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22
numpy_100.ipynb → 100_Numpy_exercises.ipynb
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@ -4,16 +4,15 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# 100 numpy exercises\n",
"\n",
"This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow \n",
"and in the numpy documentation. The goal of this collection is to offer a quick reference for both old \n",
"This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow\n",
"and in the numpy documentation. The goal of this collection is to offer a quick reference for both old\n",
"and new users but also to provide a set of exercises for those who teach.\n",
"\n",
"\n",
"If you find an error or think you've a better way to solve some of them, feel \n",
"free to open an issue at <https://github.com/rougier/numpy-100>\n"
"If you find an error or think you've a better way to solve some of them, feel\n",
"free to open an issue at <https://github.com/rougier/numpy-100>"
]
},
{
@ -27,7 +26,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"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."
"Run the `initialize.py` module, then for each question you can query the\n",
"answer or an hint with `hint(n)` or `answer(n)` for `n` question number."
]
},
{
@ -408,7 +408,7 @@
"print(sum(range(5),-1))\n",
"from numpy import *\n",
"print(sum(range(5),-1))\n",
"```\n"
"```"
]
},
{
@ -449,7 +449,7 @@
"np.array(0) / np.array(0)\n",
"np.array(0) // np.array(0)\n",
"np.array([np.nan]).astype(int).astype(float)\n",
"```\n"
"```"
]
},
{
@ -508,7 +508,7 @@
"#### 32. Is the following expressions true? (★☆☆)\n",
"```python\n",
"np.sqrt(-1) == np.emath.sqrt(-1)\n",
"```\n"
"```"
]
},
{
@ -821,7 +821,7 @@
"1, 2, 3, 4, 5\n",
"6, , , 7, 8\n",
" , , 9,10,11\n",
"```\n"
"```"
]
},
{
@ -1213,7 +1213,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 82. Compute a matrix rank (★★★) "
"#### 82. Compute a matrix rank (★★★)"
]
},
{

16
numpy_100.md → 100_Numpy_exercises.md
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@ -1,17 +1,15 @@
# 100 numpy exercises
This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow
and in the numpy documentation. The goal of this collection is to offer a quick reference for both old
This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow
and in the numpy documentation. The goal of this collection is to offer a quick reference for both old
and new users but also to provide a set of exercises for those who teach.
If you find an error or think you've a better way to solve some of them, feel
free to open an issue at <https://github.com/rougier/numpy-100>
File automatically generated. See the documentation to update questions/answers/hints programmatically.
If you find an error or think you've a better way to solve some of them, feel
free to open an issue at <https://github.com/rougier/numpy-100>File automatically generated. See the documentation to update questions/answers/hints programmatically.
#### 1. Import the numpy package under the name `np` (★☆☆)
#### 2. Print the numpy version and the configuration (★☆☆)
@ -79,7 +77,6 @@ from numpy import *
print(sum(range(5),-1))
```
#### 27. Consider an integer vector Z, which of these expressions are legal? (★☆☆)
```python
Z**Z
@ -97,7 +94,6 @@ np.array(0) // np.array(0)
np.array([np.nan]).astype(int).astype(float)
```
#### 29. How to round away from zero a float array ? (★☆☆)
#### 30. How to find common values between two arrays? (★☆☆)
@ -109,7 +105,6 @@ np.array([np.nan]).astype(int).astype(float)
np.sqrt(-1) == np.emath.sqrt(-1)
```
#### 33. How to get the dates of yesterday, today and tomorrow? (★☆☆)
#### 34. How to get all the dates corresponding to the month of July 2016? (★★☆)
@ -159,7 +154,6 @@ np.sqrt(-1) == np.emath.sqrt(-1)
, , 9,10,11
```
#### 55. What is the equivalent of enumerate for numpy arrays? (★★☆)
#### 56. Generate a generic 2D Gaussian-like array (★★☆)
@ -214,7 +208,7 @@ np.sqrt(-1) == np.emath.sqrt(-1)
#### 81. Consider an array Z = [1,2,3,4,5,6,7,8,9,10,11,12,13,14], how to generate an array R = [[1,2,3,4], [2,3,4,5], [3,4,5,6], ..., [11,12,13,14]]? (★★★)
#### 82. Compute a matrix rank (★★★)
#### 82. Compute a matrix rank (★★★)
#### 83. How to find the most frequent value in an array?

26
numpy_100_with_hints.md → 100_Numpy_exercises_with_hints.md
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@ -1,19 +1,17 @@
# 100 numpy exercises
This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow
and in the numpy documentation. The goal of this collection is to offer a quick reference for both old
This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow
and in the numpy documentation. The goal of this collection is to offer a quick reference for both old
and new users but also to provide a set of exercises for those who teach.
If you find an error or think you've a better way to solve some of them, feel
free to open an issue at <https://github.com/rougier/numpy-100>
File automatically generated. See the documentation to update questions/answers/hints programmatically.
If you find an error or think you've a better way to solve some of them, feel
free to open an issue at <https://github.com/rougier/numpy-100>File automatically generated. See the documentation to update questions/answers/hints programmatically.
#### 1. Import the numpy package under the name `np` (★☆☆)
`hint: import … as `
`hint: import … as`
#### 2. Print the numpy version and the configuration (★☆☆)
`hint: np.__version__, np.show_config)`
#### 3. Create a null vector of size 10 (★☆☆)
@ -67,7 +65,7 @@ np.nan in set([np.nan])
#### 23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆)
`hint: np.dtype`
#### 24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆)
`hint: `
`hint:`
#### 25. Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆)
`hint: >, <=`
#### 26. What is the output of the following script? (★☆☆)
@ -78,7 +76,6 @@ print(sum(range(5),-1))
from numpy import *
print(sum(range(5),-1))
```
`hint: np.sum`
#### 27. Consider an integer vector Z, which of these expressions are legal? (★☆☆)
```python
@ -96,8 +93,7 @@ np.array(0) / np.array(0)
np.array(0) // np.array(0)
np.array([np.nan]).astype(int).astype(float)
```
`No hints provided... `
`No hints provided...`
#### 29. How to round away from zero a float array ? (★☆☆)
`hint: np.uniform, np.copysign, np.ceil, np.abs`
#### 30. How to find common values between two arrays? (★☆☆)
@ -108,7 +104,6 @@ np.array([np.nan]).astype(int).astype(float)
```python
np.sqrt(-1) == np.emath.sqrt(-1)
```
`hint: imaginary number`
#### 33. How to get the dates of yesterday, today and tomorrow? (★☆☆)
`hint: np.datetime64, np.timedelta64`
@ -158,7 +153,6 @@ np.sqrt(-1) == np.emath.sqrt(-1)
6, , , 7, 8
, , 9,10,11
```
`hint: np.genfromtxt`
#### 55. What is the equivalent of enumerate for numpy arrays? (★★☆)
`hint: np.ndenumerate, np.ndindex`
@ -214,7 +208,7 @@ np.sqrt(-1) == np.emath.sqrt(-1)
`hint: minimum maximum`
#### 81. Consider an array Z = [1,2,3,4,5,6,7,8,9,10,11,12,13,14], how to generate an array R = [[1,2,3,4], [2,3,4,5], [3,4,5,6], ..., [11,12,13,14]]? (★★★)
`hint: stride_tricks.as_strided`
#### 82. Compute a matrix rank (★★★)
#### 82. Compute a matrix rank (★★★)
`hint: np.linalg.svd`
#### 83. How to find the most frequent value in an array?
`hint: np.bincount, argmax`
@ -227,7 +221,7 @@ np.sqrt(-1) == np.emath.sqrt(-1)
#### 87. Consider a 16x16 array, how to get the block-sum (block size is 4x4)? (★★★)
`hint: np.add.reduceat`
#### 88. How to implement the Game of Life using numpy arrays? (★★★)
`No hints provided... `
`No hints provided...`
#### 89. How to get the n largest values of an array (★★★)
`hint: np.argsort | np.argpartition`
#### 90. Given an arbitrary number of vectors, build the cartesian product (every combinations of every item) (★★★)
@ -247,7 +241,7 @@ np.sqrt(-1) == np.emath.sqrt(-1)
#### 97. Considering 2 vectors A & B, write the einsum equivalent of inner, outer, sum, and mul function (★★★)
`hint: np.einsum`
#### 98. Considering a path described by two vectors (X,Y), how to sample it using equidistant samples (★★★)?
`hint: np.cumsum, np.interp `
`hint: np.cumsum, np.interp`
#### 99. Given an integer n and a 2D array X, select from X the rows which can be interpreted as draws from a multinomial distribution with n degrees, i.e., the rows which only contain integers and which sum to n. (★★★)
`hint: np.logical_and.reduce, np.mod`
#### 100. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★)

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numpy_100_with_hints_with_solutions.md → 100_Numpy_exercises_with_hints_with_solutions.md
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12
numpy_100_random.ipynb → 100_Numpy_random.ipynb
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@ -4,16 +4,15 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# 100 numpy exercises\n",
"\n",
"This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow \n",
"and in the numpy documentation. The goal of this collection is to offer a quick reference for both old \n",
"This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow\n",
"and in the numpy documentation. The goal of this collection is to offer a quick reference for both old\n",
"and new users but also to provide a set of exercises for those who teach.\n",
"\n",
"\n",
"If you find an error or think you've a better way to solve some of them, feel \n",
"free to open an issue at <https://github.com/rougier/numpy-100>\n"
"If you find an error or think you've a better way to solve some of them, feel\n",
"free to open an issue at <https://github.com/rougier/numpy-100>"
]
},
{
@ -27,7 +26,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the `initialize.py` module, then call a random question with `pick()`an hint towards its solution with `hint(n)` and the answer with `answer(n)`,where n is the number of the picked question."
"Run the `initialize.py` module, then call a random question with `pick()` an hint towards its solution with\n",
"`hint(n)` and the answer with `answer(n)`, where n is the number of the picked question."
]
},
{

8
README.md
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@ -7,9 +7,11 @@ This is a collection of numpy exercises from numpy mailing list, stack overflow,
→ [Test them on Binder](http://mybinder.org:/repo/rougier/numpy-100/notebooks/100_Numpy_exercises.ipynb)
→ [Read them on GitHub](100_Numpy_exercises.md)
Note: markdown and ipython notebook are created programmatically from the dictionary in `data_source.py`.
To modify the content of these files, chance the text in `data_source.py` and then re-generate them via the
method into the `generators.py` module.
Note: markdown and ipython notebook are created programmatically from the source data in `source/exercises.ktx`.
To modify the content of these files, please change the text in the source and run the `generators.py` module with a python
interpreter with the libraries under `requirements.txt` installed.
The keyed text format (`ktx`) is a minimal human readable key-values to store text (markdown or others) indexed by keys.
This work is licensed under the MIT license.
[![DOI](https://zenodo.org/badge/10173/rougier/numpy-100.svg)](https://zenodo.org/badge/latestdoi/10173/rougier/numpy-100)

63
generators.py
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@ -2,10 +2,41 @@ import os
import nbformat as nbf
import mdutils
import data_source as ds
def ktx_to_dict(input_file, keystarter='<'):
""" parsing keyed text to a python dictionary. """
answer = dict()
with open(input_file, 'r+') as f:
lines = f.readlines()
k, val = '', ''
for line in lines:
if line.startswith(keystarter):
k = line.replace(keystarter, '').strip()
val = ''
else:
val += line
if k:
answer.update({k: val.strip()})
return answer
def create_jupyter_notebook(destination_filename='numpy_100.ipynb'):
def dict_to_ktx(input_dict, output_file, keystarter='<'):
""" Store a python dictionary to a keyed text"""
with open(output_file, 'w+') as f:
for k, val in input_dict.items():
f.write(f'{keystarter} {k}\n')
f.write(f'{val}\n\n')
HEADERS = ktx_to_dict(os.path.join('source', 'headers.ktx'))
QHA = ktx_to_dict(os.path.join('source', 'exercises100.ktx'))
def create_jupyter_notebook(destination_filename='100_Numpy_exercises.ipynb'):
""" Programmatically create jupyter notebook with the questions (and hints and solutions if required)
saved under data_source.py """
@ -15,16 +46,16 @@ def create_jupyter_notebook(destination_filename='numpy_100.ipynb'):
nb['cells'] = []
# - Add header:
nb['cells'].append(nbf.v4.new_markdown_cell(ds.HEADER))
nb['cells'].append(nbf.v4.new_markdown_cell(ds.SUB_HEADER))
nb['cells'].append(nbf.v4.new_markdown_cell(ds.JUPYTER_INSTRUCTIONS))
nb['cells'].append(nbf.v4.new_markdown_cell(HEADERS["header"]))
nb['cells'].append(nbf.v4.new_markdown_cell(HEADERS["sub_header"]))
nb['cells'].append(nbf.v4.new_markdown_cell(HEADERS["jupyter_instruction"]))
# - Add initialisation
nb['cells'].append(nbf.v4.new_code_cell('%run initialise.py'))
# - Add questions and empty spaces for answers
for n in range(1, 101):
nb['cells'].append(nbf.v4.new_markdown_cell(f'#### {n}. ' + ds.QHA[f'q{n}']))
nb['cells'].append(nbf.v4.new_markdown_cell(f'#### {n}. ' + QHA[f'q{n}']))
nb['cells'].append(nbf.v4.new_code_cell(""))
# Delete file if one with the same name is found
@ -35,7 +66,7 @@ def create_jupyter_notebook(destination_filename='numpy_100.ipynb'):
nbf.write(nb, destination_filename)
def create_jupyter_notebook_random_question(destination_filename='numpy_100_random.ipynb'):
def create_jupyter_notebook_random_question(destination_filename='100_Numpy_random.ipynb'):
""" Programmatically create jupyter notebook with the questions (and hints and solutions if required)
saved under data_source.py """
@ -45,9 +76,9 @@ def create_jupyter_notebook_random_question(destination_filename='numpy_100_rand
nb['cells'] = []
# - Add header:
nb['cells'].append(nbf.v4.new_markdown_cell(ds.HEADER))
nb['cells'].append(nbf.v4.new_markdown_cell(ds.SUB_HEADER))
nb['cells'].append(nbf.v4.new_markdown_cell(ds.JUPYTER_INSTRUCTIONS_RAND))
nb['cells'].append(nbf.v4.new_markdown_cell(HEADERS["header"]))
nb['cells'].append(nbf.v4.new_markdown_cell(HEADERS["sub_header"]))
nb['cells'].append(nbf.v4.new_markdown_cell(HEADERS["jupyter_instruction_rand"]))
# - Add initialisation
nb['cells'].append(nbf.v4.new_code_cell('%run initialise.py'))
@ -61,7 +92,7 @@ def create_jupyter_notebook_random_question(destination_filename='numpy_100_rand
nbf.write(nb, destination_filename)
def create_markdown(destination_filename='numpy_100', with_hints=False, with_solutions=False):
def create_markdown(destination_filename='100_Numpy_exercises', with_hints=False, with_solutions=False):
# Create file name
if with_hints:
destination_filename += '_with_hints'
@ -72,16 +103,16 @@ def create_markdown(destination_filename='numpy_100', with_hints=False, with_sol
mdfile = mdutils.MdUtils(file_name=destination_filename)
# Add headers
mdfile.write(ds.HEADER)
mdfile.write(ds.SUB_HEADER)
mdfile.write(HEADERS["header"])
mdfile.write(HEADERS["sub_header"])
# Add questions (and hint or answers if required)
for n in range(1, 101):
mdfile.new_header(title=f"{n}. {ds.QHA[f'q{n}']}", level=4)
mdfile.new_header(title=f"{n}. {QHA[f'q{n}']}", level=4)
if with_hints:
mdfile.write(f"`{ds.QHA[f'h{n}']}`")
mdfile.write(f"`{QHA[f'h{n}']}`")
if with_solutions:
mdfile.insert_code(ds.QHA[f'a{n}'], language='python')
mdfile.insert_code(QHA[f'a{n}'], language='python')
# Delete file if one with the same name is found
if os.path.exists(destination_filename):

8
initialise.py
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@ -1,18 +1,18 @@
import numpy as np
import data_source as ds
import generators as ge
def question(n):
print(f'{n}. ' + ds.QHA[f'q{n}'])
print(f'{n}. ' + ge.QHA[f'q{n}'])
def hint(n):
print(ds.QHA[f'h{n}'])
print(ge.QHA[f'h{n}'])
def answer(n):
print(ds.QHA[f'a{n}'])
print(ge.QHA[f'a{n}'])
def pick():

1457
source/exercises100.ktx Normal file
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25
source/headers.ktx Normal file
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@ -0,0 +1,25 @@
< header
# 100 numpy exercises
This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow
and in the numpy documentation. The goal of this collection is to offer a quick reference for both old
and new users but also to provide a set of exercises for those who teach.
If you find an error or think you've a better way to solve some of them, feel
free to open an issue at <https://github.com/rougier/numpy-100>
< sub_header
File automatically generated. See the documentation to update questions/answers/hints programmatically.
< jupyter_instruction
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.
< jupyter_instruction_rand
Run the `initialize.py` module, then call a random question with `pick()` an hint towards its solution with
`hint(n)` and the answer with `answer(n)`, where n is the number of the picked question.