Index
A
Anaconda environment
Attributes
B
Backward differentiation formula (BDF)
Bayesian statistics
conditional probability
import modules
likelihood function
linear regression model
Boolean masks
dataset
GLM model
MCMC algorithm
MCMC sampling
Seaborn library
stochastic variable
Monte Carlo simulation methods
overview
posterior probability
random variables
mc.find_MAP function
mc.sample function
mc.traceplot function
PyMC models
SciPy stats module
sampling posterior distribution
statistical modeling
unconditional probabilities
BDF. See Backward differentiation formula (BDF)
Butterworth filter
C
Clustering
confusion_matrix function
K-means method
predict method
sklearn.cluster module
Comma-separated values (CSV) format
csv.reader function
DataFrame instance
delimiter argument
loadtxt function
read_csv function
skiprows argument
TAB character
usecols argument
Computing environments
Conda
conda create command
conda info command
conda install PACKAGE
conda list command
conda update command
YAML format
Conflicting objectives
CSV format. See Comma-separated values (CSV) format
Cython library
cy_cumsum function
cy_julia_fractal function
cy_sum function
D
Delimiter-separated values (DSV)
Dense matrices
Durbin–Watson statistical test
E
Einstein summation convention
Extrapolation
F
FDM. See Finite-difference method (FDM)
FEM. See Finite-element method (FEM)
FEniCS framework
CellFunction instance
DirichletBC class
dolfin.Constant
dolfin.Expression object
dolfin.FunctionSpace class
dolfin.interactive
dolfin library
dolfin.MeshFunction object
dolfin.refine function
dolfin.solve
function instance
mesh object
RectangleMesh function
refined_mesh
vector method
Finite-difference method (FDM)
boundary-value problem
Dirichlet boundary conditions
eye function
ODE problem
reshape method
scipy.sparse module
two-dimensional generalization
Finite-element method (FEM)
Finite impulse response (FIR) filters
G
Generalized least squares (GLS)
get_values method
H
Hierarchical Data Format 5 (HDF5)
attributes
datasets
files
flush method
group objects
HDFStore object
h5py library
iterrows method
PyTables library
where method
High-and low-level languages, trade-off
Hypothesis testing
I
Infinite impulse response (IIR) filters
Installation commands
Integral transforms
Fourier transform function
Laplace transform
Interpolation
bivariate
Chebyshev polynomials
explicit matrix form
griddata function
implicit matrix form
Legendre polynomials
multivariate situations
polynomial
spline
Vandermonde matrix
Interpreter
IPython
autocompletion
command prompt
documentation
extension commands
debugger mode
file system navigation
%timeit and %time commands
profiler function
reset command
running scripts
input and output caching
notebook
cell-types
dashboard page
editing cells
features
HTML document
JSON-based file format
markdown cells
PDF format
object introspection
Qt console
system shell
J, K
JavaScript Object Notation (JSON) format
json.dump function
json.load function
L
LUdecomposition method
M
Machine learning
classification
classification_report function
data and target attributes
datasets module
linear_model module
load_iris function
sklearn.ensemble module
sklearn.metrics module
sklearn.tree module
train_test_split function
clustering
cross-validation
dimensionality reduction
feature extraction
feature selection
regression
ElasticNet class
fit method
LASSO method
LinearRegression instance
LinearRegression object
make_regression function
regularized
score method
SSE
sklearn modules
supervised learning
training
unsupervised learning
Matplotlib
annotations
axes layout managers
GridSpec
insets
plt.subplot2grid function
subplots
axes instances
axis properties
autoscale method
axis ticks
grid lines
log-scale plots
set_title method
set_xlabel and set_ylabel methods
set_xlim and set_ylim methods
set_xticks and set_yticks methods
spine attribute
tick placements
twinx method
color map graph
definition
figure instances
import
interactive mode
legends
line properties
noninteractive nodes
NumPy arrays
plot types
plt.subplots function
text formatting
3D graphs
Matrix and vector operations
elementwise multiplication
Kronecker product
matrix-vector multiplication
nontrivial matrix multiplication
Mesh-grid arrays
Miniconda environment
Multiple integrals
N
Nonparametric methods
Numba library
Heaviside step function
imshow function
JIT-compiled function
jit_julia_fractal function
NumPy universal function
NumPy-array aware function
py_cumsum function
py_sum function
NumPy vectorize function
Numerical integration methods
interpretation
midpoint rule
Newton–Cotes quadrature rule
quad function
quadrature rule
SciPy
Simpson’s rule
sympy.Lambda function
tabulated integrand
trapezoid rule
Boolean-valued indexing
constant values
creation
data types
fancy indexing
incremental sequences
indexing and slicing expressions
logarithmic sequences
matrix creation
arbitrary one-dimensional array
nonzero diagonals
memory data
mesh-grid arrays
multidimensional arrays
properties
Python lists
real and imaginary parts
reshaping and resizing
slices
vectorized expressions
aggregate function
arithmetic operations
array operations
Boolean-valued arrays
conditional expressions
elementary mathematical function
elementwise functions
logical operations
set operations
views
uninitialized values
NumPy library
O
ODEs. See Ordinary differential equations (ODEs)
Optimization
bisection method
constraints
cvxopt library
inequality function
Lagrangian function
L-BFGS-B method
linear programming
objective function
optimize.minimize function
SciPy SLSQP solver
continuous and smooth functions
convex problems
feasible method
import libraries
minimization problem
multivariate optimization
BFGS method
brute force
Hessian evaluations
Newton’s method
slice objects
steepest descent method
vectorized functions
nonlinear least square problem
Levenberg–Marquardt method
model function
nonlinear programming problem
univariate optimization
Ordinary differential equations (ODEs)
boundary value conditions
canonical form
direction field graph
dsolve function
homogeneous
initial value conditions
Laplace transformation
nonhomogeneous
numerical methods
Adams methods
adaptive stepsize/stepsize control
Euler’s method
Runge-Kutta method
SciPy See (SciPy)
source term
standard form
symbolic solution
Ordinary least squares (OLS)
P, Q
Pandas library
DataFrame object
apply method
columns attribute
drop method
groupby method
index attributes
info method
ix indexer attribute
sort_index method
sortlevel method
sort method
sum method
value_counts method
values attribute
seaborn graphics library
boxplot function
dropna method
heatmap
jointplot function
kdeplot function
sns.set function
violinplot functions
Series object
describe method
index attribute
kind argument
plot method
time series
concat function
DataFrame.plot method
date_range function
DateTimeIndex instance
DatetimeIndex object
freq keyword
groupby methods
join method
mean function
PeriodIndex class
resample method
reset_index method
to_period method
to_pydatetime method
UNIX timestamps
using read_csv
Partial differential equations (PDEs)
FDM
FEM
Dirichlet/Neumann type
libraries
strong form
test function
trial function
dolfin.CellFunction
Poisson model
Polynomials
Probability density function (PDF)
R
RandomState object
Regression
S, T
args argument
double pendulum, dynamics
eigenvalue equation
integrate.odeint
linear equation system
condition number
higher-order polynomial model
SciPy la.lstsq method
parameters/constant values
rectangular systems
square systems
symbolic variables
sympy.solve function
unique solution
unknown model parameters
Lokta–Volterra equation
nonlinear equations
Broyden’s method
multivariate equation systems
numerical techniques
optimize module
trigonometric equations
univariate function
univariate systems
vector-valued function
visualization
odeint function
odeint solver
set_integrator method
set_jac_params method
sympy.lambdify
Serialization
Signal processing
signal files
convolution filters
FIR and IIR filters
spectral analysis
Fourier transform
frequency-domain filter
spectogram
window function
Slices
Social component
Sparse matrices
eigen value problems
graphs and networks
add_edges_from
degree method
edge_color argument
Tokyo Metro graph
transfer attribute
linear algebra functions
linear equation systems
pyplot module
in SciPy
sp.sparse module
Spline interpolation
Spyder IDE
object inspector
panes
Python and IPython consoles
shell prompt
source code editor
Spyder Integrated Development Environment
Statistics
ndarray methods
population
random module
choice function
randint function
RandomState class
RandomState instance
seed function
random variable and distributions
discrete Poisson distribution
interval method
moment method
stats method
var and std methods
statsmodels library
discrete regression
logistic regression
import module
linear regression
mathematical model
multivariate linear regression
patsy library
binary-valued treatment fields
DataFrame objects
design_info attribute
design matrix
formula syntax
function call notation
np.linalg.lstsq function
simple linear regression
time-series analysis
ARMA model
AR model
plot_acf function
Steepest descent method
Sum of squared errors (SSE)
Symbolic and arbitrary-precision integration
Symbolic computing. See Symbolic Python (SymPy)
Symbolic Python (SymPy)
equation solving
linear algebra
LUsolve method
manipulating expressions
substitutions
sympy.apart function
sympy.cancel function
sympy.collect function
sympy.expand function
sympy.factor function
sympy.simplify function
sympy.together function
mathematical expressions
mathematical symbols
arbitrary function
constants and special symbols
floating-point number
integer class
lambda functions
rational number
sin function
unapplied function
undefined functions
unevaluated function
numerical evalution
symbolic calculus
derivatives
integrals
limits
series expansions
sums and products
sympy.init_printing function
U
Univariate optimization
V
Vectorized expressions
Visualization. See Matplotlib
W, X, Y, Z
Weighted least squares (WLS)
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