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[add] numpy indexing and slicing
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‎README.md

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@@ -165,6 +165,8 @@ All skills are base on the implementation of Python 3.
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<td><ul style="margin: 8px">
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<li><a href="#create-array-or-matrix">Create Array or Matrix</li>
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<li><a href="#basic-operations">Basic Operations</li>
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<li><a href="#indexing-and-slicing">Indexing and Slicing</li>
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</ul>
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</table>
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@@ -1372,6 +1374,18 @@ rng.integers(low=2, high=10, size=(10, 2)) # random integer matrix
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## [Basic Operations](numpy/basic_operations.ipynb)
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### Sort and Concatenate
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``` py
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np.sort(a, axis=None)
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np.sort(a, axis=-1)[::-1]
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a.sort()
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a[::-1].sort()
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np.concatenate((a, b), axis=None)
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np.concatenate((a, b), axis=2)
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```
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### Element-wise
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``` py
@@ -1404,11 +1418,31 @@ A.mean(axis=0) # [0.4340667 , 0.7072504 , 0.80986881, 0.74171617]
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A.mean(axis=1) # [0.69220011, 0.65425093]
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```
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## Indexing, Slicing, and Iterating
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## [Indexing and Slicing](numpy/indexing_slicing.ipynb)
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``` py
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# Index and slicing arrays
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x[1, 3] == x[1][3]
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y[1:5:2, ::3]
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# Indexing arrays
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x[np.array([0, 1, 2, -1, -2])]
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y[np.array([1, 2, 3]), 1:4:2]
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y[np.array([1, 2]), np.array([-1, -1])]
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https://numpy.org/doc/stable/user/quickstart.html#indexing-slicing-and-iterating
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https://numpy.org/doc/stable/user/quickstart.html#advanced-indexing-and-index-tricks
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https://numpy.org/doc/stable/user/basics.indexing.html
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# Masking arrays
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x[x>5]
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x[(x%2==0) | (x>7)]
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y[[True]*3 + [False] + [True] + [False], 2::2]
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# Ellipsis syntax
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x[-1, ..., 3] # same as x[-1, :, 3]
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x[:3, ...] # same as x[0:3, :, :] and x[0:3] and x[:3]
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x[::2, ..., np.array([0, 2])] # same as x[0:5:2, :, np.array([0, 2])]
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```
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## Shape Manipulation
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‎numpy/basic_operations.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"source": [
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"## Sort"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"a = np.arange(5)\n",
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"a = np.sort(a) # not an in-place sorting\n",
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"# [0, 1, 2, 3, 4]\n",
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"\n",
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"a[::-1].sort() # perform in-place sorting\n",
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"# [4, 3, 2, 1, 0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"array([[[0, 7, 6, 4],\n",
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" [4, 8, 0, 6],\n",
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" [2, 0, 5, 9]],\n",
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"\n",
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" [[7, 7, 7, 7],\n",
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" [5, 1, 8, 4],\n",
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" [5, 3, 1, 9]]], dtype=int64)"
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]
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},
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"metadata": {},
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"execution_count": 38
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}
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],
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"source": [
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"a = np.random.default_rng(42).integers(0, 10, (2,3,4))\n",
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"a"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"array([[[0, 4, 6, 7],\n",
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" [0, 4, 6, 8],\n",
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" [0, 2, 5, 9]],\n",
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"\n",
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" [[7, 7, 7, 7],\n",
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" [1, 4, 5, 8],\n",
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" [1, 3, 5, 9]]], dtype=int64)"
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]
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},
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"metadata": {},
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"execution_count": 40
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}
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],
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"source": [
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"np.sort(a, axis=-1) # default: sort on last (axis=2 in this case) dimension"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"array([[[0, 0, 0, 4],\n",
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" [2, 7, 5, 6],\n",
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" [4, 8, 6, 9]],\n",
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"\n",
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" [[5, 1, 1, 4],\n",
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" [5, 3, 7, 7],\n",
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" [7, 7, 8, 9]]], dtype=int64)"
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]
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},
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"metadata": {},
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"execution_count": 45
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}
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],
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"source": [
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"np.sort(a, axis=1) # sort on the second demension"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"array([9, 9, 8, 8, 7, 7, 7, 7, 7, 6, 6, 5, 5, 5, 4, 4, 4, 3, 2, 1, 1, 0,\n",
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" 0, 0], dtype=int64)"
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]
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},
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"metadata": {},
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"execution_count": 47
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}
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],
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"source": [
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"np.sort(a, axis=None)[::-1] # sort on all elements and flatten"
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]
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},
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{
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"source": [
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"Note: The `axis` uses in `np.sort()` is not as in `max()`, `mean()` and other functions.\n",
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"\n",
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"- `axis=None` will sort the arrays into **flattened** arrays.\n",
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"- `axis=n` will sort the arrays on the `n-th` dimension.\n",
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" - e.g., `np.sort((3, 3, 1), axis=2)` will sort the elements on the last dimension. "
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"source": [
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"## Concatenate"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"(array([[2, 1]]),\n",
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" array([[0, 1],\n",
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" [2, 3]]))"
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]
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},
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"metadata": {},
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"execution_count": 9
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}
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],
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"source": [
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"a = np.array([[2, 1]])\n",
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"b = np.arange(4).reshape(2, 2)\n",
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"a, b"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"[2 1 0 1 2 3]\n[[2 1]\n [0 1]\n [2 3]]\n[[2 0 1]\n [1 2 3]]\n"
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]
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}
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],
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"source": [
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"print( np.concatenate((a, b), axis=None) )\n",
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"print( np.concatenate((a, b), axis=0) ) # default axis\n",
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"print( np.concatenate((a.T, b), axis=1) )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 48,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"[[[0]\n [1]\n [2]]\n\n [[3]\n [4]\n [5]]\n\n [[6]\n [7]\n [8]]]\n[[[ 0 1]\n [ 2 3]\n [ 4 5]]\n\n [[ 6 7]\n [ 8 9]\n [10 11]]\n\n [[12 13]\n [14 15]\n [16 17]]]\n"
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]
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}
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],
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"source": [
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"a = np.arange(9).reshape(3, 3, 1)\n",
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"b = np.arange(18).reshape(3, 3, 2)\n",
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"print(a)\n",
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"print(b)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 50,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"[ 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14\n 15 16 17]\n[[[ 0 0 1]\n [ 1 2 3]\n [ 2 4 5]]\n\n [[ 3 6 7]\n [ 4 8 9]\n [ 5 10 11]]\n\n [[ 6 12 13]\n [ 7 14 15]\n [ 8 16 17]]]\n"
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]
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}
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],
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"source": [
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"print( np.concatenate((a, b), axis=None) )\n",
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"print( np.concatenate((a, b), axis=2) )"
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]
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},
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{
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"source": [
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"Note: The `axis` uses in `np.concatenate()` is not as in `max()`, `mean()` and other functions.\n",
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"\n",
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"- `axis=None` will merge the arrays into **flattened** arrays.\n",
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"- `axis=n` will merge the arrays on the `n-th` dimension.\n",
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" - e.g., `np.concatenate((3, 3, 1), (3, 3, 2), axis=2) = (3, 3, 3)`"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"source": [
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"## Artithemetics\n",
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"print(f\"np.all(A) = {np.all(A)}\")\n",
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"print(f\"np.any(A) = {np.any(A)}\")"
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]
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},
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{
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"source": [
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"# Reference\n",
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"\n",
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"- https://numpy.org/doc/stable/user/quickstart.html#basic-operations\n",
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"- https://numpy.org/doc/stable/user/absolute_beginners.html#adding-removing-and-sorting-elements\n",
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"- https://numpy.org/doc/stable/user/absolute_beginners.html#basic-array-operations\n",
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"- https://numpy.org/doc/stable/user/absolute_beginners.html#more-useful-array-operations"
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],
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"cell_type": "markdown",
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"metadata": {}
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}
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]
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}

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