List of active deprecations

This pages lists all active deprecations in the SymPy codebase. See the Deprecation Policy page for a description of SymPy’s deprecation policy, as well as instructions for contributors on how to deprecate things.

In particular, the deprecation policy for SymPy is for deprecations to last at least 1 year after the first major release that includes the deprecation. After that period, the deprecated functionality may be removed from SymPy, and code will need to be updated to use the replacement feature to continue working.

During the deprecation period, a SymPyDeprecationWarning message will be printed whenever the deprecated functionality is used. It is recommended for users to update their code so that it does not use deprecated functionality, as described below for each given deprecation.

Silencing SymPy Deprecation Warnings

To silence SymPy deprecation warnings, add a filter using the warnings module. For example:

import warnings
from sympy.utilities.exceptions import SymPyDeprecationWarning

warnings.filterwarnings(
    # replace "ignore" with "error" to make the warning raise an exception.
    # This useful if you want to test you aren't using deprecated code.
    "ignore",

    # message may be omitted to filter all SymPyDeprecationWarnings
    message=r"(?s).*<regex matching the warning message>",

    category=SymPyDeprecationWarning,
    module=r"<regex matching your module>"
)

Here (?s).*<regex matching the warning message> is a regular expression matching the warning message. For example, to filter a warning about sympy.printing, you might use message=r"(?s).*sympy\.printing". The leading (?s).* is there because the warnings module matches message against the start of the warning message, and because typical warning messages span multiple lines.

<regex matching your module> should be a regular expression matching your module that uses the deprecated code. It is recommended to include this so that you don’t also silence the same warning for unrelated modules.

This same pattern may be used to instead turn SymPyDeprecationWarning into an error so that you can test that you aren’t using deprecated code. To do this, replace "ignore" with "error" in the above example. You may also omit message to make this apply to all SymPyDeprecationWarning warnings.

If you are using pytest, you can use the pytest warnings filtering capabilities to either ignore SymPyDeprecationWarning or turn them into errors.

Note

The Python -W flag and PYTHONWARNINGS environment variable will NOT work to filter SymPy deprecation warnings (see this blog post by Ned Batchelder and this SymPy issue for details on why). You will need to either add a warnings filter as above or use pytest to filter SymPy deprecation warnings.

Version 1.14

There are no deprecations yet for SymPy 1.14.

Version 1.13

Deprecated mechanics Body class

The Body class in the sympy.physics.mechanics module has been deprecated. It was introduced to support the joints framework. However, it causes several problems because it represents both rigid bodies and particles. Body has now been fully replaced by RigidBody and Particle. Previously, one could create a simple rigid body or particle using only the Body class:

>>> from sympy import symbols
>>> from sympy.physics.mechanics import Body
>>> Body("rigid_body")  
rigid_body
>>> Body("particle", mass=symbols("m"))  
particle

Now they should be created using the RigidBody and Particle class:

>>> from sympy.physics.mechanics import RigidBody, Particle
>>> RigidBody("rigid_body")
rigid_body
>>> Particle("particle")
particle

Deprecated mechanics JointsMethod

The JointsMethod class in the sympy.physics.mechanics module has been deprecated. It was introduced to support the joints framework, but it has been fully replaced due to limitations in its design. Previously, one could construct as system solely consisting out of bodies and joints, which were then parsed by JointsMethod to a backend, like KanesMethod to form the equations of motion.

>>> from sympy import symbols
>>> from sympy.physics.mechanics import (
...   Body, JointsMethod, PinJoint, PrismaticJoint)
>>> g, l = symbols("g l")
>>> wall = Body("wall")
>>> cart = Body("cart")
>>> pendulum = Body("Pendulum")
>>> slider = PrismaticJoint("s", wall, cart, joint_axis=wall.x)
>>> pin = PinJoint("j", cart, pendulum, joint_axis=cart.z,
...                child_point=l * pendulum.y)
>>> pendulum.masscenter.set_vel(pendulum.frame, 0)
>>> cart.apply_force(-g * cart.mass * wall.y)
>>> pendulum.apply_force(-g * pendulum.mass * wall.y)
>>> method = JointsMethod(wall, slider, pin)  
>>> method.form_eoms()  
Matrix([
[ Pendulum_mass*l*u_j(t)**2*sin(q_j(t)) - Pendulum_mass*l*cos(q_j(t))*Derivative(u_j(t), t) - (Pendulum_mass + cart_mass)*Derivative(u_s(t), t)],
[-Pendulum_mass*g*l*sin(q_j(t)) - Pendulum_mass*l*cos(q_j(t))*Derivative(u_s(t), t) - (Pendulum_izz + Pendulum_mass*l**2)*Derivative(u_j(t), t)]])

The replacement of JointsMethod is System, which can be used to form the equations of motion of the same cart pole as follows:

>>> from sympy import symbols
>>> from sympy.physics.mechanics import (
...   Particle, PinJoint, PrismaticJoint, RigidBody, System)
>>> g, l = symbols("g l")
>>> wall = RigidBody("wall")
>>> cart = RigidBody("cart")
>>> pendulum = RigidBody("Pendulum")
>>> slider = PrismaticJoint("s", wall, cart, joint_axis=wall.x)
>>> pin = PinJoint("j", cart, pendulum, joint_axis=cart.z,
...                child_point=l * pendulum.y)
>>> system = System.from_newtonian(wall)
>>> system.add_joints(slider, pin)
>>> system.apply_uniform_gravity(-g * wall.y)
>>> system.form_eoms()
Matrix([
[ Pendulum_mass*l*u_j(t)**2*sin(q_j(t)) - Pendulum_mass*l*cos(q_j(t))*Derivative(u_j(t), t) - (Pendulum_mass + cart_mass)*Derivative(u_s(t), t)],
[-Pendulum_mass*g*l*sin(q_j(t)) - Pendulum_mass*l*cos(q_j(t))*Derivative(u_s(t), t) - (Pendulum_izz + Pendulum_mass*l**2)*Derivative(u_j(t), t)]])

Deprecated matrix mixin classes

The matrix mixin classes are deprecated. Previously the Matrix class (aka MutableDenseMatrix) was created through an inheritance hierarchy that looked like:

class MatrixRequired:
class MatrixShaping(MatrixRequired):
class MatrixSpecial(MatrixRequired):
class MatrixProperties(MatrixRequired):
class MatrixOperations(MatrixRequired):
class MatrixArithmetic(MatrixRequired):
class MatrixCommon(
    MatrixArithmetic,
    MatrixOperations,
    MatrixProperties,
    MatrixSpecial,
    MatrixShaping):
class MatrixDeterminant(MatrixCommon):
class MatrixReductions(MatrixDeterminant):
class MatrixSubspaces(MatrixReductions):
class MatrixEigen(MatrixSubspaces)
class MatrixCalculus(MatrixCommon):
class MatrixDeprecated(MatrixCommon):
class MatrixBase(MatrixDeprecated,
   MatrixCalculus,
   MatrixEigen,
   MatrixCommon,
   Printable):
class RepMatrix(MatrixBase):
class DenseMatrix(RepMatrix):
class MutableRepMatrix(RepMatrix):
class MutableDenseMatrix(DenseMatrix, MutableRepMatrix):

As of SymPy 1.13 this has been simplified and all classes above MatrixBaseare merged together so the hierarchy looks like:

class MatrixBase(Printable):
class RepMatrix(MatrixBase):
class DenseMatrix(RepMatrix):
class MutableRepMatrix(RepMatrix):
class MutableDenseMatrix(DenseMatrix, MutableRepMatrix):

The matrix mixin classes like MatrixRequired etc are still available because downstream code might be subclassing these classes but these are all deprecated and will be removed in a future version of SymPy. Subclassing these classes is deprecated and anycode that does that should be changed to not subclass them.

It is also deprecated to use these classes with isinstance like isinstance(M, MatrixCommon). Any code doing this should be changed to use isinstance(M, Matrixbase) instead which will also work with previous SymPy versions.

More generally importing anything from the sympy.matrices.common or sympy.matrices.matrices modules in which these classes are defined is deprecated. These modules will be removed in a future release of SymPy.

The reason for this change is that the convoluted inheritance hierarchy made it difficult to improve Matrix for the majority of users while still providing all of these classes that could be subclassed. Since these mixin classes are no longer used as part of Matrix they no longer serve any function within SymPy and the removal of this now unused code will simplify the codebase.

The string fallback in sympify()

The sympify function would previously convert an unrecognized object to a string and retry sympification. This was deprecated in SymPy 1.6 and was removed in SymPy 1.13.

The behavior of sympify() is that sympify(expr) tries various methods to try to convert expr into a SymPy objects. Previously if all these methods would fail, it would take str(expr) and try to parse it using parse_expr(). This string fallback feature was deprecated in SymPy 1.6 and was removed in SymPy 1.13.

This behaviour was problematic for a few reasons:

• It could affect performance in major ways. See for instance issues #18056 and #15416 where it caused up to 100x slowdowns. The issue is that SymPy functions automatically call sympify on their arguments. Whenever a function is passed something that sympify doesn’t know how to convert to a SymPy object, for instance, a Python function type, it passes the string to parse_expr(). This is significantly slower than the direct conversions that happen by default. This occurs specifically whenever sympify() is used in library code instead of _sympify() (or equivalently sympify(strict=True)), but presently this is done a lot. Using strict=True will at some point be the default for all library code, but this is a harder change to make.

• It can cause security issues, since strings are evaled, and objects can return whatever string they want in their __repr__. See also https://github.com/sympy/sympy/pull/12524.

• It really wasn’t very useful to begin with. Just because an object’s string form can be parsed into a SymPy expression doesn’t mean it should be parsed that way. This is usually correct for custom numeric types, but an object’s repr could be anything. For instance, if the string form of an object looks like a valid Python identifier, it would parse as a Symbol.

There are plenty of ways to make custom objects work inside of sympify().

• Firstly, if an object is intended to work alongside other SymPy expressions, it should subclass from Basic (or Expr). If it does, sympify() will just return it unchanged because it will already be a valid SymPy object.

• For objects that you control, you can add the _sympy_ method. The sympify docstring has an example of this.

• For objects that you don’t control, you can add a custom converter to the sympy.core.sympify.converter dictionary. The sympify() docstring also has an example of this.

Deprecate the DMP.rep attribute.

The internal type of Poly is the DMP class which previously could be used to access the coefficients of a polynomial as a list like:

>>> from sympy import symbols, Poly
>>> x = symbols('x')
>>> p = Poly(x**2 + 2*x + 3)
>>> p
Poly(x**2 + 2*x + 3, x, domain='ZZ')
>>> p.rep  
DMP([1, 2, 3], ZZ)
>>> p.rep.rep  
[1, 2, 3]

As of SymPy 1.13 the DMP type may be implemented by one of two subclasses:

• DMP_Python which is like the previous DMP type and has a list as its internal representation.

• DUP_Flint which wraps a Flint polynomial from python-flint.

The DUP_Flint type does not have an attribute that is analogous to the list that DMP_Python has. Accessing .rep will still generate a list but now gives a deprecation warning.

Instead of .rep use the DMP.to_list() method which returns an equivalent list:

>>> p.rep.to_list()
[1, 2, 3]

The .to_list() method is also available in previous versions of SymPy and its behaviour is unchanged.

Deprecate the pkgdata module

The sympy.utilities.pkdata module is deprecated and will be removed. It is no longer used anywhere in SymPy and is unsuitable for use by any downstream code. Use the stdlib importlib.resources module instead.

Deprecate Eq.rewrite(Add)

The ability to rewrite eq = Eq(x, y) like eq.rewrite(Add) to give x - y has been deprecated in favor of writing eq.lhs - eq.rhs. A replacement property/method was not deemed necessary given the clarity of the explicit use of lhs and rhs, and the inclusion of this functionality in the rewrite apparatus leads to failures when a node expecting a Boolean is re- written as an Expr.

Deprecate markers, annotations, fill, rectangles of the Plot class

The properties markers, annotations, fill, rectangles (containing user-provided numerical data to be added on a plot) are deprecated. The new implementation saves user-provided numerical data into appropriate data series, which can easily be processed by MatplotlibBackend. Instead of setting those properties directly, users should pass the homonym keyword arguments to the plotting functions.

The supported behavior is to pass keyword arguments to the plotting functions, which works fine for all versions of SymPy (before and after 1.13):

p = plot(x,
  markers=[{"args":[[0, 1], [0, 1]], "marker": "*", "linestyle": "none"}],
  annotations=[{"text": "test", "xy": (0, 0)}],
  fill={"x": [0, 1, 2, 3], "y1": [0, 1, 2, 3]},
  rectangles=[{"xy": (0, 0), "width": 5, "height": 1}])

Setting attributes on the plot object is deprecated and will raise warnings:

p = plot(x, show=False)
p.markers = [{"args":[[0, 1], [0, 1]], "marker": "*", "linestyle": "none"}]
p.annotations = [{"text": "test", "xy": (0, 0)}]
p.fill = {"x": [0, 1, 2, 3], "y1": [0, 1, 2, 3]}
p.rectangles = [{"xy": (0, 0), "width": 5, "height": 1}]
p.show()

Motivation for this deprecation: the implementation of the Plot class suggests that it is ok to add attributes and hard-coded if-statements in the MatplotlibBackend class to provide more and more functionalities for user-provided numerical data (e.g. adding horizontal lines, or vertical lines, or bar plots, etc). However, in doing so one would reinvent the wheel: plotting libraries already implements the necessary API. There is no need to hard code these things. The plotting module should facilitate the visualization of symbolic expressions. The best way to add custom numerical data is to retrieve the figure created by the plotting module and use the API of a particular plotting library. For example:

# plot symbolic expression
p = plot(cos(x))
# retrieve Matplotlib's figure and axes object
fig, ax = p._backend.fig, p._backend.ax[0]
# add the desired numerical data using Matplotlib's API
ax.plot([0, 1, 2], [0, 1, -1], "*")
ax.axhline(0.5)
# visualize the figure
fig

Moved mechanics functions

With the introduction of some new objects like the Inertia and load objects in the sympy.physics.mechanics module, some functions from sympy.physics.mechanics.functions have been moved to new modules. This removes some circular import errors and makes it easier to navigate through the source code, due to the parity between function names and module names. The following functions were moved:

• inertia has been moved to sympy.physics.mechanics.inertia

• inertia_of_point_mass has been moved to sympy.physics.mechanics.inertia

• gravity has been moved to sympy.physics.mechanics.loads

Previously you could import the functions from sympy.physics.mechanics.functions:

>>> from sympy.physics.mechanics.functions import inertia, inertia_of_point_mass, gravity

Now they should be imported from sympy.physics.mechanics:

>>> from sympy.physics.mechanics import inertia, inertia_of_point_mass
>>> from sympy.physics.mechanics.loads import gravity

Ordered comparisons like a < b with modular integers

SymPy’s GF domains represent modular integers. Previously it was possible to compare these with ordered comparisons like a < b:

>>> from sympy import GF
>>> F5 = GF(5)
>>> F5(2) < F5(3) 
True

This will now fail with TypeError when the ground types are set to flint. When the ground types are not flint these comparisons are now deprecated: they will still work but will give a deprecation warning when used.

Ordered comparisons of modular integer or finite fields do not make sense because these are not ordered fields:

>>> e = F5(4)
>>> e + 1 > e 
False

The ModularInteger.to_int() method

SymPy’s GF domains are for modular integers e.g. GF(n) is for the integers modulo n and can be used like:

>>> from sympy import GF
>>> K = GF(5)
>>> a = K(7)
>>> a
2 mod 5

The elements of a modular integer domain have a to_int() method that is deprecated since SymPy 1.13:

>>> # this is deprecated:
>>> a.to_int()  
2

Instead the preferred way to achieve equivalent behavior is to use the method on the domain (added in SymPy 1.13) or alternatively calling int might be better:

>>> K.to_int(a)
2
>>> int(a)
2

These two ways of converting to an int are not equivalent. The domain GF(p) can be defined with symmetric=True or symmetric=False. This difference affects the behavior of the to_int method:

>>> KS = GF(5, symmetric=True)
>>> KU = GF(5, symmetric=False)
>>> [KS.to_int(KS(n)) for n in range(10)]
[0, 1, 2, -2, -1, 0, 1, 2, -2, -1]
>>> [KU.to_int(KU(n)) for n in range(10)]
[0, 1, 2, 3, 4, 0, 1, 2, 3, 4]
>>> [int(KS(n)) for n in range(10)]
[0, 1, 2, 3, 4, 0, 1, 2, 3, 4]
>>> [int(KU(n)) for n in range(10)]
[0, 1, 2, 3, 4, 0, 1, 2, 3, 4]

So if symmetric=True (which is the default) then the to_int method will sometimes return negative integers. If symmetric=False or if the int(a) method is used the returned result is always a nonnegative integer. Note also that the behaviour of int(a) was changed in SymPy 1.13: in previous versions it was equivalent to a.to_int(). To write code that behaves the same way in all SymPy versions you can:

1. Use symmetric=False and use int(a).

2. Define a function like

   def to_int(K, a):
       if hasattr(K, 'to_int'):
           return K.to_int(a)
       else:
           return a.to_int()
   

The reason for this change is that it makes it possible to use python-flint’s nmod as an alternative (much faster) implementation for the elements of GF(p). It is not possible to add a to_int method to python-flint’s nmod type or to capture the equivalent of symmetric=True/False by storing data in the nmod instance. Deprecating and removing the to_int method and changing the behavior of the int method means that the element instances do not have any behavior that depends on whether the domain is considered to be “symmetric” or not. Instead the notion of “symmetric” is now purely a property of the domain object itself rather than of the elements and so the to_int method that depends on this must be a domain method rather than an element method.

Relocate symbolic functions from ntheory to functions

The following symbolic functions in ntheory have been moved to functions:

• sympy.ntheory.factor_.divisor_sigma

• sympy.ntheory.factor_.primenu

• sympy.ntheory.factor_.primeomega

• sympy.ntheory.factor_.reduce_totient

• sympy.ntheory.factor_.totient

• sympy.ntheory.generate.primepi

• sympy.partitions_.npartitions

• sympy.ntheory.residue_ntheory.jacobi_symbol

• sympy.ntheory.residue_ntheory.legendre_symbol

• sympy.ntheory.residue_ntheory.mobius

Code that imports these functions from top-level like from sympy import mobius will continue to work fine. However code that imports these from the fully qualified module like from sympy.ntheory import mobius or from sympy.ntheory.residue_ntheory import mobius will now see a deprecation warning. The new location for these functions is in sympy.functions but the intended way to import them is still from top-level like from sympy import mobius.

The following symbolic functions in ntheory have been moved to functions, but cannot be imported at top-level.

• sympy.ntheory.factor_.udivisor_sigma

The following functions have been moved from functions to ntheory because they are numeric functions.

• sympy.functions.combinatorial.numbers.carmichael.is_carmichael

• sympy.functions.combinatorial.numbers.carmichael.find_carmichael_numbers_in_range

• sympy.functions.combinatorial.numbers.carmichael.find_first_n_carmichaels

If you are using these functions, change from

>>> from sympy import carmichael
>>> carmichael.is_carmichael(561)
True

to

>>> from sympy import is_carmichael
>>> is_carmichael(561)
True

Version 1.12

The ManagedProperties metaclass

The ManagedProperties metaclass was previously the metaclass for Basic. Now Basic does not use metaclasses and so its metaclass is just type. Any code that previously subclassed Basic and wanted to do anything with metaclasses would have needed to subclass ManagedProperties to make the relevant metaclass. The only relevant method of ManagedProperties has been moved to Basic.__init_subclass__. Since ManagedProperties is not used as the metaclass for Basic any more and no longer does anything useful it should be possible for such code to just subclass type instead for any metaclass.

New Joint coordinate format

The format, i.e. type and auto generated name, of the generalized coordinates and generalized speeds of the joints in the sympy.physics.mechanics module has changed. The data type has changed from list to Matrix, which is the same as the type for the generalized coordinates within the KanesMethod. The auto naming of the generalized coordinates and generalized speeds of the PinJoint and PrismaticJoint have also changed to q_<joint.name> and u_<joint.name>. Previously each of those joints had an unique template for auto generating these names.

New Joint intermediate frames

The definition of the joint axis in the sympy.physics.mechanics module has changed. Instead of using the arguments parent_axis and child_axis to automatically determine the joint axis and an intermediate reference frame, the joints now use an intermediate frame argument for both the parent and the child body, i.e. parent_interframe and child_interframe. This means that you can now fully define the joint attachment, consisting of a point and frame, for both bodies. Furthermore, if a joint like the PinJoint has a specific joint axis, e.g. the axis about which the rotation occurs, then this axis can be specified using the joint_axis argument. An advantage of this setup is that one can more accurately define the transformation from the parent body to the child body.

For example, suppose you want a PinJoint that rotates the child body about the parent.z axis and -child.z axis. The previous way to specify this joint was:

>>> from sympy.physics.mechanics import Body, PinJoint
>>> parent, child = Body('parent'), Body('child')
>>> pin = PinJoint('pin', parent, child, parent_axis=parent.z,
...                child_axis=-child.z)   
>>> parent.dcm(child)   
Matrix([
[-cos(q_pin(t)), -sin(q_pin(t)),  0],
[-sin(q_pin(t)),  cos(q_pin(t)),  0],
[             0,              0, -1]])

When inspecting this matrix you will notice that for theta_pin = 0 the child body is rotated \(\pi\) rad about the parent.y axis. In the new definition you can see that we get the same result, but this time we have also specified this exact rotation:

>>> from sympy import pi
>>> from sympy.physics.mechanics import Body, PinJoint, ReferenceFrame
>>> parent, child, = Body('parent'), Body('child')
>>> int_frame = ReferenceFrame('int_frame')
>>> int_frame.orient_axis(child.frame, child.y, pi)
>>> pin = PinJoint('pin', parent, child, joint_axis=parent.z,
...                child_interframe=int_frame)
>>> parent.dcm(child)
Matrix([
[-cos(q_pin(t)), -sin(q_pin(t)),  0],
[-sin(q_pin(t)),  cos(q_pin(t)),  0],
[             0,              0, -1]])

However if you liked the fact that the deprecated arguments aligned the frames for you, then you can still make use of this feature by providing vectors to parent_interframe and child_interframe, which are then oriented such that the joint axis expressed in the intermediate frame is aligned with the given vector:

>>> from sympy.physics.mechanics import Body, PinJoint
>>> parent, child = Body('parent'), Body('child')
>>> pin = PinJoint('pin', parent, child, parent_interframe=parent.z,
...                child_interframe=-child.z)
>>> parent.dcm(child)
Matrix([
[-cos(q_pin(t)), -sin(q_pin(t)),  0],
[-sin(q_pin(t)),  cos(q_pin(t)),  0],
[             0,              0, -1]])

Change in joint attachment point argument

The argument names for specifying the attachment points of a joint in sympy.physics.mechanics , i.e. parent_joint_pos and child_joint_pos, have been changed to parent_point and child_point. This is because these arguments can now also be Point objects, so they can be exactly the same as the parent_point and child_point attributes.

For example, suppose you want a PinJoint in the parent to be positioned at parent.frame.x with respect to the mass center, and in the child at -child.frame.x. The previous way to specify this was:

>>> from sympy.physics.mechanics import Body, PinJoint
>>> parent, child = Body('parent'), Body('child')
>>> pin = PinJoint('pin', parent, child, parent_joint_pos=parent.frame.x,
...                child_joint_pos=-child.frame.x)   
>>> pin.parent_point.pos_from(parent.masscenter)   
parent_frame.x
>>> pin.child_point.pos_from(child.masscenter)   
- child_frame.x

Now you can do the same with either

>>> from sympy.physics.mechanics import Body, PinJoint
>>> parent, child = Body('parent'), Body('child')
>>> pin = PinJoint('pin', parent, child, parent_point=parent.frame.x,
...                child_point=-child.frame.x)
>>> pin.parent_point.pos_from(parent.masscenter)
parent_frame.x
>>> pin.child_point.pos_from(child.masscenter)
- child_frame.x

Or

>>> from sympy.physics.mechanics import Body, PinJoint, Point
>>> parent, child = Body('parent'), Body('child')
>>> parent_point = parent.masscenter.locatenew('parent_point', parent.frame.x)
>>> child_point = child.masscenter.locatenew('child_point', -child.frame.x)
>>> pin = PinJoint('pin', parent, child, parent_point=parent_point,
...                child_point=child_point)
>>> pin.parent_point.pos_from(parent.masscenter)
parent_frame.x
>>> pin.child_point.pos_from(child.masscenter)
- child_frame.x

Version 1.11

Modules sympy.tensor.array.expressions.conv_* renamed to sympy.tensor.array.expressions.from_*

In order to avoid possible naming and tab-completion conflicts with functions with similar names to the names of the modules, all modules whose name starts with conv_* in sympy.tensor.array.expressions have been renamed to from_*.

New Mathematica code parser

The old mathematica code parser defined in the module sympy.parsing.mathematica in the function mathematica is deprecated. The function parse_mathematica with a new and more comprehensive parser should be used instead.

The additional_translations parameter for the Mathematica parser is not available in parse_mathematica. Additional translation rules to convert Mathematica expressions into SymPy ones should be specified after the conversion using SymPy’s .replace( ) or .subs( ) methods on the output expression. If the translator fails to recognize the logical meaning of a Mathematica expression, a form similar to Mathematica’s full form will be returned, using SymPy’s Function object to encode the nodes of the syntax tree.

For example, suppose you want F to be a function that returns the maximum value multiplied by the minimum value, the previous way to specify this conversion was:

>>> from sympy.parsing.mathematica import mathematica
>>> mathematica('F[7,5,3]', {'F[*x]': 'Max(*x)*Min(*x)'})   
21

Now you can do the same with

>>> from sympy.parsing.mathematica import parse_mathematica
>>> from sympy import Function, Max, Min
>>> parse_mathematica("F[7,5,3]").replace(Function("F"), lambda *x: Max(*x)*Min(*x))
21

Redundant static methods in carmichael

A number of static methods in ~.carmichael are just wrappers around other functions. Instead of carmichael.is_perfect_square use sympy.ntheory.primetest.is_square and instead of carmichael.is_prime use ~.isprime. Finally, carmichael.divides can be replaced by instead checking

n % p == 0

The check argument to HadamardProduct, MatAdd and MatMul

This argument can be used to pass incorrect values to ~.HadamardProduct, ~.MatAdd, and ~.MatMul leading to later problems. The check argument will be removed and the arguments will always be checked for correctness, i.e., the arguments are matrices or matrix symbols.

Version 1.10

Some traversal functions have been moved

Some traversal functions have moved. Specifically, the functions

• bottom_up

• interactive_traversal

• postorder_traversal

• preorder_traversal

• use

have moved to different SymPy submodules.

These functions should be used from the top-level sympy namespace, like

sympy.preorder_traversal

or

from sympy import preorder_traversal

In general, end-users should use the top-level sympy namespace for any functions present there. If a name is in the top-level namespace, its specific SymPy submodule should not be relied on, as functions may move around due to internal refactorings.

sympy.core.trace

The trace object sympy.core.trace.Tr() was moved to sympy.physics.quantum.trace.Tr(). This was because it was only used in the sympy.physics.quantum submodule, so it was better to have it there than in the core.

The sympy.core.compatibility submodule

The sympy.core.compatibility submodule is deprecated.

This submodule was only ever intended for internal use. Now that SymPy no longer supports Python 2, this module is no longer necessary, and the remaining helper functions have been moved to more convenient places in the SymPy codebase.

Some of the functions that were in this module are available from the top-level SymPy namespace, i.e.,

sympy.ordered
sympy.default_sort_key

or

from sympy import ordered, default_sort_key

In general, end-users should use the top-level sympy namespace for any functions present there. If a name is in the top-level namespace, its specific SymPy submodule should not be relied on, as functions may move around due to internal refactorings.

The remaining functions in sympy.core.compatibility were only intended for internal SymPy use and should not be used by user code.

Additionally, these two functions, ordered and default_sort_key, also used to be in sympy.utilities.iterables but have been moved from there as well.

Version 1.9

expr_free_symbols

The expr_free_symbols attribute of various SymPy objects is deprecated.

expr_free_symbols was meant to represent indexed objects such as MatrixElement and Indexed as free symbols. This was intended to make derivatives of free symbols work. However, this now works without making use of the method:

>>> from sympy import Indexed, MatrixSymbol, diff
>>> a = Indexed("A", 0)
>>> diff(a**2, a)
2*A[0]
>>> X = MatrixSymbol("X", 3, 3)
>>> diff(X[0, 0]**2, X[0, 0])
2*X[0, 0]

This was a general property that was added to solve a very specific problem but it added a layer of abstraction that is not necessary in general.

1. objects that have structural “non-expression” nodes already allow one to focus on the expression node if desired, e.g.

   >>> from sympy import Derivative, symbols, Function
   >>> x = symbols('x')
   >>> f = Function('f')
   >>> Derivative(f(x), x).expr
   f(x)
   

   introduction of this property encourages imprecise thinking when requesting free_symbols since it allows one to get symbols from a specific node of an object without specifying the node

2. the property was incorrectly added to AtomicExpr so numbers are returned as expr_free_symbols:

   >>> S(2).expr_free_symbols 
   2
   

3. the application of the concept was misapplied to define Subs.expr_free_symbols: it added in expr_free_symbols of the point but the point is a Tuple so nothing was added

4. it was not used anywhere else in the codebase except in the context of differentiating a Subs object, which suggested that it was not something of general use, this is also confirmed by the fact that,

5. it was added without specific tests except for test of the derivatives of the Subs object for which it was introduced

See issue #21494 for more discussion.

sympy.stats.sample(numsamples=n)

The numsamples parameter to sympy.stats.sample() is deprecated.

numsamples makes sample() return a list of size numsamples, like

>>> from sympy.stats import Die, sample
>>> X = Die('X', 6)
>>> sample(X, numsamples=3) 
[3, 2, 3]

However, this functionality can be easily implemented by the user with a list comprehension

>>> [sample(X) for i in range(3)] 
[5, 4, 3]

Additionally, it is redundant with the size parameter, which makes sample return a NumPy array with the given shape.

>>> sample(X, size=(3,)) 
array([6, 6, 1])

Historically, sample was changed in SymPy 1.7 so it returned an iterator instead of sample value. Since an iterator was returned, a numsamples parameter was added to specify the length of the iterator.

However, this new behavior was considered confusing, as discussed in issue #21563, so it was reverted. Now, sample_iter should be used if a iterator is needed. Consequently, the numsamples parameter is no longer needed for sample().

sympy.polys.solvers.RawMatrix

The RawMatrix class is deprecated. The RawMatrix class was a subclass of Matrix that used domain elements instead of Expr as the elements of the matrix. This breaks a key internal invariant of Matrix and this kind of subclassing limits improvements to the Matrix class.

The only part of SymPy that documented the use of the RawMatrix class was the Smith normal form code, and that has now been changed to use DomainMatrix instead. It is recommended that anyone using RawMatrix with the previous Smith Normal Form code should switch to using DomainMatrix as shown in issue #21402. A better API for the Smith normal form will be added later.

Non-Expr objects in a Matrix

In SymPy 1.8 and earlier versions it was possible to put non-Expr elements in a Matrix and the matrix elements could be any arbitrary Python object:

>>> M = Matrix([[(1, 2), {}]]) 

This is not useful and does not really work, e.g.:

>>> M + M 
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for +: 'Dict' and 'Dict'

The main reason for making this possible was that there were a number of Matrix subclasses in the SymPy codebase that wanted to work with objects from the polys module, e.g.

1. RawMatrix (see above) was used in solve_lin_sys which was part of heurisch and was also used by smith_normal_form. The NewMatrix class used domain elements as the elements of the Matrix rather than Expr.

2. NewMatrix was used in the holonomic module and also used domain elements as matrix elements

3. PolyMatrix used a mix of Poly and Expr as the matrix elements and was used by risch.

All of these matrix subclasses were broken in different ways and the introduction of DomainMatrix (#20780, #20759, #20621, #19882, #18844) provides a better solution for all cases. Previous PRs have removed the dependence of these other use cases on Matrix (#21441, #21427, #21402) and now #21496 has deprecated having non-Expr in a Matrix.

This change makes it possible to improve the internals of the Matrix class but it potentially impacts on some downstream use cases that might be similar to the uses of Matrix with non-Expr elements that were in the SymPy codebase. A potential replacement for code that used Matrix with non-Expr elements is DomainMatrix if the elements are something like domain elements and a domain object can be provided for them. Alternatively if the goal is just printing support then perhaps TableForm can be used.

It isn’t clear what to advise as a replacement here without knowing more about the usecase. If you are unclear how to update your code, please open an issue or write to our mailing list so we can discuss it.

The get_segments attribute of plotting objects

The get_segments method implemented in Line2DBaseSeries is used to convert two list of coordinates, x and y, into a list of segments used by Matplotlib’s LineCollection to plot a line.

Since the list of segments is only required by Matplotlib (for example, Bokeh, Plotly, Mayavi, K3D only require lists of coordinates), this has been moved inside the MatplotlibBackend class.

Note that previously, the method get_points() always returned uniformly sampled points, which meant that some functions were not plotted correctly when using get_points() to plot with Matplotlib.

To avoid this problem, the method get_segments() could be used, which used adaptive sampling and which could be used with Matplotlib’s LineCollection. However, this has been changed, and now get_points() can also use adaptive sampling. The get_data() method can also be used.

The mdft function in sympy.physics.matrices

The sympy.physics.matrices.mdft() function is deprecated. It can be replaced with the DFT class in sympy.matrices.expressions.fourier.

In particular, replace mdft(n) with DFT(n).as_explicit(). For example:

>>> from sympy.physics.matrices import mdft
>>> mdft(3) # DEPRECATED 
Matrix([
[sqrt(3)/3,                sqrt(3)/3,                sqrt(3)/3],
[sqrt(3)/3, sqrt(3)*exp(-2*I*pi/3)/3,  sqrt(3)*exp(2*I*pi/3)/3],
[sqrt(3)/3,  sqrt(3)*exp(2*I*pi/3)/3, sqrt(3)*exp(-2*I*pi/3)/3]])

>>> from sympy.matrices.expressions.fourier import DFT
>>> DFT(3)
DFT(3)
>>> DFT(3).as_explicit()
Matrix([
[sqrt(3)/3,                sqrt(3)/3,                sqrt(3)/3],
[sqrt(3)/3, sqrt(3)*exp(-2*I*pi/3)/3,  sqrt(3)*exp(2*I*pi/3)/3],
[sqrt(3)/3,  sqrt(3)*exp(2*I*pi/3)/3, sqrt(3)*exp(-2*I*pi/3)/3]])

This was changed because the sympy.physics submodule is supposed to only contain things that are specific to physics, but the discrete Fourier transform matrix is a more general mathematical concept, so it is better located in the sympy.matrices module. Furthermore, the DFT class is a matrix expression, meaning it can be unevaluated and support symbolic shape.

The private SparseMatrix._smat and DenseMatrix._mat attributes

The ._mat attribute of Matrix and the ._smat attribute of SparseMatrix are deprecated.

The internal representation of Matrix and SparseMatrix was changed to be a DomainMatrix in #21626 so that it is no longer possible to expose a mutable list/dict as a way of mutating a Matrix. Instead of ._mat the new .flat() method can be used, which returns a new list that cannot be used to mutate the Matrix itself. Instead of ._smat the .todok() method can be used which returns a new dict.

Note that these attributes are already changed in SymPy 1.9 to return read-only copies, so that any code that relied on mutating them will be broken. Also these attributes were technically always private (they started with an underscore), so user code should not really have been using them in the first place.

laplace_transform of a Matrix with noconds=False

Prior to version 1.9, calling laplace_transform() on a Matrix with noconds=False (which is the default), resulted in a Matrix of tuples:

>>> from sympy import laplace_transform, symbols, eye
>>> t, z = symbols('t z')
>>> laplace_transform(eye(2), t, z) 
Matrix([
[(1/z, 0, True),   (0, 0, True)],
[  (0, 0, True), (1/z, 0, True)]])

However, Matrix is only designed to work with Expr objects (see Non-Expr objects in a Matrix above).

To avoid this, either use noconds=True to remove the convergence conditions

>>> laplace_transform(eye(2), t, z, noconds=True)
Matrix([
[1/z,   0],
[  0, 1/z]])

or use legacy_matrix=False to return the new behavior, which will be to return a single tuple with the Matrix in the first argument and the convergence conditions combined into a single condition for the whole matrix.

>>> laplace_transform(eye(2), t, z, legacy_matrix=False)
(Matrix([
[1/z,   0],
[  0, 1/z]]), 0, True)

When this deprecation is removed the legacy_matrix=False behavior will become the default, but the flag will be left intact for compatibility.

Version 1.8

sympy.printing.theanocode

Theano has been discontinued, and forked into a new project called Aesara. The sympy.printing.theanocode module has been renamed to sympy.printing.aesaracode, and all the corresponding functions have been renamed (e.g., theano_code has been renamed to aesara_code(), TheanoPrinter has been renamed to AesaraPrinter, and so on).

sympy.assumptions.handlers.AskHandler and related methods

Predicate has experienced a big design change. Previously, its handler was a list of AskHandler classes and registration was done by add_handler() and remove_handler() functions. Now, its handler is a multipledispatch instance and registration is done by register() or register_many() methods. Users must define a predicate class to introduce a new one.

Previously, handlers were defined and registered this way:

class AskPrimeHandler(AskHandler):
    @staticmethod
    def Integer(expr, assumptions):
        return expr.is_prime

register_handler('prime', AskPrimeHandler)

It should be changed to this:

# Predicate definition.
# Not needed if you are registering the handler to existing predicate.
class PrimePredicate(Predicate):
    name = 'prime'
Q.prime = PrimePredicate()

# Handler registration
@Q.prime.register(Integer)
def _(expr, assumptions):
    return expr.is_prime

See GitHub issue #20209.

Version 1.7.1

Calling sympy.stats.StochasticProcess.distribution with RandomIndexedSymbol

The distribution method of sympy.stats stochastic processes used to accept a RandomIndexedSymbol (that is, a stochastic process indexed with a timestamp), but should now only be called with the timestamp.

For example, if you have

>>> from sympy import symbols
>>> from sympy.stats import WienerProcess
>>> W = WienerProcess('W')
>>> t = symbols('t', positive=True)

Previously this would work

W.distribution(W(t)) # DEPRECATED

It should now be called like

>>> W.distribution(t)
NormalDistribution(0, sqrt(t))

This was change was made as part of a change to store only Basic objects in sympy.stats .args. See issue #20078 for details.

Version 1.7

sympy.stats.DiscreteMarkovChain.absorbing_probabilites()

The absorbing_probabilites method name was misspelled. The correct spelling absorbing_probabilities() (“absorbing probabilities”) should be used instead.

sympy.utilities.misc.find_executable()

The function sympy.utilities.misc.find_executable() is deprecated. Instead use the standard library shutil.which() function, which has been in the standard library since Python 3.3 and is more powerful.

Mutable attributes in sympy.diffgeom

Several parts of sympy.diffgeom have been updated to no longer be mutable, which better matches the immutable design used in the rest of SymPy.

• Passing strings for symbol names in CoordSystem is deprecated. Instead you should be explicit and pass symbols with the appropriate assumptions, for instance, instead of

  CoordSystem(name, patch, ['x', 'y']) # DEPRECATED
  

  use

  CoordSystem(name, patch, symbols('x y', real=True))
  

• Similarly, the names keyword argument has been renamed to symbols, which should be a list of symbols.

• The Manifold.patches attribute is deprecated. Patches should be tracked separately.

• The Patch.coord_systems attribute is deprecated. Coordinate systems should be tracked separately.

• The CoordSystem.transforms attribute, CoordSystem.connect_to() method, and CoordSystem.coord_tuple_transform_to() method are deprecated. Instead, use the relations keyword to the CoordSystem class constructor and the CoordSystem.transformation() and CoordSystem.transform() methods (see the docstring of CoordSystem for examples).

The unicode argument and attribute to sympy.printing.pretty.stringpict.prettyForm and the sympy.printing.pretty.pretty_symbology.xstr function

The sympy.printing.pretty.pretty_symbology.xstr function, and the unicode argument and attribute to sympy.printing.pretty.stringpict.prettyForm were both present to support the Unicode behavior of Python 2. Since Unicode strings are the default in Python 3, these are not needed any more. xstr() should be replaced with just str(), the unicode argument to prettyForm should be omitted, and the prettyForm.unicode attribute should be replaced with the prettyForm.s attribute.

Passing the arguments to lambdify as a set

Passing the function arguments to lambdify as a set is deprecated. Instead pass them as a list or tuple. For example, instead of

lambdify({x, y}, x + 2*y) # WRONG

use

lambdify((x, y), x + 2*y) # RIGHT

This is because sets are unordered. For instance, in the above example it would be impossible for lambidfy to know if it was called with {x, y} or {y, x}. Thus, when passed the arguments as a set lambdify would have to guess their order, which would lead to an incorrect function if it guessed incorrectly.

Core operators no longer accept non-Expr args

The core operator classes Add, Mul, and Pow can no longer be constructed directly with objects that are not subclasses of Expr.

Expr is the superclass of all SymPy classes that represent scalar numeric quantities. For example, sin, Symbol, and Add are all subclasses of Expr. However, may objects in SymPy are not Expr because they represent some other type of mathematical object. For example, Set, Poly, and Boolean are all non-Expr. These do not make mathematical sense inside of Add, Mul, and Pow, which are designed specifically to represent the addition, multiplication, and exponentiation of scalar complex numbers.

Manually constructing one of these classes with such an object is possible, but it will generally create something that will then break. For example

Mul(1, Tuple(2)) # This is deprecated

works and creates Tuple(2), but only because Mul is “tricked” by always treating \(1 \cdot x = x\). If instead you try

Mul(2, Tuple(2)) # This is deprecated

it fails with an exception

AttributeError: 'Tuple' object has no attribute 'as_coeff_Mul'

because it tries to call a method of Expr on the Tuple object, which does not have all the Expr methods (because it is not a subclass of Expr).

If you want to use the +, *, or ** operation on a non-Expr object, use the operator directly rather than using Mul, Add or Pow. If functional versions of these are desired, you can use a lambda or the operator module.

Version 1.6

Various sympy.utilities submodules have moved

The following submodules have been renamed.

• sympy.utilities.benchmarking → sympy.testing.benchmarking

• sympy.utilities.pytest → sympy.testing.pytest

• sympy.utilities.randtests → sympy.core.random

• sympy.utilities.runtests → sympy.testing.runtests

• sympy.utilities.tmpfiles → sympy.testing.tmpfiles

sympy.testing.randtest

sympy.testing.randtest is deprecated. The functions in it have been moved to sympy.core.random. The following functions have been moved.

• sympy.testing.randtest.random_complex_number → sympy.core.random.random_complex_number

• sympy.testing.randtest.verify_numerically sympy.core.random.verify_numerically

• sympy.testing.randtest.test_derivative_numerically → sympy.core.random.test_derivative_numerically

• sympy.testing.randtest._randrange → sympy.core.random._randrange

• sympy.testing.randtest._randint → sympy.core.random._randint

Mixing Poly and non-polynomial expressions in binary operations

In previous versions of SymPy, Poly was a subclass of Expr, but it has been changed to only be a subclass of Basic. This means that some things that used to work with Poly are now deprecated because they are only designed to work with Expr objects.

This includes combining Poly with Expr objects using binary operations, for example

Poly(x)*sin(x) # DEPRECATED

To do this, either explicitly convert the non-Poly operand to a Poly using Expr.as_poly() or convert the Poly operand to an Expr using Poly.as_expr(), depending on which type you want the result to be.

The print_cyclic flag of sympy.combinatorics.Permutation

The print_cyclic attribute of sympy.combintorics.Permutation controls whether permutations print as cycles or arrays. This would be done by setting Permutation.print_cyclic = True or Permutation.print_cyclic = False. However, this method of controlling printing is bad because it is a global flag, but printing should not depend on global behavior.

Instead, users should use the perm_cyclic flag of the corresponding printer. The easiest way to configure this is to set the flag when calling init_printing(), like

>>> from sympy import init_printing
>>> init_printing(perm_cyclic=False) # Makes Permutation print in array form 
>>> from sympy.combinatorics import Permutation
>>> Permutation(1, 2)(3, 4) 
⎛0 1 2 3 4⎞
⎝0 2 1 4 3⎠

The Permutation docstring contains more details on the perm_cyclic flag.

Using integrate with Poly

In previous versions of SymPy, Poly was a subclass of Expr, but it has been changed to only be a subclass of Basic. This means that some things that used to work with Poly are now deprecated because they are only designed to work with Expr objects.

This includes calling integrate() or Integral with Poly.

To integrate a Poly, use the Poly.integrate() method. To compute the integral as an Expr object, call the Poly.as_expr() method first.

See also Mixing Poly and non-polynomial expressions in binary operations above.

Creating an indefinite Integral with an Eq argument

Passing an Eq() object to integrate() is deprecated in the case where the integral is indefinite. This is because if \(f(x) = g(x)\), then \(\int f(x)\,dx = \int g(x)\,dx\) is not true in general, due to the arbitrary constants (which integrate does not include).

If you want to make an equality of indefinite integrals, use Eq(integrate(f(x), x), integrate(g(x), x)) explicitly.

If you already have an equality object eq, you can use Eq(integrate(eq.lhs, x), integrate(eq.rhs, x)).

Version 1.5

Tensor.fun_eval and Tensor.__call__

TensExpr.fun_eval and Tensor.__call__ (i.e., calling a tensor to evaluate it) are deprecated. The Tensor.substitute_indices() method should be used. This was changed because fun_eval was considered a confusing name and using function evaluation was considered both confusing and dangerous.

TensorType

The TensorType class is deprecated. Use tensor_heads() instead. The TensorType class had no purpose except shorter creation of TensorHead objects.

See also The tensorhead() function below.

The dummy_fmt argument to TensorIndexType

The dummy_fmt keyword argument to TensorIndexType is deprecated. Setting dummy_fmt='L' leads to _dummy_fmt='L_%d', which is confusing and uses obsolete string formatting. dummy_name should be used instead. This change was made because dummy_name is a clearer name.

The metric argument to TensorIndexType

The metric keyword argument to TensorIndexType is deprecated. The name “metric” was ambiguous because it meant “metric symmetry” in some places and “metric tensor” in others.

Either the metric_symmetry keyword or the TensorIndexType.set_metric() method should be used instead.

The get_kronecker_delta() and get_epsilon() methods of TensorIndexType

The get_kronecker_delta() and get_epsilon() methods of TensorIndexType are deprecated. Use the TensorIndexType.delta and TensorIndexType.epsilon properties instead, respectively.

The tensorsymmetry() function

The tensorsymmetry() function in sympy.tensor is deprecated. Use the TensorSymmetry class constructor instead.

TensorSymmetry is preferred over tensorsymmetry() because the latter

1. Does not have any extra functionality

2. Involves obscure Young tableau

3. Is not a member of the TensorSymmetry class

The tensorhead() function

The tensorhead() function is deprecated in favor of tensor_heads(). tensor_heads() is more consistent with other SymPy names (i.e., Symbol and symbols() or TensorIndex and tensor_indices()). It also does not use Young tableau to denote symmetries.

The is_EmptySet attribute of sets

The is_EmptySet attribute of Set objects is deprecated. Instead either use

from sympy import S
s is S.EmptySet

or

s.is_empty

The difference is that s.is_empty may return None if it is unknown if the set is empty.

ProductSet(iterable)

Passing a single iterable as the first argument to ProductSet is deprecated. Creating a product set from an iterable should be done using ProductSet(*iterable), or as each individual argument. For example

>>> from sympy import ProductSet
>>> sets = [{i} for i in range(3)]
>>> ProductSet(*sets)
ProductSet({0}, {1}, {2})
>>> ProductSet({1, 2}, {1})
ProductSet({1, 2}, {1})

This is done because sets themselves can be iterables, and sets of sets are allowed. But the product set of a single set should mathematically be that set itself (or more exactly, the set of 1-tuples of elements of that set). Automatically denesting a single iterable makes it impossible to represent this object and makes ProductSet not generalize correctly when passed 1 argument. On the other hand, treating the first argument differently if it is a set than if it is another type of iterable (which is what is currently done in the deprecated code path) is confusing behavior.

The set_potential_energy method in sympy.physics.mechanics

The set_potential_energy() methods of sympy.physics.mechanics.particle.Particle and sympy.physics.mechanics.rigidbody.RigidBody are deprecated.

Instead one should set the Particle.potential_energy and RigidBody.potential_energy attributes to set the potential energy, like

P.potential_energy = scalar

This change was made to be more Pythonic, by using setters and getters of a @property method rather than an explicit set_ method.

Using a set for the condition in ConditionSet

Using a set for the condition in ConditionSet is deprecated. A boolean should be used instead. This is because the condition is mathematically a boolean, and it is ambiguous what a set should mean in this context.

To fix this deprecation, replace

ConditionSet(symbol, set_condition)

with

ConditionSet(symbol, And(*[Eq(lhs, 0) for lhs in set_condition]))

For example,

ConditionSet((x, y), {x + 1, x + y}, S.Reals) # DEPRECATED

would become

ConditionSet((x, y), Eq(x + 1, 0) & Eq(x + y, 0), S.Reals)

The max_degree and get_upper_degree properties of sympy.polys.multivariate_resultants.DixonResultant

The max_degree property and get_upper_degree() methods of DixonResultant are deprecated. See issue #17749 for details.

Non-tuple iterable for the first argument to Lambda

Using a non-tuple as the first argument to Lambda is deprecated. If you have a non-tuple, convert it to a tuple first, like Lambda(tuple(args), expr).

This was done so that Lambda could support general tuple unpacking, like

>>> from sympy import Lambda, symbols
>>> x, y, z = symbols('x y z')
>>> f = Lambda((x, (y, z)), x + y + z)
>>> f(1, (2, 3))
6

The evaluate flag to differentiate_finite

The evaluate flag to differentiate_finite() is deprecated.

differentiate_finite(expr, x, evaluate=True) expands the intermediate derivatives before computing differences. But this usually not what you want, as it does not satisfy the product rule.

If you really do want this behavior, you can emulate it with

diff(expr, x).replace(
    lambda arg: arg.is_Derivative,
    lambda arg: arg.as_finite_difference())

See the discussion on issue #17881.

Version 1.4

TensorIndexType.data and related methods

The TensorIndexType.data property is deprecated, as well as several methods which made use of it including the get_matrix(), the __getitem__() (indexing), __iter__() (iteration), _components_data_full_destroy(), and __pow__() (**) methods. Storing data on tensor objects was a design flaw and not consistent with how the rest of SymPy works.

Instead, the TensExpr.replace_with_arrays() method should be used.