# NumPy Python Tutorial 2018 – Part 1 | NumPy and Pandas Tutorial | NumPy Tutorial 2018 | NumPy

///NumPy Python Tutorial 2018 – Part 1 | NumPy and Pandas Tutorial | NumPy Tutorial 2018 | NumPy

## NumPy Python Tutorial 2018 – Part 1 | NumPy and Pandas Tutorial | NumPy Tutorial 2018 | NumPy

NumPy Python Tutorial 2018 – Part 1 | NumPy and Pandas Tutorial | NumPy Tutorial 2018 | NumPy

There are basic libraries which are really important for the data manipulation. So, first is ‘NumPy’ that is nothing but numeric python and then there is ‘pandas’ and then there is ‘scipy’.
So, these three are the basic libraries which are very important for you to learn and these two things are very related the NumPy and pandas they are the core structure to handle the data. So, you need to understand NumPy and pandas when you are going to deal with the data.
What is NumPy?
NumPy is nothing but provides all the libraries to deal with the linear algebra.
• NumPy, which stands for Numerical Python
• NumPy is the foundational package for mathematical computing
• Mathematical and logical operations on arrays
• Operations related to linear algebra. NumPy has inbuilt functions for linear algebra and random generation
• ndarray is the core object in NumPy
Basics ndarray
• Multidimensional array
• Homogeneous collection of values
• Fast and efficient
• Support for mathematical functions
• Primary container for data exchange between python algorithms
Important attributes of an ndarray object are:
• Ndarray.ndlm: the number of axes (dimensions) of the array, the number of dimensions is referred to as rank
• Ndarray.shape: the dimensions of the array. This is tuple of integers indicating the size of the array in each dimension. For a matrix within rows and m columns, the shape will be (n,m)
• Ndarray.size: the total number of elements of the array.this is equal to the product of the elements of shape
• Ndarray.dtype: an object describing the type of the elements in the array

#numpy, #numpytutorial, #pythontutorial, #numpypandas

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By |2019-11-02T23:45:02+00:00November 2nd, 2019|Python Video Tutorials|21 Comments

1. yash bhatia channel November 2, 2019 at 11:45 pm - Reply

A subject of my choice.

2. Sailesh Patra November 2, 2019 at 11:45 pm - Reply

Can you share that file with us?

3. ABHINAV DUBEY November 2, 2019 at 11:45 pm - Reply

screen is shifted not able to see from 20 min

4. ABHINAV DUBEY November 2, 2019 at 11:45 pm - Reply

provide this whole file in description so that we can study it later also.

5. Paul Layne November 2, 2019 at 11:45 pm - Reply

I love this content. Unfortunately, around minute 19 the edges of the screen become whited out. I still gave you a like, because this video is very helpful, but in order to follow along it would be nice to see your whole screen.

6. Pisau Pisau November 2, 2019 at 11:45 pm - Reply

Generally good, but too many uses of the word "things" rather than the proper words. If only matrix multiplication were as simple as multiplying this thing with that thing and that thing with this other thing…

7. Hassane AZZI November 2, 2019 at 11:45 pm - Reply

Interesting course !!

8. Manoj Bhosle November 2, 2019 at 11:45 pm - Reply

its not visible properly

9. himanshu shekhar November 2, 2019 at 11:45 pm - Reply

I have one doubt acadglid, Why giving dtype create below difference ?
multiDimArray = [('Him',26,1234),('shek',27,4567),('bruce',28,7896)]
mdArray = np.array(multiDimArray,dtype=[('name','<U11'),('age', 'int32'),('salary','<f4')])

// output = [('Him', 26, 1234.) ('shek', 27, 4567.) ('bruce', 28, 7896.)]
//size: 3 and shape (3,)
mdArray1 = np.array(multiDimArray)
// output = [['Him' '26' '1234']

['shek' '27' '4567']

['bruce' '28' '7896']]

// size: 9 and shape (3,3)

10. Tianchi Zhang November 2, 2019 at 11:45 pm - Reply

Excellent accent………

11. Jessy Sheen November 2, 2019 at 11:45 pm - Reply

Nice video but the audio is so disturbed

12. Falola Yusuf November 2, 2019 at 11:45 pm - Reply

can we get the a copy of the notebook for the class? Thanks

13. Er Ankit sharma November 2, 2019 at 11:45 pm - Reply

Sir please improve the voice quality

14. Roger November 2, 2019 at 11:45 pm - Reply

voice quality need to be improve

15. milan319 November 2, 2019 at 11:45 pm - Reply

Good content but consider getting a pop filter for your microphone

16. Keshav Ramaiah November 2, 2019 at 11:45 pm - Reply

Is a very good video for data science beginners

17. Kiran K November 2, 2019 at 11:45 pm - Reply

Thank you

18. su lin November 2, 2019 at 11:45 pm - Reply