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Previous lesson: Python programming basics
This lesson explains how to import modules from the Python Standard Library. It introduces the concept of methods associated with a class.
Learning objectives At the end of this lesson, the learner will:
.upper()method to turn a string to all uppercase letters.
Total video time: 33m 35s
Lesson Jupyter notebook at GitHub
A module is reusable code stored in a separate file. Functions can be imported from a module using an
Modules in the Standard Library are included in the normal Python distribution. Functions in the Standard Libarary that aren’t built-in must be imported.
Forms of the import statement
Import a single function:
from math import sqrt answer = sqrt(3) # don't need to include the module name
Import the whole module:
import math answer = math.sqrt(3) # prefix the function name with the module name
Import the module and abbreviate:
import math as m answer = m.sqrt(3) # prefix the function name with the module abbreviation
Example - Import a single function:
from math import sqrt answer = sqrt(2) print(answer)
Example - Import the entire module:
import math pi = math.pi # modules can include constants (unchanging variable values) print(pi) answer = math.cos(pi) print(answer)
Example - abbreviate the imported module name
import math as m answer = m.log10(1000) print(answer)
# Import the time module import time # Print current local time as a formatted string # "Local time" may be ambiguous if running in the cloud! print(time.strftime('%H:%M:%S')) print("I'm going to go to sleep for 3 seconds!") # Suspend execution for 3 seconds time.sleep(3) print("I'm awake!") print(time.strftime('%H:%M:%S'))
Note that some functions return a value, as in the first example where the current time is returned, then printed. But some functions do not return any value. Rather, they just do something, as in the second example, which makes the script pause for a certain number of seconds.
import os working_directory = os.getcwd() print(working_directory) print(os.listdir()) # no argument gets working directory # print(os.listdir(working_directory + '/Documents'))
In the examples we have used so far, the modules we have wanted to import have been included in the Standard Library, so we have been able to load them using an
import statement. However, sometimes when you try to import a module that you’ve seen in code examples, you will receive an error message saying that the module can’t be found. In that case, the library containing that module isn’t downloaded to your computer and it must be installed. (Note: we will use “package” and “library” interchangeably and ignore their technical differences.)
The libraries that are installed automatically will depend at least partly on how you installed and are using Python. If you are using Jupyter notebooks installed as part of an Anaconda distribution, many of the commonly used modules not included in the Standard Library will be available to you without needing to install them. The same is true with Colab. If you did a stand-alone installation of Jupyter notebooks, you are more likely to need to install additional libraries.
There are two common installation methods. The standard method uses
pip. If you are using Anaconda, it comes with its own package manager,
conda. Both of these applications are used in the console (
Command prompte on Windows or
Terminal on Mac.) Here is an example of how to install a commonly used package:
requests. In the console, enter the following:
pip3 install requests
If it doesn’t work on your computer, then try it again without the “3”:
pip install requests
(Depending on your installation, the “3” may be important if
pip without the “3” installs Python 2 packages.) You will see lines of printout but eventually you should see a prompt again. Once the installation is complete, you should be able to use the library in your Jupyter notebook.
Note: The name of the library to be installed is often, but not always, the name of the module that you will load. For example, the commonly used BeautifulSoup library is installed using
pip install beautifulsoup4
but is typically loaded in code using
from bs4 import BeautifulSoup
bs4 is the actual module name. Check the documentation for the library to make sure what the name is of the library before installing (for example this for BeautifulSoup).
Since you cannot easily access the Colab runtime environment through a terminal window, you can’t issue the
pip command directly from a command line. Fortunately, the
iPython notebook system (used by both Jupyter and Colab notebooks) includes several ways to run command line commands.
One way is to use “magic commands”. When a code line begins with a percent sign (
%), it is interpreted as a special “magic” command rather than a Python command. Here’s how you would install the
requests library in a notebook environment:
%pip install requests
(Note that in Colab you don’t actually need to do this, since
requests is already installed by default.) There is also a magic
%conda command if you are using the Conda package manager that is installed with Jupyter notebooks in the Anaconda distribution.
Another option for running
pip is to begin the line with an exclamation mark (
!), which runs the following text as if it were given from the command line. Using that method, you would enter:
!pip install requests
However, the magic command method is probably better since it automatically installs the module using the version that is correct for the version of Python that is running the notebook.
If you are running Jupyter notebooks in your local environment, you don’t need to run these install commands every time you run the notebook, since the installation will persist on your local hard drive. However, Colab spins up a new cloud server for you each time you re-open a Colab notebook. So on Colab, you need to do the install each time you open the notebook for a new session. (There are ways to “permanently” install libraries in Colab, but they are more complicated than what we want to get into in this lesson. See Stack Overflow for details.)
Particular objects are instances of generic classes.
Methods are associated with particular classes of objects. Any instance of a class can use a method associated with that class.
A method is essentially a function that is linked to a class.
The form of a method is:
Arguments may be passed into a method. A method may produce a return value or it may carry out an action without returning anything.
Some string class methods are:
# instantiate a string object my_message = 'Do not yell at me, Steve!' # apply the .upper() method to the string object shouting = my_message.upper() print(shouting) ee_cummings = my_message.lower() print(ee_cummings) my_book = my_message.title() print(my_book)
When looking at someone else’s code, you can tell whether an object like
something.otherthing() is a function from the
something module or a method of a
something object by looking at the code. If the top of the code has a statement like:
then it is a function imported from the
something module. If the code has a statement somewhere like:
something = something_else
where an object is assigned to a variable named
something, then it is a method of a
The following video covers content that is not required, but you might find it useful.
The datetime module defines several kinds of objects. Two of them are:
Date objects can be instantiated by specifying their year, month, and day:
datetime.date(2001,9,11). They can also be instantiated using the
import datetime # Instantiate two date objects, numeric arguments required. sep_11 = datetime.date(2001,9,11) this_day = datetime.date.today() # method sets the date value as today print(type(sep_11)) # Create various string representations of the date objects print(sep_11.isoformat()) # use ISO 8601 format print(sep_11.weekday()) # numeric value; Monday is 0 print(sep_11.strftime('%A')) # '%A' is a string format code for the day print() print(this_day.isoformat()) print(this_day.weekday()) print(this_day.strftime('%A'))
DateTime objects can be instantitated using the current UTC (i.e. Greenwich Mean Time) time using the
import datetime # Instantiate a dateTime object # The dateTime will be expressed as Universal Coordinated Time (UTC) # a.k.a. Greenwich Mean Time (GMT) right_now = datetime.datetime.utcnow() print(type(right_now)) # Create string representations of the datetime object print(right_now.isoformat()) # See the datetime module documentation for the string format codes print(right_now.strftime('%B %d, %Y %I:%M %p'))
datetime objects are abstract and don’t have any particular representation. They can be represented as strings using several methods. The
.isoformat() method represents them as strings according to the ISO 8601 standard. The
.strftime() method represents them as strings based on user-defined string format codes.
Notice how this differs from the
strftime() function from the
time module. The
strftime() function returns a string object. It does not create an instance of any abstract object. In contrast, abstract
datetime objects are created by the
datetime module. They are not strings, but string representations of them can be created by several methods.
The questions for the practice assignment are in this assignment Colab notebook. You will need to make a copy of it in your own drive before editing it.
For feedback on the assignment, change the sharing properties to allow access for anyone with the link, and send the notebook link to the instructor.
Next lesson: Lists and dictionaries
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