دانلود کتاب Python Data Analytics: Data Analysis and Science Using Pandas, Matplotlib and the Python Programming Language

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Contents at a Glance......Page 4
Contents......Page 5
About the Author......Page 16
About the Technical Reviewer......Page 17
Acknowledgments......Page 18
Data Analysis......Page 19
Computer Science......Page 20
Professional Fields of Application......Page 21
Types of Data......Page 22
Problem Definition......Page 23
Data Extraction......Page 24
Data Exploration/Visualization......Page 25
Deployment......Page 26
Quantitative and Qualitative Data Analysis......Page 27
Open Data......Page 28
Python and Data Analysis......Page 29
Conclusions......Page 30
Python—The Programming Language......Page 31
Python—The Interpreter......Page 32
Python 2 and Python 3......Page 33
Anaconda......Page 34
Enthought Canopy......Page 35
Run an Entire Program Code......Page 36
Writing Python Code......Page 37
Import New Libraries and Functions......Page 38
Data Structure......Page 39
Functional Programming (Only for Python 3.4)......Page 40
IPython Shell......Page 42
IPython Notebook......Page 44
The Jupyter Project......Page 45
The IDEs for Python......Page 46
Spyder......Page 47
Sublime......Page 48
Liclipse......Page 49
SciPy......Page 50
Pandas......Page 51
Conclusions......Page 52
The NumPy Installation......Page 53
Ndarray: The Heart of the Library......Page 54
Create an Array......Page 55
Types of Data......Page 56
Intrinsic Crea tion of an Array......Page 57
Basic Operations......Page 58
Arit hmetic Operators......Page 59
The M atrix Product......Page 60
Increm ent and Decrement Operators......Page 61
Aggregat e Functions......Page 62
Indexing......Page 63
Slicing......Page 64
Iterating an Array......Page 66
Shape Manipulation......Page 68
Joining Arrays......Page 69
Splitting Arrays......Page 70
Copies or Views of Objects......Page 72
Broadcasting......Page 73
Structured Arrays......Page 76
Loading and Saving Data in Binary Files......Page 77
Reading File with T abular Data......Page 78
Conclusions......Page 79
pandas: The Python Data Analysis Library......Page 80
Installation from Anaconda......Page 81
Installation on Linux......Page 82
Test Your pandas Installation......Page 83
Introduction to pandas Data Structures......Page 84
Declaring a Series......Page 85
Selecting the Internal Elements......Page 86
Defining Series from NumPy Arrays and Other Series......Page 87
Operations and Mathematical Functions......Page 88
Evaluating Values......Page 89
NaN Values......Page 90
Operations between Series......Page 91
Defining a DataFrame......Page 92
Selecting Elements......Page 94
Assigning Values......Page 95
Filtering......Page 97
The Index Objects......Page 98
Index with Duplicate Labels......Page 99
Reindexing......Page 100
Dropping......Page 102
Arithmetic and Data Alignment......Page 103
Operations between Data Structures......Page 104
Operations between DataFrame and Series......Page 105
Functions by Element......Page 106
Functions by Row or Column......Page 107
Sorting and Ranking......Page 108
Correlation and Covariance......Page 111
“Not a Number” Data......Page 112
Filtering Out NaN Values......Page 113
Hierarchical Indexing and Leveling......Page 114
Summary Statistic by Level......Page 117
Conclusions......Page 118
I/O API Tools......Page 119
Reading Data in CSV or Text Files......Page 120
Using RegExp for Parsing TXT Files......Page 122
Reading TXT Files into Parts or Partially......Page 124
Writing Data in CSV......Page 125
Writing Data in HTML......Page 127
Reading Data from an HTML File......Page 129
Reading Data from XML......Page 130
Reading and Writing Data on Microsoft Excel Files......Page 132
JSON Data......Page 134
The Format HDF5......Page 137
Serialize a Python Object with cPickle......Page 138
Pickling with pandas......Page 139
Loading and Writing Data with SQLite3......Page 140
Loading and Writing Data with PostgreSQL......Page 142
Reading and Writing Data with a NoSQL Database: MongoDB......Page 144
Conclusions......Page 146
Data Preparation......Page 147
Merging......Page 148
Merging on Index......Page 151
Concatenating......Page 152
Combining......Page 155
Pivoting with Hierarchical Indexing......Page 156
Pivoting from “Long” to “Wide” Format......Page 157
Removing......Page 158
Removing Duplicates......Page 159
Replacing Values via Mapping......Page 160
Adding Values via Mapping......Page 161
Rename the Indexes of the Axes......Page 162
Discretization and Binning......Page 164
Detecting and Filtering Outliers......Page 167
Random Sampling......Page 168
Built-in Methods for Manipulation of Strings......Page 169
Regular Expressions......Page 171
Data Aggregation......Page 172
GroupBy......Page 173
A Practical Example......Page 174
Hierarchical Grouping......Page 175
Chain of Transformations......Page 176
Functions on Groups......Page 177
Advanced Data Aggregation......Page 178
Conclusions......Page 181
The matplotlib Library......Page 182
IPython and IPython QtConsole......Page 183
Backend Layer......Page 185
Artist Layer......Page 186
pylab and pyplot......Page 187
A Simple Interactive Chart......Page 188
Set the Properties of the Plot......Page 192
matplotlib and NumPy......Page 194
Using the kwargs......Page 196
Working with Multiple Figures and Axes......Page 197
Adding Text......Page 199
Adding a Grid......Page 203
Adding a Legend......Page 204
Saving the Code......Page 207
Converting Your Session as an HTML File......Page 208
Saving Your Chart Directly as an Image......Page 210
Handling Date Values......Page 211
Line Chart......Page 213
Line Charts with pandas......Page 220
Histogram......Page 221
Bar Chart......Page 222
Horizontal Bar Chart......Page 225
Multiserial Bar Chart......Page 226
Multiseries Bar Chart with pandas DataFrame......Page 228
Multiseries Stacked Bar Charts......Page 230
Stacked Bar Charts with pandas DataFrame......Page 232
Other Bar Chart Representations......Page 233
Pie Charts......Page 234
Pie Charts with pandas DataFrame......Page 237
Contour Plot......Page 238
Polar Chart......Page 240
3D Surfaces......Page 242
Scatter Plot in 3D......Page 244
Bar Chart 3D......Page 245
Display Subplots within Other Subplots......Page 246
Grids of Subplots......Page 248
Conclusions......Page 250
Supervised and Unsupervised Learning......Page 251
The Iris Flower Dataset......Page 252
The PCA Decomposition......Page 256
K-Nearest Neighbors Classifier......Page 258
Diabetes Dataset......Page 261
Linear Regression: The Least Square Regression......Page 262
Support Vector Classification ( SVC)......Page 267
Nonlinear SVC......Page 271
Plotting Different SVM Classifiers Using the Iris Dataset......Page 273
Support Vector Regression (SVR)......Page 276
Conclusions......Page 278
The System in the Study: The Adriatic Sea and the Po Valley......Page 279
Data Source......Page 282
Data Analysis on IPython Notebook......Page 284
The RoseWind......Page 298
Calculating the Distribution of the Wind Speed Means......Page 301
Conclusions......Page 302
The Open Data Source for Demographics......Page 303
The JavaScript D3 Library......Page 307
Drawing a Clustered Bar Chart......Page 310
The Choropleth Maps......Page 314
The Choropleth Map of the US Population in 2014......Page 318
Conclusions......Page 323
Recognizing Handwritten Digits with scikit-learn......Page 324
The Digits Dataset......Page 325
Learning and Predicting......Page 328
Conclusions......Page 329
With IPython Notebook in a Python 2 Cell......Page 330
Radicals......Page 331
Accents......Page 332
Political and Government Data......Page 340
Social Data......Page 341
Climatic Data......Page 342
Musical Data......Page 343
Index......Page 344