Index
A
- adjusted autoregressive model
- ARMA model
- about / Forecasting with an ARMA model
- used, for forecasting / Forecasting with an ARMA model
- array() function / Creating a multidimensional array
- array shapes, NumPy
- manipulating / Manipulating array shapes
- arrays, flattening / Manipulating array shapes
- arrays, stacking / Stacking arrays
- arrays, splitting / Splitting arrays
- array attributes / Array attributes
- arrays, converting / Converting arrays
- assert functions, NumPy
- about / Assert functions
- assert_almost_equal function / The assert_almost_equal function
- assert_approx_equal function / Approximately equal arrays
- assert_array_almost_equal function / The assert_array_almost_equal function
- assert_almost_equal function / Assert functions, The assert_almost_equal function
- assert_approx_equal function / Assert functions, Approximately equal arrays
- assert_array_almost_equal function / Assert functions
- assert_array_equal function / Assert functions
- assert_array_less function / Assert functions
- assert_equal function / Assert functions
- assert_raises function / Assert functions
- assert_string_equal function / Assert functions
- assert_warns function / Assert functions
- atmospheric humidity
- atmospheric humidity, KNMI De Bilt data
- analyzing / Analyzing atmospheric humidity in De Bilt
- atmospheric pressure
- atmospheric pressure, KNMI De Bilt data file
- analyzing / Analyzing atmospheric pressure in De Bilt
- Augmented Dickey Fuller (ADF) test / Demonstrating cointegration
- Autoregressive (AR) model
- Autoregressive Moving Average (ARMA) model
- average De Bilt temperature
- outliers analysis / Outliers analysis of average De Bilt temperature
- average temperature autocorrelation
- examining, with pandas / Examining autocorrelation of average temperature with pandas
B
- basic data analysis
- dataset / Introducing the dataset
- Blaze
- NumPy, comparing with / Comparing NumPy to Blaze
- about / Comparing NumPy to Blaze
- URL / Comparing NumPy to Blaze
- Boolean indexing
- about / Indexing arrays with Booleans
- performing / Indexing arrays with Booleans
C
- character codes
- about / Character codes
- clustering
- cointegration
- about / Demonstrating cointegration
- demonstrating / Demonstrating cointegration
- column stacking, NumPy arrays
- about / Stacking arrays
- column_stack() function / Stacking arrays
- concatenate() function / Stacking arrays
- corner detection
- about / Detecting corners
- performing / Detecting corners
- Cython
- about / Using Cython with NumPy
- using, with NumPy / Using Cython with NumPy
D
- daily temperature range, KNMI De Bilt data file
- about / Determining the daily temperature range
- determining / Determining the daily temperature range
- data analysis, KNMI weather station
- daily temperature range, determining / Determining the daily temperature range
- yearly average temperature, determining / Looking for evidence of global warming
- solar radiation, comparing with temperature / Comparing solar radiation versus temperature
- wind direction, analyzing / Analyzing wind direction
- wind speed, analyzing / Analyzing wind speed
- precipitation, analyzing / Analyzing precipitation and sunshine duration
- sunshine duration, analyzing / Analyzing precipitation and sunshine duration
- De Bilt precipitation data, analyzing / Analyzing monthly precipitation in De Bilt
- De Bilt atmospheric pressure, analyzing / Analyzing atmospheric pressure in De Bilt
- De Bilt atmospheric humidity, analyzing / Analyzing atmospheric humidity in De Bilt
- data type objects
- about / Data type objects
- day-of-the-year temperature model
- about / Introducing the day-of-the-year temperature model
- used, for modeling temperature / Day-of-year temperature take two
- debugging
- about / Debugging with IPython
- IPython, used / Debugging with IPython
- decorators
- applying / Nose tests decorators
- deprecated decorator / Nose tests decorators
- depth-wise splitting, NumPy arrays
- about / Splitting arrays
- depth stacking, NumPy arrays
- about / Stacking arrays
- Dow Jones Industrial (DJI)
- dsplit() function
- about / Splitting arrays
- dtype attributes
- about / dtype attributes
- dtype constructors
- about / dtype constructors
E
- Empirical Mode Decomposition (EMD) / Introducing the Sunspot data
F
- fancy indexing
- about / Fancy indexing
- performing / Fancy indexing
- filter
- designing / Designing the filter
- flat attribute, ndarray / Array attributes
- flatten() function
- about / Manipulating array shapes
- forecasting
- ARMA model, used / Forecasting with an ARMA model
G
- Gaussian integral / Numerical integration
H
- horizontal splitting, NumPy arrays
- about / Splitting arrays
- horizontal stacking, NumPy arrays
- about / Stacking arrays
I
- iirdesign function / Filtering a signal
- imag attribute, ndarray / Array attributes
- inter-quartile range / Outliers analysis of average De Bilt temperature
- interp1d class / Interpolation
- interpolation
- about / Interpolation
- intra-year daily average temperatures
- analysing / Analyzing intra-year daily average temperatures
- Intrinsic Mode Functions (IMF)
- about / Introducing the Sunspot data
- extracting, via sifting / Introducing the Sunspot data
- IPython
- installing, on Windows / Installing NumPy, Matplotlib, SciPy, and IPython on Windows
- installing, on Linux / Installing NumPy, Matplotlib, SciPy, and IPython on Linux
- program, profiling with / Profiling a program with IPython
- about / Profiling a program with IPython
- debugging with / Debugging with IPython
- itemsize attribute, ndarray / Array attributes
- ix_() function
K
- KNMI
- URL / Introducing the dataset
- about / Introducing the dataset
- knownfailureif decorator / Nose tests decorators
L
- linear combination
- about / Forecasting with an ARMA model
- Linux
- NumPy, installing / Installing NumPy, Matplotlib, SciPy, and IPython on Linux
- Matplotlib, installing / Installing NumPy, Matplotlib, SciPy, and IPython on Linux
- SciPy, installing / Installing NumPy, Matplotlib, SciPy, and IPython on Linux
- IPython, installing / Installing NumPy, Matplotlib, SciPy, and IPython on Linux
- Linux distributions
- Arch Linux / Installing NumPy, Matplotlib, SciPy, and IPython on Linux
- Debian / Installing NumPy, Matplotlib, SciPy, and IPython on Linux
- Fedora / Installing NumPy, Matplotlib, SciPy, and IPython on Linux
- Gentoo / Installing NumPy, Matplotlib, SciPy, and IPython on Linux
- OpenSUSE / Installing NumPy, Matplotlib, SciPy, and IPython on Linux
- Slackware / Installing NumPy, Matplotlib, SciPy, and IPython on Linux
- loadtxt function
- about / Introducing the dataset
M
- Mac OS X
- NumPy, installing / Installing NumPy, Matplotlib, and SciPy on Mac OS X
- Matplotlib, installing / Installing NumPy, Matplotlib, and SciPy on Mac OS X
- SciPy, installing / Installing NumPy, Matplotlib, and SciPy on Mac OS X
- Matplotlib
- installing, on Windows / Installing NumPy, Matplotlib, SciPy, and IPython on Windows
- installing, on Linux / Installing NumPy, Matplotlib, SciPy, and IPython on Linux
- installing, on Mac OS X / Installing NumPy, Matplotlib, and SciPy on Mac OS X
- monthly precipitation, KNMI De Bilt data file
- analyzing / Analyzing monthly precipitation in De Bilt
- Moving Average (MA) model
- moving averages
- about / Moving averages
- ploting / Moving averages
- moving average temperature model
- multidimensional NumPy array
- creating / Creating a multidimensional array
N
- nbytes attribute, ndarray / Array attributes
- ndarray
- about / The NumPy array object
- ndim attribute / Array attributes
- size attribute / Array attributes
- itemsize attribute / Array attributes
- nbytes attribute / Array attributes
- T attribute / Array attributes
- real attribute / Array attributes
- imag attribute / Array attributes
- flat attribute / Array attributes
- ndim attribute, ndarray / Array attributes
- nose
- about / Nose tests decorators
- decorators, using / Nose tests decorators
- installing / Nose tests decorators
- Not a Number (NaN)
- about / Introducing the dataset
- numerical integration
- about / Numerical integration
- NumPy
- about / Python
- installing, on Windows / Installing NumPy, Matplotlib, SciPy, and IPython on Windows
- installing, on Linux / Installing NumPy, Matplotlib, SciPy, and IPython on Linux
- installing, on Mac OS X / Installing NumPy, Matplotlib, and SciPy on Mac OS X
- building, from source / Building from source
- online resources / Online resources and help
- forum link / Online resources and help
- assert functions / Assert functions
- Cython, using with / Using Cython with NumPy
- comparing, to Blaze / Comparing NumPy to Blaze
- numpy.testing module
- decorators / Nose tests decorators
- NumPy array object
- about / The NumPy array object
- NumPy arrays
- about / NumPy arrays
- adding / Adding arrays
- advantages / The advantages of using NumPy arrays
- array elements, selecting / Selecting array elements
- record data type, creating / Creating a record data type
- one-dimensional, slicing / One-dimensional slicing and indexing
- one-dimensional, indexing / One-dimensional slicing and indexing
- converting / Converting arrays
- views, creating / Creating views and copies
- fancy indexing / Fancy indexing
- indexing, performing with list of locations / Indexing with a list of locations
- indexing, with Booleans / Indexing arrays with Booleans
- stride tricks, applying for Sudoku / Stride tricks for Sudoku
- broadcasting / Broadcasting arrays
- NumPy basics
- NumPy array object / The NumPy array object
- NumPy numerical types
- overview / NumPy numerical types
- bool / NumPy numerical types
- inti / NumPy numerical types
- int8 / NumPy numerical types
- int16 / NumPy numerical types
- int32 / NumPy numerical types
- int64 / NumPy numerical types
- uint8 / NumPy numerical types
- uint16 / NumPy numerical types
- uint32 / NumPy numerical types
- uint64 / NumPy numerical types
- float16 / NumPy numerical types
- float32 / NumPy numerical types
- float64 / NumPy numerical types
- complex64 / NumPy numerical types
- complex / NumPy numerical types
- complex128 / NumPy numerical types
- data type objects / Data type objects
- character codes / Character codes
- dtype constructors / dtype constructors
- dtype attributes / dtype attributes
O
- one-dimensional NumPy arrays
- slicing / One-dimensional slicing and indexing
- indexing / One-dimensional slicing and indexing
- outliers analysis, average De Bilt temperature
- performing / Outliers analysis of average De Bilt temperature
P
- pandas DataFrame
- used, for descriptive statistics / Describing data with pandas DataFrames
- pandas library
- about / Examining autocorrelation of average temperature with pandas
- used, for examining average temperature autocorrelation / Examining autocorrelation of average temperature with pandas
- used, for correlating weather and stocks / Correlating weather and stocks with pandas
- precipitation, KNMI De Bilt data file
- analyzing / Analyzing precipitation and sunshine duration
- predictive analytics
- average temperature autocorrelation, examining with pandas / Examining autocorrelation of average temperature with pandas
- data, describing with pandas DataFrames / Describing data with pandas DataFrames
- weather and stocks, correlating with pandas / Correlating weather and stocks with pandas
- temperature, predicting / Predicting temperature
- intra-year daily average temperatures, analysing / Analyzing intra-year daily average temperatures
- day-of-the-year temperature model / Introducing the day-of-the-year temperature model
- temperature, modeling with SciPy leastsq function / Modeling temperature with the SciPy leastsq function
- day-of-the-year temperature / Day-of-year temperature take two
- moving average temperature model / Moving-average temperature model with lag 1
- program
- profiling, with IPython / Profiling a program with IPython
- Python
- about / Python
- PyUnit API
- about / Performing Unit tests
Q
- quad function / Numerical integration
R
- ravel() function
- about / Manipulating array shapes
- real attribute, ndarray / Array attributes
- record data type
- about / Creating a record data type
- creating / Creating a record data type
- resize() method
- about / Manipulating array shapes
- robust statistics / Using more robust statistics
- row stacking, NumPy arrays
- about / Stacking arrays
S
- #scipy channel / Online resources and help
- scikit-learn
- about / Clustering stocks with scikit-learn
- used, for clustering stocks / Clustering stocks with scikit-learn
- SciPy
- installing, on Windows / Installing NumPy, Matplotlib, SciPy, and IPython on Windows
- installing, on Linux / Installing NumPy, Matplotlib, SciPy, and IPython on Linux
- installing, on Mac OS X / Installing NumPy, Matplotlib, and SciPy on Mac OS X
- online resources / Online resources and help
- forum link / Online resources and help
- scipy.integrate / Numerical integration
- scipy.interpolate function / Interpolation
- SciPy leastsq function
- used, for modeling temperature / Modeling temperature with the SciPy leastsq function
- setastest decorator / Nose tests decorators
- shape() function
- about / Manipulating array shapes
- sifting process
- about / Introducing the Sunspot data
- steps / Sifting continued
- signal
- filtering / Filtering a signal
- signal processing techniques
- Sunspot data / Introducing the Sunspot data
- moving averages / Moving averages
- size attribute, ndarray / Array attributes
- skipif decorator / Nose tests decorators
- slow decorator / Nose tests decorators
- smoothing
- about / Smoothing functions
- smoothing functions
- about / Smoothing functions
- solar radiation
- comparing, with temperature / Comparing solar radiation versus temperature
- split() function
- about / Splitting arrays
- splitting, NumPy arrays
- performing / Splitting arrays
- horizontal splitting / Splitting arrays
- vertical splitting / Splitting arrays
- depth-wise splitting / Splitting arrays
- stacking, NumPy arrays
- performing / Stacking arrays
- horizontal stacking / Stacking arrays
- vertical stacking / Stacking arrays
- depth stacking / Stacking arrays
- column stacking / Stacking arrays
- row stacking / Stacking arrays
- stocks
- clustering, with scikit-learn / Clustering stocks with scikit-learn
- stride tricks
- applying, to Sudoku / Stride tricks for Sudoku
- sunshine duration, KNMI De Bilt data file
- analyzing / Analyzing precipitation and sunshine duration
- sunspot data
- about / Introducing the Sunspot data
- sunspots
- about / Introducing the Sunspot data
T
- T attribute, ndarray / Array attributes
- temperature
- predicting / Predicting temperature
- autoregressive model with lag 1 / Autoregressive model with lag 1
- autoregressive model with lag 2 / Autoregressive model with lag 2
- modeling, with SciPy leastsq function / Modeling temperature with the SciPy leastsq function
- transpose() function
- about / Manipulating array shapes
U
- unit tests
- performing / Performing Unit tests
V
- vertical splitting, NumPy arrays
- about / Splitting arrays
- vertical stacking, NumPy arrays
- about / Stacking arrays
- views, NumPy arrays
- creating / Creating views and copies
- vsplit() function
- about / Splitting arrays
W
- weather and stocks
- correlating, with pandas / Correlating weather and stocks with pandas
- wind direction, KNMI De Bilt data file
- analyzing / Analyzing wind direction
- Windows
- NumPy, installing / Installing NumPy, Matplotlib, SciPy, and IPython on Windows
- IPython, installing / Installing NumPy, Matplotlib, SciPy, and IPython on Windows
- SciPy, installing / Installing NumPy, Matplotlib, SciPy, and IPython on Windows
- Matplotlib, installing / Installing NumPy, Matplotlib, SciPy, and IPython on Windows
- wind speed, KNMI De Bilt data file
- analyzing / Analyzing wind speed
Y
- yearly average temperature, KNMI De Bilt data file
- determining / Looking for evidence of global warming