{
    "info": {
        "author": "semihM",
        "author_email": "",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Topic :: Scientific/Engineering :: Mathematics"
        ],
        "description": "# <a href=\"https://pypi.org/project/MatricesM/\">MatricesM</a>\n#### A stand-alone library for Python 3.6 and higher to create and manipulate matrices used in linear algebra and dataframes in statistics\n#### [Join MathStuff's Slack workspace](https://join.slack.com/t/mathstuffm/shared_invite/enQtNjE1NzE4NjM2ODM0LTk3ODEyNDVhY2Y5OGU1ZjZmZDc0YjQwMmE2YTJkZTczMGI1ODdmZGY2ZTQ2ZGRiMTM3MmQ0NjczODdmMzBiYjI) for questions and discussions.\n#### Check out [the wiki](https://github.com/MathStuff/MatricesM/wiki) for better documentation\n\n### Install using pip:\n\n   <code>pip install MatricesM</code>\n\n### Import by using:\n   ```python \n   import MatricesM as mm #Use by calling : mm.Matrix(arguments)\n   ```\n   #### OR\n   ```python \n   from MatricesM import * #Use matrices directly : Matrix(arguments)\n   ```\n### Import and print example matrices:\n   ```python \n   from MatricesM.exampleMatrices import *\n   ```\n\n### Basic syntax:\n```python \n\nMatrix(dim=dimensions,#Required(UNLESS 'data' is given), int | list/tuple as [rows,cols]\n\n       data=data, #Optional, list of numbers | list of lists containing numbers | string | dictionary. If no argument is passed matrix is filled depending on the 'fill' and 'ranged' \n\n       fill=value, #Optional,Available distributions: uniform|triangular|gauss|gammavariate|betavariate|expovariate|lognormvariate; also accepts int|float|complex|str|list|range, fills the matrix with chosen distribution or number, None will force uniform distribution. Doesn't affect the matrix if \"data\" is given\n\n       ranged=[*args] | dict;\"\"\"\n                        ->To apply all the elements give a list | tuple\n                        ->To apply every column individually give a dictionary as {\"Column_name\":[*args], ...}\n                        ->Arguments should follow one of the following rules:\n                              1)If 'fill' is uniform, interval to pick numbers from as [minimum,maximum]; \n                              2)If 'fill' is gauss or lognormvariate mean and standard deviation are picked from this attribute as [mean,standard_deviation];\n                              3)If 'fill' is triangular, range of the numbers and the mode as [minimum,maximum,mode];\n                              4)If 'fill' is gammavariate or betavariate, alpha and beta values are picked as [alpha,beta]\n                              5)If 'fill' is expovariate, lambda value have to be given in a list as [lambda]\"\"\"                   \n\n       features=column_names #Optional, list of strings. If no argument given, columns get named \"col_1\",\"col_2\" and so on\n\n       seed=integer_seed #Optional, int. Seed to generate the random numbers from, doesn't affect anything if numbers are provided.\n\n       dtype=matrix_dtype #Optional, int|float|complex|dataframe. Data type the matrix will hold, default is float.\n\n       coldtypes=column_dtypes #Requires dtype==dataframe to work. Contains the data types each column will hold. If nothing is passed, types get declared by the first row.\n\n       decimal=decimals_to_round #Optional, int (default is 4). Decimal digits to round to while printing\n\n       index=index_column #Optional, Matrix|list|tuple; indices to use for rows. Only works if dtype is set to dataframe\n\n       indexname=index_col_name #Optional, str; name of the index column\n\n       implicit=False #Optional, boolean. If necessary parameters are given, this can be set to True to speed up the setup process. Don't change if you aren't sure what your matrix requires to work properly.\n      )\n\n#Alternative way for creating dataframes, inherits from Matrix class, dtype is passed as dataframe to Matrix\ndataframe(data=data,\n          features=column_names,\n          coldtypes=column_dtypes,\n          decimal=decimals_to_round, #Decimal defaults to 3\n          index=index_column,\n          indexname=index_col_name, \n          **kwargs #Rest of the arguments passed to Matrix's __init__\n         )\n\n```         \n   ##### -[matrix.py](https://github.com/MathStuff/MatricesM/blob/master/MatricesM/matrix.py) contains the main Matrix class.\n\n   ##### -[matrices.py](https://github.com/MathStuff/MatricesM/blob/master/MatricesM/constructors/matrices.py) contains functions to create special matrices.\n\n   ##### -[exampleMatrices.py](https://github.com/MathStuff/MatricesM/blob/master/MatricesM/exampleMatrices.py) contains example matrices.\n\n   ##### -Check the [project tab](https://github.com/semihM/Matrices/projects) to see the progress\n-------------- \nSome examples:\n--------------\n##### Create matrices filled with random numbers or given values\n```python \n#Creates a 4x4 matrix filled with random float numbers\nA = Matrix(4) \n\n#Creates a 3x5 matrix with elements uniformly distributed in the range from 10 to 25\nB = Matrix([3,5],ranged=[10,25]) \n\n#Create a 6x6 square matrix filled with random integer numbers in the default range: [0,1]\nE = Matrix(6,dtype=int) \n\n#Create a 200x5 matrix using Gauss distribution with mean=50 and standard deviation=10\nF = Matrix([200,5],fill=gauss,ranged=[50,10]) \n\n#Create a 10x10 matrix filled with 1's\nG = Matrix(10,fill=1)\n\n#Create a 200x4 matrix filled with integer numbers using triangular distribution where the range is [0,20] and mode is around if not 18\nH = Matrix((200,4),fill=triangular,ranged=[0,20,18],dtype=int) \n\n#Create a 50x50 matrix filled with complex numbers using beta distribution for both real and imaginary parts with alpha=2 and beta=5\nC1 = Matrix(50,fill=betavariate,ranged=[2,5],dtype=complex)\n\n#Create a 10x1 matrix filled with the given string\nS = Matrix((10,1),fill=\"hello\",dtype=dataframe)\n```\n----------------------------------------\n##### Generate randomly filled matrices using special distributions\n```python\n#Create a 10000x3 matrix using a triangular distribution with integer values.\nrandomData1 = Matrix((10000,3),\n                     fill=triangular,\n                     ranged={\"feature1\":(0,100,50),\"feature2\":(-50,50,25),\"feature3\":(10,20,20)},\n                     seed=32141,\n                     dtype=int)\n\n#Create a 10000x4 matrix using gamma distribution with float numbers.\nrandomData2 = Matrix([10000,4],\n                     fill=gammavariate,\n                     ranged={\"feature1\":[1,1.2],\"feature2\":[12,24],\"feature3\":[15,100],\"feature4\":[1.5,3]},\n                     seed=39598)\n\n#Create a 10000x4 matrix using normal(gauss) distribution with integer numbers.\nrandomData3 = Matrix([10000,4],\n                     fill=gauss,\n                     ranged={\"feature1\":[0,25],\"feature2\":[100,200],\"feature3\":[1000,10000],\"feature4\":[1,100]},\n                     seed=4472142,\n                     dtype=int)\n\n#Create a 20000x4 matrix using exponential distribution with float numbers.\nrandomData4 = Matrix([20000,4],\n                     fill=expovariate,\n                     ranged={\"feature1\":[0.1],\"feature2\":[0.95],\"feature3\":[0.5],\"feature4\":[0.00025]},\n                     seed=21751923)\n```\n----------------------------------------\n##### Create special matrices\n```python \n#3x3 identity matrix\nid3 = Matrix(data=Identity(3))\n\n#A 8x8 symmetrical matrix filled with numbers in range from 0 to 1 with uniform distribution \nsym1 = Matrix(data=Symmetrical(8))\n\n``` \n----------------------------------------\n##### Give list of numbers to create matrices\n```python \n#Creates a matrix with the given list of numbers\nfilled_rows = [[1,2,3],[4,5,6],[7,8,9]]\n\nC = Matrix(data=filled_rows) \n\n#Create a dataframe from a list\ndata = [[\"James\",180.4,85],\n        [\"Tom\",172,73],\n        [\"Sophia\",168.25,65]]\n\ndf = dataframe(data=data,\n               features=[\"Name\",\"Height\",\"Weight\"],\n               decimal=1)\n\n#Same as:        \ndf = Matrix(data=data,\n            dtype=dataframe,\n            features=[\"Name\",\"Height\",\"Weight\"],\n            decimal=1)\n\n#coldtypes parameter may be required in cases where the data given doesn't represent the desired data types\n``` \n----------------------------------------\n##### Give a string filled with data and use the numbers in it to create a matrix\n```python \n#Creates a 3x3 matrix from the given string\nC1 = Matrix(3,\"1 0 -1 4 5 5 1 2 2\") \n\n#Creates a 2x4 matrix from the given string\nC2 = Matrix([2,4],\"5 -2 -3 2 1 0 0 4\")\n\n#Create a matrix from the given string, dimension is *required* as [dataAmount,features]. Only numbers are picked up\ndata=\"\"\"1,K,60,69900,6325\n2,K,30,79000,5200\n3,E,52,85500,7825\n4,E,57,17100,8375\n5,E,55,5500,5450\n6,E,68,27200,8550\n7,E,41,20500,4500\n8,E,20,69000,5050\n9,K,33,13200,8325\n10,E,37,31800,5975\"\"\"\n\n#As an integer matrix\nintMat = Matrix(dim=[10,4],\n                data=data,\n                features=[\"id\",\"age\",\"num1\",\"num2\"],\n                dtype=int) \n\n#Or as a dataframe\ndf = Matrix(dim=[10,4],\n            data=data,\n            features=[\"id\",\"age\",\"num1\",\"num2\"],\n            dtype=dataframe,\n            coldtypes=[int]*4)\n\n```\n----------------------------------------\n##### Read data from files \n```python \n#Create a dataframe matrix from a csv file. read_file accepts 2 optional parameters: encoding, delimiter\ndata_matrix = read_file(data_directory) \n\n#Example dataset: https://www.kaggle.com/uciml/red-wine-quality-cortez-et-al-2009\nwine = read_file(\".../Data/winequality-red.csv\")\n```\n----------------------------------------\n##### Get specific parts of the matrix (Assuming default column names)\n```python\n#All rows' second to forth columns as a matrix\nMatrix[:,1:4] == Matrix.t[1:4,:].t\n\n#Nineth column of every even numbered row as a matrix\nMatrix[::2,8] == Matrix[::2,8:9] == Matrix[\"col_9\"][::2] == Matrix.col_9[::2] \n#Using methods\nMatrix.select((\"col_9\"))[::2] == Matrix.col(9)[::2]\n\n#Forth to seventh rows as a matrix\nMatrix[3:7] \n\n#Fifth row's eighth element (returns the value as it is, not a new matrix)\nMatrix[4,7] == Matrix.matrix[4][7]\n\n#Use column names\nMatrix[\"col_3\",\"col_1\",\"col_2\"] == Matrix.select((\"col_3\",\"col_1\",\"col_2\"))\n\n#Use index column for row indices\n#Return the rows where the index matches the 'value'\nMatrix.ind[value]\n\n#Return the rows from val1's first appearance until val2's first appearance with only 'col_4' column\nMatrix.ind[val1:val2,\"col_4\"]\n```\n----------------------------------------\n##### Filter out depending on what you need\n```python \n#Using example dataset, get the rows where the \"quality\" feature is higher or equal to 6 and pH in range (3,3.3)\n#All statements should be properly closed with parentheses\nwineOverSix = winedata.where(\"(quality>6) and ((pH<3.3) and (pH>3))\")\n#Alternative way (2x faster)\nwineOverSix = winedata[(winedata[\"quality\"]>6) & ((winedata[\"pH\"]<3.3) & (winedata[\"pH\"]>3))]\n\n#Select the columns of pH and quality and assign them to another matrix\nfiltered = winedata.select((\"pH\",\"quality\"))\n#Alternative way (2x faster)\nfiltered = winedata[\"pH\",\"quality\"] \n\n#Use 'quality' column as indices\nwinedata.index = winedata.quality\n\n#Sort by given column and shuffle the data\nwinedata.sortBy(\"quality\") #Data is sorted in increasing order, use reverse=True for decreasing order\n\n#Shuffle the rows\nwinedata.shuffle()\n\n#Get 20 samples from the data under desired conditions\nwinedata.sample(20,\"(quality>5) and ((alcohol<11) or (density>0.95))\")\n#Alternative way (1.5x faster)\nwinedata[(winedata[\"quality\"]>5) & ((winedata[\"alcohol\"]<11) | (winedata[\"density\"]>0.95))].sample(20)\n\n#Return all the rows and select 'alcohol' and 'quality' columns where quality is higher than 6\nwinedata[winedata[\"quality\"]>6,(\"alcohol\", \"quality\")]\n\n#Return the rows of all email adresses using gmail.com domain in the column 'mail'\nMatrix.match(expression=r\"\\w+@gmail.com\",\n             columns=\"mail\",\n             as_row=True)\n```\n----------------------------------------\n##### Apply arithmetic operations to individual rows and columns.\n```python\n#Create a 1000x2 dataframe filled using normal distribution with given arguments\nmarketData = Matrix((1000,2),fill=gauss,ranged={\"Price\":(250,60),\"Discount\":(8,2)},dtype=dataframe)\n\n#Change invalid values in \"Discount\" column where it's less than 0 to 0\nmarketData[marketData[\"Discount\"]<0,\"Discount\"] = 0\n\n#Explore the data\nmarketData.describe\n\n#Multiply 'Price' with 0.9 and subtract 5 also add 10 to 'Discount' under the conditions: Price>100 and Discount<5\nmarketData.apply( (\"*0.9 -5\",\"+10\"), (\"Price\",\"Discount\"), \"(Price>100) and (Discount<5)\" )\n```\n----------------------------------------\n##### Replace values in the matrix\n```python\n#Replace all 0's with 1's\ndata.replace(old=0,new=1)\n\n#Replace all \"Pending\" values to \"Done\" in \"Order1\" and \"Order2\" columns\ndata.replace(old=\"Pending\", #(data[\"Order1\"]==\"Pending\") & (data[\"Order2\"]==\"Pending\") can also be used\n             new=\"Done\",\n             column=(\"Order1\",\"Order2\")\n             )\n\n#Replace all '' values in the column \"Length\" with the mean of the \"Length\" column\ndata.replace=(old='', #data[\"Length\"]==\"\" can also be used\n              new=data.mean(\"Length\",get=0),\n              column=\"Length\"\n              )\n\n#Replace all \"FF\" values in \"Grade\" column with \"AA\" in the column \"Grade\" where \"Year\" is less than 2019\ndata.replace(old=\"FF\", #data[\"Grade\"]==\"FF\" can also be used\n             new=\"AA\",\n             column=\"Grade\",\n             condition=data[\"Year\"]<=2019\n             )\n\n#Replace all numbers below 0 in with 0's in column named \"F5\" where \"Score1\" is less than \"Score2\"\ndata.replace(old=data[\"F5\"]<0,\n             new=0,\n             column=\"F5\",\n             condition=data[\"Score1\"]<data[\"Score2\"]\n             )\n\n#Change value of 'Feature5' to 0 in the rows where the 'Feature1' is lower than 0\ndata[data[\"Feature1\"]<0,\"Feature5\"] = 0\n\n#Create a matrix with a square filled with 0's in the middle, 5's outside\ns = Matrix(5,fill=5,dtype=int)\ns[1:4,1:4] = 0 #Same as using Matrix(3,fill=0)\n#Visually:\n\"\"\"\n5 5 5 5 5              5 5 5 5 5\n5 5 5 5 5  Changes to  5 0 0 0 5\n5 5 5 5 5     ---->    5 0 0 0 5\n5 5 5 5 5              5 0 0 0 5\n5 5 5 5 5              5 5 5 5 5\n\"\"\"\n\n#Change the values in the 2nd to 4th rows' 1st and 3rd columns to 0 and 99 \nMatrix[1:4,(\"col_1\",\"col_3\")] = [0,9]\n#Visually:\n\"\"\"\n3 9 6 10               3 9  6  10\n5 0 4  2   Changes to  0 0 99   2\n5 8 2  2      ---->    0 8 99   2\n6 1 7  0               0 1 99   0\n\"\"\"\n\n#Change all values in the 2nd column to 7's\nMatrix.col_2 = 7\n#Visually:\n\"\"\"\n3 9 6 10               3 7 6 10\n5 0 4  2   Changes to  5 7 4  2\n5 8 2  2      ---->    5 7 2  2\n6 1 7  0               6 7 7  0\n\"\"\"\n\n#Change odd numbered rows' even numbered columns to the values in the given matrix\nMatrix[0::2,1::2] = Matrix(2,fill=999)\n#Visually:\n\"\"\"\n3 9 6 10               3 999 6 999\n5 0 4  2   Changes to  5   7 4   2\n5 8 2  2      ---->    5 999 2 999\n6 1 7  0               6   7 7   0\n\"\"\"\n```\n----------------------------------------\n##### Concatenate a matrix to your matrix.\n```python\n#Concatenate a new column named 'Discounted_Price' containing the product of the 'Price' and 'Discount' columns\nmarketData[\"Discounted_Price\"] = marketData[\"Price\"] - marketData[\"Price\"]*(marketData[\"Discount\"]/100)\n```\n----------------------------------------\n#### Use your matrix's methods and properties\n##### Basics\n```python \nMatrix.grid #Prints ALL of the matrix's elements as a grid, if dtype is dataframe, column names also get printed\n\nMatrix.p #Prints the dimensions, wheter or not the matrix is square and the grid. If dtype is dataframe, column names are also printed\n\nMatrix.decimal #Returns the chosen amount of decimal digits to round while printing. Can be used to set it's value\n\nMatrix.matrix #Returns the matrix's rows as lists in a list. >>> dataframe.data Returns the same thing if dataframe was used instead of Matrix\n\nMatrix.dim #Returns the dimension of the matrix; can be used to change the dimensions, ex: [4,8] can be set to [1,32] where rows carry over as columns in order from left to right\n\nMatrix.d0 #Returns the amount of rows\n\nMatrix.d1 #Returns the amount of columns\n\nMatrix.col(n,as_matrix) #Returns the nth column if n is an integer or returns the column named n, as a list or matrix, set as_matrix to True to get the list as a matrix\n\nMatrix.row(n,as_matrix) #Returns nth row of the matrix as a list or matrix, set as_matrix to True to get the list as a matrix\n\nMatrix.concat(matrix,axis,fillnull) #Concatenate a matrix to self. Set 'axis' to 0 to concatenate as rows, 1(default) to concatenate as columns. 'fillnull' to enable filling missing values with null objects\n\nMatrix.add(values,row,col,feature,dtype,index,fillnull) #Adds list to given index in row or col, indeces start from 1. If a column is added, dtype and feature are used determine type and name. If a row is added, 'index' can be used to determine its row label. 'fillnull' to enable any missing values as null objects\n\nMatrix.remove(row,col) #Removes the desired row and/or column\n\nMatrix.swap(index1,index2,axis) #Swap the row or column in index1 with index2. Set 'axis' 0 to use indices for rows, 1 to use as column indices. Column names can be used with axis=1.\n\nMatrix.copy #Returns a copy of the matrix\n\nMatrix.obj #Returns the string form of the Matrix object which can be evaluated to create the same matrix\n\nMatrix.seed #Returns the seed used to generate the random numbers in the matrix, returns None if matrix wasn't filled randomly. Can be used to refill the matrix inplace if set to a new value\n\nMatrix.fill #Returns the value or distribution of which the matrix was filled with. Can be used to refill the matrix inplace if set to a new value\n\nMatrix.initRange #Returns the value of 'ranged' used while creating the matrix. Can be used to refill the matrix inplace if set to a new value\n\nMatrix.intForm #Returns integer form of the matrix\n\nMatrix.floatForm #Returns float form of the matrix\n\nMatrix.ceilForm #Returns a matrix of all the elements' ceiling values\n\nMatrix.floorForm #Returns the same matrix as \"intForm\"\n\nMatrix.roundForm(n) #Returns a matrix of elements' rounded up to n decimal digits. Same as round(Matrix,n)\n\nMatrix.kwargs #Returns a dictionary of the matrix's basic attributes\n\nMatrix.ROW_LIMIT #Attribute to determine the amount of rows to print while representing the matrix, default is 30.\n\nMatrix.QR_ITERS #Attribute to determine how many iterations will be done in eigenvalue calculation with QR algorithm, default is 50. Play around with this value if the values you get don't seem right.\n\nMatrix.EIGENVEC_ITERS #Attribute to determine how many iterations will be done in eigenvector calculation with shifted inverse iteration method, default is 10.\n\nMatrix.col_1, Matrix.col_2, ... #Returns the column named col_1,col_2 ...\n\n#Available arithmetic operators : \"@\", \"+\", \"-\", \"*\", \"/\", \"//\", \"**\", \"%\"\n\n#Available comparison operators : \"<\" ,\"<=\", \">\", \">=\", \"==\", \"!=\", \"&\", \"|\", \"~\"\n\n```\n##### Algebric properties\n```python\nMatrix.det #Returns the determinant of the matrix\n\nMatrix.t #Returns the transposed matrix\n\nMatrix.ht #Returns the hermitian-transpose of the matrix\n\nMatrix.adj #Returns the adjoint matrix\n\nMatrix.inv #Returns the inversed matrix\n\nMatrix.pseudoinv #Returns the pseudo inverse of the matrix\n\nMatrix.rank #Returns the rank of the matrix\n\nMatrix.echelon #Returns the echelon form of the matrix\n\nMatrix.rrechelon #Returns the reduced row echelon form of the matrix\n\nMatrix.eigenvalues #Returns the eigenvalues #Currently doesn't work with singular matrices\n\nMatrix.eigenvectors #Returns a list of eigenvectors as matrices\n\nMatrix.EIGENDEC #Returns the matrices from eigenvalue decomposition in a tuple\n\nMatrix.eigenvecmat #Returns a matrix with eigenvectors as columns\n\nMatrix.diagmat #Returns the diagonal matrix from eigenvalue decomposition\n\nMatrix.SVD #Returns the U,sigma and the V.ht matrices in a tuple from the singular value decomposition\n\nMatrix.LU #Returns both L and U matrices from LU decomposition in a tuple\n\nMatrix.lowtri #Returns the lower triangular form (L matrix from LU decomposition) of the matrix\n\nMatrix.uptri #Returns the upper triangular form (U matrix from LU decomposition) of the matrix\n\nMatrix.symdec #Returns both symmetrical and anti-symmetrical parts of the matrix\n\nMatrix.sym #Returns the symmetric part of the matrix \n\nMatrix.anti #Returns the antisymmetric part of the matrix\n\nMatrix.perma #Returns the permanent of the matrix\n\nMatrix.conj #Returns the conjugated forms of the elements in a matrix\n\nMatrix.QR #Returns both Q and R matrices from QR decomposition in a tuple\n\nMatrix.Q #Returns the orthonormal matrix from the QR decomposition\n\nMatrix.R #Returns the upper-triangular matrix from the QR decomposition\n\nMatrix.trace #Returns the trace of the matrix\n\nMatrix.isSquare #Returns True if the matrix is a square matrix\n\nMatrix.isSymmetric #Returns True if the matrix is a symmetric matrix\n\nMatrix.isAntiSymmetric #Returns True if the matrix is an antisymmetric matrix\n\nMatrix.isPerSymmetric #Returns True if the matrix is a persymmetric matrix\n\nMatrix.isHermitian #Returns True if the matrix is a hermitian matrix\n\nMatrix.isTriangular #Returns True if the matrix is a triangular matrix\n\nMatrix.isUpperTri #Returns True if the matrix is a upper-trianguar matrix\n\nMatrix.isLowerTri #Returns True if the matrix is a lower-triangular matrix\n\nMatrix.isDiagonal #Returns True if the matrix is a diagonal matrix\n\nMatrix.isUpperBidiagonal #Returns True if the matrix is an upper-bidiagonal matrix\n\nMatrix.isLowerBidiagonal #Returns True if the matrix is a lower-bidiagonal matrix\n\nMatrix.isBidiagonal #Returns True if the matrix is an upper-bidiagonal or a lower-bidiagonal matrix\n\nMatrix.isTridiagonal #Returns True if the matrix is a tridiagonal matrix\n\nMatrix.isUpperHessenberg #Returns True if the matrix is an upper-Hessenberg matrix\n\nMatrix.isLowerHessenberg #Returns True if the matrix is a lower-Hessenberg matrix\n\nMatrix.isHessenberg #Returns True if the matrix is an upper-Hessenberg or a lower-Hessenberg matrix\n\nMatrix.isToeplitz #Returns True if the matrix is a Toeplitz matrix\n\nMatrix.isUnitary #Returns True if the matrix is a unitary matrix\n\nMatrix.isIdempotent #Returns True if the matrix is an idempotent matrix\n\nMatrix.isOrthogonal #Returns True if the matrix is an orthogonal matrix\n\nMatrix.isCircular #Returns True if the matrix is a circular matrix\n\nMatrix.isPositive #Returns True if the matrix is a positive valued matrix\n\nMatrix.isNonNegative #Returns True if the matrix is a non-negative matrix\n\nMatrix.isProjection #Returns True if the matrix is a projection matrix\n\nMatrix.isZero #Returns True if the all the elements in the matrix is 0\n\nMatrix.isDefective #Returns True if the nxn matrix has m linearly independent eigenvalues where m<n\n\nMatrix.minor(m,n,returndet) #Returns the mth row's nth element's minor matrix's determinant, set returndet to False to get the matrix of which the determinant was calculated\n\nMatrix.setdiag(val) #Change diagonal elements to given 'val' or the values in 'val'. If 'val' is a Matrix, diagonals are picked from given matrix's diagonals\n\nMatrix.nilpotency(limit) #Returns the nilpotency degree of the matrix, returns None if some elements diverge. Limit parameter is for iteration amount\n\n```\n##### Statistical properties \n```python \nMatrix.features #Returns the column names if given, can also be used to set column names\n\nMatrix.rename(old_names,new_names) #Rename columns. Multiple names should be given in a list or a tuple\n\nMatrix.name_update(prefix,suffix,changechar) #Add prefix and/or suffix to column names, change characters in column names with 'changechar'.\n\nMatrix.namereset(start=1) #Reset column names to 'col_1','col_2', ... . Start is 1 by default\n\nMatrix.index #Returns the values in the index column in a list, can bu used to set new indices\n\nMatrix.indexname #Returns the index column's name\n\nMatrix.index_update(prefix,suffix,changechar) #Add prefix and/or suffix to string type row labels, change string characters with 'changechar'.\n\nMatrix.indexreset(start) #Reset index column to range(start,Matrix.d0+start)\n\nMatrix.coldtypes #Returns what type of data each column carries, can be used to set the values.\n\nMatrix.head(n) #Returns the first n rows (if there are less than n rows it returns all the rows)\n\nMatrix.tail(n) #Returns the last n rows (if there are less than n rows it returns all the rows)\n\nMatrix.describe #Returns a description matrix with columns describing the matrix holding column, count, dtype, mean, sdev, min, 25%, 50%, 75%, max.\n\nMatrix.info #Returns information about columns: Dtype, Valid data amount, Invalid data amount, Unique data amount\n\nMatrix.uniques(column) #Returns the unique set of the 'column'; If 'column' is None, all columns' unique values are returned in a list of lists \n\nMatrix.groupBy(columns) #Return a 'Group' object containing the Matrix grouped by given 'columns'.\n\nMatrix.sum(n,get) #Returns the sum of the elements in the column with name/index 'n'. If 'n' is None, all column sums are returned. Use 'get' to choose what to return, 0 for a list, 1 for a dictionary(default), 2 for a Matrix.\n\nMatrix.prod(n,get) #Returns the product of the elements in the column with name/index 'n'. If 'n' is None, all column products are returned. Use 'get' to choose what to return, 0 for a list, 1 for a dictionary(default), 2 for a Matrix.\n\nMatrix.find(element,indexStart) #Returns a list of the element's indeces as tuples. Returns None if element not in matrix\n\nMatrix.select(columns) #Returns a matrix where the desired columns are concatenated in order. Only works if 'columns' is a tuple or a list\n\nMatrix.where(condition) #Returns a matrix where the given condition(s) are True. 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Set returnmat wheter or not to return self.\n\nMatrix.sortBy(column,reverse,returnmat) #Sort the matrix by the desired 'column', do it in decreasing order if 'reverse'==True, and return self if 'returnmat'==True\n\nMatrix.shuffle(iterations,returnmat) #Shuffle the rows 'iterations' times and return self if 'returnmat'==True\n\nMatrix.sample(size,condition) #Get a sample sized 'size' where the 'condition' is True\n\nMatrix.count(column,get) #Returns how many of the values are valid (same type as given in coldtypes) for each or desired column(s).  Use 'get' to choose what to return, 0 for a list, 1 for a dictionary(default), 2 for a Matrix.\n\nMatrix.mean(n,get) #Returns the nth column or column named n's average, give None as argument to get the all columns' averages;  Use 'get' to choose what to return, 0 for a list, 1 for a dictionary(default), 2 for a Matrix.\n\nMatrix.ranged(n,get) #Returns the nth column or column named n's range, give None as argument to get the all columns' ranges; Use 'get' to choose what to return, 0 for a list, 1 for a dictionary(default), 2 for a Matrix.\n\nMatrix.median(n,get) #Returns the nth column or column named n's median, give None to get all columns' medians;  Use 'get' to choose what to return, 0 for a list, 1 for a dictionary(default), 2 for a Matrix.\n\nMatrix.freq(n,get) #Returns the nth column or column named n's elements frequency as a dictionary where elements are keys and how often they repeat as values. If called without arguments, returns every column\"s frequencies; Use 'get' to choose what to return, 0 for a list, 1 for a dictionary(default), 2 for a list of Matrix object for each column.\n\nMatrix.mode(n,get) #Returns the nth column or column named n's mode, give None to get all columns' modes;  Use 'get' to choose what to return, 0 for a list, 1 for a dictionary(default), 2 for a list of Matrix object for each column.\n\nMatrix.ranked(n,reverse,key,get) #Returns the nth column or column named n's ranks(that are the indices the values would get if they were sorted) give None to get all columns' modes;  Use 'get' to choose what to return,-1 for ranks in-place, 0 for a list, 1 for a dictionary(default), 2 for a list of Matrix object for each column.\n\nMatrix.iqr(n,as_quartiles,get) #Returns the nth column or column named n's iqr, give None to get all columns' iqr values. If first,second and third quartiles is desired, give as_quartiles parameter bool(True);  Use 'get' to choose what to return, 0 for a list, 1 for a dictionary(default), 2 for a Matrix.\n\nMatrix.sdev(n,population,get) #Returns the nth column or column named n's standard deviation, if None is given as an argument returns all columns' standard deviations. Give population parameter 1 if calculation is not for samples, 0 otherwise; Use 'get' to choose what to return, 0 for a list, 1 for a dictionary(default), 2 for a Matrix.\n\nMatrix.var(n,population,get) #Returns the nth column or column named n's variance, if None is given as an argument returns all columns' variance. Give population parameter 1 if calculation is not for samples, 0 otherwise;  Use 'get' to choose what to return, 0 for a list, 1 for a dictionary(default), 2 for a Matrix.\n\nMatrix.cov(col1,col2,population) #Returns the col1 and col2's covariance. Give population parameter True if calculation is not for samples\n\nMatrix.z(col,population) #Returns the z-scores of the desired  column, call without arguments to get the all z-scores as a matrix. Give population parameter 1 if calculation is not for samples, 0 otherwise.\n\nMatrix.corr(column_1,column_2,population,method) #Returns linear correlation of 2 columns chosen from the matrix. If no argument given, returns the correlation matrix. Give population parameter 1 if calculation is not for samples, 0 otherwise; methods available: 'pearson'(default), 'kandell', 'spearman'\n\nMatrix.normalize(column,inplace) #Normalize the data in the desired column, None to normalize all columns. Give inplace parameter \"True\" boolean value to make normalization in-place, \"False\" to return a new matrix with normalized data\n\nMatrix.stdize(column,inplace) #Standardize the data in the desired column, None to standardize all columns. Give inplace parameter \"True\" boolean value to make standardization in-place, \"False\" to return a new matrix with standardized data\n\nMatrix.oneHotEncode(column,concat) #One-hot encode a 'column', 'concat' to decide wheter or not to concatenate the encoded matrix or return it\n\n```\n\n----------------------------------------\n##### Printing options\n```python\n#All the values + column names if it's a dataframe\n\nmyMatrix.grid \n\n#Dimensions + wheter its square or not + the string printed in 'grid' property\n\nmyMatrix.p #Same as print(myMatrix)\n\n#Similar to 'grid' but rows and columns are limited by myMatrix.ROW_LIMIT and myMatrix.COL_LIMIT\n\nmyMatrix\n\n#Issues about dtypes not matching and/or missing data can be solved by using 'replace' method\n```\n----------------------------------------\n##### Copying the matrix\n```python\n#Using 'copy' property (Fastest)\n\nnewMatrix = oldMatrix.copy\n\n#Using 'kwargs' property, 'copy' uses this one so it's as fast as 'copy' is\n\nnewMatrix = Matrix(kwargs=oldMatrix.kwargs)\n\n#Using 'obj' property (Slowest)\n\nnewMatrix = eval(oldMatrix.obj)\n\n```\n----------------------------------------\n##### All calculations below returns a matrix filled with 1's where the condition is True, otherwise 0\n```python \n   A**2 == A*A\n\n   A*2 == A+A\n\n   A.t.t == A\n\n   A.adj.matrix[2][0] == A.minor(1,3)\n\n   #bool object can be called to get a boolean value of the matrix, if all elements are 1's then it will return True and False in any other case.\n   bool(Matrix(10,fill=1)) == True\n\n   #round call is currently required for the next examples due to <~%1e-5 error rate on some calculations\n\n   round(A @ Matrix(data=Identity(A.dim[0])),4) == round(A, 4) #A assumed to be a square matrix\n\n   round(A @ A.inv)== Matrix(data=Identity(A.dim[0]))\n\n   round(A,4) == round(A.sym + A.anti,4)\n\n   round(A.inv.inv,4) == round(A, 4)\n\n   round(A.lowtri @ A.uptri, 4) == round(A, 4)\n\n   round(A.Q @ A.R, 4) == round(A, 4)\n\n\n``` \n----------------------------------------\n\n#### More examples can be found in [exampleMatrices.py](https://github.com/MathStuff/MatricesM/blob/master/MatricesM/exampleMatrices.py)\n\n\n",
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