Index
A
- .at operator
- about / The .iat and .at operators
- Active State Python
- aggregate method
- using / Using the aggregate method
- aggregation, in R
- about / Aggregation in R
- aliases, for Time Series frequencies
- alpha
- about / The alpha and p-values
- alternative hypothesis
- Anaconda
- about / Continuum Analytics Anaconda
- URL / Continuum Analytics Anaconda, Final step for all platforms, Other numeric or analytics-focused Python distributions
- installing / Installing Anaconda
- URL, for download / Installing Anaconda
- installing, on Linux / Linux
- installing, on Mac OS/X / Mac OS X
- installing, on Windows / Windows
- installing, final steps / Final step for all platforms
- numeric or analytics-focused Python distributions / Other numeric or analytics-focused Python distributions
- IPython installation / Install via Anaconda (for Linux/Mac OS X)
- scikit-learn, installing via / Installing via Anaconda
- append
- using / Using append
- arithmetic operations
- applying, on columns / Arithmetic operations on columns
B
- Bayesian analysis example
- switchpoint detection / Bayesian analysis example – Switchpoint detection
- Bayesians
- about / How the model is defined
- Bayesian statistical analysis
- conducting, steps / Conducting Bayesian statistical analysis
- Bayesian statistics
- about / Introduction to Bayesian statistics
- reference link / Introduction to Bayesian statistics
- mathematical framework / Mathematical framework for Bayesian statistics
- references / Mathematical framework for Bayesian statistics, Applications of Bayesian statistics, References
- applications / Applications of Bayesian statistics
- versus Frequentist statistics / Bayesian statistics versus Frequentist statistics
- Bayes theory
- about / Bayes theory and odds
- Bernoulli distribution
- about / The Bernoulli distribution
- reference link / The Bernoulli distribution
- big data
- references / We live in a big data world
- 4Vs / 4 V's of big data
- about / 4 V's of big data
- examples / The move towards real-time analytics
- binomial distribution
- about / The binomial distribution
- Boolean indexing
- about / Boolean indexing
- any() method / The is in and any all methods
- isin method / The is in and any all methods
- all method / The is in and any all methods
- where() method, using / Using the where() method
- indexes, operations / Operations on indexes
C
- 4-4-5 calendar
- reference link / pandas/tseries
- central limit theorem
- reference link / Background
- central limit theorem (CLT)
- about / The mean
- classes, converter.py
- Converter / pandas/tseries
- Formatters / pandas/tseries
- Locators / pandas/tseries
- classes, offsets.py
- DateOffset / pandas/tseries
- BusinessMixin / pandas/tseries
- MonthOffset / pandas/tseries
- MonthBegin / pandas/tseries
- MonthEnd / pandas/tseries
- BusinessMonthEnd / pandas/tseries
- BusinessMonthBegin / pandas/tseries
- YearOffset / pandas/tseries
- YearBegin / pandas/tseries
- YearEnd / pandas/tseries
- BYearEnd / pandas/tseries
- BYearBegin / pandas/tseries
- Week / pandas/tseries
- WeekDay / pandas/tseries
- WeekOfMonth / pandas/tseries
- LastWeekOfMonth / pandas/tseries
- QuarterOffset / pandas/tseries
- QuarterEnd / pandas/tseries
- QuarterrBegin / pandas/tseries
- BQuarterEnd / pandas/tseries
- BQuarterBegin / pandas/tseries
- FY5253Quarter / pandas/tseries
- FY5253 / pandas/tseries
- Easter / pandas/tseries
- Tick / pandas/tseries
- classes, parsers.py
- classes, plm.py
- PanelOLS / pandas/stats
- MovingPanelOLS / pandas/stats
- NonPooledPanelOLS / pandas/stats
- classes, sql.py
- column
- multiple functions, applying to / Applying multiple functions
- column name
- specifying, in R / Specifying column name in R
- specifying, in pandas / Specifying column name in pandas
- columns
- arithmetic operations, applying on / Arithmetic operations on columns
- concat function
- about / The concat function
- concat function, elements
- objs function / The concat function
- axis function / The concat function
- join function / The concat function
- join_axes function / The concat function
- keys function / The concat function
- concat operation
- reference link / The join function
- Conda
- documentation, URL / Final step for all platforms
- conda command
- Confidence (Frequentist) interval
- versus Credible (Bayesian) interval / Confidence (Frequentist) versus Credible (Bayesian) intervals
- confidence interval
- about / Confidence intervals
- example / An illustrative example
- container types, R
- Vector / R data types
- List / R data types
- DataFrame / R data types
- Matrix / R data types
- continuous probability distributions
- about / Continuous probability distributions
- continuous uniform distribution / The continuous uniform distribution
- exponential distribution / The exponential distribution
- normal distribution / The normal distribution
- continuous uniform distribution
- Continuum Analytics
- correlation
- about / Correlation and linear regression, Correlation
- reference link / Correlation, An illustrative example
- Credible (Bayesian) interval
- versus Confidence (Frequentist) interval / Confidence (Frequentist) versus Credible (Bayesian) intervals
- cross-sections / Cross sections
- cut() function, pandas
- about / The pandas solution
- cut() method, R
- about / An R example using cut()
- reference link / An R example using cut()
- Cython / What is pandas?
- URL / Source installation
- Cython documentation
- reference link / Improving performance using Python extensions
D
- data
- grouping / Grouping of data
- reshaping / Pivots and reshaping data
- resampling / Resampling of data
- data analysis
- motivation / Motivation for data analysis
- big data / We live in a big data world
- time limitation / So much data, so little time for analysis
- URL / So much data, so little time for analysis
- real-time analytics / The move towards real-time analytics
- DataFrame
- about / DataFrame
- creating / DataFrame Creation
- creating, with dictionaries of Series / Using dictionaries of Series
- creating, with dictionary of ndarrays/lists / Using a dictionary of ndarrays/lists
- creating, with structured array / Using a structured array
- creating, with Series structure / Using a Series structure
- constructors / Using a Series structure
- operations / Operations
- single row, appending to / Appending a single row to a DataFrame
- DataFrame.join function / The join function
- DataFrame constructors
- DataFrame.from_dict / Using a Series structure
- DataFrame.from_records / Using a Series structure
- DataFrame.from_items / Using a Series structure
- pandas.io.parsers.read_csv / Using a Series structure
- pandas.io.parsers.read_table / Using a Series structure
- pandas.io.parsers.read_fwf / Using a Series structure
- DataFrame objects
- SQL-like merging/joining / SQL-like merging/joining of DataFrame objects
- DataFrame operations
- selection / Selection
- assignment / Assignment
- deletion / Deletion
- alignment / Alignment
- mathematical operations / Other mathematical operations
- dataset, Python
- measures of central tendency, computing of / Computing measures of central tendency of a dataset in Python
- data structure, pandas
- data types, Numpy
- reference link / R data types
- data types, R
- about / R data types
- reference link / R data types
- DateOffset object
- about / DateOffset and TimeDelta objects
- features / DateOffset and TimeDelta objects
- ddply
- reference link / Split-apply-combine
- Debian Python page
- URL / Linux
- decision trees / Decision trees
- dependence
- reference link / Correlation
- descriptive statistics
- versus inferential statistics / Descriptive statistics versus inferential statistics
- deviation
- about / Deviation and variance
- dimensionality reduction / Dimensionality reduction
- discrete probability distributions
- discrete uniform distribution
- about / Discrete uniform distributions
- Bernoulli distribution / The Bernoulli distribution
- binomial distribution / The binomial distribution
- Poisson distribution / The Poisson distribution
- Geometric distribution / The Geometric distribution
- negative binomial distribution / The negative binomial distribution
- distribution
- fitting / Fitting a distribution
- downsampling
- about / Resampling of data
E
- Enhancing Performance, documentation
- reference link / Improving performance using Python extensions
- Enthought
- Enthought Canopy
- exponential distribution
- about / The exponential distribution
- reference link / The exponential distribution
F
- Facebook (FB)
- factors / categorical data
- about / Factors/categorical data
- Fedora software installs
- URL / Linux
- file hierarchy, pandas
- pandas/core / Introduction to pandas' file hierarchy, pandas/core
- pandas/src / Introduction to pandas' file hierarchy
- pandas/io / Introduction to pandas' file hierarchy, pandas/io
- pandas/tools / Introduction to pandas' file hierarchy, pandas/tools
- pandas/sparse / Introduction to pandas' file hierarchy, pandas/sparse
- pandas/stats / Introduction to pandas' file hierarchy, pandas/stats
- pandas/util / Introduction to pandas' file hierarchy, pandas/util
- pandas/rpy / Introduction to pandas' file hierarchy, pandas/rpy
- pandas/tests / pandas/tests
- pandas/compat / pandas/compat
- pandas/computation / pandas/computation
- pandas/tseries / pandas/tseries
- pandas/sandbox / pandas/sandbox
- filtering
- applying, on groupby object / Filtering
- FM regression
- reference link / pandas/stats
- frequency aliases
- reference link / Frequency conversion
- frequency conversion / Frequency conversion
- Frequentists
- about / How the model is defined
- Frequentist statistics
- versus Bayesian statistics / Bayesian statistics versus Frequentist statistics
G
- Geometric distribution
- about / The Geometric distribution
- get-pip script
- GitHub
- IPython download, URL / Windows
- groupby-transform function / The transform() method
- groupby.py submodule
- Splitter classes / pandas/core
- Grouper/Grouping classes / pandas/core
- groupby object
- filtering, applying on / Filtering
- groupby operation
- about / The groupby operation
- using, with MultiIndex / Using groupby with a MultiIndex
- GroupBy operator
- about / Aggregation and GroupBy
- using / The pandas' GroupBy operator
H
- histograms, versus bar plots
- reference link / Computing measures of central tendency of a dataset in Python
- hyperparameters / The scikit-learn ML/classifier interface
- hypothesis testing
- about / Hypothesis testing – the null and alternative hypotheses
- null hypothesis / The null and alternative hypotheses
- alternative hypothesis / The null and alternative hypotheses
I
- %in% operator, R / R %in% operator
- .iat operator
- about / The .iat and .at operators
- .iloc operator
- .ix operator
- about / Label, integer, and mixed indexing
- indexing, mixing with / Mixed indexing with the .ix operator
- illustration, with document classification
- about / Illustration using document classification
- supervised learning / Supervised learning
- unsupervised learning / Unsupervised learning
- independent samples t-tests / Types of t-tests
- indexing, pandas
- about / Basic indexing
- attributes, accessing with dot operator / Accessing attributes using dot operator
- range slicing / Range slicing
- mixing, with .ix operator / Mixed indexing with the .ix operator
- inferential statistics
- versus descriptive statistics / Descriptive statistics versus inferential statistics
- integer-oriented indexing
- Intel
- Interactive Python (IPython)
- interpolate() function
- reference link / Handling missing values
- IPython
- installation / IPython installation
- installation, on Linux / Linux
- installation, on Windows / Windows
- installation, URL / Windows
- installation, on Mac OS/X / Mac OS X
- installation, via Anaconda / Install via Anaconda (for Linux/Mac OS X)
- installation, Wakari / Wakari by Continuum Analytics
- installation, with virtualenv / Virtualenv
- IPython Notebook
- URL / IPython Notebook
- isin() function, pandas / The pandas isin() function
J
- join function
- about / The join function
- joining
- about / Merging and joining
- join operation
- reference link / The join function
K
- K-means clustering / K-means clustering
- K-means clustering, scikit-learn
- reference link / K-means clustering
- Kaggle
- Kaggle Titanic competition application
- about / Application of machine learning – Kaggle Titanic competition, The titanic: machine learning from disaster problem
- problem of overfitting / The problem of overfitting
L
- .loc operator
- label-oriented indexing / Label, integer, and mixed indexing, Label-oriented indexing
- about / Label-oriented indexing
- selection, Boolean array used / Selection using a Boolean array
- lagging / Shifting/lagging
- lambda functions
- reference link / The groupby operation
- law of large numbers (LLN)
- reference link / The mean
- levels
- swapping / Swapping and reordering levels
- re-ordering / Swapping and reordering levels
- linear regression
- about / Correlation and linear regression, Linear regression
- example / An illustrative example
- Linux
- logical operators, NumPy array
- np.all() / Logical operators
- np.any() / Logical operators
- logical subsetting
- about / Logical subsetting
- in R / Logical subsetting in R
- in pandas / Logical subsetting in pandas
- logistic regression
- about / Logistic regression
- reference link / Logistic regression
M
- machine learning
- about / Introduction to machine learning
- reference link / Introduction to machine learning
- machine learning application
- Kaggle Titanic competition / Application of machine learning – Kaggle Titanic competition
- machine learning systems
- working / How machine learning systems learn
- Mac OS/X
- Python, installing / Linux, Mac OS X
- Python, installing from compressed tarball / Installing Python from compressed tarball
- Python installation, URL / Installation using a package manager
- Anaconda installation / Mac OS X
- panda installation / Mac
- IPython installation / Mac OS X
- IPython installation, URL / Mac OS X
- Markov Chain Monte Carlo (MCMC)
- Markov Chain Monte Carlo Maximum Likelihood
- reference link / Monte Carlo estimation of the likelihood function and PyMC
- matching operators
- comparing, in R and pandas / Comparing matching operators in R and pandas
- mathematical framework, Bayesian statistics
- matplotlib
- using, for plotting / Plotting using matplotlib
- reference link / Plotting using matplotlib
- maximum likelihood estimator (MLE)
- about / How the model is defined
- mean
- measure of central tendency
- about / Measures of central tendency and variability, Measures of central tendency
- mean / The mean
- median / The median
- mode / The mode
- computing, for dataset in Python / Computing measures of central tendency of a dataset in Python
- measure of dispersion
- about / Measures of variability, dispersion, or spread
- range / Range
- quartile / Quartile
- measure of spread
- measure of variability
- median
- melt() function, pandas
- about / The pandas melt() function
- melt() function, R
- about / The R melt() function
- melt function
- using / Using the melt function
- used, for reshaping / Reshaping using melt
- merge function
- merge function, arguments
- left / SQL-like merging/joining of DataFrame objects
- right / SQL-like merging/joining of DataFrame objects
- how / SQL-like merging/joining of DataFrame objects
- on / SQL-like merging/joining of DataFrame objects
- left_on / SQL-like merging/joining of DataFrame objects
- right_on / SQL-like merging/joining of DataFrame objects
- left_index / SQL-like merging/joining of DataFrame objects
- right_index / SQL-like merging/joining of DataFrame objects
- sort / SQL-like merging/joining of DataFrame objects
- suffixes / SQL-like merging/joining of DataFrame objects
- copy / SQL-like merging/joining of DataFrame objects
- merge operation
- reference link / The join function
- merging
- about / Merging and joining
- reference link / The concat function, SQL-like merging/joining of DataFrame objects
- methods, for reshaping DataFrames
- about / Other methods to reshape DataFrames
- melt function / Using the melt function
- pandas.get_dummies() function / The pandas.get_dummies() function
- methods, math.py
- rank(..) / pandas/stats
- solve(..) / pandas/stats
- inv(..) / pandas/stats
- is_psd(..) / pandas/stats
- newey_west(..) / pandas/stats
- calc_F(..) / pandas/stats
- methods, parsers.py
- methods, pickle.py
- methods, plotting.py
- scatter_matrix(..) / pandas/tools
- andrews_curves(..) / pandas/tools
- parallel_coordinates(..) / pandas/tools
- lag_plot(..) / pandas/tools
- autocorrelation_plot(..) / pandas/tools
- bootstrap_plot(..) / pandas/tools
- radviz(..) / pandas/tools
- methods, sql.py
- methods, util.py
- isleapyear(..) / pandas/tseries
- pivot_annual(..) / pandas/tseries
- MinGW installation, on Windows
- URL / Source installation
- missing data
- handling / Handling missing data
- missing values
- handling / Handling missing values
- mode
- Monte Carlo (MC) integration
- Monte Carlo estimation, likelihood function
- Monte Carlo estimation, PyMC
- MSI packages
- URL, for download / Core Python installation
- multi-indexing / MultiIndexing
- MultiIndex
- groupby operation, using with / Using groupby with a MultiIndex
- multiple columns
- selecting, in R / Multicolumn selection in R
- selecting, in pandas / Multicolumn selection in pandas
- multiple functions
- applying, to column / Applying multiple functions
- multiple object classes, internals.py
- Block / pandas/core
- NumericBlock / pandas/core
- FloatOrComplexBlock / pandas/core
- ComplexBlock / pandas/core
- FloatBlock / pandas/core
- IntBlock / pandas/core
- BoolBlock / pandas/core
- TimeDeltaBlock / pandas/core
- DatetimeBlock / pandas/core
- ObjectBlock / pandas/core
- SparseBlock / pandas/core
- BlockManager / pandas/core
- SingleBlockManager / pandas/core
- JoinUnit / pandas/core
N
- N-dimensional version, DataFrame
- reference link / pandas/core
- naïve approach, to Titanic problem / A naïve approach to Titanic problem
- negative binomial distribution
- normal distribution
- about / The normal distribution
- NoSQL
- URL / Variety of big data
- np.nan* aggregation functions, NumPy
- reference link / Handling missing data
- np.newaxis function / Adding a dimension
- np.reshape function
- URL / Reshaping
- null, and alternative hypotheses
- alpha value / The alpha and p-values
- p-value / The alpha and p-values
- null hypothesis
- Null Signifcance Hypothesis Testing (NHST) / A t-test example
- numexpr
- reference link / pandas/computation
- NumPy
- ndarrays / NumPy ndarrays
- URL / NumPy ndarrays
- datatypes / NumPy datatypes
- datatypes, URL / NumPy datatypes
- indexing / NumPy indexing and slicing
- slicing / NumPy indexing and slicing
- array, slicing / Array slicing
- array, masking / Array masking
- complex indexing / Complex indexing
- Numpy
- URL / Source installation
- numpy.dot
- URL / Basic operations
- numpy.percentile function
- reference link / Quartile
- NumPy array
- URL / NumPy ndarrays
- creating / NumPy array creation
- creating, via numpy.array / NumPy arrays via numpy.array
- creating, via numpy.arange / NumPy array via numpy.arange
- creating, via numpy.linspace / NumPy array via numpy.linspace
- creating, via various other functions / NumPy array via various other functions
- indexing, URL / Array slicing
- copies / Copies and views
- views / Copies and views
- operations / Operations
- btoadcasting / Broadcasting
- shape manipulation / Array shape manipulation
- sorting / Array sorting
- Numpy array
- versus R-matrix / R-matrix and NumPy array compared
- NumPy array, creating via various function
- about / NumPy array via various other functions
- numpy.ones / numpy.ones
- numpy.eye / numpy.eye
- numpy.diag / numpy.diag
- numpy.random.rand / numpy.random.rand
- numpy.empty / numpy.empty
- numpy.tile / numpy.tile
- NumPy ndarrays
- about / NumPy ndarrays
O
- objects
- slicing / Slicing and selection
- odds
- about / Bayes theory and odds
- one sample independent t-test / Types of t-tests
- Open Suse
- URL / Linux
- operations, NumPy array
- basic operations / Basic operations
- reduction operations / Reduction operations
- statistical operators / Statistical operators
- logical operators / Logical operators
- Ordinary Least Squares (OLS) / pandas/stats
- overfitting / The problem of overfitting
P
- p-value
- references / The alpha and p-values
- pad method
- reference link / Handling missing values
- paired samples t-test / Types of t-tests
- Pandas
- installing, from third-party vendor / Installation of Python and pandas from a third-party vendor
- pandas
- about / How Python and pandas fit into the data analytics mix, What is pandas?
- features / What is pandas?
- URL / What is pandas?
- benefits / Benefits of using pandas
- installing, from third-party vendor / Installation of Python and pandas from a third-party vendor
- downloading / Downloading and installing pandas
- installing / Downloading and installing pandas
- installing, on Linux / Linux
- installing, on Mac / Mac
- installing, on Windows / Windows
- URL, for download / Source installation
- data structures / Data structures in pandas
- data structures, URL / Data structures in pandas
- indexing / Basic indexing
- file hierarchy / Introduction to pandas' file hierarchy
- column name, specifying in / Specifying column name in pandas
- multiple columns, selecting in / Multicolumn selection in pandas
- isin() function / The pandas isin() function
- logical subsetting / Logical subsetting in pandas
- split-apply-combine, implementing in / Implementation in pandas
- melt() function / The pandas melt() function
- cut() function / The pandas solution
- used, for data analysis / Data analysis and preprocessing using pandas
- used, for preprocessing / Data analysis and preprocessing using pandas
- data, examining / Examining the data
- missing values, handling / Handling missing values
- pandas.DataFrame.any
- pandas.get_dummies() function
- pandas/compat
- submodules / pandas/compat
- pandas/computation
- submodules / pandas/computation
- pandas/core
- about / Introduction to pandas' file hierarchy
- submodules / pandas/core
- pandas/io
- about / Introduction to pandas' file hierarchy
- submodules / pandas/io
- pandas/rpy
- about / Introduction to pandas' file hierarchy
- submodules / pandas/rpy
- reference link / pandas/rpy
- pandas/sparse
- about / Introduction to pandas' file hierarchy
- submodules / pandas/sparse
- reference link / pandas/sparse
- pandas/src
- pandas/stats
- about / Introduction to pandas' file hierarchy
- submodules / pandas/stats
- pandas/tools
- about / Introduction to pandas' file hierarchy
- submodules / pandas/tools
- pandas/tseries
- submodules / pandas/tseries
- pandas/util
- about / Introduction to pandas' file hierarchy
- submodules / pandas/util
- pandas DataFrames
- versus R DataFrames / R's DataFrames versus pandas' DataFrames
- pandas installation, on Linux
- for Ubuntu/Debian / Ubuntu/Debian
- for Red Hat / Red Hat
- for Fedora / Fedora
- for OpenSuse / OpenSuse
- pandas installation, on Mac
- source installation / Source installation
- binary installation / Binary installation
- pandas installation, on Windows
- binary installation / Binary Installation
- binary installation, URL / Binary Installation
- source installation / Source installation
- Interactive Python (IPython) tool / IPython
- Interactive Python (IPython) tool / IPython
- IPython Notebook / IPython Notebook
- pandas series
- versus R lists / R lists and pandas series compared
- panel
- about / Panel
- items / Panel
- major_axis / Panel
- minor_axis / Panel
- 3D NumPy array, using with axis labels / Using 3D NumPy array with axis labels
- Python dictionary of DataFrame structures, using / Using a Python dictionary of DataFrame objects
- parsers.py
- reference link / pandas/io
- Patsy
- model, constructing for scikit-learn / Constructing a model using Patsy for scikit-learn
- reference link / Constructing a model using Patsy for scikit-learn
- performance
- improving, Python extensions used / Improving performance using Python extensions
- pip / Third-party Python software installation
- pivots
- about / Pivots and reshaping data
- pivot_table
- references / Pivots and reshaping data
- plotting
- performing, with matplotlib / Plotting using matplotlib
- Poisson distribution
- about / The Poisson distribution
- reference link / The Poisson distribution
- power law
- reference link / Linear regression
- Principal Component Analysis (PCA) / Dimensionality reduction
- probability
- about / What is probability?
- probability density function (PDF) / Continuous probability distributions
- probability distributions
- about / Probability distributions
- probability mass function (pmf)
- PYMC Pandas Example
- URL / IPython Notebook
- PyPI Readline package
- URL / Windows
- Python
- about / How Python and pandas fit into the data analytics mix
- features / How Python and pandas fit into the data analytics mix
- URL / How Python and pandas fit into the data analytics mix, Selecting a version of Python to use, Installing Python from compressed tarball
- libraries / How Python and pandas fit into the data analytics mix
- version, selecting / Selecting a version of Python to use
- installation, on Linux / Linux
- installation, on Windows / Core Python installation
- installation, on Mac OS/X / Mac OS X
- Anaconda package, URL / Installation of Python and pandas from a third-party vendor
- Python(x,y)
- Python 3.0
- URL / Selecting a version of Python to use
- references / Selecting a version of Python to use
- Python decorators
- reference link / pandas/util
- Python dictionary, DataFrame objects
- DataFrame.to_panel method, using / Using the DataFrame.to_panel method
- DataFrame.to_panel method, references / Using the DataFrame.to_panel method
- other operations / Other operations
- Python extensions
- used, for improving performance / Improving performance using Python extensions
- Python installation, on Linux
- about / Linux
- from compressed tarball / Installing Python from compressed tarball
- Python installation, on Mac OS/X
- about / Mac OS X
- URL / Mac OS X
- package manager, using / Installation using a package manager
- Python installation, on Windows
- about / Windows
- core Python installation / Core Python installation
- third-party software install / Third-party Python software installation
- URL / Third-party Python software installation
- Python Lexical Analysis
Q
R
- R
- data types / R data types
- column name, specifying in / Specifying column name in R
- multiple columns, selecting in / Multicolumn selection in R
- %in% operator / R %in% operator
- logical subsetting / Logical subsetting in R
- split-apply-combine, implementing in / Implementation in R
- melt() function / The R melt() function
- cut() method / An R example using cut()
- R, and pandas
- matching operators, comparing in / Comparing matching operators in R and pandas
- R-matrix
- versus Numpy array / R-matrix and NumPy array compared
- random forest / Random forest
- random walk hypothesis
- reference link / The exponential distribution
- range / Range
- R DataFrames
- about / R DataFrames
- versus pandas DataFrames / R's DataFrames versus pandas' DataFrames
- README file, scikit-learn
- reference link / Installing on Windows
- R lists
- about / R lists
- versus pandas series / R lists and pandas series compared
- role of pandas, in machine learning / Role of pandas in machine learning
S
- sample covariance
- reference link / The mean
- sample mean
- reference link / The mean
- scikit-learn
- about / Role of pandas in machine learning
- installing / Installation of scikit-learn
- installing, via Anacondas / Installing via Anaconda
- installing, on Unix (Linux/Mac OSX) / Installing on Unix (Linux/Mac OS X)
- installing, on Windows / Installing on Windows
- reference link / Installing on Windows
- model. constructing for / Constructing a model using Patsy for scikit-learn
- scikit-learn ML/classifier interface
- about / The scikit-learn ML/classifier interface
- reference link / The scikit-learn ML/classifier interface
- scipy.stats function
- reference link / Quartile
- Scipy Lecture Notes, Interfacing with C
- reference link / Improving performance using Python extensions
- Series
- creating / Series creation
- creating, with numpy.ndarray / Using numpy.ndarray
- creating, with Python dictionary / Using Python dictionary
- creating, with scalar values / Using scalar values
- operations / Operations on Series
- Series operations
- assignment / Assignment
- slicing / Slicing
- arithmetic and statistical operations / Other operations
- Setuptools
- shape manipulation, NumPy array
- about / Array shape manipulation
- multi-dimensional array, flattening / Flattening a multi-dimensional array
- reshaping / Reshaping
- resizing / Resizing
- dimension, adding / Adding a dimension
- shifting / Shifting/lagging
- single row
- appending, to DataFrame / Appending a single row to a DataFrame
- sortlevel() method / MultiIndexing
- sparse.py
- reference link / pandas/core
- split-apply-combine
- about / Split-apply-combine
- implementing, in R / Implementation in R
- implementing, in pandas / Implementation in pandas
- SQL-like merging/joining, of DataFrame objects / SQL-like merging/joining of DataFrame objects
- SQL joins
- reference link / SQL-like merging/joining of DataFrame objects
- stack() function
- about / The stack() function
- stacking
- about / Stacking and unstacking
- statistical hypothesis tests
- about / Statistical hypothesis tests
- background / Background
- z-test / The z-test
- t-test / The t-test
- structured array, DataFrame
- URL / Using a structured array
- submodules, pandas/compat
- chainmap.py / pandas/compat
- chainmap_impl.py / pandas/compat
- pickle_compat.py / pandas/compat
- openpyxl_compat.py / pandas/compat
- submodules, pandas/computation
- api.py / pandas/computation
- align.py / pandas/computation
- common.py / pandas/computation
- engines.py / pandas/computation
- eval.py / pandas/computation
- expressions.py / pandas/computation
- ops.py / pandas/computation
- pytables.py / pandas/computation
- scope.py / pandas/computation
- submodules, pandas/core
- api.py / pandas/core
- array.py / pandas/core
- base.py / pandas/core
- common.py / pandas/core
- config.py / pandas/core
- datetools.py / pandas/core
- frame.py / pandas/core
- generic.py / pandas/core
- categorical.py / pandas/core
- format.py / pandas/core
- groupby.py / pandas/core
- ops.py / pandas/core
- index.py / pandas/core
- internals.py / pandas/core
- matrix.py / pandas/core
- nanops.py / pandas/core
- panel.py / pandas/core
- panel4d.py / pandas/core
- panelnd.py / pandas/core
- series.py / pandas/core
- sparse.py / pandas/core
- strings.py / pandas/core
- submodules, pandas/io
- api.py / pandas/io
- auth.py / pandas/io
- common.py / pandas/io
- data.py / pandas/io
- date_converters.py / pandas/io
- excel.py / pandas/io
- ga.py / pandas/io
- gbq.py / pandas/io
- html.py / pandas/io
- json.py / pandas/io
- packer.py / pandas/io
- parsers.py / pandas/io
- pickle.py / pandas/io
- pytables.py / pandas/io
- sql.py / pandas/io
- to_sql(..) / pandas/io
- stata.py / pandas/io
- wb.py / pandas/io
- submodules, pandas/rpy
- base.py / pandas/rpy
- common.py / pandas/rpy
- mass.py / pandas/rpy
- var.py / pandas/rpy
- submodules, pandas/sparse
- api.py / pandas/sparse
- array.py / pandas/sparse
- frame.py / pandas/sparse
- list.py / pandas/sparse
- panel.py / pandas/sparse
- series.py / pandas/sparse
- submodules, pandas/stats
- api.py / pandas/stats
- common.py / pandas/stats
- fama_macbeth.py / pandas/stats
- interface.py / pandas/stats
- math.py / pandas/stats
- misc.py / pandas/stats
- moments.py / pandas/stats
- ols.py / pandas/stats
- plm.py / pandas/stats
- var.py / pandas/stats
- submodules, pandas/tools
- util.py / pandas/tools
- tile.py / pandas/tools
- rplot.py / pandas/tools
- plotting.py / pandas/tools
- pivot.py / pandas/tools
- merge.py / pandas/tools
- describe.py / pandas/tools
- submodules, pandas/tseries
- api.py / pandas/tseries
- converter.py / pandas/tseries
- frequencies.py / pandas/tseries
- holiday.py / pandas/tseries
- index.py / pandas/tseries
- interval.py / pandas/tseries
- offsets.py / pandas/tseries
- period.py / pandas/tseries
- plotting.py / pandas/tseries
- resample.py / pandas/tseries
- timedeltas.py / pandas/tseries
- tools.py / pandas/tseries
- util.py / pandas/tseries
- submodules, pandas/util
- terminal.py / pandas/util
- print_versions.py / pandas/util
- misc.py / pandas/util
- decorators.py / pandas/util
- clipboard.py / pandas/util
- supervised learning
- versus unsupervised learning / Supervised versus unsupervised learning
- about / Supervised learning
- supervised learning algorithms
- about / Supervised learning algorithms
- model, constructing for scikit-learn with Patsy / Constructing a model using Patsy for scikit-learn
- general boilerplate code explanation / General boilerplate code explanation
- logistic regression / Logistic regression
- support vector machine (SVM) / Support vector machine
- decision trees / Decision trees
- random forest / Random forest
- supervised learning problems
- classification / Supervised versus unsupervised learning
- regression / Supervised versus unsupervised learning
- support vector machine (SVM) / Support vector machine
- URL / Support vector machine
- swaplevel function / Swapping and reordering levels
- SWIG Documentation
- reference link / Improving performance using Python extensions
- switchpoint detection, Bayesian analysis example / Bayesian analysis example – Switchpoint detection
T
- t-distribution
- reference link / The t-test
- t-test
- about / The t-test
- one sample independent t-test / Types of t-tests
- independent samples t-tests / Types of t-tests
- paired samples t-test / Types of t-tests
- reference link / Types of t-tests
- example / A t-test example
- tailed test
- reference link / Statistical hypothesis tests
- time-series-related instance methods
- about / Time series-related instance methods
- shifting/lagging / Shifting/lagging
- frequency conversion / Frequency conversion
- data, resampling / Resampling of data
- aliases, for Time Series frequencies / Aliases for Time Series frequencies
- Time-Series-related objects
- datetime.datetime / A summary of Time Series-related objects
- Timestamp / A summary of Time Series-related objects
- DatetimeIndex / A summary of Time Series-related objects
- Period / A summary of Time Series-related objects
- PeriodIndex / A summary of Time Series-related objects
- DateOffset / A summary of Time Series-related objects
- timedelta / A summary of Time Series-related objects
- TimeDelta object / DateOffset and TimeDelta objects
- time series
- handling / Handling time series
- TimeSeries.resample function
- about / Resampling of data
- Time series concepts
- time series data
- reading in / Reading in time series data
- TimeDelta object / DateOffset and TimeDelta objects
- DateOffset object / DateOffset and TimeDelta objects
- Time series datatypes
- about / Time series concepts and datatypes
- Period / Period and PeriodIndex
- PeriodIndex / PeriodIndex
- Time Series datatypes
- conversion between / Conversions between Time Series datatypes
- time series datatypes
- PeriodIndex / PeriodIndex
- Time Series frequencies
- aliases / Aliases for Time Series frequencies
- Titanic problem
- naïve approach / A naïve approach to Titanic problem
- transform() method / The transform() method
- Type I Error / Type I and Type II errors
- Type II Error / Type I and Type II errors
U
- UEFA Champions League
- URL / The groupby operation
- unbiased estimator
- reference link / Deviation and variance
- Unix (Linux/Mac OSX)
- scikit-learn, installing on / Installing on Unix (Linux/Mac OS X)
- unstacking
- about / Stacking and unstacking
- unsupervised learning
- versus supervised learning / Supervised versus unsupervised learning
- about / Unsupervised learning
- unsupervised learning algorithms
- about / Unsupervised learning algorithms
- dimensionality reduction / Dimensionality reduction
- K-means clustering / K-means clustering
- upsampling
- about / Resampling of data
V
- 4Vs of big data
- about / 4 V's of big data, Veracity of big data
- volume / Volume of big data
- velocity / Velocity of big data
- variety / Variety of big data
- veracity / Veracity of big data
- variance
- about / Deviation and variance
- variety, big data / Variety of big data
- vector auto-regression classes, var.py
- VAR / pandas/stats
- PanelVAR / pandas/stats
- vector autoregression
- reference link / pandas/stats
- velocity, big data / Velocity of big data
- veracity, big data / Veracity of big data
- virtualenv tool
- about / Virtualenv
- installing / Virtualenv installation and usage
- using / Virtualenv installation and usage
- URL / Virtualenv installation and usage
- volume, big data / Volume of big data
W
- Wakari
- where() method / Using the where() method
- Windows
- Python, installing / Windows, Core Python installation
- Anaconda installation / Windows
- panda installation / Windows
- IPython installation / Windows
- scikit-learn, installing on / Installing on Windows
- WinPython
- World Bank Economic data
- URL / Benefits of using pandas
X
- xs method / Cross sections
Z
- z-test
- about / The z-test
- zettabytes
- URL / Volume of big data