Módulos#
Introducción#
Hemos visto que una parte fundamental de la programación en Python (y en cualquier lenguaje) consiste en estructurar la solución que estamos desarrollando en funciones.
A partir de este concepto, surgen las módulos (bibliotecas o paquetes). Las bibliotecas reunen conjuntos de funciones específicas asociadas a un tema en particular. En esta clase y las próximas, recorreremos varias bibliotecas útiles para la programación en Python.
Módulos estandard#
El lenguaje originalmente provee de una cantidad limitada de módulos, que puede consultarse acá. Estas bibliotecas ya vienen pre-instaladas con Python.
Bibliotecas adicionales#
Sin embargo, la mayor riqueza y flexibilidad de Python proviene de la enorme cantidad de bibliotecas que han sido desarrolladas a lo largo de los años por encima de las bibliotecas propias del lenguaje. Las bibliotecas pueden instalarse directamente desde Anaconda Navigator, que es la distribución de Python que estamos usando. Un listado de todos los módulos que provee Anaconda es éste.
En ésta página pueden encontrar una lista curada de módulos.
Verán con la práctica que puede haber varios módulos sobre un tema en particular. Por ejemplo, para graficar tenemos (tomado prestado de SciPy: https://scipy.org/topical-software.html):
matplotlib: a Python 2-D plotting library, which produces publication-quality figures used in a variety of hardcopy formats (PNG, JPG, PS, SVG) and interactive GUI environments (WX, GTK, Tkinter, FLTK, Qt) across platforms. matplotlib can be used in Python scripts, interactively from the Python shell (à la MATLAB or Mathematica), in web application servers generating dynamic charts, or embedded in GUI applications. For interactive use, IPython provides a special mode which integrates with matplotlib. See the matplotlib gallery for recipes.
Bokeh: an interactive web visualization library for large datasets. Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity over large data to thin clients.
Gnuplot.py: a Python package that interfaces to gnuplot, the popular open-source plotting program. It allows you to use gnuplot from within Python to plot arrays of data from memory, data files, or mathematical functions. If you use Python to perform computations or as “glue” for numerical programs, you can use this package to plot data on the fly as they are computed. IPython includes additional enhancements to Gnuplot.py (but which require the base package) to make it more efficient in interactive usage.
Graceplot: a Python interface to the Grace 2-D plotting program.
y la lista sigue y sigue.
Atributos y métodos#
Además de funciones, los módulos pueden definir nuevos tipos de variables. Las funciones dentro de los módulos se llaman métodos, mientras que las variables suelen denominarse atributos. Por ejemplo, imaginemos un módulo llamado maradona que define:
diego = 'Diego Armando'
maradona = 'Maradona'
def hola(nombre):
print("Hola ", nombre, ", te saluda ", diego)
Este módulo define dos variables (atributos), diego y maradona, y una función (método) hola(nombre). Para acceder a los métodos y atributos se usa el .:
a = maradona.diego # a contiene 'Diego Armando'
maradona.hola('Flavio') # imprime "Hola Flavio, te saluda Diego Armando"
Usando módulos#
Para variar, existen varias maneras de usar módulos. Para todas ellas se usa la palabra reservada import.
Importando todo el módulo#
import random
Con lo anterior, importamos el módulo completo. Ahora bien, ¿qué atributos y clases provee el módulo? Podemos usar el comando help,
help(random)
Help on module random:
NAME
random - Random variable generators.
MODULE REFERENCE
https://docs.python.org/3.9/library/random
The following documentation is automatically generated from the Python
source files. It may be incomplete, incorrect or include features that
are considered implementation detail and may vary between Python
implementations. When in doubt, consult the module reference at the
location listed above.
DESCRIPTION
bytes
-----
uniform bytes (values between 0 and 255)
integers
--------
uniform within range
sequences
---------
pick random element
pick random sample
pick weighted random sample
generate random permutation
distributions on the real line:
------------------------------
uniform
triangular
normal (Gaussian)
lognormal
negative exponential
gamma
beta
pareto
Weibull
distributions on the circle (angles 0 to 2pi)
---------------------------------------------
circular uniform
von Mises
General notes on the underlying Mersenne Twister core generator:
* The period is 2**19937-1.
* It is one of the most extensively tested generators in existence.
* The random() method is implemented in C, executes in a single Python step,
and is, therefore, threadsafe.
CLASSES
_random.Random(builtins.object)
Random
SystemRandom
class Random(_random.Random)
| Random(x=None)
|
| Random number generator base class used by bound module functions.
|
| Used to instantiate instances of Random to get generators that don't
| share state.
|
| Class Random can also be subclassed if you want to use a different basic
| generator of your own devising: in that case, override the following
| methods: random(), seed(), getstate(), and setstate().
| Optionally, implement a getrandbits() method so that randrange()
| can cover arbitrarily large ranges.
|
| Method resolution order:
| Random
| _random.Random
| builtins.object
|
| Methods defined here:
|
| __getstate__(self)
| # Issue 17489: Since __reduce__ was defined to fix #759889 this is no
| # longer called; we leave it here because it has been here since random was
| # rewritten back in 2001 and why risk breaking something.
|
| __init__(self, x=None)
| Initialize an instance.
|
| Optional argument x controls seeding, as for Random.seed().
|
| __reduce__(self)
| Helper for pickle.
|
| __setstate__(self, state)
|
| betavariate(self, alpha, beta)
| Beta distribution.
|
| Conditions on the parameters are alpha > 0 and beta > 0.
| Returned values range between 0 and 1.
|
| choice(self, seq)
| Choose a random element from a non-empty sequence.
|
| choices(self, population, weights=None, *, cum_weights=None, k=1)
| Return a k sized list of population elements chosen with replacement.
|
| If the relative weights or cumulative weights are not specified,
| the selections are made with equal probability.
|
| expovariate(self, lambd)
| Exponential distribution.
|
| lambd is 1.0 divided by the desired mean. It should be
| nonzero. (The parameter would be called "lambda", but that is
| a reserved word in Python.) Returned values range from 0 to
| positive infinity if lambd is positive, and from negative
| infinity to 0 if lambd is negative.
|
| gammavariate(self, alpha, beta)
| Gamma distribution. Not the gamma function!
|
| Conditions on the parameters are alpha > 0 and beta > 0.
|
| The probability distribution function is:
|
| x ** (alpha - 1) * math.exp(-x / beta)
| pdf(x) = --------------------------------------
| math.gamma(alpha) * beta ** alpha
|
| gauss(self, mu, sigma)
| Gaussian distribution.
|
| mu is the mean, and sigma is the standard deviation. This is
| slightly faster than the normalvariate() function.
|
| Not thread-safe without a lock around calls.
|
| getstate(self)
| Return internal state; can be passed to setstate() later.
|
| lognormvariate(self, mu, sigma)
| Log normal distribution.
|
| If you take the natural logarithm of this distribution, you'll get a
| normal distribution with mean mu and standard deviation sigma.
| mu can have any value, and sigma must be greater than zero.
|
| normalvariate(self, mu, sigma)
| Normal distribution.
|
| mu is the mean, and sigma is the standard deviation.
|
| paretovariate(self, alpha)
| Pareto distribution. alpha is the shape parameter.
|
| randbytes(self, n)
| Generate n random bytes.
|
| randint(self, a, b)
| Return random integer in range [a, b], including both end points.
|
| randrange(self, start, stop=None, step=1)
| Choose a random item from range(start, stop[, step]).
|
| This fixes the problem with randint() which includes the
| endpoint; in Python this is usually not what you want.
|
| sample(self, population, k, *, counts=None)
| Chooses k unique random elements from a population sequence or set.
|
| Returns a new list containing elements from the population while
| leaving the original population unchanged. The resulting list is
| in selection order so that all sub-slices will also be valid random
| samples. This allows raffle winners (the sample) to be partitioned
| into grand prize and second place winners (the subslices).
|
| Members of the population need not be hashable or unique. If the
| population contains repeats, then each occurrence is a possible
| selection in the sample.
|
| Repeated elements can be specified one at a time or with the optional
| counts parameter. For example:
|
| sample(['red', 'blue'], counts=[4, 2], k=5)
|
| is equivalent to:
|
| sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5)
|
| To choose a sample from a range of integers, use range() for the
| population argument. This is especially fast and space efficient
| for sampling from a large population:
|
| sample(range(10000000), 60)
|
| seed(self, a=None, version=2)
| Initialize internal state from a seed.
|
| The only supported seed types are None, int, float,
| str, bytes, and bytearray.
|
| None or no argument seeds from current time or from an operating
| system specific randomness source if available.
|
| If *a* is an int, all bits are used.
|
| For version 2 (the default), all of the bits are used if *a* is a str,
| bytes, or bytearray. For version 1 (provided for reproducing random
| sequences from older versions of Python), the algorithm for str and
| bytes generates a narrower range of seeds.
|
| setstate(self, state)
| Restore internal state from object returned by getstate().
|
| shuffle(self, x, random=None)
| Shuffle list x in place, and return None.
|
| Optional argument random is a 0-argument function returning a
| random float in [0.0, 1.0); if it is the default None, the
| standard random.random will be used.
|
| triangular(self, low=0.0, high=1.0, mode=None)
| Triangular distribution.
|
| Continuous distribution bounded by given lower and upper limits,
| and having a given mode value in-between.
|
| http://en.wikipedia.org/wiki/Triangular_distribution
|
| uniform(self, a, b)
| Get a random number in the range [a, b) or [a, b] depending on rounding.
|
| vonmisesvariate(self, mu, kappa)
| Circular data distribution.
|
| mu is the mean angle, expressed in radians between 0 and 2*pi, and
| kappa is the concentration parameter, which must be greater than or
| equal to zero. If kappa is equal to zero, this distribution reduces
| to a uniform random angle over the range 0 to 2*pi.
|
| weibullvariate(self, alpha, beta)
| Weibull distribution.
|
| alpha is the scale parameter and beta is the shape parameter.
|
| ----------------------------------------------------------------------
| Class methods defined here:
|
| __init_subclass__(**kwargs) from builtins.type
| Control how subclasses generate random integers.
|
| The algorithm a subclass can use depends on the random() and/or
| getrandbits() implementation available to it and determines
| whether it can generate random integers from arbitrarily large
| ranges.
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| __dict__
| dictionary for instance variables (if defined)
|
| __weakref__
| list of weak references to the object (if defined)
|
| ----------------------------------------------------------------------
| Data and other attributes defined here:
|
| VERSION = 3
|
| ----------------------------------------------------------------------
| Methods inherited from _random.Random:
|
| getrandbits(self, k, /)
| getrandbits(k) -> x. Generates an int with k random bits.
|
| random(self, /)
| random() -> x in the interval [0, 1).
|
| ----------------------------------------------------------------------
| Static methods inherited from _random.Random:
|
| __new__(*args, **kwargs) from builtins.type
| Create and return a new object. See help(type) for accurate signature.
class SystemRandom(Random)
| SystemRandom(x=None)
|
| Alternate random number generator using sources provided
| by the operating system (such as /dev/urandom on Unix or
| CryptGenRandom on Windows).
|
| Not available on all systems (see os.urandom() for details).
|
| Method resolution order:
| SystemRandom
| Random
| _random.Random
| builtins.object
|
| Methods defined here:
|
| getrandbits(self, k)
| getrandbits(k) -> x. Generates an int with k random bits.
|
| getstate = _notimplemented(self, *args, **kwds)
|
| randbytes(self, n)
| Generate n random bytes.
|
| random(self)
| Get the next random number in the range [0.0, 1.0).
|
| seed(self, *args, **kwds)
| Stub method. Not used for a system random number generator.
|
| setstate = _notimplemented(self, *args, **kwds)
|
| ----------------------------------------------------------------------
| Methods inherited from Random:
|
| __getstate__(self)
| # Issue 17489: Since __reduce__ was defined to fix #759889 this is no
| # longer called; we leave it here because it has been here since random was
| # rewritten back in 2001 and why risk breaking something.
|
| __init__(self, x=None)
| Initialize an instance.
|
| Optional argument x controls seeding, as for Random.seed().
|
| __reduce__(self)
| Helper for pickle.
|
| __setstate__(self, state)
|
| betavariate(self, alpha, beta)
| Beta distribution.
|
| Conditions on the parameters are alpha > 0 and beta > 0.
| Returned values range between 0 and 1.
|
| choice(self, seq)
| Choose a random element from a non-empty sequence.
|
| choices(self, population, weights=None, *, cum_weights=None, k=1)
| Return a k sized list of population elements chosen with replacement.
|
| If the relative weights or cumulative weights are not specified,
| the selections are made with equal probability.
|
| expovariate(self, lambd)
| Exponential distribution.
|
| lambd is 1.0 divided by the desired mean. It should be
| nonzero. (The parameter would be called "lambda", but that is
| a reserved word in Python.) Returned values range from 0 to
| positive infinity if lambd is positive, and from negative
| infinity to 0 if lambd is negative.
|
| gammavariate(self, alpha, beta)
| Gamma distribution. Not the gamma function!
|
| Conditions on the parameters are alpha > 0 and beta > 0.
|
| The probability distribution function is:
|
| x ** (alpha - 1) * math.exp(-x / beta)
| pdf(x) = --------------------------------------
| math.gamma(alpha) * beta ** alpha
|
| gauss(self, mu, sigma)
| Gaussian distribution.
|
| mu is the mean, and sigma is the standard deviation. This is
| slightly faster than the normalvariate() function.
|
| Not thread-safe without a lock around calls.
|
| lognormvariate(self, mu, sigma)
| Log normal distribution.
|
| If you take the natural logarithm of this distribution, you'll get a
| normal distribution with mean mu and standard deviation sigma.
| mu can have any value, and sigma must be greater than zero.
|
| normalvariate(self, mu, sigma)
| Normal distribution.
|
| mu is the mean, and sigma is the standard deviation.
|
| paretovariate(self, alpha)
| Pareto distribution. alpha is the shape parameter.
|
| randint(self, a, b)
| Return random integer in range [a, b], including both end points.
|
| randrange(self, start, stop=None, step=1)
| Choose a random item from range(start, stop[, step]).
|
| This fixes the problem with randint() which includes the
| endpoint; in Python this is usually not what you want.
|
| sample(self, population, k, *, counts=None)
| Chooses k unique random elements from a population sequence or set.
|
| Returns a new list containing elements from the population while
| leaving the original population unchanged. The resulting list is
| in selection order so that all sub-slices will also be valid random
| samples. This allows raffle winners (the sample) to be partitioned
| into grand prize and second place winners (the subslices).
|
| Members of the population need not be hashable or unique. If the
| population contains repeats, then each occurrence is a possible
| selection in the sample.
|
| Repeated elements can be specified one at a time or with the optional
| counts parameter. For example:
|
| sample(['red', 'blue'], counts=[4, 2], k=5)
|
| is equivalent to:
|
| sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5)
|
| To choose a sample from a range of integers, use range() for the
| population argument. This is especially fast and space efficient
| for sampling from a large population:
|
| sample(range(10000000), 60)
|
| shuffle(self, x, random=None)
| Shuffle list x in place, and return None.
|
| Optional argument random is a 0-argument function returning a
| random float in [0.0, 1.0); if it is the default None, the
| standard random.random will be used.
|
| triangular(self, low=0.0, high=1.0, mode=None)
| Triangular distribution.
|
| Continuous distribution bounded by given lower and upper limits,
| and having a given mode value in-between.
|
| http://en.wikipedia.org/wiki/Triangular_distribution
|
| uniform(self, a, b)
| Get a random number in the range [a, b) or [a, b] depending on rounding.
|
| vonmisesvariate(self, mu, kappa)
| Circular data distribution.
|
| mu is the mean angle, expressed in radians between 0 and 2*pi, and
| kappa is the concentration parameter, which must be greater than or
| equal to zero. If kappa is equal to zero, this distribution reduces
| to a uniform random angle over the range 0 to 2*pi.
|
| weibullvariate(self, alpha, beta)
| Weibull distribution.
|
| alpha is the scale parameter and beta is the shape parameter.
|
| ----------------------------------------------------------------------
| Class methods inherited from Random:
|
| __init_subclass__(**kwargs) from builtins.type
| Control how subclasses generate random integers.
|
| The algorithm a subclass can use depends on the random() and/or
| getrandbits() implementation available to it and determines
| whether it can generate random integers from arbitrarily large
| ranges.
|
| ----------------------------------------------------------------------
| Data descriptors inherited from Random:
|
| __dict__
| dictionary for instance variables (if defined)
|
| __weakref__
| list of weak references to the object (if defined)
|
| ----------------------------------------------------------------------
| Data and other attributes inherited from Random:
|
| VERSION = 3
|
| ----------------------------------------------------------------------
| Static methods inherited from _random.Random:
|
| __new__(*args, **kwargs) from builtins.type
| Create and return a new object. See help(type) for accurate signature.
FUNCTIONS
betavariate(alpha, beta) method of Random instance
Beta distribution.
Conditions on the parameters are alpha > 0 and beta > 0.
Returned values range between 0 and 1.
choice(seq) method of Random instance
Choose a random element from a non-empty sequence.
choices(population, weights=None, *, cum_weights=None, k=1) method of Random instance
Return a k sized list of population elements chosen with replacement.
If the relative weights or cumulative weights are not specified,
the selections are made with equal probability.
expovariate(lambd) method of Random instance
Exponential distribution.
lambd is 1.0 divided by the desired mean. It should be
nonzero. (The parameter would be called "lambda", but that is
a reserved word in Python.) Returned values range from 0 to
positive infinity if lambd is positive, and from negative
infinity to 0 if lambd is negative.
gammavariate(alpha, beta) method of Random instance
Gamma distribution. Not the gamma function!
Conditions on the parameters are alpha > 0 and beta > 0.
The probability distribution function is:
x ** (alpha - 1) * math.exp(-x / beta)
pdf(x) = --------------------------------------
math.gamma(alpha) * beta ** alpha
gauss(mu, sigma) method of Random instance
Gaussian distribution.
mu is the mean, and sigma is the standard deviation. This is
slightly faster than the normalvariate() function.
Not thread-safe without a lock around calls.
getrandbits(k, /) method of Random instance
getrandbits(k) -> x. Generates an int with k random bits.
getstate() method of Random instance
Return internal state; can be passed to setstate() later.
lognormvariate(mu, sigma) method of Random instance
Log normal distribution.
If you take the natural logarithm of this distribution, you'll get a
normal distribution with mean mu and standard deviation sigma.
mu can have any value, and sigma must be greater than zero.
normalvariate(mu, sigma) method of Random instance
Normal distribution.
mu is the mean, and sigma is the standard deviation.
paretovariate(alpha) method of Random instance
Pareto distribution. alpha is the shape parameter.
randbytes(n) method of Random instance
Generate n random bytes.
randint(a, b) method of Random instance
Return random integer in range [a, b], including both end points.
random() method of Random instance
random() -> x in the interval [0, 1).
randrange(start, stop=None, step=1) method of Random instance
Choose a random item from range(start, stop[, step]).
This fixes the problem with randint() which includes the
endpoint; in Python this is usually not what you want.
sample(population, k, *, counts=None) method of Random instance
Chooses k unique random elements from a population sequence or set.
Returns a new list containing elements from the population while
leaving the original population unchanged. The resulting list is
in selection order so that all sub-slices will also be valid random
samples. This allows raffle winners (the sample) to be partitioned
into grand prize and second place winners (the subslices).
Members of the population need not be hashable or unique. If the
population contains repeats, then each occurrence is a possible
selection in the sample.
Repeated elements can be specified one at a time or with the optional
counts parameter. For example:
sample(['red', 'blue'], counts=[4, 2], k=5)
is equivalent to:
sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5)
To choose a sample from a range of integers, use range() for the
population argument. This is especially fast and space efficient
for sampling from a large population:
sample(range(10000000), 60)
seed(a=None, version=2) method of Random instance
Initialize internal state from a seed.
The only supported seed types are None, int, float,
str, bytes, and bytearray.
None or no argument seeds from current time or from an operating
system specific randomness source if available.
If *a* is an int, all bits are used.
For version 2 (the default), all of the bits are used if *a* is a str,
bytes, or bytearray. For version 1 (provided for reproducing random
sequences from older versions of Python), the algorithm for str and
bytes generates a narrower range of seeds.
setstate(state) method of Random instance
Restore internal state from object returned by getstate().
shuffle(x, random=None) method of Random instance
Shuffle list x in place, and return None.
Optional argument random is a 0-argument function returning a
random float in [0.0, 1.0); if it is the default None, the
standard random.random will be used.
triangular(low=0.0, high=1.0, mode=None) method of Random instance
Triangular distribution.
Continuous distribution bounded by given lower and upper limits,
and having a given mode value in-between.
http://en.wikipedia.org/wiki/Triangular_distribution
uniform(a, b) method of Random instance
Get a random number in the range [a, b) or [a, b] depending on rounding.
vonmisesvariate(mu, kappa) method of Random instance
Circular data distribution.
mu is the mean angle, expressed in radians between 0 and 2*pi, and
kappa is the concentration parameter, which must be greater than or
equal to zero. If kappa is equal to zero, this distribution reduces
to a uniform random angle over the range 0 to 2*pi.
weibullvariate(alpha, beta) method of Random instance
Weibull distribution.
alpha is the scale parameter and beta is the shape parameter.
DATA
__all__ = ['Random', 'SystemRandom', 'betavariate', 'choice', 'choices...
FILE
/opt/miniconda3/lib/python3.9/random.py
La función random() del módulo random, que llamamos como random.random() genera un número pseudo-aleatorio entre 0 y 1:
random.random()
0.6257711637488371
La función randrange() genera números al azar en un rango:
random.randrange(1,6,1)
3
import datetime
Con lo anterior, importamos el módulo completo. Ahora bien, ¿qué atributos y clases provee el módulo? Podemos usar el comando help,
help(datetime)
Help on module datetime:
NAME
datetime - Fast implementation of the datetime type.
MODULE REFERENCE
https://docs.python.org/3.9/library/datetime
The following documentation is automatically generated from the Python
source files. It may be incomplete, incorrect or include features that
are considered implementation detail and may vary between Python
implementations. When in doubt, consult the module reference at the
location listed above.
CLASSES
builtins.object
date
datetime
time
timedelta
tzinfo
timezone
class date(builtins.object)
| date(year, month, day) --> date object
|
| Methods defined here:
|
| __add__(self, value, /)
| Return self+value.
|
| __eq__(self, value, /)
| Return self==value.
|
| __format__(...)
| Formats self with strftime.
|
| __ge__(self, value, /)
| Return self>=value.
|
| __getattribute__(self, name, /)
| Return getattr(self, name).
|
| __gt__(self, value, /)
| Return self>value.
|
| __hash__(self, /)
| Return hash(self).
|
| __le__(self, value, /)
| Return self<=value.
|
| __lt__(self, value, /)
| Return self<value.
|
| __ne__(self, value, /)
| Return self!=value.
|
| __radd__(self, value, /)
| Return value+self.
|
| __reduce__(...)
| __reduce__() -> (cls, state)
|
| __repr__(self, /)
| Return repr(self).
|
| __rsub__(self, value, /)
| Return value-self.
|
| __str__(self, /)
| Return str(self).
|
| __sub__(self, value, /)
| Return self-value.
|
| ctime(...)
| Return ctime() style string.
|
| isocalendar(...)
| Return a named tuple containing ISO year, week number, and weekday.
|
| isoformat(...)
| Return string in ISO 8601 format, YYYY-MM-DD.
|
| isoweekday(...)
| Return the day of the week represented by the date.
| Monday == 1 ... Sunday == 7
|
| replace(...)
| Return date with new specified fields.
|
| strftime(...)
| format -> strftime() style string.
|
| timetuple(...)
| Return time tuple, compatible with time.localtime().
|
| toordinal(...)
| Return proleptic Gregorian ordinal. January 1 of year 1 is day 1.
|
| weekday(...)
| Return the day of the week represented by the date.
| Monday == 0 ... Sunday == 6
|
| ----------------------------------------------------------------------
| Class methods defined here:
|
| fromisocalendar(...) from builtins.type
| int, int, int -> Construct a date from the ISO year, week number and weekday.
|
| This is the inverse of the date.isocalendar() function
|
| fromisoformat(...) from builtins.type
| str -> Construct a date from the output of date.isoformat()
|
| fromordinal(...) from builtins.type
| int -> date corresponding to a proleptic Gregorian ordinal.
|
| fromtimestamp(timestamp, /) from builtins.type
| Create a date from a POSIX timestamp.
|
| The timestamp is a number, e.g. created via time.time(), that is interpreted
| as local time.
|
| today(...) from builtins.type
| Current date or datetime: same as self.__class__.fromtimestamp(time.time()).
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| __new__(*args, **kwargs) from builtins.type
| Create and return a new object. See help(type) for accurate signature.
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| day
|
| month
|
| year
|
| ----------------------------------------------------------------------
| Data and other attributes defined here:
|
| max = datetime.date(9999, 12, 31)
|
| min = datetime.date(1, 1, 1)
|
| resolution = datetime.timedelta(days=1)
class datetime(date)
| datetime(year, month, day[, hour[, minute[, second[, microsecond[,tzinfo]]]]])
|
| The year, month and day arguments are required. tzinfo may be None, or an
| instance of a tzinfo subclass. The remaining arguments may be ints.
|
| Method resolution order:
| datetime
| date
| builtins.object
|
| Methods defined here:
|
| __add__(self, value, /)
| Return self+value.
|
| __eq__(self, value, /)
| Return self==value.
|
| __ge__(self, value, /)
| Return self>=value.
|
| __getattribute__(self, name, /)
| Return getattr(self, name).
|
| __gt__(self, value, /)
| Return self>value.
|
| __hash__(self, /)
| Return hash(self).
|
| __le__(self, value, /)
| Return self<=value.
|
| __lt__(self, value, /)
| Return self<value.
|
| __ne__(self, value, /)
| Return self!=value.
|
| __radd__(self, value, /)
| Return value+self.
|
| __reduce__(...)
| __reduce__() -> (cls, state)
|
| __reduce_ex__(...)
| __reduce_ex__(proto) -> (cls, state)
|
| __repr__(self, /)
| Return repr(self).
|
| __rsub__(self, value, /)
| Return value-self.
|
| __str__(self, /)
| Return str(self).
|
| __sub__(self, value, /)
| Return self-value.
|
| astimezone(...)
| tz -> convert to local time in new timezone tz
|
| ctime(...)
| Return ctime() style string.
|
| date(...)
| Return date object with same year, month and day.
|
| dst(...)
| Return self.tzinfo.dst(self).
|
| isoformat(...)
| [sep] -> string in ISO 8601 format, YYYY-MM-DDT[HH[:MM[:SS[.mmm[uuu]]]]][+HH:MM].
| sep is used to separate the year from the time, and defaults to 'T'.
| The optional argument timespec specifies the number of additional terms
| of the time to include. Valid options are 'auto', 'hours', 'minutes',
| 'seconds', 'milliseconds' and 'microseconds'.
|
| replace(...)
| Return datetime with new specified fields.
|
| time(...)
| Return time object with same time but with tzinfo=None.
|
| timestamp(...)
| Return POSIX timestamp as float.
|
| timetuple(...)
| Return time tuple, compatible with time.localtime().
|
| timetz(...)
| Return time object with same time and tzinfo.
|
| tzname(...)
| Return self.tzinfo.tzname(self).
|
| utcoffset(...)
| Return self.tzinfo.utcoffset(self).
|
| utctimetuple(...)
| Return UTC time tuple, compatible with time.localtime().
|
| ----------------------------------------------------------------------
| Class methods defined here:
|
| combine(...) from builtins.type
| date, time -> datetime with same date and time fields
|
| fromisoformat(...) from builtins.type
| string -> datetime from datetime.isoformat() output
|
| fromtimestamp(...) from builtins.type
| timestamp[, tz] -> tz's local time from POSIX timestamp.
|
| now(tz=None) from builtins.type
| Returns new datetime object representing current time local to tz.
|
| tz
| Timezone object.
|
| If no tz is specified, uses local timezone.
|
| strptime(...) from builtins.type
| string, format -> new datetime parsed from a string (like time.strptime()).
|
| utcfromtimestamp(...) from builtins.type
| Construct a naive UTC datetime from a POSIX timestamp.
|
| utcnow(...) from builtins.type
| Return a new datetime representing UTC day and time.
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| __new__(*args, **kwargs) from builtins.type
| Create and return a new object. See help(type) for accurate signature.
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| fold
|
| hour
|
| microsecond
|
| minute
|
| second
|
| tzinfo
|
| ----------------------------------------------------------------------
| Data and other attributes defined here:
|
| max = datetime.datetime(9999, 12, 31, 23, 59, 59, 999999)
|
| min = datetime.datetime(1, 1, 1, 0, 0)
|
| resolution = datetime.timedelta(microseconds=1)
|
| ----------------------------------------------------------------------
| Methods inherited from date:
|
| __format__(...)
| Formats self with strftime.
|
| isocalendar(...)
| Return a named tuple containing ISO year, week number, and weekday.
|
| isoweekday(...)
| Return the day of the week represented by the date.
| Monday == 1 ... Sunday == 7
|
| strftime(...)
| format -> strftime() style string.
|
| toordinal(...)
| Return proleptic Gregorian ordinal. January 1 of year 1 is day 1.
|
| weekday(...)
| Return the day of the week represented by the date.
| Monday == 0 ... Sunday == 6
|
| ----------------------------------------------------------------------
| Class methods inherited from date:
|
| fromisocalendar(...) from builtins.type
| int, int, int -> Construct a date from the ISO year, week number and weekday.
|
| This is the inverse of the date.isocalendar() function
|
| fromordinal(...) from builtins.type
| int -> date corresponding to a proleptic Gregorian ordinal.
|
| today(...) from builtins.type
| Current date or datetime: same as self.__class__.fromtimestamp(time.time()).
|
| ----------------------------------------------------------------------
| Data descriptors inherited from date:
|
| day
|
| month
|
| year
class time(builtins.object)
| time([hour[, minute[, second[, microsecond[, tzinfo]]]]]) --> a time object
|
| All arguments are optional. tzinfo may be None, or an instance of
| a tzinfo subclass. The remaining arguments may be ints.
|
| Methods defined here:
|
| __eq__(self, value, /)
| Return self==value.
|
| __format__(...)
| Formats self with strftime.
|
| __ge__(self, value, /)
| Return self>=value.
|
| __getattribute__(self, name, /)
| Return getattr(self, name).
|
| __gt__(self, value, /)
| Return self>value.
|
| __hash__(self, /)
| Return hash(self).
|
| __le__(self, value, /)
| Return self<=value.
|
| __lt__(self, value, /)
| Return self<value.
|
| __ne__(self, value, /)
| Return self!=value.
|
| __reduce__(...)
| __reduce__() -> (cls, state)
|
| __reduce_ex__(...)
| __reduce_ex__(proto) -> (cls, state)
|
| __repr__(self, /)
| Return repr(self).
|
| __str__(self, /)
| Return str(self).
|
| dst(...)
| Return self.tzinfo.dst(self).
|
| isoformat(...)
| Return string in ISO 8601 format, [HH[:MM[:SS[.mmm[uuu]]]]][+HH:MM].
|
| The optional argument timespec specifies the number of additional terms
| of the time to include. Valid options are 'auto', 'hours', 'minutes',
| 'seconds', 'milliseconds' and 'microseconds'.
|
| replace(...)
| Return time with new specified fields.
|
| strftime(...)
| format -> strftime() style string.
|
| tzname(...)
| Return self.tzinfo.tzname(self).
|
| utcoffset(...)
| Return self.tzinfo.utcoffset(self).
|
| ----------------------------------------------------------------------
| Class methods defined here:
|
| fromisoformat(...) from builtins.type
| string -> time from time.isoformat() output
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| __new__(*args, **kwargs) from builtins.type
| Create and return a new object. See help(type) for accurate signature.
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| fold
|
| hour
|
| microsecond
|
| minute
|
| second
|
| tzinfo
|
| ----------------------------------------------------------------------
| Data and other attributes defined here:
|
| max = datetime.time(23, 59, 59, 999999)
|
| min = datetime.time(0, 0)
|
| resolution = datetime.timedelta(microseconds=1)
class timedelta(builtins.object)
| Difference between two datetime values.
|
| timedelta(days=0, seconds=0, microseconds=0, milliseconds=0, minutes=0, hours=0, weeks=0)
|
| All arguments are optional and default to 0.
| Arguments may be integers or floats, and may be positive or negative.
|
| Methods defined here:
|
| __abs__(self, /)
| abs(self)
|
| __add__(self, value, /)
| Return self+value.
|
| __bool__(self, /)
| True if self else False
|
| __divmod__(self, value, /)
| Return divmod(self, value).
|
| __eq__(self, value, /)
| Return self==value.
|
| __floordiv__(self, value, /)
| Return self//value.
|
| __ge__(self, value, /)
| Return self>=value.
|
| __getattribute__(self, name, /)
| Return getattr(self, name).
|
| __gt__(self, value, /)
| Return self>value.
|
| __hash__(self, /)
| Return hash(self).
|
| __le__(self, value, /)
| Return self<=value.
|
| __lt__(self, value, /)
| Return self<value.
|
| __mod__(self, value, /)
| Return self%value.
|
| __mul__(self, value, /)
| Return self*value.
|
| __ne__(self, value, /)
| Return self!=value.
|
| __neg__(self, /)
| -self
|
| __pos__(self, /)
| +self
|
| __radd__(self, value, /)
| Return value+self.
|
| __rdivmod__(self, value, /)
| Return divmod(value, self).
|
| __reduce__(...)
| __reduce__() -> (cls, state)
|
| __repr__(self, /)
| Return repr(self).
|
| __rfloordiv__(self, value, /)
| Return value//self.
|
| __rmod__(self, value, /)
| Return value%self.
|
| __rmul__(self, value, /)
| Return value*self.
|
| __rsub__(self, value, /)
| Return value-self.
|
| __rtruediv__(self, value, /)
| Return value/self.
|
| __str__(self, /)
| Return str(self).
|
| __sub__(self, value, /)
| Return self-value.
|
| __truediv__(self, value, /)
| Return self/value.
|
| total_seconds(...)
| Total seconds in the duration.
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| __new__(*args, **kwargs) from builtins.type
| Create and return a new object. See help(type) for accurate signature.
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| days
| Number of days.
|
| microseconds
| Number of microseconds (>= 0 and less than 1 second).
|
| seconds
| Number of seconds (>= 0 and less than 1 day).
|
| ----------------------------------------------------------------------
| Data and other attributes defined here:
|
| max = datetime.timedelta(days=999999999, seconds=86399, microseconds=9...
|
| min = datetime.timedelta(days=-999999999)
|
| resolution = datetime.timedelta(microseconds=1)
class timezone(tzinfo)
| Fixed offset from UTC implementation of tzinfo.
|
| Method resolution order:
| timezone
| tzinfo
| builtins.object
|
| Methods defined here:
|
| __eq__(self, value, /)
| Return self==value.
|
| __ge__(self, value, /)
| Return self>=value.
|
| __getinitargs__(...)
| pickle support
|
| __gt__(self, value, /)
| Return self>value.
|
| __hash__(self, /)
| Return hash(self).
|
| __le__(self, value, /)
| Return self<=value.
|
| __lt__(self, value, /)
| Return self<value.
|
| __ne__(self, value, /)
| Return self!=value.
|
| __repr__(self, /)
| Return repr(self).
|
| __str__(self, /)
| Return str(self).
|
| dst(...)
| Return None.
|
| fromutc(...)
| datetime in UTC -> datetime in local time.
|
| tzname(...)
| If name is specified when timezone is created, returns the name. Otherwise returns offset as 'UTC(+|-)HH:MM'.
|
| utcoffset(...)
| Return fixed offset.
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| __new__(*args, **kwargs) from builtins.type
| Create and return a new object. See help(type) for accurate signature.
|
| ----------------------------------------------------------------------
| Data and other attributes defined here:
|
| max = datetime.timezone(datetime.timedelta(seconds=86340))
|
| min = datetime.timezone(datetime.timedelta(days=-1, seconds=60))
|
| utc = datetime.timezone.utc
|
| ----------------------------------------------------------------------
| Methods inherited from tzinfo:
|
| __getattribute__(self, name, /)
| Return getattr(self, name).
|
| __reduce__(...)
| -> (cls, state)
class tzinfo(builtins.object)
| Abstract base class for time zone info objects.
|
| Methods defined here:
|
| __getattribute__(self, name, /)
| Return getattr(self, name).
|
| __reduce__(...)
| -> (cls, state)
|
| dst(...)
| datetime -> DST offset as timedelta positive east of UTC.
|
| fromutc(...)
| datetime in UTC -> datetime in local time.
|
| tzname(...)
| datetime -> string name of time zone.
|
| utcoffset(...)
| datetime -> timedelta showing offset from UTC, negative values indicating West of UTC
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| __new__(*args, **kwargs) from builtins.type
| Create and return a new object. See help(type) for accurate signature.
DATA
MAXYEAR = 9999
MINYEAR = 1
__all__ = ('date', 'datetime', 'time', 'timedelta', 'timezone', 'tzinf...
FILE
/opt/miniconda3/lib/python3.9/datetime.py
aunque lo que devuelve es bastante ilegible. Sin embargo, nos indica la página web https://docs.python.org/3.7/library/datetime que es un poco mejor.
print(datetime.date.today())
2023-07-19
print(date.today())
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[8], line 1
----> 1 print(date.today())
NameError: name 'date' is not defined
findeanio = datetime.date(2020,12,31)
print(findeanio)
print(type(findeanio))
Importando todo el módulo, versión 2#
Hay veces en que uno no quiere tener que referirse al módulo en particular usando el .. Para eso se puede importar el módulo así:
from random import *
print(randrange(1,10,1))
from math import *
print(sin(0.5))
import math
print(math.sin(0.5))
Es importante en estos casos que los módulos que uno importan no tengan funciones que se llamen igual, porque no habría manera de distinguirlas.
Renombrando un módulo#
Hay veces que puede ser útil renombrar el módulo al momento de usarlo. Típicamente se usa cuando el módulo es muy complejo y tiene varios niveles de métodos y atributos.
import datetime as dt
print(dt.date.today())
import numpy as np
Importando partes de un módulo#
Los módulos pueden importarse parcialmente, ya sea una función particular, o algunos objetos solamente.
Para ver cómo funciona, reseteamos el Kernel (Kernel -> Restart), de modo tal que se descarguen todos los módulos que cargamos hasta ahora.
from random import randint,random
print(randint(2,20))
11
random()
0.636243366768532
Lo siguiente da error al resetear el Kernel, porque el módulo random no está cargado en el notebook.
print(random.randint(2,20)) #Esto da error al resetear el Kernel, porque el módulo random no está cargado
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-7-203a3d038875> in <module>
----> 1 print(random.randint(2,20)) #Esto da error al resetear el Kernel, porque el módulo random no está cargado
AttributeError: 'builtin_function_or_method' object has no attribute 'randint'
from math import sin
print(sin(0.5))
0.479425538604203
print(cos(0.5))
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-10-968d4a906a79> in <module>
----> 1 print(cos(0.5))
NameError: name 'cos' is not defined
from math import sin,cos
print(sin(0.5))
print(cos(0.5))
print(math.sin(0.5))
0.479425538604203
0.8775825618903728
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-12-573735fd7a12> in <module>
1 print(sin(0.5))
2 print(cos(0.5))
----> 3 print(math.sin(0.5))
NameError: name 'math' is not defined
Usos y costumbres#
Se estila llamar a los módulos muy usados de la siguiente manera:
from math import *
import numpy as np