Complete Numpy Manual. Numpy Reference Guide Numpy. Scipy 0.7 Reference Guide. Guide to Numpy PDF book by Travis Oliphant (2006, free) Guide to Numpy: 2nd. Browse the docs online or download a copy of your own. Python's documentation, tutorials, and guides are constantly evolving. Get started here, or scroll down for documentation broken out by type and subject. In case you’re interested, we also have complete cheat sheets for Bootstrap, HTML, CSS, MySQL, and JavaScript. So download a copy of our Python cheat sheet and get that first.py program up and running! PDF Version of Python Cheat Sheet. Python Cheat Sheet (Download PDF) Infographic Version of Python Cheat Sheet (PNG) Python Cheat Sheet. Development team behind Python 3 has reiterated that there is an end of life for Python 2 support, more libraries have been ported to Python 3. The increased adoption of Python 3 can be shown by the number of Python packages that now provide Python 3 support, which at the time of writing includes 339 of the 360 most popular Python packages. Download Python 3.9.2 Documentation. Last updated on: Mar 06, 2021. To download an archive containing all the documents for this version of Python in one of various formats, follow one of links in this table.

• Python The Complete Reference Pdf Download

Welcome! This is the documentation for Numpy and Scipy.

For contributors:

Latest releases:

Others:

• For newer versions see https://www.numpy.org/doc

  Numpy 1.17.0 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.17.0 User Guide, [PDF]

  Numpy 1.16.1 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.16.1 User Guide, [PDF]

  Numpy 1.16.0 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.16.0 User Guide, [PDF]

  Numpy 1.15.4 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.15.4 User Guide, [PDF]

  Numpy 1.15.1 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.15.1 User Guide, [PDF]

  Numpy 1.15.0 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.15.0 User Guide, [PDF]

  Numpy 1.14.5 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.14.5 User Guide, [PDF]

  Numpy 1.14.1 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.14.1 User Guide, [PDF]

  Numpy 1.14.0 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.14.0 User Guide, [PDF]

  Numpy 1.13.0 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.13.0 User Guide, [PDF]

  Numpy 1.12.0 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.12.0 User Guide, [PDF]

  Numpy 1.11.0 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.11.0 User Guide, [PDF]

  Numpy 1.10.4 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.10.4 User Guide, [PDF]

  Numpy 1.10.1 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.10.1 User Guide, [PDF]

  Numpy 1.10.0 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.10.0 User Guide, [PDF]

  Numpy 1.9.3 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.9.3 User Guide, [PDF]

  Numpy 1.9.2 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.9.2 User Guide, [PDF]

  Numpy 1.9.1 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.9.1 User Guide, [PDF]

  Numpy 1.9.0 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.9.0 User Guide, [PDF]

  Numpy 1.8.1 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.8.1 User Guide, [PDF]

  Numpy 1.8.0 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.8.0 User Guide, [PDF]

  Numpy 1.7.0 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.7.0 User Guide, [PDF]

  Numpy 1.6 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.6 User Guide, [PDF]

  Numpy 1.5 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.5 User Guide, [PDF]

  Numpy 1.4 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.4 User Guide, [PDF]

  Numpy 1.3 Reference Guide, [HTML+zip], [PDF]

  Numpy 1.3 User Guide, [PDF]

• Scipy 1.6.1 Reference Guide, [HTML+zip], [PDF]

  Scipy 1.6.0 Reference Guide, [HTML+zip], [PDF]

  Scipy 1.5.4 Reference Guide, [HTML+zip], [PDF]

  Scipy 1.5.3 Reference Guide, [HTML+zip], [PDF]

  Scipy 1.5.2 Reference Guide, [HTML+zip], [PDF]

  Scipy 1.5.1 Reference Guide, [HTML+zip], [PDF]

  Scipy 1.5.0 Reference Guide, [HTML+zip], [PDF]

  Scipy 1.4.1 Reference Guide, [HTML+zip], [PDF]

  Scipy 1.4.0 Reference Guide, [HTML+zip], [PDF]

  Scipy 1.3.3 Reference Guide, [HTML+zip], [PDF]

  Scipy 1.3.2 Reference Guide, [HTML+zip], [PDF]

  Scipy 1.3.1 Reference Guide, [HTML+zip], [PDF]

  Scipy 1.3.0 Reference Guide, [HTML+zip], [PDF]

  Scipy 1.2.3 Reference Guide, [HTML+zip], [PDF]

  Scipy 1.2.1 Reference Guide, [HTML+zip], [PDF]

  Scipy 1.2.0 Reference Guide, [HTML+zip], [PDF]

  Scipy 1.1.0 Reference Guide, [HTML+zip], [PDF]

  Scipy 1.0.0 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.19.1 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.19.0 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.18.1 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.18.0 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.17.1 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.17.0 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.16.1 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.16.0 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.15.1 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.15.0 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.14.1 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.14.0 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.13.0 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.12.0 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.11.0 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.10.1 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.10.0 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.9.0 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.8 Reference Guide, [HTML+zip], [PDF]

  Scipy 0.7 Reference Guide, [HTML+zip], [PDF]

Original Numpy documentation:

Python 3 is a truly versatile programming language, loved both by web developers, data scientists, and software engineers. And there are several good reasons for that!

• Python is open-source and has a great support community,
• Plus, extensive support libraries.
• Its data structures are user-friendly.

Best of all:

Once you get a hang of it, your development speed and productivity will soar!

If you want to master two lucrative codings skills at once — server-side website development and machine learning — Python is your best bet.

In case you’re interested, we also have complete cheat sheets for Bootstrap, HTML, CSS, MySQL, and JavaScript.

So download a copy of our Python cheat sheet and get that first .py program up and running!

PDF Version of Python Cheat Sheet

Infographic Version of Python Cheat Sheet (PNG)

Python Cheat Sheet

Python Basics: Getting Started

Most Windows and Mac computers come with Python pre-installed. You can check that via a Command-Line search. If you don’t have a copy, download one.

The particular appeal of Python is that you can write a program in any text editor, save it in .py format and then run via a Command-Line.

But as you learn to write more complex code or venture into data science, you might want to switch to an IDE or IDLE.

What is IDLE (Integrated Development and Learning)?

IDLE (Integrated Development and Learning Environment) comes with every Python installation. Its advantage over other text editors is that it highlights important keywords (e.g. string functions), making it easier for you to interpret code.

Shell is the default mode of operation for Python IDLE. In essence, it’s a simple loop that performs that following four steps:

• Reads the Python statement
• Evaluates the results of it
• Prints the result on the screen
• And then loops back to read the next statement.

Python shell is a great place to test various small code snippets.

Main Python Data Types

Every value in Python is called an “object”. And every object has a specific data type. The three most-used data types are as follows:

• Integers (int) — an integer number to represent an object such as “number 3”.

• Floating-point numbers (float) — use them to represent floating-point numbers.

• Strings — codify a sequence of characters using a string. For example, the word “hello”. In Python 3, strings are immutable. If you already defined one, you cannot change it later on.

While you can modify a string with commands such as replace() or join(), they will create a copy of a string and apply the modification to it, rather than rewrite the original one.

Plus, another three types worth mentioning are lists, dictionaries, and tuples. All of them are discussed in the next sections.

For now, let’s focus on the strings.

How to Create a String in Python

You can create a string in three ways using single, double, or triple quotes.

Here’s an example of every option:

Basic Python String

Whichever option you choose, you should stick to it and use it consistently within your program.

As the next step, you can use the print() function to output your string in the console window. This lets you review your code and ensure that all functions well.

Here’s a snippet for that:

String Concatenation

The next thing you can master is concatenation — a way to add two strings together using the “+” operator.

Here’s how it’s done:

Note: You can’t apply + operator to two different data types e.g. string + integer. If you try to do that, you’ll get the following Python error:

String Replication

As the name implies, this command lets you repeat the same string several times. This is done using * operator.

Mind that this operator acts as a replicator only with string data types. When applied to numbers, it acts as a multiplier.

String replication example:

And with print ()

And your output will be Alice written five times in a row.

Math Operators

For reference, here’s a list of other math operations you can apply towards numbers:

How to Store Strings in Variables

Variables in Python 3 are special symbols that assign a specific storage location to a value that’s tied to it. In essence, variables are like special labels that you place on some value to know where it’s stored.

Strings incorporate data. So you can “pack” them inside a variable. Doing so makes it easier to work with complex Python programs.

Here’s how you can store a string inside a variable.

Let’s break it down a bit further:

• my_str is the variable name.
• = is the assignment operator.
• “Just a random string” is a value you tie to the variable name.

Now when you print this out, you receive the string output.

print(my_str)

= Hello World

See? By using variables, you save yourself heaps of effort as you don’t need to retype the complete string every time you want to use it.

Built-in Functions in Python

You already know the most popular function in Python — print().

Now let’s take a look at its equally popular cousins that are in-built in the platform.

Input() Function

input() function is a simple way to prompt the user for some input (e.g. provide their name). All user input is stored as a string.

Here’s a quick snippet to illustrate this:

When you run this short program, the results will look like this:

Hi! What’s your name? “Jim”

Nice to meet you, Jim!

How old are you? 25

So, you are already 25 years old, Jim!

len() Function

len() function helps you find the length of any string, list, tuple, dictionary, or another data type.

It’s a handy command to determine excessive values and trim them to optimize the performance of your program.

Here’s an input function example for a string:

Output:

The length of the string is: 35

filter()

Use the filter() function to exclude items in an iterable object (lists, tuples, dictionaries, etc.).

Optional: The PDF version of the checklist can also include a full table of all the in-built functions.

How to Define a Function

Apart from using in-built functions, Python 3 also allows you to define your own functions for your program.

To recap, a function is a block of coded instructions that perform a certain action. Once properly defined, a function can be reused throughout your program i.e. re-use the same code.

Here’s a quick walkthrough explaining how to define a function in Python:

First, use def keyword followed by the function name():. The parentheses can contain any parameters that your function should take (or stay empty).

def name():

Next, you’ll need to add a second code line with a 4-space indent to specify what this function should do.

Now, you have to call this function to run the code.

Now, let’s take a look at a defined function with a parameter — an entity, specifying an argument that a function can accept.

In this case, you pass the number 1 in for the x parameter, 2 in for the y parameter, and 3 in for the z parameter. The program will that do the simple math of adding up the numbers:

Output:

a = 1 + 2

b = 1 + 3

c = 2 + 3

Python The Complete Reference Pdf Download

How to Pass Keyword Arguments to a Function

A function can also accept keyword arguments. In this case, you can use parameters in random order as the Python interpreter will use the provided keywords to match the values to the parameters.

Here’s a simple example of how you pass a keyword argument to a function.

Output:

Productname: White T-shirt

Price: 15

Productname: Jeans

Price: 45

Lists

Lists are another cornerstone data type in Python used to specify an ordered sequence of elements. In short, they help you keep related data together and perform the same operations on several values at once. Unlike strings, lists are mutable (=changeable).

Each value inside a list is called an item and these are placed between square brackets.

Example Lists

Alternatively, you can use list() function to do the same:

How to Add Items to a List

You have two ways to add new items to existing lists.

The first one is using append() function:

The second option is to insert() function to add an item at the specified index:

How to Remove an Item from a List

Again, you have several ways to do so. First, you can use remove() function:

Secondly, you can use the pop() function. If no index is specified, it will remove the last item.

The last option is to use del keyword to remove a specific item:

P.S. You can also apply del towards the entire list to scrap it.

Combine Two Lists

To mash up two lists use the + operator.

Create a Nested List

You can also create a list of your lists when you have plenty of them.

Sort a List

Use the sort() function to organize all items on your list.

Slice a List

Now, if you want to call just a few elements from your list (e.g. the first 4 items), you need to specify a range of index numbers separated by a colon [x:y].

Here’s an example:

Change Item Value on Your List

You can easily overwrite the value of one list items:

Output:

Loop Through The List

Using for loop you can multiply the usage of certain items, similarly to what * operator does.

Here’s an example:

Copy a List

Use the built-in copy() function to replicate your data:

Alternatively, you can copy a list with the list() method:

List Comprehensions

List comprehensions are a handy option for creating lists based on existing lists. When using them you can build by using strings and tuples as well.

List Comprehensions Examples

Here’s a more complex example that features math operators, integers, and the range() function:

Tuples

Tuples are similar to lists — they allow you to display an ordered sequence of elements. However, they are immutable and you can’t change the values stored in a tuple.

The advantage of using tuples over lists is that the former is slightly faster. So it’s a nice way to optimize your code.

How to Create a Tuple

Note: Once you create a tuple, you can’t add new items to it or change it in any other way!

How to Slide a Tuple

The process is similar to slicing lists.

Output:

Convert Tuple to a List

Since Tuples are immutable, you can’t change them. What you can do though is convert a tuple into a list, make an edit and then convert it back to a tuple.

Here’s how to accomplish this:

Dictionaries

A dictionary holds indexes with keys that are mapped to certain values. These key-value pairs offer a great way of organizing and storing data in Python. They are mutable, meaning you can change the stored information.

A key value can be either a string, Boolean, or integer. Here’s an example dictionary illustrating this:

How to Сreate a Python Dictionary

Here’s a quick example showcasing how to make an empty dictionary.

Option 1:new_dict = {}

Option 2:other_dict= dict()

And you can use the same two approaches to add values to your dictionary:

How to Access a Value in a Dictionary

You can access any of the values in your dictionary the following way:

You can also use the following methods to accomplish the same.

• dict.keys() isolates keys
• dict.values() isolates values
• dict.items() returns items in a list format of (key, value) tuple pairs

Change Item Value

To change one of the items, you need to refer to it by its key name:

Loop Through the Dictionary

Again to implement looping, use for loop command.

Note: In this case, the return values are the keys of the dictionary. But, you can also return values using another method.

If Statements (Conditional Statements) in Python

Just like other programming languages, Python supports the basic logical conditions from math:

• Equals: a b
• Not Equals: a != b
• Less than: a < b
• Less than or equal to a <= b
• Greater than: a > b
• Greater than or equal to: a >= b

You can leverage these conditions in various ways. But most likely, you’ll use them in “if statements” and loops.

If Statement Example

The goal of a conditional statement is to check if it’s True or False.

Output:

That’s True!

Nested If Statements

For more complex operations, you can create nested if statements. Here’s how it looks:

Elif Statements

elif keyword prompts your program to try another condition if the previous one(s) was not true.

Here’s an example:

If Else Statements

else keyword helps you add some additional filters to your condition clause. Here’s how an if-elif-else combo looks:

If-Not-Statements

Not keyword lets you check for the opposite meaning to verify whether the value is NOT True:

Pass Statements

If statements can’t be empty. But if that’s your case, add the pass statement to avoid having an error:

Python Loops

Python has two simple loop commands that are good to know:

• for loops
• while loops

Let’s take a look at each of these.

For Loop

As already illustrated in the other sections of this Python checklist, for loop is a handy way for iterating over a sequence such as a list, tuple, dictionary, string, etc.

Here’s an example showing how to loop through a string:

While Loops

While loop enables you to execute a set of statements as long as the condition for them is true.

How to Break a Loop

You can also stop the loop from running even if the condition is met. For that, use the break statement both in while and for loops:

Class

Since Python is an object-oriented programming language almost every element of it is an object — with its methods and properties.

Class acts as a blueprint for creating different objects. Objects are an instance of a class, where the class is manifested in some program.

How to Create a Class

Let’s create a class named TestClass, with one property named z:

How To Create an Object

As a next step, you can create an object using your class. Here’s how it’s done:

Further, you can assign different attributes and methods to your object. The example is below:

How to Create a Subclass

Every object can be further sub-classified. Here’s an example:

Dealing with Python Exceptions (Errors)

Python has a list of in-built exceptions (errors) that will pop up whenever you make a mistake in your code. As a newbie, it’s good to know how to fix these.

The Most Common Python Exceptions

• AttributeError — pops up when an attribute reference or assignment fails.
• IOError — emerges when some I/O operation (e.g. an open() function) fails for an I/O-related reason, e.g., “file not found” or “disk full”.
• ImportError — comes up when an import statement cannot locate the module definition. Also, when a from… import can’t find a name that must be imported.
• IndexError — emerges when a sequence subscript is out of range.
• KeyError — raised when a dictionary key isn’t found in the set of existing keys.
• KeyboardInterrupt — lights up when the user hits the interrupt key (such as Control-C or Delete).
• NameError — shows up when a local or global name can’t be found.
• OSError — indicated a system-related error.
• SyntaxError — pops up when a parser encounters a syntax error.
• TypeError — comes up when an operation or function is applied to an object of inappropriate type.
• ValueError — raised when a built-in operation/function gets an argument that has the right type but not an appropriate value, and the situation is not described by a more precise exception such as IndexError.
• ZeroDivisionError — emerges when the second argument of a division or modulo operation is zero.

How to Troubleshoot The Errors

Python has a useful statement, design just for the purpose of handling exceptions – try/except statement.

Here’s a code snippet showing how you can catch KeyErrors in a dictionary using this statement:

You can also detect several exceptions at once with a single statement. Here’s an example of that:

Try/Except With Else Clause

Adding an else clause will help you confirm that no errors were found:

Iphone 4 Hacktivate Tool All V2 free download - All Video Downloader, SWAT 4, iCloud Remover Tool, and many more programs. Iphone 5 Hacktivate Tools Free Download Link. Update Intruduction. Download And Unzip Use Tools. Open Shh Rd Tools Click 1 For R un. Run Hacktivate Tool For 3. Run 4 For Exite. Iphone hacktivate tool free download. Iphone 4 Hacktivate Tool free download - SWAT 4, iCloud Remover Tool, Windows USB/DVD Download Tool, and many more programs. Click here if your download doesn't start automatically in 10 seconds. The file has been deleted. Get WhatsTools from Google Play Download App. Click here to download WhatsTools - Share Archives.

Conclusion

Now you know the core Python concepts!

By no means is this Python checklist comprehensive. But it includes all the key data types, functions, and commands you should learn as a beginner.

As always, we welcome your feedback in the comment section below!