Index
A
- AdaBoost
- AdaBoostRegressor class
- advanced Pandas use cases
- agglomerative clustering / Applying hierarchical clustering on images
- AgglomerativeClustering class
- aggregated counts
- aggregated data
- Alembic
- alias command / The alias command
- American Standard Code for Information Interchange (ASCII)
- Anaconda
- analytic signal
- ANAOVA
- angle() function
- annotate method
- annotations
- Anscombe's quartet
- Apache Spark
- approximation / Applying the discrete wavelet transform
- arbitrary precision
- area under the curve (AUC,ROC AUC or AUROC)
- argrelmax() function
- array creation
- array functions
- artificial intelligence (AI) / Data analysis and processing
- association tables
- assortativity coefficient, graph
- asyncio module
- autoregressive models
- average clustering coefficient
- average_clustering() function
B
C
- Cache algorithms
- Caffe
- Calmar ratio
- Capital Asset Pricing Model (CAPM)
- cascade classifier
- categorized corpus
- CategorizedPlaintextCorpusReader
- centrality / Calculating social network closeness centrality
- central tendency, of noisy data
- cepstrum
- chebyfit() function
- clip() function
- clique
- clique;about / Getting the clique number of a graph
- clique number, graph
- closeness centrality
- clustering coefficient
- co-occurrence matrix
- code style
- Cohen's kappa
- ColorBrewer tool
- color quantization
- color quantization;about / Quantizing colors
- colors
- command-line history
- command-line tools / Command-line tools
- common tab separated values (TSV) / Displaying geographical maps
- community package
- complete graph
- computational tools
- concurrent.futures module
- conda
- confidence intervals
- of mean, determining / Determining confidence intervals for mean, variance, and standard deviation, How to do it...
- of variance, determining / Determining confidence intervals for mean, variance, and standard deviation, How to do it...
- of standard deviation, determining / Determining confidence intervals for mean, variance, and standard deviation, How to do it...
- reference link / See also
- confusion matrix
- confusion_matrix() function
- consensus set / Fitting noisy data with the RANSAC algorithm
- contingency table
- continuous variable
- continuumio/miniconda3 image
- contour plot
- coroutines
- corpora
- correlate() function
- correlation coefficient
- cosine similarity
- cosine_similarity() function
- Count-min sketch
- cross-correlation
- cross-validation
- cross-validation (CV)
- csvkit tool / Data munging
- custom magics
D
- d3.js
- data
- indexing / Indexing and selecting data
- selecting / Indexing and selecting data
- grouping / Grouping data
- fitting, to exponential distribution / Fitting data to the exponential distribution, How to do it..., How it works…, See also
- winsorizing / Winsorizing data, How to do it...
- winsorizing, reference link / See also
- transforming, with power ladder / Transforming data with the power ladder, How to do it...
- transforming, with logarithms / Transforming data with logarithms, How to do it...
- rebinning / Rebinning data, How to do it...
- clustering, with Spark / Clustering data with Spark, How to do it…, How it works…
- clustering hierarchically / Hierarchically clustering data, How to do it...
- data, in binary format
- data, in MongoDB
- data, in Redis
- data, in text format
- data access
- data aggregation
- data analysis / Introduction
- database indices
- database migration scripts
- DataFrame
- data munging
- data points
- data processing, using arrays
- Data Science Toolbox (DST)
- data structure, Pandas
- data types
- date and time objects
- decision tree learning / Learning with random forests
- degree / Calculating the assortativity coefficient of a graph
- degree distribution
- degree_assortativity_coefficient() function
- density() function
- detail coefficients / Applying the discrete wavelet transform
- determinants
- DFFITS
- dilation
- dimension tables
- discrete cosine transform (DCT)
- discrete wavelet transform (DVT)
- distance
- distributed processing
- Docker
- docker-clean script
- Docker images
- docker tips
- document graph, with cosine similarity
- dummy classifier
- DummyClassifier class
- dummy regressor
- DummyRegressor class
- Duncan dataset
E
F
G
H
I
- image
- denoising / Denoising images, How to do it...
- patches, extracting from / Extracting patches from an image, Getting ready, How to do it...
- metadata, extracting from / Extracting metadata from images, How to do it...
- texture features, extracting from / Extracting texture features from images, How to do it..., See also
- hierarchical clustering, applying / Applying hierarchical clustering on images, How to do it...
- segmenting, with spectral clustering / Segmenting images with spectral clustering, How to do it...
- image processing / Introduction
- image segmentation
- image texture
- indices
- individual stocks
- influence plots
- instantaneous phase
- integral image
- interpolation
- interquartile mean (IQM) / Measuring central tendency of noisy data
- inverse document frequency / Stemming, lemmatizing, filtering, and TF-IDF scores
- IPython
- IPython Notebook
- IPython notebooks / IPython notebooks
- IPython notebooks and open data
- IPython notebook widgets
- Iris-Setosa
- Iris-Versicolour
- Iris-Virginica
J
K
L
M
- machine learning (ML) / Data analysis and processing
- machine learning models
- Mahout
- main sequence / Clipping and filtering outliers
- Mallet
- mathematics and statistics
- Matplotlib
- matplotlib
- Matplotlib API Primer
- matplotlib color maps
- matrix of scatterplots
- Matthews correlation coefficient (MCC)
- matthews_corrcoef() function
- maximum clique / Getting the clique number of a graph
- maximum drawdown / Ranking stocks with the Calmar and Sortino ratios
- maximum likelihood estimation (MLE) method / How to do it...
- MayaVi
- mean
- mean absolute deviation (MAD) / How it works…
- mean absolute error (MeanAE)
- Mean Absolute Percentage Error (MAPE)
- Mean Percentage Error (MPE)
- mean silhouette coefficient
- mean squared error (MSE)
- Mean Squared Error (MSE) / Exponential smoothing
- mean_absolute_error() function
- mean_squared_error() function
- medfilt() documentation
- median absolute error (MedAE)
- median_absolute_error() function
- mel frequency spectrum
- mel scale
- Memory class
- memory leaks / Profiling memory usage
- memory usage
- memory_profiler module
- metadata
- methods
- Miniconda
- Mirador
- missing data
- MLpack
- models
- Modern Portfolio Theory (MPT)
- Modern Portfolio Theory (MPT);about / Optimizing an equal weights two-asset portfolio
- Modular toolkit for data processing (MDP)
- Monte Carlo method
- moving block bootstrapping time series data
- mpld3
- mpmath / Introduction
- MultiBoost
- multiple models
- multiple tasks
- multiple threads
N
O
P
- Pandas
- pandas
- pandas library
- Pandas objects
- PCA class
- pdist() function
- peaks
- Pearson's correlation
- PEP8
- pep8 analyzer
- periodogram() function
- periodograms
- pesentations
- phase synchronization
- plot types
- point biserial correlation
- Poisson distribution
- posterior distribution / Determining bias
- power ladder
- power spectral density
- precision
- precision_score() function
- prediction performance
- principal component analysis (PCA)
- Principal Component Analysis (PCA)
- principal component regression (PCR)
- principal components / Applying principal component analysis for dimension reduction
- prior distribution / Determining bias
- probability weights
- probplot() function
- Proj.4
- proportions
- PyMongo
- PyOpenCL
- PyOpenCL 2015.2.3
- Python applications
- Python data visualization tools
- Python libraries, in data analysis
- Python threading
Q
R
- R
- RandomForestClassifier class
- random forests
- random number generators
- random walk hypothesis (RWH)
- random walks
- RANSAC algorithm
- RapidMiner
- recall
- recall_score() function
- receiver operating characteristic (ROC)
- reports
- reproducible data analysis
- reproducible sessions
- requests-cache website
- rescaled range / Applying the discrete wavelet transform
- residual sum of squares (RSS)
- Resilient Distributed Datasets (RDDs)
- resources
- returns / Computing simple and log returns
- returns statistics
- RFE class
- RGB (red, green and blue) / Quantizing colors
- risk-free rate / Exploring risk and return
- risk and return
- robust error checking
- robust linear model
- robust regression / Fitting a robust linear model
- roc_auc_score() function
S
- savgol_filter() function
- Savitzky-Golay filter
- Scale-invariant Feature Transform (SIFT)
- scatter plot
- scikit-learn / Using ggplot2-like plots
- scikit-learn documentation
- scikit-learn library
- scikit-learn modules
- SciPy
- SciPy documentation
- for exponential distribution, reference link / See also
- for Poisson distribution / See also
- seaborn
- seaborn color palettes
- search engine indexing
- security market line (SML) / Exploring risk and return
- Selenium
- Series
- shapefile format / Displaying geographical maps
- shared nothing architecture
- Sharpe ratio
- short-time Fourier transform (STFT) / Analyzing the frequency spectrum of audio
- signals
- silhouette coefficients
- silhouette_score() function
- simple and log returns
- skewness
- smoothing
- social network closeness centrality
- social network density
- software aspects / Introduction
- software performance
- Solving a Problem in the Doctrine of Chances essay
- Sortino ratio
- Spark
- Spearman rank correlation
- spectral analysis
- spectral clustering
- spectral_clustering() function
- Speeded Up Robust Features (SURF)
- split() function
- SQLAlchemy
- square root of the MSE (RMSE) / Computing MSE and median absolute error
- stacking
- Stanford Network Analysis Project (SNAP)
- star schema
- statistics functions / Functional statistics
- Statsmodels
- statsmodels
- statsmodels documentation
- stemming
- STFT
- stock prices database
- stop words
- streaming algorithms / Streaming counting with the Count-min sketch
- supervised learning
- Support Vector Machine (SVM)
- SURF
- documentation, reference link / See also
T
U
V
- Vagrant
- validation
- validation curves / Introduction
- variables
- variance
- Viola-Jones object detection framework
- violin plots
- VirtualBox
- virtualenv
- virtual environment
- virtualenvwrapper
- visualization toolkit (VTK) / MayaVi
- VotingClassifier class
- Vowpal Wabbit
W
- Wald-Wolfowitz runs test
- watermark extension
- weak learners / Boosting for better learning
- web
- web browsing
- weighted least squares
- Weka
- Welch's method
- welch() function
- winsorizing technique / Winsorizing data, How to do it...
- Within Cluster Sum of Squares (WCSS)
- Within Set Sum Squared Error (WSSSE)
- WordNetLemmatizer class
X
Y
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