diff --git a/algorithms/simplex/python/simplex.py b/algorithms/simplex/python/simplex.py
new file mode 100644
index 00000000..d7b20ec5
--- /dev/null
+++ b/algorithms/simplex/python/simplex.py
@@ -0,0 +1,207 @@
+from sys import stdin
+import sys
+
+M = 99
+
+def Read_Input():
+    n, m = list(map(int, stdin.readline().split()))
+    A = []
+    for i in range(n):
+        A += [list(map(int, stdin.readline().split()))]
+    b = list(map(int, stdin.readline().split()))
+    b.append(0)
+    c = list(map(int, stdin.readline().split()))
+    for i in range(len(c)):
+        c[i] = 0 - c[i]
+    A.append(c)
+    return n, m, A, b
+
+# Add slack variable for each inequality such that
+# for row i of A,  s_j = 1 if i = j and 0 otherwise
+def Add_Slack_Variables(A, m):
+    for i in range(len(A)):
+        for j in range(len(A) - 1):
+            A[i].append(0)
+    for i in range(len(A) - 1):
+        A[i][i + m] = 1
+
+# Add artificial variable to every row i of A
+# that has a negative solution b[i]
+def Add_Artificial_Variables(A, b):
+    artificial_var = 0
+    for i in range(len(b)):
+        if b[i] < 0:
+            artificial_var += 1
+            b[i] = 0 - b[i]
+            for j in range(len(A[i])):
+                A[i][j] = 0 - A[i][j]
+            for k in range(len(A)):
+                A[k].append(0)
+            A[i][-1] = 1
+            A[-1][-1] = M
+    return artificial_var
+
+# Complete the matrix by appending P and b as
+# the final columns
+def Add_b_and_P(A, b):
+    for i in range(len(A)):
+        A[i].append(0)
+    A[-1][-1] = 1
+    for j in range(len(b)):
+        A[j].append(b[j])
+
+# Check whether there is only 1 non-zero entry in column
+def Test_Basis(A, column):
+    appearances = 0
+    row = -1
+    for i in range(len(A)):
+        if A[i][column] != 0:
+            appearances += 1
+            row = i
+    if appearances == 1:
+        return row
+    return -1
+
+# Build up basis of columns that satisfy test criteria
+def Create_Basis(A, basis, m):
+    for i in range(m, len(A[0])):
+        row = Test_Basis(A, i)
+        if row != -1:
+            val = A[row][-1] / A[row][i]
+            basis[i] = (row, val)
+
+# check for invalid (negative) values in basis
+def Validate_Basis(A, basis, P):
+    for val in basis:
+        if basis[val][1] < 0 and val != P:
+            return False
+    return True
+
+def Move_M(A):
+    pivot_row = -1
+    pivot_column = -1
+    for i in range(len(A[-1])):
+        if A[-1][i] == M:
+            pivot_column = i
+    for j in range(len(A)):
+        if A[j][pivot_column] == 1:
+            pivot_row = j
+    if pivot_row == -1 or pivot_column == -1:
+        return False
+    for k in range(len(A[-1])):
+        A[-1][k] = A[-1][k] - (M * A[pivot_row][k])
+    return True
+
+# Perform normal Pivot operations until all val on bottom row are non-negative
+def Find_Pivot_Column(A, m):
+    min_val = 0
+    min_index = -1
+    for i in range(len(A[0]) - 1):
+        if A[-1][i] < min_val:
+            min_val = A[-1][i]
+            min_index = i
+    return min_index
+
+def Find_Pivot_Row(A, column):
+    if column == -1:
+        return -1
+    min_val = 999999999
+    row = -1
+    for i in range(len(A) - 1):
+        if A[i][column] > 0:
+            val = A[i][-1] / A[i][column]
+            if val < min_val:
+                min_val = val
+                row = i
+    return row
+
+def Pivot(A, pivot_row, pivot_column):
+    val = A[pivot_row][pivot_column]
+    for i in range(len(A[pivot_row])):
+        A[pivot_row][i] = A[pivot_row][i] / val
+    for i in range(len(A)):
+        if i != pivot_row:
+            Scale_Row(A, i, pivot_row, pivot_column)
+
+def Scale_Row(A, row, pivot_row, pivot_column):
+    scale = A[row][pivot_column]
+    if scale != 0:
+        for i in range(len(A[row])):
+            comp = A[pivot_row][i] * scale
+            A[row][i] = A[row][i] - comp
+
+
+def Update_Basis(basis, pivot_row, pivot_column, m):
+    entering = pivot_column
+    for val in basis:
+        if basis[val][0] == pivot_row:
+            exiting = val
+    del basis[exiting]
+    val = A[pivot_row][-1] / A[pivot_row][pivot_column]
+    basis[pivot_column] = (pivot_row, val)
+    for val in basis:
+        row = basis[val][0]
+        basis[val] = (row, A[row][-1] / A[row][val])
+
+def Test_No_Solution(basis, real_var, slack_var, artificial_var):
+    lower = real_var + slack_var
+    upper = lower + artificial_var
+    for val in basis:
+        if val >= lower and val < upper and basis[val][1] > .0005:
+            return True
+
+def Get_Solution(basis, real_var, solution):
+    for var in basis:
+        if var < real_var:
+            solution[var] = basis[var][1]
+    return solution
+
+def Print_Solution(solution):
+    print("Bounded solution")
+    print(' '.join(list(map(lambda x : '%.18f' % x, solution))))
+
+def Solve(n, m, A, b):
+    # Create matrix
+    Add_Slack_Variables(A,m)
+    artificial_var = Add_Artificial_Variables(A, b)
+    Add_b_and_P(A, b)
+    slack_var = n
+    real_var = m
+    P = real_var + slack_var + artificial_var
+
+    # Create basis
+    basis = {}
+    Create_Basis(A, basis, m)
+
+    # Validate basis
+    valid = Validate_Basis(A, basis, P)
+    i = 0
+    while not valid:
+        flag = Move_M(A)
+        if not flag:
+            break
+        basis = {}
+        Create_Basis(A, basis, m)
+        valid = Validate_Basis(A, basis, P)
+
+    # Perform pivots while negative val in bottom row
+    col = Find_Pivot_Column(A, m)
+    while col != -1:
+        row = Find_Pivot_Row(A, col)
+        if row == -1:
+            print("Infinity")
+            return
+        Pivot(A, row, col)
+        Update_Basis(basis, row, col, m)
+        col = Find_Pivot_Column(A, m)
+    if Test_No_Solution(basis, real_var, slack_var, artificial_var):
+        print("No solution")
+        return
+
+    solution = [0 for i in range(real_var)]
+    Get_Solution(basis, real_var, solution)
+    Print_Solution(solution)
+
+if __name__ == "__main__":
+    n, m, A, b = Read_Input()
+    Solve(n, m, A, b)