Index
A
- advanced Pandas use cases
- for data analysis / Advanced uses of Pandas for data analysis
- hierarchical indexing / Hierarchical indexing
- panel data / The Panel data
- annotate method
- about / Legends and annotations
- annotations
- about / Legends and annotations
- array creation
- about / Array creation
- array functions
- about / Array functions
- artificial intelligence (AI) / Data analysis and processing
B
- bar plot
- about / Bar plots
- Berkeley Vision and Learning Center (BVLC)
- Bokeh
C
- Caffe
- computational tools
- about / Computational tools
- contour plot
- about / Contour plots
- cross-validation (CV)
- about / Measuring prediction performance
- csvkit tool / Data munging
D
- data
- indexing / Indexing and selecting data
- selecting / Indexing and selecting data
- grouping / Grouping data
- data, in binary format
- interacting with / Interacting with data in binary format
- HDF5 / HDF5
- data, in MongoDB
- interacting with / Interacting with data in MongoDB
- data, in Redis
- interacting with / Interacting with data in Redis
- simple value / The simple value
- list / List
- set / Set
- ordered set / Ordered set
- data, in text format
- interacting with / Interacting with data in text format
- reading / Reading data from text format
- writing / Writing data to text format
- data aggregation
- about / Data aggregation
- data analysis
- about / Data analysis and processing
- process / Data analysis and processing
- domain knowledge / Data analysis and processing
- computer science / Data analysis and processing
- artificial intelligence / Data analysis and processing
- machine learning / Data analysis and processing
- statistics / Data analysis and processing
- mathematics / Data analysis and processing
- knowledge domain / Data analysis and processing
- steps / Data analysis and processing
- data requirements / Data analysis and processing
- data collection / Data analysis and processing
- data processing / Data analysis and processing
- data cleaning / Data analysis and processing
- exploratory data analysis / Data analysis and processing
- modelling / Data analysis and processing
- algorithms / Data analysis and processing
- data product / Data analysis and processing
- libraries / An overview of the libraries in data analysis
- Python libraries / Python libraries in data analysis
- DataFrame
- about / The DataFrame
- data munging
- about / Data munging
- data, cleaning / Cleaning data
- filtering / Filtering
- data, merging / Merging data
- data, reshaping / Reshaping data
- data processing, using arrays
- about / Data processing using arrays
- data, saving / Saving an array
- data, loading / Loading an array
- data structure, Pandas
- about / The Pandas data structure
- Series / Series
- DataFrame / The DataFrame
- data types
- about / Data types
- date and time objects
- working with / Working with date and time objects
E
- equal (eq) function / Binary operations
- essential functionalities
- about / The essential basic functionality
- labels, reindexing / Reindexing and altering labels
- labels, altering / Reindexing and altering labels
- head and tail / Head and tail
- binary operations / Binary operations
- functional statistics / Functional statistics
- function application / Function application
- sorting / Sorting
F
- fancy indexing
- about / Fancy indexing
- FASTLab
- features
- functions
- plotting, with Pandas / Plotting functions with Pandas
G
- greater equal (ge) function / Binary operations
- greater than (gt) function / Binary operations
H
- histogram plot
- about / Histogram plots
I
- interpolation
- about / Resampling time series
- Iris-Setosa
- Iris-Versicolour
- Iris-Virginica
J
- jq tool / Data munging
L
- legends
- about / Legends and annotations
- less equal (le) function / Binary operations
- less than (lt) function / Binary operations
- libraries, for data processing
- Statsmodels / An overview of the libraries in data analysis
- Modular toolkit for data processing (MDP) / An overview of the libraries in data analysis
- Orange / An overview of the libraries in data analysis
- Mirador / An overview of the libraries in data analysis
- RapidMiner / An overview of the libraries in data analysis
- Theano / An overview of the libraries in data analysis
- Natural language processing toolkit (NLTK) / An overview of the libraries in data analysis
- libraries, implemented in C++
- Vowpal Wabbit / An overview of the libraries in data analysis
- MultiBoost / An overview of the libraries in data analysis
- MLpack / An overview of the libraries in data analysis
- Caffe / An overview of the libraries in data analysis
- libraries, in data analysis
- linear algebra
- about / Linear algebra with NumPy
- with NumPy / Linear algebra with NumPy
M
- machine learning (ML) / Data analysis and processing
- machine learning models
- defining / An overview of machine learning models
- supervised learning / An overview of machine learning models
- unsupervised learning / An overview of machine learning models
- Mahout
- Mallet
- Matplotlib
- about / Matplotlib
- Matplotlib API Primer
- about / The matplotlib API primer
- line properties / Line properties
- figures / Figures and subplots
- subplots / Figures and subplots
- MayaVi
- about / MayaVi
- methods
- for manipulating documents / Interacting with data in MongoDB
- Mirador
- missing data
- working with / Working with missing data
- MLpack
- Modular toolkit for data processing (MDP)
- MultiBoost
N
- Natural language processing toolkit (NLTK)
- not equal (ne) function / Binary operations
- NumPy
- about / NumPy
- linear algebra with / Linear algebra with NumPy
- random numbers / NumPy random numbers
- NumPy arrays
- about / NumPy arrays
- data type / Data types
- array creation / Array creation
- indexing / Indexing and slicing
- slicing / Indexing and slicing
- fancy indexing / Fancy indexing
- numerical operations on arrays / Numerical operations on arrays
O
- Orange
- overfitting
- about / Measuring prediction performance
P
- Pandas
- about / Pandas
- package overview / An overview of the Pandas package
- data structure / The Pandas data structure
- parsing functions / Reading data from text format
- Pandas objects
- parameters / Reading data from text format
- PEP8
- URL / NumPy arrays
- about / NumPy arrays
- plot types
- exploring / Exploring plot types
- scatter plot / Scatter plots
- bar plot / Bar plots
- contour plot / Contour plots
- histogram plot / Histogram plots
- prediction performance
- measuring / Measuring prediction performance
- Principal Component Analysis (PCA)
- PyMongo
- about / PyMongo
- Python data visualization tools
- about / Additional Python data visualization tools
- Bokeh / Bokeh
- MayaVi / MayaVi
- Python libraries, in data analysis
- about / Python libraries in data analysis
- NumPy / NumPy
- Pandas / Pandas
- Matplotlib / Matplotlib
- PyMongo / PyMongo
- scikit-learn library / The scikit-learn library
Q
- q tool / Data munging
R
- RapidMiner
S
- scatter plot
- about / Scatter plots
- scikit-learn library
- about / The scikit-learn library
- scikit-learn modules
- defining, for different models / The scikit-learn modules for different models
- data representation, defining / Data representation in scikit-learn
- Series
- about / Series
- Spark
- statistics functions / Functional statistics
- Statsmodels
- supervised learning
- classification problems / An overview of machine learning models
- regression problems / An overview of machine learning models
- about / Supervised learning – classification and regression
- classification / Supervised learning – classification and regression
- regression / Supervised learning – classification and regression
- Support Vector Machine (SVM)
T
- text method
- about / Legends and annotations
- Theano
- Timedeltas
- about / Timedeltas
- time series
- reference, Pandas documentation / Working with date and time objects
- resampling / Resampling time series
- plotting / Time series plotting
- time series data
- downsampling / Downsampling time series data
- unsampling / Upsampling time series data
- time series primer
- about / Time series primer
- time zone handling
- about / Time zone handling
U
- unsupervised learning
- defining / Unsupervised learning – clustering and dimensionality reduction
- clustering / Unsupervised learning – clustering and dimensionality reduction
- dimensionality reduction / Unsupervised learning – clustering and dimensionality reduction
V
- visualization toolkit (VTK) / MayaVi
- Vowpal Wabbit
W
- Weka
X
- xmlstarlet tool / Data munging