₦airaland Forum

HARDDON:
Herein lies the vilest of all ills a man can inflict on himself
Herein lies the undoing of royal balls.
Herein lies the baby in Boys, the boys in Men.

The need to bribe, to short-circuit, to con. Forgetting that SHE ate the apple first...

words made of gold brought on a platter for men
yet some shall make waste of it

HARDDON:

boss where did I go wrong

IAmBloody:
Hey guys,I need ur fu*king help asap...am a shy guy ...don't know how to toast a lady(I feel bad)...I do get lost of word whenever am with a girl I intend dating

Your pills are found in this thread go and start from page 1
come back later and share your testimonies with harddon and the fam

Castlewick:
Alright.... I’ll put that in mind. Apart from that, is there any other thing you see as an issue?

nothing wrong for me
the first statement of u not being killed left me smiling

lahp:
u should know that u don't need to proclaim being a bad guy to show u are a bad guy if u are one u don't use megaphone to announce bad guy coming give way u just do what a bad guy does

for the stripping part I might add

so that's what u do to get all this Tripper boys jerking for your attention
hmmm I need to tell mum

what makes u think if u strip I will make use of the vaseline?

Castlewick:
Hey guys, so I’ve been following for quite sometime now. I’ve been treated badly by girls coz I’m always doing nice guy and many of my friends (both male and female) say that I’m wasting my God given resources (shredded body, fine face, etc). Even a very close relative said I’m a “softie” when it comes to females. After the last Amaka that disappointed I made up my mind to change things. So I’m on this platform called “tinder” and I and a girl got texting. I want you guys to rate it.

u should know that u don't need to proclaim being a bad guy to show u are a bad guy if u are one u don't use megaphone to announce bad guy coming give way u just do what a bad guy does

Castlewick:
Hey guys, so I’ve been following for quite sometime now. I’ve been treated badly by girls coz I’m always doing nice guy and many of my friends (both male and female) say that I’m wasting my God given resources (shredded body, fine face, etc). Even a very close relative said I’m a “softie” when it comes to females. After the last Amaka that disappointed I made up my mind to change things. So I’m on this platform called “tinder” and I and a girl got texting. I want you guys to rate it.

what do we get to rate??
what have u decided to change?

kollinzgee:
fitst of all it depends on your girls responds to the guy ,does she pick the calls from the guy,does she accept the money or gifts from the guy if she does tell her to stop or else you both will have problems then manup ask her to give you the phone whenever he calls if she does warn him never to call her again with some light threats that she is in a relationship.

this shows how needy and jealous are
the best way forward is backward when with women
let her have her fun
u have yours

lahp:
i would have told her when she talked to the guy 'hey i have noticed u '

i wont advice u do the jealous act play cool keep your distance have your fun
she will come in handy when she cools down

truth is she wants to do the jealous thing on you
just do the opposite play cool dont talk about what she is doing

606thearena:
Nice one brother I just started reading some of your drills baba you dope
I have a current issue that I don't know how to resolve it
Here is my problem I was talking to a girl in my class I wanted to have her for sex only kind of friendship,she was really into me I felt it would be a smooth sail for me because the girl was into me .unknow to me my babe has told her am dating her,so they had some kind of friendship I was unaware of this is the beginning of my problem she went and showed my babe all our chat all the calls I made to her she told my girl everything .
I love my babe very much she is a new catch though and also a virgin tested and confirmed now she said she has lost the trust she has for me I have apologize as a man should right now she is giving me attitudes she also did something that really angered me as we were going to our different location she made a call to a guy asking if he was at home and he said he was at home ,I was really pissed but I played it cool and left her and went to my direction .i called her twice later in the day she never picked never return my calls
This issue is just two days old
What do I do to regain her trust and take charge of relationship like I was before
I really love this girl and I want her for keeps. The dons in the house please if there is any help that can be rendered I would be very grateful for it .

i would have told her when she talked to the guy 'hey i have noticed u '

i wont advice u do the jealous act play cool keep your distance have your fun
she will come in handy when she cools down

VERTEX1:
. Thanks bro, you see that's my fear, even though she romance me a lot in public especially my head. but she won't allow me to even touch her hand, we discuss nude things a lot she will said I'm corrupt and naughty, wicked and stingy if I refuse her plea. I don't know whether to be harsh or soft with her.

next time she touches ur head tell her don't do that smallie your not meant to touch the heads of ur elders

she is playing u bro step up don't be harsh too much so as not to drive her away just do so when necessary
have thus outlook of she is your younger sister

VERTEX1:
Please guys, I've not dated any girl in fact I'm not sexually active, so there's this girl that came for IT in our office, we play a lot and we insult each other much in a playful manner. she always jokingly said I'm her hubby and hold my head as to kiss me but removes her head immediately I headed towards kissing her. Many times she slaps me and I slap back.she even told me her health issues and about her bf, as she's sexually active. The problem now is that she's begging me to subscribe for her . even though I've done financial something before for her. she's pretty and doesn't look hungry. So guys please help I don't know whether she wants to dry me or just testing if I'm stingy. we're not dating and I don't have plan of sex with anybody.

cc:
Kollinzgee
Harddon
Davidthegeek

My advice dont sub for her bro if u start giving her things at every request she will see u as her giver
if u dont sub for her bro others will even then u can still get to talk with her
if u observe so well she does the same with u why will she want to kiss u and when u oblige she runs away?

ufuncs which i shall use as an alias for universal functions exist in two flavours : unary ufuncs which operates in a single input and binary ufuncs which operates in double outputs we shall talk about these two flavours

Array arithmetics:
ufuncs should feel natural to use if u are well absorbed with pythons native arithmetics
the standard addition subtraction and multipliaction can be used

import numpy as np

x = np.arange(4)
print('x = ' x )
print('x + 5 = ' x + 5 )

[out] x = [0, 1, 2, 3]
[out] x + 5 = [5, 6, 7, 8]

we also have operators for exponentiation and modulus which are : a** , a%

u can add this arithmetic operation together if u choose


Operator Equivalent ufunc Description
+ np.add Addition (e.g., 1 + 1 = 2)
- np.subtract Subtraction (e.g., 3 - 2 = 1)
- np.negative Unary negation (e.g., -2)
* np.multiply Multiplication (e.g., 2 * 3 = 6)
/ np.divide Division (e.g., 3 / 2 = 1.5)
// np.floor_ divide Floor division (e.g., 3 // 2 = 1)
** np.power Exponentiation (e.g., 2 ** 3 =
% np.mod Modulus/remainder (e.g., 9 % 4 = 1)

the above is a table of arithmetic operators implemented in numpy

Hi all good evening

we have learnt the basics of creating a numpy array using : numpy.array[[1, 2,4,5]] after importing the numpy package

in this class we shall see how we can do basic computing on numpy arrays and we shall focus of universal functions

The Bible is divided into two sections the Hebrew and the Greek Scriptures
the Hebrew Scriptures starts with creation, the law God gave to Moses to be relayed to the Israelites then ends in the book of Malachi.
the Greek Scriptures which was written in Greek due to the fact that Greek was the most influential language by then starts with the life of Jesus down to revelations by God to John.
In the Hebrew Scriptures there are so many laws, some of which Christians are not obliged to obey, but can still do so.
Example tithing. Christians are not obliged to thithe but can do so voluntarily and not publicly so as to receive praise or so as to let anybody know what they did. Matthew 6;;1
but the most important of all laws which every part of the Scriptures is based on is found at mark 12;28-34
here love of neighbors and God is emphasised. because if u do not love God u can't keep his commands.
if u don't love your neighbours u can't do what God commands u won't live your enemies rather u will wish they do die.


NOTE:: THE BIBLE IS NOT CONTRADICTORY IT IS A BOOK THAT TOUCHES ALL ASPECT OF LIFE': HEALTH, SCIENCE, HISTORY, RELATIONSHIP WITH OTHERS. THE BIBLE IS NOT AN OLD BOOK FOR OUR TIME IT CONSISTS OF PRACTICAL WISDOM THAT WE ALL NEED TO APPLY. MOST OF ITS PROPHECIES HAVE BEEN FULFILLED WE ALL NEED TO GRAB OUR COMPUTER AND HISTORY BOOKS TO SEE HOW TRUE IT IS.. DID U K OW THE BIBLE SAID TALKED ABOUT THE ANGLO AMERICAN WORLD POWER AND IT'S RELATIONSHIP WITH PEOPLE DANIEL: 3;1-7
SO THEREFORE DO NOT LOOK AT THE BIBLE AS FICTIONAL DOING SO SHOWS U ARE NOT AS REASONABLE AS U THINK
WHAT U NEED IS AN OPEN HEART WITH A BOOK OF THE BIBLE AND YOUR ACCESS TO INTERNET

Afonjas:
[s][/s]




THE BIBLE SHOULD NOT BE ON THE LIST AS INTELLIGENT PEOPLE DON'T PATRONIZE IT.

you must be intelligent
such a big fool u are

the guy bombing squad no reach international standards na why dem sack am

wow nice she is good but u know me for collect oh ... even if I won't return all I pick
I shall return small and take the rest that way they will think I am honest
there is a law Robert green called this in his book 48 laws of power

There are several ways to create arrays. For example, you can create an array from a regular Python list or tuple using the array function. The type of the resulting array is deduced from the type of the elements in the sequences. A frequent error consists in calling array with multiple numeric arguments, rather than providing a single list of numbers as an argument. array transforms sequences of sequences into two- dimensional arrays, sequences of sequences of sequences into three-dimensional arrays, and so on. The type of the array can also be explicitly specified at creation time: Often, the elements of an array are originally unknown, but its size is known. Hence, NumPy offers several functions to create arrays with initial placeholder content. These minimize the necessity of growing arrays, an expensive operation. The function zeros creates an array full of zeros, the function ones creates an array full of ones, and the function empty creates an array whose initial content is random and depends on the state of the memory. By default, the dtype of the created array is float64 . To create sequences of numbers, NumPy provides a function analogous to range that returns arrays instead of lists. When arange is used with floating point arguments, it is generally not possible to predict the number of elements obtained, due to the finite floating point precision. For this reason, it is usually better to use the function linspace that receives as an argument the number of elements that we want, instead of the step: See also: array , zeros, zeros_like , ones , ones_like , empty , empty_like , arange, linspace, numpy.random.rand , numpy.random.randn , fromfunction, fromfile Printing Arrays When you print an array, NumPy displays it in a similar way to nested lists, but with the following layout: the last axis is printed from left to right, the second-to-last is printed from top to bottom, the rest are also printed from top to bottom, with each slice separated from the next by an empty line. One-dimensional arrays are then printed as rows, bidimensionals as matrices and tridimensionals as lists of matrices. See below to get more details on reshape . If an array is too large to be printed, NumPy automatically skips the central part of the array and only prints the corners: To disable this behaviour and force NumPy to print the entire array, you can change the printing options using set_printoptions . Basic Operations Arithmetic operators on arrays apply elementwise . A new array is created and filled with the result. Unlike in many matrix languages, the product operator * operates elementwise in NumPy arrays. The matrix product can be performed using the @ operator (in python >=3.5) or the dot function or method: Some operations, such as += and *=, act in place to modify an existing array rather than create a new one. When operating with arrays of different types, the type of the resulting array corresponds to the more general or precise one (a behavior known as upcasting). Many unary operations, such as computing the sum of all the elements in the array, are implemented as methods of the ndarray class. By default, these operations apply to the array as though it were a list of numbers, regardless of its shape. However, by specifying the axis parameter you can apply an operation along the specified axis of an array: Universal Functions NumPy provides familiar mathematical functions such as sin, cos, and exp. In NumPy, these are called “universal functions”( ufunc ). Within NumPy, these functions operate elementwise on an array, producing an array as output. See also: all , any , apply_along_axis , argmax, argmin , argsort, average , bincount , ceil, clip , conj , corrcoef , cov , cross, cumprod , cumsum , diff, dot, floor, inner , inv, lexsort, max , maximum , mean , median , min, minimum, nonzero , outer , prod, re , round, sort , std, sum , trace, transpose , var, vdot , vectorize , where Indexing, Slicing and Iterating One-dimensional arrays can be indexed, sliced and iterated over, much like lists and other Python sequences. Multidimensional arrays can have one index per axis. These indices are given in a tuple separated by commas: When fewer indices are provided than the number of axes, the missing indices are considered complete slices : The expression within brackets in b[i] is treated as an i followed by as many instances of : as needed to represent the remaining axes. NumPy also allows you to write this using dots as b[i,...] . The dots ( ... ) represent as many colons as needed to produce a complete indexing tuple. For example, if x is an array with 5 axes, then x[1,2,...] is equivalent to x[1,2,:,:,:] , x[...,3] to x[:,:,:,:,3] and x[4,...,5,:] to x[4,:,:,5,:] . Iterating over multidimensional arrays is done with respect to the first axis: However, if one wants to perform an operation on each element in the array, one can use the flat attribute which is an iterator over all the elements of the array: See also: Indexing , Indexing (reference), newaxis, ndenumerate , indices Shape Manipulation Changing the shape of an array An array has a shape given by the number of elements along each axis: The shape of an array can be changed with various commands. Note that the following three commands all return a modified array, but do not change the original array: The order of the elements in the array resulting from ravel () is normally “C-style”, that is, the rightmost index “changes the fastest”, so the element after a[0,0] is a [0,1]. If the array is reshaped to some other shape, again the array is treated as “C-style”. NumPy normally creates arrays stored in this order, so ravel() will usually not need to copy its argument, but if the array was made by taking slices of another array or created with unusual options, it may need to be copied. The functions ravel() and reshape () can also be instructed, using an optional argument, to use FORTRAN-style arrays, in which the leftmost index changes the fastest. The reshape function returns its argument with a modified shape, whereas the ndarray.resize method modifies the array itself: If a dimension is given as -1 in a reshaping operation, the other dimensions are automatically calculated: See also: ndarray.shape , reshape , resize , ravel Stacking together different arrays Several arrays can be stacked together along different axes: The function column_stack stacks 1D arrays as columns into a 2D array. It is equivalent to hstack only for 2D arrays: On the other hand, the function row_stack is equivalent to vstack for any input arrays. In general, for arrays of with more than two dimensions, hstack stacks along their second axes, vstack stacks along their first axes, and concatenate allows for an optional arguments giving the number of the axis along which the concatenation should happen. Note In complex cases, r_ and c_ are useful for creating arrays by stacking numbers along one axis. They allow the use of range literals (“:”) When used with arrays as arguments, r_ and c_ are similar to vstack and hstack in their default behavior, but allow for an optional argument giving the number of the axis along which to concatenate. See also: hstack , vstack, column_stack, concatenate, c_ , r_ Splitting one array into several smaller ones Using hsplit, you can split an array along its horizontal axis, either by specifying the number of equally shaped arrays to return, or by specifying the columns after which the division should occur: vsplit splits along the vertical axis, and array_split allows one to specify along which axis to split. Copies and Views When operating and manipulating arrays, their data is sometimes copied into a new array and sometimes not. This is often a source of confusion for beginners. There are three cases: No Copy at All Simple assignments make no copy of array objects or of their data. Python passes mutable objects as references, so function calls make no copy. View or Shallow Copy Different array objects can share the same data. The view method creates a new array object that looks at the same data. Slicing an array returns a view of it: Deep Copy The copy method makes a complete copy of the array and its data. Functions and Methods Overview Here is a list of some useful NumPy functions and methods names ordered in categories. See Routines for the full list. Array Creation arange, array, copy , empty , empty_like , eye, fromfile , fromfunction , identity, linspace, logspace , mgrid , ogrid , ones , ones_like , r , zeros, zeros_like Conversions ndarray.astype , atleast_1d , atleast_2d , atleast_3d , mat Manipulations array_split , column_stack, concatenate , diagonal , dsplit, dstack , hsplit, hstack , ndarray.item , newaxis , ravel , repeat , reshape , resize , squeeze, swapaxes , take , transpose, vsplit , vstack Questions all , any , nonzero , where Ordering argmax, argmin , argsort , max , min, ptp , searchsorted , sort Operations choose , compress , cumprod , cumsum , inner , ndarray.fill , imag , prod, put , putmask , real, sum Basic Statistics cov , mean , std, var Basic Linear Algebra cross, dot , outer , linalg.svd , vdot Less Basic Broadcasting rules Broadcasting allows universal functions to deal in a meaningful way with inputs that do not have exactly the same shape. The first rule of broadcasting is that if all input arrays do not have the same number of dimensions, a “1” will be repeatedly prepended to the shapes of the smaller arrays until all the arrays have the same number of dimensions. The second rule of broadcasting ensures that arrays with a size of 1 along a particular dimension act as if they had the size of the array with the largest shape along that dimension. The value of the array element is assumed to be the same along that dimension for the “broadcast” array. After application of the broadcasting rules, the sizes of all arrays must match. More details can be found in Broadcasting. Fancy indexing and index tricks NumPy offers more indexing facilities than regular Python sequences. In addition to indexing by integers and slices, as we saw before, arrays can be indexed by arrays of integers and arrays of booleans. Indexing with Arrays of Indices When the indexed array a is multidimensional, a single array of indices refers to the first dimension of a. The following example shows this behavior by converting an image of labels into a color image using a palette. We can also give indexes for more than one dimension. The arrays of indices for each dimension must have the same shape. Naturally, we can put i and j in a sequence (say a list) and then do the indexing with the list. However, we can not do this by putting i and j into an array, because this array will be interpreted as indexing the first dimension of a. Another common use of indexing with arrays is the search of the maximum value of time-dependent series: You can also use indexing with arrays as a target to assign to: However, when the list of indices contains repetitions, the assignment is done several times, leaving behind the last value: This is reasonable enough, but watch out if you want to use Python’s += construct, as it may not do what you expect: Even though 0 occurs twice in the list of indices, the 0th element is only incremented once. This is because Python requires “a+=1” to be equivalent to “a = a + 1”. Indexing with Boolean Arrays When we index arrays with arrays of (integer) indices we are providing the list of indices to pick. With boolean indices the approach is different; we explicitly choose which items in the array we want and which ones we don’t. The most natural way one can think of for boolean indexing is to use boolean arrays that have the same shape as the original array: This property can be very useful in assignments: You can look at the following example to see how to use boolean indexing to generate an image of the Mandelbrot set : The second way of indexing with booleans is more similar to integer indexing; for each dimension of the array we give a 1D boolean array selecting the slices we want: Note that the length of the 1D boolean array must coincide with the length of the dimension (or axis) you want to slice. In the previous example, b1 has length 3 (the number of rows in a), and b2 (of length 4) is suitable to index the 2nd axis (columns) of a. The ix_() function The ix_ function can be used to combine different vectors so as to obtain the result for each n-uplet. For example, if you want to compute all the a+b*c for all the triplets taken from each of the vectors a, b and c: You could also implement the reduce as follows: and then use it as: The advantage of this version of reduce compared to the normal ufunc.reduce is that it makes use of the Broadcasting Rules in order to avoid creating an argument array the size of the output times the number of vectors. Indexing with strings See Structured arrays . Linear Algebra Work in progress. Basic linear algebra to be included here. Simple Array Operations See linalg.py in numpy folder for more. Tricks and Tips Here we give a list of short and useful tips. “Automatic” Reshaping To change the dimensions of an array, you can omit one of the sizes which will then be deduced automatically: Vector Stacking How do we construct a 2D array from a list of equally- sized row vectors? In MATLAB this is quite easy: if x and y are two vectors of the same length you only need do m=[x;y] . In NumPy this works via the functions column_stack , dstack , hstack and vstack , depending on the dimension in which the stacking is to be done. For example: The logic behind those functions in more than two dimensions can be strange. See also: NumPy for Matlab users Histograms The NumPy histogram function applied to an array returns a pair of vectors: the histogram of the array and the vector of bins. Beware: matplotlib also has a function to build histograms (called hist, as in Matlab) that differs from the one in NumPy. The main difference is that pylab.hist plots the histogram automatically, while numpy.histogram only generates the data. Further reading The Python tutorial NumPy Reference SciPy Tutorial SciPy Lecture Notes A matlab, R, IDL, NumPy/SciPy dictionary Table Of Contents Quickstart tutorial Prerequisites The Basics An example Array Creation Printing Arrays Basic Operations Universal Functions Indexing, Slicing and Iterating Shape Manipulation Changing the shape of an array Stacking together different arrays Splitting one array into several smaller ones Copies and Views No Copy at All View or Shallow Copy Deep Copy Functions and Methods Overview Less Basic Broadcasting rules Fancy indexing and index tricks Indexing with Arrays of Indices Indexing with Boolean Arrays The ix_() function Indexing with strings Linear Algebra Simple Array Operations Tricks and Tips “Automatic” Reshaping Vector Stacking Histograms Further reading Previous topic Installing NumPy Next topic NumPy basics Quick search search [[ 1. , 0., 0.], [ 0. , 1., 2.]] >>> import numpy as np >>> a = np .arange( 15 ).reshape( 3, 5) >>> a array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) >>> a .shape (3, 5) >>> a .ndim 2 >>> a .dtype .name 'int64' >>> a .itemsize 8 >>> a .size 15 >>> type (a) <type 'numpy.ndarray'> >>> b = np .array([ 6, 7, 8 ]) >>> b array([6, 7, 8]) >>> type (b) <type 'numpy.ndarray'> >>> >>> import numpy as np >>> a = np .array([ 2, 3,4]) >>> a array([2, 3, 4]) >>> a .dtype dtype('int64') >>> b = np .array([ 1.2 , 3.5 , 5.1 ]) >>> b .dtype dtype('float64') >>> >>> a = np .array( 1,2 ,3,4) # WRONG >>> a = np .array([ 1, 2,3,4 ]) # RIGHT >>> >>> b = np .array([( 1.5 ,2, 3), ( 4,5,6 )]) >>> b array([[ 1.5, 2. , 3. ], [ 4. , 5. , 6. ]]) >>> >>> c = np .array( [ [ 1,2], [ 3, 4] ], dtype =complex ) >>> c array([[ 1.+0.j, 2.+0.j], [ 3.+0.j, 4.+0.j]]) >>> >>> np .zeros( ( 3,4) ) array([[ 0., 0., 0., 0.], [ 0., 0., 0., 0.], [ 0., 0., 0., 0.]]) >>> np .ones( ( 2 ,3,4), dtype =np.int16 ) # dtype can also be specified array([[[ 1, 1, 1, 1], [ 1, 1, 1, 1], [ 1, 1, 1, 1]], [[ 1, 1, 1, 1], [ 1, 1, 1, 1], [ 1, 1, 1, 1]]], dtype=int16) >>> np .empty ( (2, 3) ) # uninitialized, output may vary array([[ 3.73603959e-262, 6.02658058e-154, 6.55490914e-260], [ 5.30498948e-313, 3.14673309e-307, 1.00000000e+000]]) >>> >>> np .arange( 10, 30 , 5 ) array([10, 15, 20, 25]) >>> np .arange( 0, 2, 0.3 ) # it accepts float arguments array([ 0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8]) >>> >>> from numpy import pi >>> np .linspace( 0 , 2, 9 ) # 9 numbers from 0 to 2 array([ 0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75, 2. ]) >>> x = np .linspace( 0, 2*pi, 100 ) # useful to evaluate function at lots of points >>> f = np .sin(x)

NumPy Arrays:

NOTE: [IN]<- whenever u see this it shows the line of codes u will type into your notebook
[OUT] <- the expected result after u print out your code

Unlike python lists NumPy arrays is an array which must be of the same type(int64, float64, int32, float32, strings, booleans).
if the array is made of diffrent types by default it will upcast if possible

you can create a numpy array like this

[IN] x = np.array([1,2,3,4])

[IN] print (x)

[OUT] array([1, 2, 3, 4])

#this is an array of type int that will upcast to a floating point
[IN] x = np.array([3.147,2,3,4])

[IN] print(x)

[OUT] array([3.147, 2., 3., 4.])


we can explicitly set the datatype of an array using the dtype keyword



[IN] x = np.array([1,2,3,4], dtype = int64)



[OUT] array([1, 2, 3, 4, dtype = int64])



There are other ways to create NumPy arrays from scratch we shall see such ways in the next class

greatface:
Most of them were half completed.

I hope i don't die yet but if i get to live for 112 years i plan to be rest assured this tutorial will be completed

hope you guys are ready to get your hands soiled

cos i am going to start vomiting topics on numpy in a minute

so setup your environments i will be expecting u have the jupyter notebook

if u dont have the jupyter i also expect whatever way u run your python script

you got numpy package installed

for those who installed python directly without the use of anaconda
pip install "numpy‑1.14.2+mkl‑cp36‑cp36m‑win32.whl"
2

go to ur cmd and type pip

greatface:
Am with you on this lahp, just that the nairaland bread of tutorials always keep me doubting.

doubting on what?

EngrBouss:
Will this be a WhatsApp group because I am interested

for now no whatsapp group lets see later on

Nbote:
But I don't have any programming knowledge.. Will it b a problem

through this tutorial u shall learn syntaxes of the python language
but I might recommend you taking a tutorial on the python language
I can Lead u through the fundamentals of python or better yet u can get a tutorial in their website

DAY 2: python packages: Numpy


Data science is the new oil because with data science
companies have gained insight into the market and how best to improve it's services.
As a data scientist there are times where we have to change data to numerical values that the machine/computer understands.
Data is so large to be quantified, making manipulation stressful and time consuming u might end up sleeping while on ur machine..
Python came to the rescue with a package/ library called NumPy

WHAT IS NumPy??
NumPy is an acronym for numerical python
is a library for the Python
programming language , adding support for large, multi-
dimensional arrays and matrices , along with a large
collection of high-level mathematical functions to
operate on these arrays. The ancestor of NumPy,
Numeric, was originally created by Jim Hugunin with
contributions from several other developers. In 2005,
Travis Oliphant created NumPy by incorporating features
of the competing Numarray into Numeric, with extensive
modifications. NumPy is open-source software and has
many contributors.
You can get NumPy package via
http:// www.numpy. org

cool will need u on https://www.nairaland.com/5026193/data-science-python-tutorial

Nbote:
I must follow U wherever U go

pls do. I promise to hold u by the hand and take u on a tour of this beautiful jungle called data science...
there shall be pools of knowledge so clear like crystal
feel free and secured to take a drink and feel refreshed


There are a whole lot of things data can be done with

u can mention friends too

if any installation error occurs feel free to call my attention and is will help u out

WHAT LANGUAGE IS SUITABLE FOR DATA SCIENCE


There are variety of languages used in data science
but the most used is R and python

but python is widely acceptable because Python is emerging as the popular
language used more in data science applications. ...
Python has other advantages that speed up it's
upward swing to the top of data science tools. It
integrates well with the most cloud as well as
platform-as-a-service providers.
it's has packages which perform mathematical operations quickly than other languages
it's syntax is also easy to understand
Python can also be extended with modules in c/c++


HOW TO DOWNLOAD PYTHON:

www.python.org/downloads

you can download the jupyter notebook which has a python interface (ipython)
which quickly compiles and runs the python script quickly.
jupyter can be downloaded with anaconda

https://www.anaconda.com


follow the instructions to download and install

NOTE: JUPYTER NOTEBOOK COMES WITH PYTHON PACKAGES ALREADY INSTALLED...
DOWNLOAD PYTHON 3.7 WHICH IS THE LATEST VERSION
PYTHON 2.7 IS THE LAST AND FINAL VERSION OF PYTHON 2