To show off a more exciting use-case, let’s create a notebook that plots some data for us. We’ll use matplotlib, a Python library that is used for plotting data, and NumPy, the premiere library for doing numerical work in Python. We will import these libraries for use in our code. Next we’ll define some mathematical functions. And finally, we’ll plot these functions evaluated on a large number of closely-spaced points on the domain.
[ ]:
+[1]:
diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt
index 5da1e765..8abe7d3d 100644
--- a/docs/_sources/index.rst.txt
+++ b/docs/_sources/index.rst.txt
@@ -6,7 +6,7 @@ Python Like You Mean It
Edited by `David Mascharka Python Like You Mean It
Written by Ryan Soklaski (Twitter/GitHub:@rsokl)
Edited by David Mascharka
-中文版 (Chinese Version)
+中文版 (Chinese Version)
What this is¶
diff --git a/docs/intro.html b/docs/intro.html
index eef92cda..6ae45e87 100644
--- a/docs/intro.html
+++ b/docs/intro.html
@@ -201,7 +201,7 @@ Python Like You Mean It
Written by Ryan Soklaski (Twitter/GitHub:@rsokl)
Edited by David Mascharka
-中文版 (Chinese Version)
+中文版 (Chinese Version)
What this is¶
diff --git a/docs_backup/.doctrees/Module1_GettingStartedWithPython/Informal_Intro_Python.doctree b/docs_backup/.doctrees/Module1_GettingStartedWithPython/Informal_Intro_Python.doctree
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diff --git a/docs_backup/Module1_GettingStartedWithPython/Informal_Intro_Python.html b/docs_backup/Module1_GettingStartedWithPython/Informal_Intro_Python.html
index aa79f6e3..10a7170f 100644
--- a/docs_backup/Module1_GettingStartedWithPython/Informal_Intro_Python.html
+++ b/docs_backup/Module1_GettingStartedWithPython/Informal_Intro_Python.html
@@ -413,8 +413,8 @@ An Informal Introduction to Python
Dabbling with Numbers¶
Time to execute some Python code that performs simple arithmetic. Typing 2 + 3 into the IPython console and hitting the <ENTER> key, you should see the following input and output in the console:
-
-[ ]:
+
+
+[1]:
+
+
+
+5
+
+
+
This console session is persistent, meaning that we can define a variable and then reference it in our code later on within this console session. Let’s define the variable x and assign it to the integer 10 (please follow along in the IPython console)
[2]:
diff --git a/docs_backup/Module1_GettingStartedWithPython/Installing_Python.html b/docs_backup/Module1_GettingStartedWithPython/Installing_Python.html
index dfed845a..36ceca32 100644
--- a/docs_backup/Module1_GettingStartedWithPython/Installing_Python.html
+++ b/docs_backup/Module1_GettingStartedWithPython/Installing_Python.html
@@ -206,7 +206,7 @@
Installing Python¶
-Without further adieu, we now provide instructions for installing Python and other useful Python libraries on your machine via the Anaconda platform. Installing the Anaconda platform will install the following:
+Without further ado, we now provide instructions for installing Python and other useful Python libraries on your machine via the Anaconda platform. Installing the Anaconda platform will install the following:
- Python; specifically the CPython interpreter that we discussed in the previous section.
- A number of useful Python packages, like matplotlib, NumPy, and SciPy.
diff --git a/docs_backup/Module1_GettingStartedWithPython/Jupyter_Notebooks.html b/docs_backup/Module1_GettingStartedWithPython/Jupyter_Notebooks.html
index df76bda4..a60d2345 100644
--- a/docs_backup/Module1_GettingStartedWithPython/Jupyter_Notebooks.html
+++ b/docs_backup/Module1_GettingStartedWithPython/Jupyter_Notebooks.html
@@ -453,7 +453,7 @@ Notebook Cells¶
To show off a more exciting use-case, let’s create a notebook that plots some data for us. We’ll use matplotlib, a Python library that is used for plotting data, and NumPy, the premiere library for doing numerical work in Python. We will import these libraries for use in our code. Next we’ll define some mathematical functions. And finally, we’ll plot these functions evaluated on a large number of closely-spaced points on the domain.
-[1]:
+[ ]:
diff --git a/docs_backup/Module1_GettingStartedWithPython/SiteFormatting.html b/docs_backup/Module1_GettingStartedWithPython/SiteFormatting.html
index f292f6b5..f0de554a 100644
--- a/docs_backup/Module1_GettingStartedWithPython/SiteFormatting.html
+++ b/docs_backup/Module1_GettingStartedWithPython/SiteFormatting.html
@@ -435,8 +435,8 @@ A Quick Guide to Formatting
-[ ]:
+
+[1]:
@@ -444,6 +444,15 @@ A Quick Guide to Formatting
+[1]:
+
+
+
+5
+
+
+
diff --git a/docs_backup/Module2_EssentialsOfPython/Basic_Objects.html b/docs_backup/Module2_EssentialsOfPython/Basic_Objects.html
index a8fede7f..2edd355a 100644
--- a/docs_backup/Module2_EssentialsOfPython/Basic_Objects.html
+++ b/docs_backup/Module2_EssentialsOfPython/Basic_Objects.html
@@ -274,8 +274,8 @@ Number Types
-
Operation
@@ -299,7 +299,7 @@ Number Typesx % yx “modulo”: y: The remainder of x / yx “modulo”: y: The remainder of x / y for positive x, y
x ** yx raised to the power yNumber Types>>> (1 + 2) * 3
9
-# finding the remainder of division
+# finding the remainder of division between two positive numbers
>>> 11 % 5
1
@@ -368,7 +368,7 @@ Number Types
Reading Comprehension: Understanding the modulo operator
-The modulo operator, %, is not commonly seen in mathematics textbooks. It is, however, a very useful operation to have at our disposal. x % y (said as x “mod” y in programmer’s jargon) returns the remainder of x / y. For example:
+The modulo operator, %, is not commonly seen in mathematics textbooks. It is, however, a very useful operation to have at our disposal. x % y (said as x “mod” y in programmer’s jargon) returns the remainder of x / y, when x and `y are non-negative numbers. For example:
- \(\frac{3}{2} = 1 + \frac{1}{2}\). 2 “goes into” 3 one time, leaving a remainder of 1. Thus
3 % 2 returns 1
- \(\frac{9}{3} = 3\). 3 “goes into” 9 three times, and leaves no remainder. Thus
9 % 3 returns 0
diff --git a/docs_backup/Module3_IntroducingNumpy/BasicIndexing.html b/docs_backup/Module3_IntroducingNumpy/BasicIndexing.html
index 7234faaa..8f506440 100644
--- a/docs_backup/Module3_IntroducingNumpy/BasicIndexing.html
+++ b/docs_backup/Module3_IntroducingNumpy/BasicIndexing.html
@@ -544,7 +544,7 @@ In-Place Assignments
Augmented Assignments¶
Recall from our discussion of basic mathematical expressions in Python, that augmented assignment expressions provide a nice shorthand notation for updating the value of a variable. For example, the assignment expression x = x + 5 can be rewritten using the augmented assignment x += 5.
-While x += 5 is truly only a shorthand in the context of basic Python objects (integer,s floats, etc.), augmented assignments on NumPy arrays behave fundamentally different than their long-form counterparts. Specifically, they directly update the underlying data referenced by the updated array, rather than creating a distinct array, thus affecting any arrays that are views of that data. We will demonstrate this here.
+While x += 5 is truly only a shorthand in the context of basic Python objects (integers floats, etc.), augmented assignments on NumPy arrays behave fundamentally different than their long-form counterparts. Specifically, they directly update the underlying data referenced by the updated array, rather than creating a distinct array, thus affecting any arrays that are views of that data. We will demonstrate this here.
# Demonstrating that augmented assignments on NumPy
# arrays update the underlying data reference by that
# array.
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--- a/docs_backup/Module4_OOP/Applications_of_OOP.html
+++ b/docs_backup/Module4_OOP/Applications_of_OOP.html
@@ -182,204 +182,6 @@