[PYTHON] Raise "numpy power" with [100 numpy exercises] (1st to 10th questions)

I'm checking paiza's programming skills, but I felt that I had to master the "numpy" array to move from B rank to A rank.

When I checked if there were any good exercises, I found "100 numpy exercises", so I tried it.

100 exercises related to numpy are published on GitHub in Jupyter Notebook format, and you can download them locally to solve the problems.

https://github.com/rougier/numpy-100/

Select "Download ZIP" from the "Clone or download" button on the above page to download the entire file locally.

There is something called "100_Numpy_exercises.ipynb" in it. This is a collection of problems, so let's open it in Jupyter Notebook.

When you read the first description

Run the initialize.py module, then for each question you can query the answer or an hint with hint(n) or answer(n) for n question number.

There is. Of the first cell

`100_Numpy_exercises.ipynb`


%run initialise.py

It seems that hints are displayed by hint (question number) and answers are displayed by answer (question number).

When I tried to execute it immediately, I got an error saying "There is no module called mdutils", so I installed a module called mdutils with pip install mdutils and it worked.

Let's solve it now. First of all, the 1st to 10th questions.

Import the numpy package under the name np (★☆☆)

** "Import numpy with the name np" **

This is easy, isn't it?

`100_Numpy_exercises.ipynb-(1)answer`


import numpy as np

answer(1) If you write and execute it, the answer will be displayed, so let's match the answers.

Print the numpy version and the configuration (★☆☆)

** "Show numpy version and settings" **

`100_Numpy_exercises.ipynb-(2)answer`


print(np.__version__)
np.show_config()

The execution result is displayed as shown below.

`100_Numpy_exercises.ipynb-(2)output`


1.18.1
blas_mkl_info:
    libraries = ['mkl_rt']
    library_dirs = ['C:/ProgramData/Anaconda3\\Library\\lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['C:\\Program Files (x86)\\IntelSWTools\\compilers_and_libraries_2019.0.117\\windows\\mkl', 'C:\\Program Files (x86)\\IntelSWTools\\compilers_and_libraries_2019.0.117\\windows\\mkl\\include', 'C:\\Program Files (x86)\\IntelSWTools\\compilers_and_libraries_2019.0.117\\windows\\mkl\\lib', 'C:/ProgramData/Anaconda3\\Library\\include']
blas_opt_info:
    libraries = ['mkl_rt']
    library_dirs = ['C:/ProgramData/Anaconda3\\Library\\lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['C:\\Program Files (x86)\\IntelSWTools\\compilers_and_libraries_2019.0.117\\windows\\mkl', 'C:\\Program Files (x86)\\IntelSWTools\\compilers_and_libraries_2019.0.117\\windows\\mkl\\include', 'C:\\Program Files (x86)\\IntelSWTools\\compilers_and_libraries_2019.0.117\\windows\\mkl\\lib', 'C:/ProgramData/Anaconda3\\Library\\include']
lapack_mkl_info:
    libraries = ['mkl_rt']
    library_dirs = ['C:/ProgramData/Anaconda3\\Library\\lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['C:\\Program Files (x86)\\IntelSWTools\\compilers_and_libraries_2019.0.117\\windows\\mkl', 'C:\\Program Files (x86)\\IntelSWTools\\compilers_and_libraries_2019.0.117\\windows\\mkl\\include', 'C:\\Program Files (x86)\\IntelSWTools\\compilers_and_libraries_2019.0.117\\windows\\mkl\\lib', 'C:/ProgramData/Anaconda3\\Library\\include']
lapack_opt_info:
    libraries = ['mkl_rt']
    library_dirs = ['C:/ProgramData/Anaconda3\\Library\\lib']
    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
    include_dirs = ['C:\\Program Files (x86)\\IntelSWTools\\compilers_and_libraries_2019.0.117\\windows\\mkl', 'C:\\Program Files (x86)\\IntelSWTools\\compilers_and_libraries_2019.0.117\\windows\\mkl\\include', 'C:\\Program Files (x86)\\IntelSWTools\\compilers_and_libraries_2019.0.117\\windows\\mkl\\lib', 'C:/ProgramData/Anaconda3\\Library\\include']

What is show_config () used to check ...?

Create a null vector of size 10 (★☆☆)

** "Create a one-dimensional array with size 10 and all elements" 0 "" **

You can generate an array with zero elements with np.zeros (size).

`100_Numpy_exercises.ipynb-(3)answer`


Z = np.zeros(10)
print(Z)

Execution result

`100_Numpy_exercises.ipynb-(3)output`


[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]

It's OK.

How to find the memory size of any array (★☆☆)

** "How do I know the memory size of an array? 』**

If array is an array, You can see the number of elements in the array with array.size and the number of bytes per element with array.itemsize. Therefore, the memory size of the entire array can be calculated by array.size * array.itemsize.

`100_Numpy_exercises.ipynb-(4)answer`


Z = np.zeros((10,10))
print("%d bytes" % (Z.size * Z.itemsize))

Execution result

`100_Numpy_exercises.ipynb-(4)output`


800 bytes

How to get the documentation of the numpy add function from the command line? (★☆☆)

** "How do I see the numpy add function documentation from the command line?" 』**

In the official answer ``%run `python -c "import numpy; numpy.info(numpy.add)"``` It was, but

`100_Numpy_exercises.ipynb-(5)answer`


np.add?

But you can see it, so I think it's okay here as well.

6. Create a null vector of size 10 but the fifth value which is 1 (★☆☆)

*"The size is 10, all the elements"0"Make a one-dimensional array. However, the fifth element is"1"To be. 』*

This is an application of Problem 3. First np.zeros()All in"0"After making the array of, the fifth element"1"Rewrite to.

`100_Numpy_exercises.ipynb-(6)answer`


Z = np.zeros(10)
Z[4] = 1
print(Z)

Execution result

`100_Numpy_exercises.ipynb-(6)output`


[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]

7. Create a vector with values ranging from 10 to 49 (★☆☆)

"Make an array with elements from 10 to 49"

In other words, you just have to make an arithmetic progression with a tolerance of 1.np.arange()Is used.

np.arange(start, stop, step)It is how to use. This time start=10、stop=50、step=It will be 1. ※step=If it is 1, step can be omitted.

`100_Numpy_exercises.ipynb-(7)answer`


Z = np.arange(10,50,1)
print(Z)

`100_Numpy_exercises.ipynb-(7)output`


[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49]

8. Reverse a vector (first element becomes last) (★☆☆)

"Reverse the array(So that the first element is the last)』

Is it also a function that reverses the array? When I thought about it and looked at hint ...

hint: array[::-1]

I see, you can just use slices and reverse the order.

`100_Numpy_exercises.ipynb-(8)answer`


Z = np.arange(1,10)
print("Z:", Z)
Z_reverse = Z[::-1]
print("Z_reverse:", Z_reverse)

Execution result

`100_Numpy_exercises.ipynb-(8)output`


Z: [1 2 3 4 5 6 7 8 9]
Z_reverse: [9 8 7 6 5 4 3 2 1]

9. Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆)

"3 rows and 3 columns array(Elements are 0-8)Make

First make a one-dimensional array, then reshape()I think it's easy to transform it into 3 rows and 3 columns using.

`100_Numpy_exercises.ipynb-(9)answer`


Z = np.arange(0,9)
 Z = np.reshape (Z, (3,3)) # Z is a 3 × 3 2D array transformation
print(Z)

Execution result

`100_Numpy_exercises.ipynb-(9)output`


[[0 1 2]
 [3 4 5]
 [6 7 8]]

10. Find indices of non-zero elements from [1,2,0,0,4,0](★☆☆)

『[1,2,0,0,4,0]From the array"Not 0"Find the index of the element ”

np.nonzero(a) #a is arrayReturns the index of the non-zero element of the array called a.

`100_Numpy_exercises.ipynb-(10)answer`


Z = np.array([1,2,0,0,4,0])
print(np.nonzero(Z))

Execution result

`100_Numpy_exercises.ipynb-(10)output`


(array([0, 1, 4], dtype=int64),)

That's all for now. Next time, I will solve the 11th to 20th questions.

The next article is here. With [100 numpy exercises]"numpy power"Raise the bottom(11th to 20th questions)