Merge pull request #22980 from KuldeepBorkar/documentating

Import Sequence changed and updated documentation

.mailmap

@@ -629,7 +629,7 @@ Kristian Brünn <hello@kristianbrunn.com> Kristianmitk <hello@kristianbrunn.com>
 Krit Karan <kritkaran.b13@iiits.in> <kritkaran94@users.noreply.github.com>
 Kshitij <kshitijparwani.mat18@itbhu.ac.in> Kshitij Parwani <44468674+P-Kshitij@users.noreply.github.com>
 Kshitij Saraogi <KshitijSaraogi@gmail.com>
-Kuldeep Borkar Jr <kuldeepborkarjr765@gmail.com>
+Kuldeep Borkar Jr <kuldeepborkarjr765@gmail.com> Kuldeep Borkar Jr <74557588+KuldeepBorkar@users.noreply.github.com>
 Kumar Krishna Agrawal <kumar.1994.14@gmail.com>
 Kunal Arora <kunalarora.135@gmail.com>
 Kunal Singh <ksingh19136@gmail.com> kunal singh <ksingh19136@gmail.com>

sympy/stats/__init__.py

@@ -24,7 +24,7 @@
 ========
 
 >>> from sympy.stats import P, E, variance, Die, Normal
->>> from sympy import Eq, simplify
+>>> from sympy import simplify
 >>> X, Y = Die('X', 6), Die('Y', 6) # Define two six sided dice
 >>> Z = Normal('Z', 0, 1) # Declare a Normal random variable with mean 0, std 1
 >>> P(X>3) # Probability X is greater than 3
@@ -84,7 +84,7 @@
 3. If you want to create a Finite Random Variable:
 
 >>> from sympy.stats import FiniteRV, P, E
->>> from sympy import Rational
+>>> from sympy import Rational, Eq
 >>> pmf = {1: Rational(1, 3), 2: Rational(1, 6), 3: Rational(1, 4), 4: Rational(1, 4)}
 >>> X = FiniteRV('X', pmf)
 >>> E(X)

sympy/stats/stochastic_process_types.py

@@ -1,9 +1,7 @@
 import random
-
 import itertools
 from typing import (Sequence as tSequence, Union as tUnion, List as tList,
         Tuple as tTuple, Set as tSet)
-
 from sympy.concrete.summations import Sum
 from sympy.core.add import Add
 from sympy.core.basic import Basic
@@ -908,8 +906,6 @@ class DiscreteMarkovChain(DiscreteTimeStochasticProcess, MarkovProcess):
 
     Symbolic probability queries are also supported
 
-    >>> from sympy import symbols, Matrix, Rational, Eq, Gt
-    >>> from sympy.stats import P, DiscreteMarkovChain
     >>> a, b, c, d = symbols('a b c d')
     >>> T = Matrix([[Rational(1, 10), Rational(4, 10), Rational(5, 10)], [Rational(3, 10), Rational(4, 10), Rational(3, 10)], [Rational(7, 10), Rational(2, 10), Rational(1, 10)]])
     >>> Y = DiscreteMarkovChain("Y", [0, 1, 2], T)
@@ -1554,8 +1550,7 @@ class ContinuousMarkovChain(ContinuousTimeStochasticProcess, MarkovProcess):
 
     Symbolic probability queries are also supported
 
-    >>> from sympy import S, symbols, Matrix, Rational, Eq, Gt
-    >>> from sympy.stats import P, ContinuousMarkovChain
+    >>> from sympy import symbols
     >>> a,b,c,d = symbols('a b c d')
     >>> G = Matrix([[-S(1), Rational(1, 10), Rational(9, 10)], [Rational(2, 5), -S(1), Rational(3, 5)], [Rational(1, 2), Rational(1, 2), -S(1)]])
     >>> C = ContinuousMarkovChain('C', state_space=[0, 1, 2], gen_mat=G)
@@ -2256,8 +2251,9 @@ def split(self, l1, l2):
 class WienerProcess(CountingProcess):
     """
     The Wiener process is a real valued continuous-time stochastic process.
-    In physics it is used to study Brownian motion and therefore also known as
-    Brownian Motion.
+    In physics it is used to study Brownian motion and it is often also called
+    Brownian motion due to its historical connection with physical process of the
+    same name originally observed by Scottish botanist Robert Brown.
 
     Parameters
     ==========