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  • Book Overview & Buying Python Data Analysis
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Python Data Analysis

Python Data Analysis

By : Idris
4.5 (13)
Buy this Book
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Python Data Analysis

Python Data Analysis

4.5 (13)
By: Idris
Buy this Book

Overview of this book

This book is for programmers, scientists, and engineers who have knowledge of the Python language and know the basics of data science. It is for those who wish to learn different data analysis methods using Python and its libraries. This book contains all the basic ingredients you need to become an expert data analyst.
Table of Contents (20 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Get in touch
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1
Section 1: Foundation for Data Analysis
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Section 1: Foundation for Data Analysis
2
Getting Started with Python Libraries
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Getting Started with Python Libraries
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Understanding data analysis
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The standard process of data analysis
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The KDD process
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SEMMA
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CRISP-DM
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Comparing data analysis and data science
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The skillsets of data analysts and data scientists
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Installing Python 3
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Software used in this book
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Using IPython as a shell
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Using JupyterLab
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Using Jupyter Notebooks
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Advanced features of Jupyter Notebooks
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Summary
3
NumPy and pandas
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NumPy and pandas
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Technical requirements
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Understanding NumPy arrays
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NumPy array numerical data types
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Manipulating array shapes
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The stacking of NumPy arrays
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Partitioning NumPy arrays
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Changing the data type of NumPy arrays
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Creating NumPy views and copies
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Slicing NumPy arrays
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Boolean and fancy indexing
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Broadcasting arrays
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Creating pandas DataFrames
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Understanding pandas Series
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Reading and querying the Quandl data
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Describing pandas DataFrames
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Grouping and joining pandas DataFrame
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Working with missing values
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Creating pivot tables
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Dealing with dates
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Summary
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References
4
Statistics
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Statistics
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Technical requirements
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Understanding attributes and their types
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Measuring central tendency
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Measuring dispersion
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Skewness and kurtosis
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Understanding relationships using covariance and correlation coefficients
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Central limit theorem
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Collecting samples
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Performing parametric tests
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Performing non-parametric tests
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Summary
5
Linear Algebra
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Linear Algebra
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Technical requirements
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Fitting to polynomials with NumPy
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Determinant
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Finding the rank of a matrix
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Matrix inverse using NumPy
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Solving linear equations using NumPy
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Decomposing a matrix using SVD
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Eigenvectors and Eigenvalues using NumPy
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Generating random numbers
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Binomial distribution
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Normal distribution
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Testing normality of data using SciPy
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Creating a masked array using the numpy.ma subpackage
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Summary
6
Section 2: Exploratory Data Analysis and Data Cleaning
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Section 2: Exploratory Data Analysis and Data Cleaning
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Data Visualization
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Data Visualization
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Technical requirements
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Visualization using Matplotlib
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Advanced visualization using the Seaborn package
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Interactive visualization with Bokeh
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Summary
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Retrieving, Processing, and Storing Data
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Retrieving, Processing, and Storing Data
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Technical requirements
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Reading and writing CSV files with NumPy
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Reading and writing CSV files with pandas
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Reading and writing data from Excel
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Reading and writing data from JSON
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Reading and writing data from HDF5
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Reading and writing data from HTML tables
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Reading and writing data from Parquet
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Reading and writing data from a pickle pandas object
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Lightweight access with sqllite3
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Reading and writing data from MySQL
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Reading and writing data from MongoDB
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Reading and writing data from Cassandra
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Reading and writing data from Redis
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PonyORM
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Summary
9
Cleaning Messy Data
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Cleaning Messy Data
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Technical requirements
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Exploring data
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Filtering data to weed out the noise
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Handling missing values
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Handling outliers
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Feature encoding techniques
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Feature scaling
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Feature transformation
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Feature splitting
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Summary
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Signal Processing and Time Series
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Signal Processing and Time Series
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Technical requirements
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The statsmodels modules
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Moving averages
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Window functions
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Defining cointegration
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STL decomposition
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Autocorrelation
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Autoregressive models
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ARMA models
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Generating periodic signals
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Fourier analysis
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Spectral analysis filtering
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Summary
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Section 3: Deep Dive into Machine Learning
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Section 3: Deep Dive into Machine Learning
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Supervised Learning - Regression Analysis
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Supervised Learning - Regression Analysis
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Technical requirements
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Linear regression
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Understanding multicollinearity
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Dummy variables
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Developing a linear regression model
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Evaluating regression model performance
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Fitting polynomial regression
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Regression models for classification
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Logistic regression
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Implementing logistic regression using scikit-learn
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Summary
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Supervised Learning - Classification Techniques
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Supervised Learning - Classification Techniques
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Technical requirements
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Classification
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Naive Bayes classification
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Decision tree classification
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KNN classification
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SVM classification
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Splitting training and testing sets
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Evaluating the classification model performance
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ROC curve and AUC
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Summary
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Unsupervised Learning - PCA and Clustering
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Unsupervised Learning - PCA and Clustering
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Technical requirements
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Unsupervised learning
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Reducing the dimensionality of data
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Clustering
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Partitioning data using k-means clustering
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Hierarchical clustering
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DBSCAN clustering
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Spectral clustering
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Evaluating clustering performance
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Summary
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Section 4: NLP, Image Analytics, and Parallel Computing
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Section 4: NLP, Image Analytics, and Parallel Computing
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Analyzing Textual Data
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Analyzing Textual Data
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Technical requirements
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Installing NLTK and SpaCy
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Text normalization
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Tokenization
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Removing stopwords
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Stemming and lemmatization
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POS tagging
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Recognizing entities
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Dependency parsing
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Creating a word cloud
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Bag of Words
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TF-IDF
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Sentiment analysis using text classification
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Text similarity
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Summary
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Analyzing Image Data
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Analyzing Image Data
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Technical requirements
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Installing OpenCV
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Understanding image data
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Color models
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Drawing on images
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Writing on images
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Resizing images
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Flipping images
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Changing the brightness
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Blurring an image
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Face detection
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Summary
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Parallel Computing Using Dask
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Parallel Computing Using Dask
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Parallel computing using Dask
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Dask data types
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Dask Delayed
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Preprocessing data at scale
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Machine learning at scale
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Summary
19
Other Books You May Enjoy
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Other Books You May Enjoy
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Leave a review - let other readers know what you think

The bag-of-words model

In the bag-of-words model, we create from a document a bag containing words found in the document. In this model, we don't care about the word order. For each word in the document, we count the number of occurrences. With these word counts, we can do statistical analysis, for instance, to identify spam in e-mail messages.

If we have a group of documents, we can view each unique word in the corpus as a feature; here, "feature" means parameter or variable. Using all the word counts, we can build a feature vector for each document; "vector" is used here in the mathematical sense. If a word is present in the corpus but not in the document, the value of this feature will be 0. Surprisingly, NLTK doesn't have a handy utility currently to create a feature vector. However, the machine learning Python library, scikit-learn, does have a CountVectorizer class that we can use. In the next chapter, Chapter 10, Predictive Analytics and Machine Learning, we will do more with scikit...

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