Python>=3.6 library for creating and using matrices used in linear algebra and statistics
Project description
MatricesM
A standalone library for Python 3.6 and higher to create and manipulate matrices used in linear algebra and dataframes in statistics
Join MathStuff's Slack workspace for questions and discussions.
Check out the wiki for better documentation
Install using pip:
pip install MatricesM
Import by using:
import MatricesM as mm #Use by calling : mm.Matrix(arguments)
OR
from MatricesM import * #Use matrices directly : Matrix(arguments)
Import and print example matrices:
from MatricesM.exampleMatrices import *
Basic syntax:
MatricesM.matrix.Matrix(
dim: int  [int,int]  (int,int) = None
Dimensions of the matrix. Required(Unless data is given), mainly used for random matrix creation or to reshape the given data.
data: [Any]  [[Any],...]  str  dict = []
Data to use in matrix, Optional, matrix's elements are picked from this parameter. Matrix filling related parameters are ignored if valid values given to this parameter. If no argument is passed matrix is filled depending on the fill and ranged
fill: Any = None
Object,method or value to use for filling the matrix with. Optional . If a list or range is given, given object will be repeated as rows. Accepts custom functions. default is null for dataframe dtype, uniform for other dtypes.
Available special distributions:

uniform

triangular

gauss

gammavariate,betavariate

expovariate

lognormvariate
ranged: [*args]  (*args)  dict = (0,1)
Arguments to pass to the function in fill. Optional. To apply all the elements give a list  tuple. To apply every column individually give a dictionary as {"Column_name":[*args], ...}.
Arguments should follow one of the following rules:

If 'fill' is uniform, interval to pick numbers from as [minimum,maximum]

If 'fill' is gauss or lognormvariate mean and standard deviation are picked from this attribute as [mean,standard_deviation]

If 'fill' is triangular, range of the numbers and the mode as [minimum,maximum,mode]

If 'fill' is gammavariate or betavariate, alpha and beta values are picked as [alpha,beta]

If 'fill' is expovariate, lambda value have to be given in a list as [lambda]"""
features:[str, ...] = []
Column names. Optional. If no argument is given, columns get named "col_1","col_2" and so on.
seed: int = None
Seed to use while picking up random numbers. Optional.
dtype: int  float  complex  dataframe = float
Type of values the Matrix will carry. Optional. dataframe to enable all types.
coldtypes: [type,...]_ = []
List of type object for each column's data type. Optional. Requires dataframe dtype to work.
decimal: int = 4
Amount of decimal places to print. Optional.
index: [Any, ...]  (Any, ...)  Matrix  Label = Label()
Row labels for each row. Optional. Only works with dataframe dtype.
implicit: bool = False
Skip setup proccess for faster initiation, if necessary arguments are passed. Optional. Don't change if you aren't sure what your matrix requires to work properly.
**kwargs: NOTES, PRECISION, DEFAULT_NULL, ROW_LIMIT, QR_ITERS, EIGENDEC_ITERS, DISPLAY_OPTIONS, DEFAULT_BOOL. )
MatricesM.matrix.dataframe(
data=: [Any]  [[Any],...]  str  dict = []
Data to use in the dataframe, Optional, matrix's elements are picked from this parameter. Matrix filling related parameters are ignored if valid values given to this parameter. If no argument is passed matrix is filled depending on the fill and ranged
features:[str, ...] = []
Column names. Optional. If no argument is given, columns get named "col_1","col_2" and so on.
coldtypes: [type,...]_ = []
List of type object for each column's data type. Optional.
decimal: int = 3
Amount of decimal places to print. Optional.
index: [Any, ...]  (Any, ...)  Matrix  Label = Label()
Row labels for each row. Optional.
**kwargs #Rest of the arguments passed to Matrix )
matrix.py contains the main Matrix class.
matrices.py contains functions to create special matrices.
exampleMatrices.py contains example matrices.
Check the project tab to see the progress
Some examples:
Create matrices filled with random numbers or given values
#Creates a 4x4 matrix filled with random float numbers
A = Matrix(4)
#Creates a 3x5 matrix with elements uniformly distributed in the range from 10 to 25
B = Matrix([3,5],ranged=[10,25])
#Create a 6x6 square matrix filled with random integer numbers in the default range: [0,1]
E = Matrix(6,dtype=int)
#Create a 200x5 matrix using Gauss distribution with mean=50 and standard deviation=10
F = Matrix([200,5],fill=gauss,ranged=[50,10])
#Create a 10x10 matrix filled with 1's
G = Matrix(10,fill=1)
#Create a 200x4 matrix filled with integer numbers using triangular distribution where the range is [0,20] and mode is around if not 18
H = Matrix((200,4),fill=triangular,ranged=[0,20,18],dtype=int)
#Create a 50x50 matrix filled with complex numbers using beta distribution for both real and imaginary parts with alpha=2 and beta=5
C1 = Matrix(50,fill=betavariate,ranged=[2,5],dtype=complex)
#Create a 10x1 matrix filled with the given string
S = Matrix((10,1),fill="hello",dtype=dataframe)
Generate randomly filled matrices using special distributions
#Create a 10000x3 matrix using a triangular distribution with integer values.
randomData1 = Matrix((10000,3),
fill=triangular,
ranged={"feature1":(0,100,50),"feature2":(50,50,25),"feature3":(10,20,20)},
seed=32141,
dtype=int)
#Create a 10000x4 matrix using gamma distribution with float numbers.
randomData2 = Matrix([10000,4],
fill=gammavariate,
ranged={"feature1":[1,1.2],"feature2":[12,24],"feature3":[15,100],"feature4":[1.5,3]},
seed=39598)
#Create a 10000x4 matrix using normal(gauss) distribution with integer numbers.
randomData3 = Matrix([10000,4],
fill=gauss,
ranged={"feature1":[0,25],"feature2":[100,200],"feature3":[1000,10000],"feature4":[1,100]},
seed=4472142,
dtype=int)
#Create a 20000x4 matrix using exponential distribution with float numbers.
randomData4 = Matrix([20000,4],
fill=expovariate,
ranged={"feature1":[0.1],"feature2":[0.95],"feature3":[0.5],"feature4":[0.00025]},
seed=21751923)
Create special matrices
#3x3 identity matrix
id3 = Identity(3)
#A 8x8 symmetrical matrix filled with numbers in range from 0 to 1 with uniform distribution
sym1 = Symmetrical(8)
Give list of numbers to create matrices
#Creates a matrix with the given list of numbers
filled_rows = [[1,2,3],[4,5,6],[7,8,9]]
C = Matrix(data=filled_rows)
#Create a dataframe from a list
data = [["James",180.4,85],
["Tom",172,73],
["Sophia",168.25,65]]
df = dataframe(data=data,
features=["Name","Height","Weight"],
decimal=1)
#Same as:
df = Matrix(data=data,
dtype=dataframe,
features=["Name","Height","Weight"],
decimal=1)
#coldtypes parameter may be required in cases where the data given doesn't represent the desired data types
Give a string filled with data and use the numbers in it to create a matrix
#Creates a 3x3 matrix from the given string
C1 = Matrix(3,"1 0 1 4 5 5 1 2 2")
#Creates a 2x4 matrix from the given string
C2 = Matrix([2,4],"5 2 3 2 1 0 0 4")
#Create a matrix from the given string, dimension is *required* as [dataAmount,features]. Only numbers are picked up
data="""1,K,60,69900,6325
2,K,30,79000,5200
3,E,52,85500,7825
4,E,57,17100,8375
5,E,55,5500,5450
6,E,68,27200,8550
7,E,41,20500,4500
8,E,20,69000,5050
9,K,33,13200,8325
10,E,37,31800,5975"""
#As an integer matrix
intMat = Matrix(dim=[10,4],
data=data,
features=["id","age","num1","num2"],
dtype=int)
#Or as a dataframe
df = Matrix(dim=[10,4],
data=data,
features=["id","age","num1","num2"],
dtype=dataframe,
coldtypes=[int]*4)
Read data from files
#Create a dataframe matrix from a csv file. read_file accepts 2 optional parameters: encoding, delimiter
data_matrix = read_file(data_directory)
#Example dataset: https://www.kaggle.com/uciml/redwinequalitycortezetal2009
wine = read_file(".../Data/winequalityred.csv")
Get specific parts of the matrix (Assuming default column names)
#All rows' second to forth columns as a matrix
Matrix[:,1:4] == Matrix.t[1:4,:].t
#Nineth column of every even numbered row as a matrix
Matrix[::2,8] == Matrix[::2,8:9] == Matrix["col_9"][::2] == Matrix.col_9[::2]
#Using methods
Matrix.select(("col_9"))[::2] == Matrix.col(9)[::2]
#Forth to seventh rows as a matrix
Matrix[3:7]
#Fifth row's eighth element (returns the value as it is, not a new matrix)
Matrix[4,7] == Matrix.matrix[4][7]
#Use column names
Matrix["col_3","col_1","col_2"] == Matrix.select(("col_3","col_1","col_2"))
#Use index column for row indices
#Return the rows where the level 1 label matches the value
Matrix.level[1].ind[value]
#Return the "col_4" column of rows using level 3 labels starting with val1's first appearance and ending with the row before val2's first appearance
Matrix.level[3].ind[val1:val2,"col_4"]
Filter out depending on what you need
#Using example dataset, get the rows where the "quality" feature is higher or equal to 6 and pH in range (3,3.3)
#All statements should be properly closed with parentheses
wineOverSix = winedata.where("(quality>6) and ((pH<3.3) and (pH>3))")
#Alternative way (2x faster)
wineOverSix = winedata[(winedata["quality"]>6) & ((winedata["pH"]<3.3) & (winedata["pH"]>3))]
#Select the columns of pH and quality and assign them to another matrix
filtered = winedata.select(("pH","quality"))
#Alternative way (2x faster)
filtered = winedata["pH","quality"]
#Use 'quality' column as row labels
winedata.index = winedata.quality
#Add 'alcohol' to as level 2 row labels
winedata.index.add_level(winedata.alcohol)
#Sort by given column and shuffle the data
winedata.sortBy("quality") #Data is sorted in increasing order, use reverse=True for decreasing order
#Shuffle the rows
winedata.shuffle()
#Get 20 samples from the data under desired conditions
winedata.sample(20,"(quality>5) and ((alcohol<11) or (density>0.95))")
#Alternative way (1.5x faster)
winedata[(winedata["quality"]>5) & ((winedata["alcohol"]<11)  (winedata["density"]>0.95))].sample(20)
#Return all the rows and select 'alcohol' and 'quality' columns where quality is higher than 6
winedata[winedata["quality"]>6,("alcohol", "quality")]
#Return the rows of all email adresses using gmail.com domain in the column 'mail'
Matrix.match(expression=r"\w+@gmail.com",
columns="mail",
as_row=True)
Apply arithmetic operations to individual rows and columns.
#Create a 1000x2 dataframe filled using normal distribution with given arguments
marketData = Matrix((1000,2),fill=gauss,ranged={"Price":(250,60),"Discount":(8,2)},dtype=dataframe)
#Change invalid values in "Discount" column where it's less than 0 to 0
marketData[marketData["Discount"]<0,"Discount"] = 0
#Explore the data
marketData.describe
#Multiply 'Price' with 0.9 and subtract 5 also add 10 to 'Discount' under the conditions: Price>100 and Discount<5
marketData.apply( ("*0.9 5","+10"), ("Price","Discount"), "(Price>100) and (Discount<5)" )
Replace values in the matrix
#Replace all 0's with 1's
data.replace(old=0,new=1)
#Replace all "Pending" values to "Done" in "Order1" and "Order2" columns
data.replace(old="Pending", #(data["Order1"]=="Pending") & (data["Order2"]=="Pending") can also be used
new="Done",
column=("Order1","Order2")
)
#Replace all '' values in the column "Length" with the mean of the "Length" column
data.replace=(old='', #data["Length"]=="" can also be used
new=data.mean("Length",get=0),
column="Length"
)
#Replace all "FF" values in "Grade" column with "AA" in the column "Grade" where "Year" is less than 2019
data.replace(old="FF", #data["Grade"]=="FF" can also be used
new="AA",
column="Grade",
condition=data["Year"]<=2019
)
#Replace all numbers below 0 in with 0's in column named "F5" where "Score1" is less than "Score2"
data.replace(old=data["F5"]<0,
new=0,
column="F5",
condition=data["Score1"]<data["Score2"]
)
#Change value of 'Feature5' to 0 in the rows where the 'Feature1' is lower than 0
data[data["Feature1"]<0,"Feature5"] = 0
#Create a matrix with a square filled with 0's in the middle, 5's outside
s = Matrix(5,fill=5,dtype=int)
s[1:4,1:4] = 0 #Same as using Matrix(3,fill=0)
#Visually:
"""
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 Changes to 5 0 0 0 5
5 5 5 5 5 > 5 0 0 0 5
5 5 5 5 5 5 0 0 0 5
5 5 5 5 5 5 5 5 5 5
"""
#Change the values in the 2nd to 4th rows' 1st and 3rd columns to 0 and 99
Matrix[1:4,("col_1","col_3")] = [0,99]
#Visually:
"""
3 9 6 10 3 9 6 10
5 0 4 2 Changes to 0 0 99 2
5 8 2 2 > 0 8 99 2
6 1 7 0 0 1 99 0
"""
Concatenate a matrix to your matrix.
#Concatenate a new column named 'Discounted_Price' containing the product of the 'Price' and 'Discount' columns
marketData["Discounted_Price"] = marketData["Price"]  marketData["Price"]*(marketData["Discount"]/100)
Use your matrix's methods and properties
Basics
Matrix.grid #Prints ALL of the matrix's elements as a grid, if dtype is dataframe, column names also get printed
Matrix.p #Prints the dimensions, wheter or not the matrix is square and the grid. If dtype is dataframe, column names are also printed
Matrix.decimal #Returns the chosen amount of decimal digits to round while printing. Can be used to set it's value
Matrix.matrix #Returns the matrix's rows as lists in a list. >>> dataframe.data Returns the same thing if dataframe was used instead of Matrix
Matrix.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
Matrix.d0 #Returns the amount of rows
Matrix.d1 #Returns the amount of columns
Matrix.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
Matrix.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
Matrix.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
Matrix.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
Matrix.remove(row,col) #Removes the desired row and/or column
Matrix.swap(index1,index2,axis,returnmat) #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. 'returnmat' to decide wheter or not to return self if 'inplace' is True
Matrix.copy #Returns a copy of the matrix
Matrix.obj #Returns the string form of the Matrix object which can be evaluated to create the same matrix
Matrix.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
Matrix.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
Matrix.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
Matrix.intForm #Returns integer form of the matrix
Matrix.floatForm #Returns float form of the matrix
Matrix.ceilForm #Returns a matrix of all the elements' ceiling values
Matrix.floorForm #Returns the same matrix as "intForm"
Matrix.roundForm(n) #Returns a matrix of elements' rounded up to n decimal digits. Same as round(Matrix,n)
Matrix.kwargs #Returns a dictionary of the matrix's basic attributes
Matrix.ROW_LIMIT #Maximum amount of rows to display using 'repr' method, default is 30.
Matrix.PRECISION #Decimal places to round to during calculations
Matrix.NOTES #String to attach to the string form of self. Notes get displayed after the labels and the grid is processed.
Matrix.EIGENVEC_ITERS #How many iterations will be done in eigenvector calculation with shifted inverse iteration method, default is 10.
Matrix.QR_ITERS #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.
Matrix.DISPLAY_OPTIONS #Display options as dict; default:{
"allow_label_dupes":False,
"dupe_place_holder":"//",
"label_seperator":",",
"left_top_corner":"+",
"left_seperator":"",
"top_seperator":"",
"col_place_holder":"...",
"row_place_holder":"..."}
Matrix.DEFAULT_NULL #Object to use as null values, default is null.
Matrix.DEFAULT_BOOL #Boolean values to use as True and False in a dictionary. {True:1,False:0} is default.
Matrix.col_1, Matrix.col_2, ... #Tries to return a 'Matrix.level.name' object pointing at 'Matrix's 'col_1','col_2', ... column name row, if it fails, tries to return all columns with 'col_1','col_2', ... column names in level1
#Element wise arithmetic operators : "@", "+", "", "*", "/", "//", "**", "%"
#Element wise comparison operators : "<" ,"<=", ">", ">=", "==", "!=", "&", "", "~"
Algebric properties
Matrix.det #Returns the determinant of the matrix
Matrix.t #Returns the transposed matrix
Matrix.ht #Returns the hermitiantranspose of the matrix
Matrix.adj #Returns the adjoint matrix
Matrix.inv #Returns the inversed matrix
Matrix.pseudoinv #Returns the pseudo inverse of the matrix
Matrix.rank #Returns the rank of the matrix
Matrix.echelon #Returns the echelon form of the matrix
Matrix.rrechelon #Returns the reduced row echelon form of the matrix
Matrix.eigenvalues #Returns the eigenvalues #Currently doesn't work with singular matrices
Matrix.eigenvectors #Returns a list of eigenvectors as matrices
Matrix.EIGENDEC #Returns the matrices from eigenvalue decomposition in a tuple
Matrix.eigenvecmat #Returns a matrix with eigenvectors as columns
Matrix.diagmat #Returns the diagonal matrix from eigenvalue decomposition
Matrix.SVD #Returns the U,sigma and the V.ht matrices in a tuple from the singular value decomposition
Matrix.LU #Returns both L and U matrices from LU decomposition in a tuple
Matrix.lowtri #Returns the lower triangular form (L matrix from LU decomposition) of the matrix
Matrix.uptri #Returns the upper triangular form (U matrix from LU decomposition) of the matrix
Matrix.symdec #Returns both symmetrical and antisymmetrical parts of the matrix
Matrix.sym #Returns the symmetric part of the matrix
Matrix.anti #Returns the antisymmetric part of the matrix
Matrix.perma #Returns the permanent of the matrix
Matrix.conj #Returns the conjugated forms of the elements in a matrix
Matrix.QR #Returns both Q and R matrices from QR decomposition in a tuple
Matrix.Q #Returns the orthonormal matrix from the QR decomposition
Matrix.R #Returns the uppertriangular matrix from the QR decomposition
Matrix.trace #Returns the trace of the matrix
Matrix.isSquare #Returns True if the matrix is a square matrix
Matrix.isSymmetric #Returns True if the matrix is a symmetric matrix
Matrix.isAntiSymmetric #Returns True if the matrix is an antisymmetric matrix
Matrix.isPerSymmetric #Returns True if the matrix is a persymmetric matrix
Matrix.isHermitian #Returns True if the matrix is a hermitian matrix
Matrix.isTriangular #Returns True if the matrix is a triangular matrix
Matrix.isUpperTri #Returns True if the matrix is a uppertrianguar matrix
Matrix.isLowerTri #Returns True if the matrix is a lowertriangular matrix
Matrix.isDiagonal #Returns True if the matrix is a diagonal matrix
Matrix.isUpperBidiagonal #Returns True if the matrix is an upperbidiagonal matrix
Matrix.isLowerBidiagonal #Returns True if the matrix is a lowerbidiagonal matrix
Matrix.isBidiagonal #Returns True if the matrix is an upperbidiagonal or a lowerbidiagonal matrix
Matrix.isTridiagonal #Returns True if the matrix is a tridiagonal matrix
Matrix.isUpperHessenberg #Returns True if the matrix is an upperHessenberg matrix
Matrix.isLowerHessenberg #Returns True if the matrix is a lowerHessenberg matrix
Matrix.isHessenberg #Returns True if the matrix is an upperHessenberg or a lowerHessenberg matrix
Matrix.isToeplitz #Returns True if the matrix is a Toeplitz matrix
Matrix.isUnitary #Returns True if the matrix is a unitary matrix
Matrix.isIdempotent #Returns True if the matrix is an idempotent matrix
Matrix.isOrthogonal #Returns True if the matrix is an orthogonal matrix
Matrix.isCircular #Returns True if the matrix is a circular matrix
Matrix.isPositive #Returns True if the matrix is a positive valued matrix
Matrix.isNonNegative #Returns True if the matrix is a nonnegative matrix
Matrix.isProjection #Returns True if the matrix is a projection matrix
Matrix.isZero #Returns True if the all the elements in the matrix is 0
Matrix.isDefective #Returns True if the nxn matrix has m linearly independent eigenvalues where m<n
Matrix.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
Matrix.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
Matrix.nilpotency(limit) #Returns the nilpotency degree of the matrix, returns None if some elements diverge. Limit parameter is for iteration amount
Statistical properties
Matrix.features #Returns the column names if given, can also be used to set column names
Matrix.index #Returns the values in the index column in a list, can bu used to set new indices
Matrix.coldtypes #Returns what type of data each column carries, can be used to set the values.
Matrix.head(n) #Returns the first n rows (if there are less than n rows it returns all the rows)
Matrix.tail(n) #Returns the last n rows (if there are less than n rows it returns all the rows)
Matrix.describe #Returns a description matrix with columns describing the matrix holding column, count, dtype, mean, sdev, min, 25%, 50%, 75%, max.
Matrix.info #Returns information about columns: Dtype, Valid data amount, Invalid data amount, Unique data amount
Matrix.uniques(column) #Returns the unique set of the 'column'; If 'column' is None, all columns' unique values are returned in a list of lists
Matrix.groupBy(columns) #Return a 'Group' object containing the Matrix grouped by given 'columns'.
Matrix.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.
Matrix.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.
Matrix.find(element,indexStart) #Returns a list of the element's indeces as tuples. Returns None if element not in matrix
Matrix.select(columns) #Returns a matrix where the desired columns are concatenated in order. Only works if 'columns' is a tuple or a list
Matrix.where(condition) #Returns a matrix where the given condition(s) are True. Example: Matrix.where("(col_1>=0.5) and (col_2!=0)")
Matrix.match(regex,columns,as_row) #Return the rows or the values in the matrix depending on 'as_row', in the given column names/numbers in 'columns' as a list/tuple or str/int, matching given 'regex' regular expressions
Matrix.apply(expressions,columns,conditions,returnmat) #Apply given 'expression' to given 'columns' where the 'conditions' are True, set returnmat wheter or not to return self. If 'columns' is None, 'expressions' is applied to all columns. Executed as: value=eval('value'+operation)
Matrix.transform(function,columns,conditions,returnmat #Pass values into the given 'function' and change them to what it outputs. Rest of the parameters works same as 'apply' method. Executed as: value = function(value)
Matrix.replace(old,new,columns,conditions,returnmat) #Change 'old' values to 'new' in the 'columns' where the 'conditions' are True. Set returnmat wheter or not to return self.
Matrix.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
Matrix.shuffle(iterations,returnmat) #Shuffle the rows 'iterations' times and return self if 'returnmat'==True
Matrix.sample(size,condition) #Get a sample sized 'size' where the 'condition' is True
Matrix.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.
Matrix.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.
Matrix.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.
Matrix.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.
Matrix.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.
Matrix.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.
Matrix.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 inplace, 0 for a list, 1 for a dictionary(default), 2 for a list of Matrix object for each column.
Matrix.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.
Matrix.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.
Matrix.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.
Matrix.cov(col1,col2,population) #Returns the col1 and col2's covariance. Give population parameter True if calculation is not for samples
Matrix.z(col,population) #Returns the zscores of the desired column, call without arguments to get the all zscores as a matrix. Give population parameter 1 if calculation is not for samples, 0 otherwise.
Matrix.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'
Matrix.normalize(column,inplace,declare,returnmat) #Normalize the data in the desired column, None to normalize all columns. Give inplace parameter "True" boolean value to make normalization inplace, "False" to return a new matrix with normalized data. 'declare' to guess the new value's data type and replace it in 'coldtypes'. 'returnmat' to decide wheter or not to return self if 'inplace' is True
Matrix.stdize(column,inplace,declare,returnmat) #Standardize the data in the desired column, None to standardize all columns. Give inplace parameter "True" boolean value to make standardization inplace, "False" to return a new matrix with standardized data. 'declare' to guess the new value's data type and replace it in 'coldtypes'. 'returnmat' to decide wheter or not to return self if 'inplace' is True
Matrix.oneHotEncode(column,concat) #Onehot encode a 'column', 'concat' to decide wheter or not to concatenate the encoded matrix or return it
Matrix.fix_coldtypes(column,retur_dtype) #Guess the data type for the given column(s), return dtypes with return_dtype or apply them directly to coldtypes
dataframe.level #Inner level class used for passing the level to 'ind' class for row labeling.
dataframe.level.ind #Row label indexing class, syntax : dataframe.level[integer_level].ind[row_label]
dataframe.level.name #Column name indexing class, syntax : dataframe.level[integer_level].ind[column_name]
dataframe.colname #Allows column names' level names over integers, syntax : dataframe.colname[label_name].name[column_name]
dataframe.rowname #Allows row labels' level names over integers, syntax : dataframe.rowname[label_name].ind[column_name]
Printing options
#All the values + column names if it's a dataframe
myMatrix.grid
#Dimensions + wheter its square or not + the string printed in 'grid' property
myMatrix.p #Same as print(myMatrix)
#Similar to 'grid' but rows and columns are limited by myMatrix.ROW_LIMIT
myMatrix
#Issues about dtypes not matching and/or missing data can be solved by using 'replace' method
Copying the matrix
#Using 'copy' property (Fastest)
newMatrix = oldMatrix.copy
#Using 'kwargs' property, 'copy' uses this one so it's as fast as 'copy' is
newMatrix = Matrix(**oldMatrix.kwargs)
#Using 'obj' property (Slowest)
newMatrix = eval(oldMatrix.obj)
All calculations below returns a matrix filled with 1's where the condition is True, otherwise 0
A**2 == A*A
A*2 == A+A
A.t.t == A
A.adj[2,0] == A.minor(1,3)
#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.
bool(Matrix(10,fill=1)) == True
#round call is currently required for the next examples due to <~%1e5 error rate on some calculations
round(A @ Matrix(data=Identity(A.dim[0])),4) == round(A, 4) #A assumed to be a square matrix
round(A @ A.inv)== Matrix(data=Identity(A.dim[0]))
round(A,4) == round(A.sym + A.anti,4)
round(A.inv.inv,4) == round(A, 4)
round(A.lowtri @ A.uptri, 4) == round(A, 4)
round(A.Q @ A.R, 4) == round(A, 4)
More examples can be found in exampleMatrices.py
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