From 16453bf98accce9f0b83293e3b73586cc7541cba Mon Sep 17 00:00:00 2001 From: Hem-W Date: Tue, 10 Oct 2023 08:50:36 +0000 Subject: [PATCH] deploy: 3de0b1af918648c2c671e8231229520331913e8c --- .../chapters/data-analytics/numpy-basic.ipynb | 43 ++++++++++--------- chapters/data-analytics/numpy-basic.html | 32 +++++++------- searchindex.js | 2 +- 3 files changed, 40 insertions(+), 37 deletions(-) diff --git a/_sources/chapters/data-analytics/numpy-basic.ipynb b/_sources/chapters/data-analytics/numpy-basic.ipynb index dfb89c2..60f3b11 100644 --- a/_sources/chapters/data-analytics/numpy-basic.ipynb +++ b/_sources/chapters/data-analytics/numpy-basic.ipynb @@ -7,7 +7,7 @@ "source": [ "# NumPy tutorial\n", "\n", - "[NumPy](http://www.numpy.org/) is the core library for scientific computing in Python. It vastly simplifies manipulating and crunching vectors and matrices. Many of python-based leading packages rely on NumPy as a infrastructure piece." + "[NumPy](http://www.numpy.org/) is the **core** package for scientific computing in Python. It vastly simplifies manipulating and crunching vectors and matrices. Many of other leading packages rely on NumPy as a infrastructure piece." ] }, { @@ -167,9 +167,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[0.69381927]\n", - " [0.62248209]\n", - " [0.41387215]]\n" + "[[0.67742525]\n", + " [0.0784592 ]\n", + " [0.6098676 ]]\n" ] } ], @@ -178,7 +178,7 @@ "d = np.zeros((2, 2)) # Create an array of all zeros with shape (2, 2)\n", "d = np.ones((1, 2)) # Create an array of all ones with shape (1, 2)\n", "d = np.random.random((3, 1)) # Create an array of random values with shape (3, 1)\n", - "# try printing them\n", + "# Try printing them\n", "print(d)" ] }, @@ -191,7 +191,7 @@ "source": [ "## Array Indexing\n", "\n", - "There are several ways to pull out a section of arrays. The most common ways include **slicing**, **integer array indexing** and **Boolean array indexing**. We may choose the appropriate indexing methods for different purposes." + "The most common ways to pull out a section of arrays include **slicing**, **integer array indexing** and **Boolean array indexing**. We may choose the appropriate indexing methods for different purposes." ] }, { @@ -398,17 +398,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "[4 5 6] (3,)\n", - "[[4 5 6]] (1, 3)\n" + "[1 2 3] (3,)\n", + "[[1 2 3]] (1, 3)\n" ] } ], "source": [ "a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])\n", - "row_r1 = a[1, :] # Mix integer indexing with slice indexing\n", - "row_r2 = a[1:2, :] # Slice indexing\n", - "print(row_r1, row_r1.shape) # Lower rank\n", - "print(row_r2, row_r2.shape)" + "a_1row = a[0, :] # Mix integer indexing with slice indexing\n", + "a_2rows = a[0:1, :] # Slice indexing\n", + "print(a_1row, a_1row.shape) # Lower rank\n", + "print(a_2rows, a_2rows.shape) " ] }, { @@ -481,15 +481,18 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 9 10 11 12]\n" - ] + "data": { + "text/plain": [ + "array([ 9, 10, 11, 12])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "print(a[a > 8])" + "a[a > 8]" ] }, { @@ -689,7 +692,7 @@ } ], "source": [ - "x = np.array([[1, 2], [3, 4]], dtype=np.float64)\n", + "x = np.array([[1, 2], [3, 4]], dtype=np.float64) # Set data types of elements by dtype\n", "y = np.array([[5, 6], [7, 8]], dtype=np.float64)\n", "\n", "# Elementwise sum; both produce an array\n", @@ -989,7 +992,7 @@ "id": "89e2FXxFL9jQ" }, "source": [ - "Broadcasting typically makes your code more ***concise***, ***readable***, and more importantly, ***faster***, so you should strive to use it where possible." + "Broadcasting typically makes your code more ***concise***, ***readable***, and more importantly, ***faster***." ] }, { diff --git a/chapters/data-analytics/numpy-basic.html b/chapters/data-analytics/numpy-basic.html index 3c9f9a2..6d69b73 100644 --- a/chapters/data-analytics/numpy-basic.html +++ b/chapters/data-analytics/numpy-basic.html @@ -424,7 +424,7 @@

Contents

2.1. NumPy tutorial#

-

NumPy is the core library for scientific computing in Python. It vastly simplifies manipulating and crunching vectors and matrices. Many of python-based leading packages rely on NumPy as a infrastructure piece.

+

NumPy is the core package for scientific computing in Python. It vastly simplifies manipulating and crunching vectors and matrices. Many of other leading packages rely on NumPy as a infrastructure piece.

In this tutorial, we will cover:

2.1.6. References#

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