Index
A
- .at operator
- Active State Python
- aggregate method
- aggregation, in R
- aliases, for Time Series frequencies
- alpha
- alternative hypothesis
- Anaconda
- append
- arithmetic operations
B
- Bayesian analysis example
- Bayesians
- Bayesian statistical analysis
- Bayesian statistics
- Bayes theory
- Bernoulli distribution
- big data
- binomial distribution
- Boolean indexing
C
- 4-4-5 calendar
- central limit theorem
- central limit theorem (CLT)
- classes, converter.py
- classes, offsets.py
- classes, parsers.py
- classes, plm.py
- classes, sql.py
- column
- column name
- columns
- concat function
- concat function, elements
- concat operation
- Conda
- conda command
- Confidence (Frequentist) interval
- confidence interval
- container types, R
- continuous probability distributions
- continuous uniform distribution
- Continuum Analytics
- correlation
- Credible (Bayesian) interval
- cross-sections / Cross sections
- cut() function, pandas
- cut() method, R
- Cython / What is pandas?
- Cython documentation
D
- data
- data analysis
- DataFrame
- DataFrame.join function / The join function
- DataFrame constructors
- DataFrame objects
- DataFrame operations
- dataset, Python
- data structure, pandas
- data types, Numpy
- data types, R
- DateOffset object
- ddply
- Debian Python page
- decision trees / Decision trees
- dependence
- descriptive statistics
- deviation
- dimensionality reduction / Dimensionality reduction
- discrete probability distributions
- discrete uniform distribution
- distribution
- downsampling
E
- Enhancing Performance, documentation
- Enthought
- Enthought Canopy
- exponential distribution
F
- Facebook (FB)
- factors / categorical data
- Fedora software installs
- file hierarchy, pandas
- filtering
- FM regression
- frequency aliases
- frequency conversion / Frequency conversion
- Frequentists
- Frequentist statistics
G
- Geometric distribution
- get-pip script
- GitHub
- groupby-transform function / The transform() method
- groupby.py submodule
- groupby object
- groupby operation
- GroupBy operator
H
I
- %in% operator, R / R %in% operator
- .iat operator
- .iloc operator
- .ix operator
- illustration, with document classification
- independent samples t-tests / Types of t-tests
- indexing, pandas
- inferential statistics
- integer-oriented indexing
- Intel
- Interactive Python (IPython)
- interpolate() function
- IPython
- IPython Notebook
- isin() function, pandas / The pandas isin() function
J
- join function
- joining
- join operation
K
- K-means clustering / K-means clustering
- K-means clustering, scikit-learn
- Kaggle
- Kaggle Titanic competition application
L
M
- machine learning
- machine learning application
- machine learning systems
- Mac OS/X
- Markov Chain Monte Carlo (MCMC)
- Markov Chain Monte Carlo Maximum Likelihood
- matching operators
- mathematical framework, Bayesian statistics
- matplotlib
- maximum likelihood estimator (MLE)
- mean
- measure of central tendency
- measure of dispersion
- measure of spread
- measure of variability
- median
- melt() function, pandas
- melt() function, R
- melt function
- merge function
- merge function, arguments
- merge operation
- merging
- methods, for reshaping DataFrames
- methods, math.py
- methods, parsers.py
- methods, pickle.py
- methods, plotting.py
- methods, sql.py
- methods, util.py
- MinGW installation, on Windows
- missing data
- missing values
- mode
- Monte Carlo (MC) integration
- Monte Carlo estimation, likelihood function
- Monte Carlo estimation, PyMC
- MSI packages
- multi-indexing / MultiIndexing
- MultiIndex
- multiple columns
- multiple functions
- multiple object classes, internals.py
N
- N-dimensional version, DataFrame
- naïve approach, to Titanic problem / A naïve approach to Titanic problem
- negative binomial distribution
- normal distribution
- NoSQL
- np.nan* aggregation functions, NumPy
- np.newaxis function / Adding a dimension
- np.reshape function
- null, and alternative hypotheses
- null hypothesis
- Null Signifcance Hypothesis Testing (NHST) / A t-test example
- numexpr
- NumPy
- Numpy
- numpy.dot
- numpy.percentile function
- NumPy array
- Numpy array
- NumPy array, creating via various function
- NumPy ndarrays
O
P
- p-value
- pad method
- paired samples t-test / Types of t-tests
- Pandas
- 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
- pandas/computation
- pandas/core
- pandas/io
- pandas/rpy
- pandas/sparse
- pandas/src
- pandas/stats
- pandas/tools
- pandas/tseries
- pandas/util
- pandas DataFrames
- pandas installation, on Linux
- pandas installation, on Mac
- pandas installation, on Windows
- pandas series
- panel
- parsers.py
- Patsy
- performance
- pip / Third-party Python software installation
- pivots
- pivot_table
- plotting
- Poisson distribution
- power law
- Principal Component Analysis (PCA) / Dimensionality reduction
- probability
- probability density function (PDF) / Continuous probability distributions
- probability distributions
- probability mass function (pmf)
- PYMC Pandas Example
- PyPI Readline package
- Python
- Python(x,y)
- Python 3.0
- Python decorators
- Python dictionary, DataFrame objects
- Python extensions
- Python installation, on Linux
- Python installation, on Mac OS/X
- Python installation, on Windows
- Python Lexical Analysis
Q
R
S
- sample covariance
- sample mean
- scikit-learn
- scikit-learn ML/classifier interface
- scipy.stats function
- Scipy Lecture Notes, Interfacing with C
- Series
- Series operations
- Setuptools
- shape manipulation, NumPy array
- shifting / Shifting/lagging
- single row
- sortlevel() method / MultiIndexing
- sparse.py
- split-apply-combine
- SQL-like merging/joining, of DataFrame objects / SQL-like merging/joining of DataFrame objects
- SQL joins
- stack() function
- stacking
- statistical hypothesis tests
- structured array, DataFrame
- submodules, pandas/compat
- submodules, pandas/computation
- submodules, pandas/core
- submodules, pandas/io
- submodules, pandas/rpy
- submodules, pandas/sparse
- submodules, pandas/stats
- submodules, pandas/tools
- submodules, pandas/tseries
- submodules, pandas/util
- supervised learning
- supervised learning algorithms
- supervised learning problems
- support vector machine (SVM) / Support vector machine
- swaplevel function / Swapping and reordering levels
- SWIG Documentation
- switchpoint detection, Bayesian analysis example / Bayesian analysis example – Switchpoint detection
T
U
- UEFA Champions League
- unbiased estimator
- Unix (Linux/Mac OSX)
- unstacking
- unsupervised learning
- unsupervised learning algorithms
- upsampling
V
W
X
Z
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