Page 128
Fitting to polynomials with NumPy......Page 129
Finding the rank of a matrix......Page 131
Matrix inverse using NumPy......Page 132
Solving linear equations using NumPy......Page 133
Decomposing a matrix using SVD......Page 134
Eigenvectors and Eigenvalues using NumPy......Page 136
Generating random numbers......Page 137
Binomial distribution......Page 138
Normal distribution......Page 140
Testing normality of data using SciPy......Page 141
Creating a masked array using the numpy.ma subpackage......Page 145
Summary......Page 147
Section 2: Exploratory Data Analysis and Data Cleaning......Page 148
Technical requirements......Page 149
Visualization using Matplotlib......Page 150
Accessories for charts......Page 151
Scatter plot......Page 153
Line plot......Page 154
Pie plot......Page 156
Bar plot......Page 157
Histogram plot......Page 158
Bubble plot......Page 160
pandas plotting......Page 162
Advanced visualization using the Seaborn package......Page 164
lm plots......Page 165
Bar plots......Page 168
Distribution plots......Page 169
Box plots......Page 170
KDE plots......Page 171
Violin plots......Page 172
Count plots......Page 173
Joint plots......Page 175
Heatmaps......Page 176
Pair plots......Page 178
Plotting a simple graph......Page 180
Glyphs......Page 182
Layouts......Page 183
Nested layout using row and column layouts......Page 187
Multiple plots......Page 189
Hide click policy......Page 191
Mute click policy......Page 193
Annotations......Page 194
Hover tool......Page 197
Widgets......Page 198
Tab panel......Page 199
Slider......Page 200
Summary......Page 203
Chapter 6: Retrieving, Processing, and Storing Data......Page 204
Reading and writing CSV files with NumPy......Page 205
Reading and writing CSV files with pandas......Page 206
Reading and writing data from Excel......Page 208
Reading and writing data from JSON......Page 209
Reading and writing data from HDF5......Page 210
Reading and writing data from HTML tables......Page 211
Reading and writing data from Parquet......Page 212
Reading and writing data from a pickle pandas object......Page 213
Lightweight access with sqllite3......Page 214
Reading and writing data from MySQL......Page 215
Inserting a whole DataFrame into the database......Page 218
Reading and writing data from MongoDB......Page 219
Reading and writing data from Cassandra......Page 220
Reading and writing data from Redis......Page 221
PonyORM......Page 222
Summary......Page 223
Chapter 7: Cleaning Messy Data......Page 224
Exploring data......Page 225
Filtering data to weed out the noise......Page 228
Column-wise filtration ......Page 229
Row-wise filtration ......Page 231
Handling missing values......Page 234
Filling in a missing value......Page 235
Handling outliers......Page 237
One-hot encoding......Page 240
Label encoding......Page 242
Ordinal encoder......Page 243
Feature scaling......Page 244
Methods for feature scaling......Page 245
Feature transformation......Page 248
Feature splitting......Page 249
Summary......Page 250
Chapter 8: Signal Processing and Time Series......Page 251
The statsmodels modules......Page 252
Moving averages......Page 253
Window functions......Page 256
Defining cointegration......Page 258
STL decomposition......Page 260
Autocorrelation......Page 262
Autoregressive models......Page 264
ARMA models......Page 268
Generating periodic signals......Page 271
Fourier analysis......Page 273
Spectral analysis filtering......Page 276
Summary......Page 278
Section 3: Deep Dive into Machine Learning......Page 279
Chapter 9: Supervised Learning - Regression Analysis......Page 280
Linear regression......Page 281
Understanding multicollinearity......Page 283
Removing multicollinearity......Page 284
Dummy variables......Page 286
Developing a linear regression model......Page 288
R-squared......Page 290
MAE......Page 291
RMSE......Page 292
Fitting polynomial regression......Page 293
Logistic regression......Page 296
Characteristics of the logistic regression model......Page 298
Advantages and disadvantages of logistic regression......Page 299
Implementing logistic regression using scikit-learn......Page 300
Summary......Page 302
Chapter 10: Supervised Learning - Classification Techniques......Page 303
Classification......Page 304
Naive Bayes classification......Page 306
Decision tree classification......Page 310
KNN classification......Page 313
Terminology......Page 316
Holdout......Page 319
Bootstrap method......Page 320
Confusion matrix......Page 321
Accuracy......Page 324
F-measure......Page 325
ROC curve and AUC......Page 326
Summary......Page 329
Chapter 11: Unsupervised Learning - PCA and Clustering......Page 330
Unsupervised learning......Page 331
Reducing the dimensionality of data......Page 332
PCA......Page 333
Performing PCA......Page 334
Clustering......Page 337
Finding the number of clusters......Page 338
The elbow method......Page 339
The silhouette method......Page 341
Partitioning data using k-means clustering......Page 343
Hierarchical clustering......Page 346
DBSCAN clustering......Page 350
Spectral clustering......Page 352
Evaluating clustering performance......Page 355
The silhouette coefficient......Page 356
The Jaccard score......Page 357
The Fowlkes-Mallows score......Page 358
Summary......Page 361
Section 4: NLP, Image Analytics, and Parallel Computing......Page 362
Chapter 12: Analyzing Textual Data......Page 363
Installing NLTK and SpaCy......Page 364
Text normalization......Page 365
Tokenization......Page 366
Removing stopwords......Page 370
Stemming and lemmatization......Page 372
POS tagging......Page 374
Recognizing entities......Page 375
Dependency parsing......Page 376
Creating a word cloud......Page 377
Bag of Words......Page 379
TF-IDF......Page 380
Sentiment analysis using text classification......Page 381
Classification using BoW......Page 382
Classification using TF-IDF......Page 387
Text similarity......Page 390
Jaccard similarity......Page 391
Cosine similarity......Page 392
Summary......Page 393
Chapter 13: Analyzing Image Data......Page 394
Installing OpenCV......Page 395
Binary images......Page 396
Color images......Page 397
Color models......Page 398
Drawing on images......Page 401
Writing on images......Page 406
Resizing images......Page 407
Flipping images......Page 409
Changing the brightness......Page 412
Blurring an image......Page 413
Face detection......Page 417
Summary......Page 421
Chapter 14: Parallel Computing Using Dask......Page 422
Parallel computing using Dask......Page 423
Dask data types......Page 424
Dask Arrays......Page 425
Dask DataFrames......Page 426
DataFrame Indexing......Page 427
Filter data......Page 430
Groupby......Page 431
Converting a Dask DataFrame into a pandas DataFrame......Page 432
Creating a Dask Bag using Python iterable items......Page 433
Creating a Dask Bag using a text file......Page 434
Storing a Dask Bag in a DataFrame......Page 435
Dask Delayed......Page 436
Feature scaling in Dask......Page 438
Feature encoding in Dask......Page 440
Machine learning at scale......Page 442
Parallel computing using scikit-learn......Page 443
Logistic regression......Page 445
Clustering......Page 447
Summary......Page 449
Other Books You May Enjoy......Page 451
Index......Page 454
