Module 2: Managing Files with Python

Python File System Automation Tutorial

Welcome to the world of automating file system tasks with Python! This tutorial will guide you through essential techniques to interact with files and directories effectively.

Get Ready:

• Install Python: Ensure you have Python installed on your system. Download it from https://www.python.org/downloads/ if needed.
• Choose an IDE: Select a code editor or IDE like IDLE, PyCharm, or Visual Studio Code to write and run your Python scripts.

Key Concepts:

• Paths:
  • Absolute Paths: Specify the full location of a file or directory from the root of the file system (e.g., /home/user/Documents/file.txt on Linux).
  • Relative Paths: Indicate a location relative to the current working directory (e.g., ../data/ to go up one directory and then into the data directory).
• Modules: Import the os module to work with file systems in Python.

Common Operations:

1. Checking File Existence:

import os
if os.path.exists("my_file.txt"):
    print("The file exists!")
else:
    print("The file does not exist.")

1. Getting Directory Contents:

contents = os.listdir("my_directory")
for item in contents:
    print(item)

1. Creating Directories:

os.mkdir("new_directory")

1. Creating Files:

with open("new_file.txt", "w") as file:
    file.write("Hello, world!")

1. Reading File Contents:

with open("my_file.txt", "r") as file:
    contents = file.read()
    print(contents)

1. Renaming Files and Directories:

os.rename("old_name.txt", "new_name.txt")

1. Deleting Files and Directories:

os.remove("file_to_delete.txt")
os.rmdir("empty_directory")

Working with CSV Files:

• Import the csv module to read and write data in CSV format.
• Use csv.reader() to read CSV files and csv.writer() to write to them.

Practice and Explore:

• Experiment with these operations in your Python environment.
• Explore other file system-related functions in the os module.
• Learn about advanced techniques like pathlib for more object-oriented file handling.

Remember:

• Always handle files and directories with care to avoid data loss or unintended modifications.
• Practice regularly to become proficient in Python file system automation.

Welcome back. Let’s take a quick moment
to think back on what you’ve already accomplished in this course. In the previous module, you discovered
how to install Python and set up your own programming environment. Then, you learned how to write and
execute Python scripts locally. We then discussed when to consider
automating, and what problems we might encounter when doing that. To finish up, you were introduced to
Qwiklabs, the platform that will help you keep growing your skills throughout
this course. Wow. You should be impressed with
yourself, and I hope you’re as excited as I am for what’s coming up next. Over the next few videos, we’ll check
out some ways you can use Python to interact with file systems. As an IT specialist, it’s likely that
you’ll need to manipulate files and directories on a computer a lot. When you need to work on a large number
of files and directories, that’s when automation can be a huge help. As a sysadmin, in my job, I interact
with files and directories all the time, sometimes even if I have no intention
of modifying them. For example, I use Python to check if a
certain file exists on the file system before doing any other operations. As you might remember, operating
systems like Mac OS, Windows, and Linux use file systems to organize and
control how data is stored and accessed. Data is usually stored on a disk and
saved in files which are held in containers called directories or
folders. File systems are usually organized in a
tree structure with directories and files nested under their parents. We know where a resource like a
directory or a file is located within that tree structure by its path. An absolute path is a full path to the
resource in the file system. For example, on a Windows computer, the
absolute path to the folder for the user Jordan would be C:\Users\Jordan. On a Linux computer, the absolute path
to the equivalent directory would be /home/jordan. We call it absolute path
because it doesn’t matter where in the file system our script is running, the
absolute path will always lead us to the resource. On the flip side, relative
paths use only a portion of a path to show where the resource is located in
relation to the current working directory. Relative paths are a
shortcut that you can use so you don’t have to write out the full file path. But keep in mind, they only make sense
relative to the current location. So for example, if we list the contents
of the directory examples, we’ll get different outputs depending on what the
current directory is. If our current directory is
/home/jordan, we’ll get the contents of /home/jordan/examples. But if the
current directory is /user/share/doc/python3, we’ll get the
contents of /user/share/doc/python3/examples. In
the upcoming videos, we’ll do a rundown of the many things that we can do with
Python to manipulate files and directories. We’ll also look at
specific file format called CSV that we’ll use to read and write data. So we’ve got lots of good stuff ahead. Let’s get to it.

What is the difference between the readline() and read() methods?

The readline() method reads a single line from the current position, the read() method reads from the current position until the end of the file.

Right on! Both methods read from the current position. The readline() method reads one line, while read() reads until the end of the file.

In the introductory course to Python, we
provided information to our scripts by creating variables and passing
parameters to our functions with the values that we wanted. This works fine
for small scripts, but it’s usually not ideal for larger jobs. When processing large chunks of data,
it’s a good idea to read that data from files. In programming, we work with
files all the time. It’s such a useful task that most
programming languages have the ability to work with files baked into the core
set of features. Python is no exception. It gives us
file objects which we can use to read and write to files. To open a file on a
computer called spider.txt, we can write this code. What we’re doing here is creating a new
file object and assigning it to a variable called File. The parameter we’ve passed to the open
function is the name of the file we want to open. In this case, we’re assuming
the file we want to read is in the same directory as a script we’re running but
we can just as easily pass an absolute path to open a file in a different
directory. When we open a file, like we’re doing
in this example, the operating system checks that we have permissions to
access that file and then gives our code a file descriptor. This is a token
generated by the OS that allows programs to do more operations with the file. In Python, this file descriptor is
stored as an attribute of the files object. The file object gives us a
bunch of methods that we can use to operate with the file. Now, with this
file object, we can read the contents of the file and print them to the screen. Here we’ve used the readline method. It lets us read a single line of a
file. Let’s call it again, see what happens. Nice. This time, we got the second line
of a file. So how does this work? Well, each time
we call the readline method, the file object updates the current position in
the file. So it keeps moving forward. We can also call the read method, which
reads from the current position until the end of the file instead of just one
line. And washed the spider out, out came the
sun, wait don’t go. You don’t like my singing? That’s okay. Let’s check out the other example. The file here has six lines in total. We read the first two with our calls to
readline and then the other four altogether with the call to read. Just like readline, the read method
starts reading from wherever we currently are in the file. But instead of just one line, it reads
all the way through to the file’s end. Finally, we close the file using the
close method. This open-use-close pattern is a
typical way of working with files in most programming languages. It’s a good idea to close files after
you’ve opened them for a few reasons. First, when a file is opening your
script, your file system usually locks it down and so no other programs or
scripts can use it until you’re finished. Second, there’s a limited
number of file descriptors that you can create before your file system runs out
of them. Although this number might be high,
it’s possible to open a lot of files and deplete your file system resources. This can happen if we’re opening files
in a loop, for example. Third, leaving open files hanging
around can lead to race conditions which occur when multiple processes try to
modify and read from one resource at the same time and can cause all sorts of
unexpected behavior. No one wins in a race condition. Now, I’m going to let you in on a
little secret. I’m the worst at remembering to close
my files, and you might agree with me it could get pretty hard to keep track of
what files you’ve opened and then to remember to close them. Fortunately,
the creators of Python agree. So to help us remember to close the
file after we’re done using it, Python lets us create a block of code by using
the keyword “with”. Let’s see what that looks like. As you can see, the “with” keyword lets
us create a block of code with the work we’d want to do with the file inside of
it. In this case, we want to take a line of
data from the file and print it to the screen, which is what print file
readline does. When we use a “with” block, Python will
automatically close the file. So we don’t need to remember to do that
ourselves. One last thing to think about, thanks
Python. Both the open-use-close approach and
the “with” approach have their advantages. Using a “with” block is a
good way to open and work on a single file then have the file automatically
closed at the end of the block. On the flip side, using open outside of
a block means we can use a file object in other places in our code. So we’re not restricted to just one
single block. But when taking this approach, we need
to remember to close it when we’re finished. Okay. I hope this helps you feel pretty
comfortable with how to open, read, and display a file. In the next video,
we’re going to check out some ways of iterating through the contents of the
file.

Here’s a tutorial on File Handling in Python:

Introduction

• Files store persistent data on your computer.
• Python offers built-in functions and methods to interact with files effectively.

Opening Files

• Use the open() function to open a file:

Python

file_object = open("filename.txt", "mode")

• File modes:
  • "r": Read mode (default)
  • "w": Write mode (overwrites existing content)
  • "a": Append mode (adds content to the end)
  • "x": Create mode (fails if the file exists)
  • "r+": Read and write mode
  • "w+": Write and read mode (overwrites existing content)
  • "a+": Append and read mode

Reading from Files

• read(): Reads the entire file contents into a string:

Python

contents = file_object.read()

• readline(): Reads a single line at a time:

Python

line = file_object.readline()

• readlines(): Reads all lines into a list:

Python

lines = file_object.readlines()

• Iteration: Iterate over lines using a for loop:

Python

for line in file_object:
    # Process each line

Writing to Files

• write(): Writes strings to a file:

Python

file_object.write("Text to write\n")  # \n for a newline

• writelines(): Writes a list of strings:

Python

lines = ["Line 1\n", "Line 2\n"]
file_object.writelines(lines)

Closing Files

• Always close files using close() to release resources:

Python

file_object.close()

Additional Tips

• Use with statement for automatic closing:

Python

with open("filename.txt", "mode") as file_object:
    # Work with the file

• Be mindful of file paths and permissions.
• Consider using context managers for proper resource management.
• Choose appropriate file modes based on your operations.

Common Operations

• Reading data from files
• Writing data to files
• Appending data to files
• Copying files
• Deleting files
• Renaming files

Practice

• Experiment with different file handling techniques to solidify your understanding.

I’m ready to provide more specific guidance or examples if you have particular questions. Feel free to ask!

Can you identify which code snippet will correctly open a file and print lines one by one without whitespace?

with open("hello_world.txt") as text:
    for line in text:
	    print(line.strip())

Good work! Here, we are iterating line by line, and the strip() command is used to remove extra whitespace.

We just took a look at the read line and
read methods, which allow us to read a single line of the file or the whole
contents of the file right to the end. Along with this, file objects can be
iterated in the same way as other Python sequences like list or strings. This is really useful when you want to
process a file line by line. Say for example you want to make a
whole line uppercase before printing it. You can do that with something like
this. Cool. Looks like that worked. But what are these weird empty lines
between the contents? Any idea what’s going on? What’s
happening is that the file has a new line character at the end of each line. So when Python reads the file line by
line, the line variable will always have a new line character at the end. In other words, the newline character
is not removed when calling read line. When we ask Python to print the line,
the print function adds another new line character, creating an empty line. What can we do to avoid getting the
empty lines? We can use a string method, strip to
remove all surrounding white space, including tabs and new lines like this. Awesome. Now that we use strip to
remove the newline character, we get the output without empty lines, just like
we wanted. Another way we can work with the
contents of the file is to read the file lines into a list. Then, we can do something with the
lists like sort contents. To do that, we open the file and use
the.readlines method. Let’s see how that looks. First, we
open the file. Then, read all the lines. Now, we close the file. Even though the file object is now
closed, the lines variable has the list of lines in the file, so we can operate
on it. For example, let’s sort it and print
it. There are two things to check out on
this code. First, the lines have been sorted
alphabetically, so they’re no longer in the order that they were in the file. Second, we can see that Python displays
a newline character using “\n” symbol when printing a list of strings. This is a way of explicitly showing
that there’s a new line character in those strings. In general, to display a
character that’s not printable, Python uses escape sequences with backslash,
like \n. Another common escape sequence is \t,
for tab. We can also use it for escaping quotes,
if we have a string that contains either a single or double quote. A quick word of caution, methods like
read or readlines that read the whole file at once are useful, but we should
be careful when reading the entire contents of a file into a variable of
our programs. If the file is super large, it can take
a lot of our computer’s memory to hold it, which can lead to poor performance. If a file is just a few kilobytes like
in our example here, it’s fine to read it and process it completely in memory. But for large files, like the big log
file of hundreds and hundreds of megabytes of data, it’s more efficient
to process it line by line. How’s this all sounding? Let’s do a
quick recap. We’ve just learned how to open files
using the open function and how to read those files using methods like read and
read lines. We also learned how to iterate through
a file line by line using the read line function and for loops. Up next, we’re going to learn how to
write content to files.

What happens to the previous contents of a file when we open it using "w" ("write" mode)?

The old contents get deleted as soon as we open the file.

You nailed it! When using write mode, the old contents get deleted as soon as the file is opened.

Now that we know how to read files in
Python, the syntax for writing to them probably won’t seem too strange. Check this out. So what do we have here? You can see that we’re using the with
block pattern we discussed in the previous video to open a file called
novel.txt. You might also guess that using the
write method on a file object writes contents to it instead of reading from
it. The second argument to the open method
is new though. So what does the w mean? File objects can be opened in several
different modes. A mode is similar to a file permission. It governs what you can do with the
file you’ve just opened. By default, the open function uses the
r mode, which stands for read only. You get an error if you try to write to
a file opened in read only mode. Since read only is the default, we
don’t have to pass the R as a second argument when we just want to read the
file. Writing however is a whole different
story. The w character tells the open function
that we want to open the file for writing only. If the file doesn’t exist
then Python will create it. If the file does exist, then its
current contents will be overwritten by whatever we decide to write using our
scripts. It’s important to remember that when
opening a file in write only mode, you can’t read its contents. If you try to, the interpreter raises
an error. If you want to add content to a file
that already exists, you can do that by using other modes like a for appending
content at the end of an existing file. Or r+ for read-write mode, where you
can both read contents and overwrite it. This has tripped up a lot of us more
than once. So I’ll say this again. If you open a
file for writing and the file already exists, the old contents will be
deleted as soon as the file is opened. Yikes, imagine accidentally deleting
important content in a file. So remember, double check that you’re
opening the right file using the right mode. For example, if you’re generating
a log file of events that your program came across, you probably want to open
the file using append, a mode. Opening it in write, w mode, would mean
you’d write over any previous entries in that file, and that’s not a good idea
for a log file. Or if you’re generating a report and
want to write it out to a new file using the write, w mode, you probably want to
check if the file exists, to avoid losing any previous contents. We’ll learn how to check if a file
exists in an upcoming video. And finally, what’s that lonely looking
30 doing hanging out at the end of our code? That’s a return value of the
write method. When successful, this method returns
the number of characters that it wrote. So in this case, 30. Along with read only, write, append,
and read-write, the open function supports a bunch of other modes. You’ll find all the documentation on
this in the official reference, which we’ll link to in the next reading. Okay, and breathe, nice job. You just learned some complex things. It might not make sense right away. So have a look at the cheat sheet
coming up, which puts all the information in one place for you. And remember, if you feel stuck at any
point, you can always rewatch the videos or ask for help in the discussion
forums. Over the last few videos, you learned
how to open and read a file. How to iterate through a file and
finally how to write a file. We use these operations a lot when
working with files. So they’re super important to know how
to use. So head on over to check out the cheat
sheet now, and after that, we get some hands-on practice with all this and in
Jupyter Notebook.

Python’s OS Module for File Manipulation: A Hands-On Tutorial

Introduction:

Welcome to this tutorial on using the powerful os module in Python to interact with files and directories on your computer! This module provides a layer of abstraction, allowing you to perform file operations seamlessly across different operating systems. Let’s dive in!

1. Importing the Module:

Python

import os

2. Deleting Files:

• Use os.remove(filename) to delete a specified file.
• Handle potential errors using try-except blocks:

Python

try:
    os.remove("my_file.txt")
    print("File deleted successfully!")
except FileNotFoundError:
    print("File not found.")

3. Renaming Files:

• Use os.rename(old_name, new_name) to rename a file.
• Similarly, handle potential errors:

Python

try:
    os.rename("old_file.txt", "new_file.txt")
    print("File renamed successfully!")
except FileNotFoundError:
    print("File not found.")

4. Checking File Existence:

• Use os.path.exists(filename) to check if a file exists. Returns True or False.

Python

if os.path.exists("my_data.csv"):
    print("File exists!")
else:
    print("File does not exist.")

5. Other File Information:

• Retrieve additional file details:

Python

import os.path

file_size = os.path.getsize("my_document.docx")
print("File size:", file_size, "bytes")

last_modified_time = os.path.getmtime("my_script.py")
print("Last modified:", last_modified_time)

6. Creating Directories:

• Use os.mkdir(directory_name) to create a new directory.

7. Listing Directory Contents:

• Use os.listdir(directory_path) to list files and directories within a path.

Remember:

• Adapt absolute file paths when working across different platforms.
• Explore the os module’s documentation for more comprehensive functions: https://docs.python.org/3/library/os.html

How can we check if a file exists inside a Python script?

Using the os.path.exists function.

You got it! The os.path.exists function will return True if the file exists, False if it doesn’t.

In earlier videos, we saw how we can
read files, iterate through the contents of files, and also write contents of
files. Those are the most common operations
we’ll do with files, but there’s plenty of other things we might need to do
when working with files in our scripts. We may need to delete, rename or move
files, or we might need information about a file, like the time it was last
modified or its current size. Let’s explore some of the many things
that we can do with files in Python. For these operations, we’ll be using
functions provided by the OS module. This module provides a layer of
abstraction between Python and the operating system. It allows us to
interact with the underlying system without us knowing whether we’re
working on a Windows, Mac, Linux, or any other operating system supported by
Python. This means that you can write and test
a script on one operating system like Windows and then run it on a different
operating system like Linux. But one thing to watch out for, paths
can be different across different operating systems. So whenever we’re
using an absolute path in our code, we need to make sure we can provide
alternatives for the platforms we want to support. The OS module lets us do
pretty much all the same tasks that we can normally do when working with files
from the command line. We can change the file permissions and
delete or rename files through our code. This means you can write scripts to do
these operations for you automatically. I bet you’re already thinking about how
useful the OS module is going to be in your IT role. To delete a file, we can
use the, “Remove” function from the OS module. Let’s see this in action. So we first import the OS module. Then we call the remove function the OS
module gives us and pass the string novel.txt which is the file we created
earlier. The file has now been deleted. Let’s see what happens if we try to
remove it again. Fail. If we try to remove the file that
doesn’t exist, the function will raise an error. We’ve got a file not found
error. The error type’s pretty explanatory and
the message also tells us the name of the file we couldn’t find: novel.txt. We can easily rename a file with the
rename function. The first parameter to rename function
is the old name of the file and the second is new name. Let’s check it out. Yeah, in Python, it’s that easy to
write a masterpiece. If we tried to do this for a file that
didn’t exist, we’d get a file not found error again. So how do we check if the
file exists or not? There’s a sub-module inside the OS
module for dealing with things related to file information like whether they
exist or not. This is called the OS path sub-module. And we can use the exists function in
this module to check whether a file exists. Let’s try this out with a
couple of examples. In this example, the exists function
returns a value of true telling us that the finished_masterpiece.txt exists but
userlist.txt doesn’t exist, so the function returns the value of false. The exists function is super useful. We can use it to check that a file
exists before trying to read it or verify that it doesn’t exist before
trying to write it which helps us avoid losing any data. Python gives us some
basic ways to interact with files stored on a computer. We’re going to take a
look at them in our next video. We’ll cover how to get other
information files like their size, the last time they were modified, and a
bunch more info. See you there.

Here’s a comprehensive tutorial on File Manipulation in Python:

Key Modules:

• os: Provides functions for interacting with the operating system, including file and directory operations.
• os.path: Offers path-related operations for handling file and directory paths conveniently.

Basic Operations:

1. Opening Files:
   • Use the open() function: Pythonfile = open("my_file.txt", "mode")
   • Common modes:
     • “r”: Read (default)
     • “w”: Write (overwrites existing content)
     • “a”: Append (adds to existing content)
     • “x”: Create (fails if file exists)
     • “r+”: Read and write
2. Reading from Files:
   • read(): Reads the entire file content into a string.
   • readline(): Reads a single line at a time.
   • readlines(): Reads all lines into a list of strings.
3. Writing to Files:
   • write(): Writes a string to the file.
4. Closing Files:
   • Always close files using file.close() to ensure proper resource management.
5. Renaming Files:
   • os.rename(old_name, new_name)
6. Deleting Files:
   • os.remove(filename)
7. Checking File Existence:
   • os.path.exists(filename)

Getting File Information:

• File Size:
  • os.path.getsize(filename) (returns size in bytes)
• Last Modification Time:
  • os.path.getmtime(filename) (returns a Unix timestamp)
    • Convert to human-readable format: datetime.datetime.fromtimestamp(timestamp)

Paths:

• Relative Paths: Specify file locations relative to the current working directory.
• Absolute Paths: Indicate the full path to a file from the root directory.
• Getting Absolute Path: os.path.abspath(filename)

Additional Operations:

• Creating Directories: os.mkdir(directory_name)
• Changing Directories: os.chdir(directory_name)
• Listing Directory Contents: os.listdir(directory_name)
• Iterating Over Files: Use os.walk() to traverse a directory tree.

Remember:

• Explore the official documentation for more functions and details: https://docs.python.org/3/library/os.html
• Practice with examples to solidify your understanding.

Some more functions of the os.path module include getsize() and isfile() which get information on the file size and determine if a file exists, respectively. In the following code snippet, what do you think will print if the file does not exist?

import os
file= "file.dat"
if os.path.isfile(file):
    print(os.path.isfile(file))
    print(os.path.getsize(file))
else:
	print(os.path.isfile(file))
    print("File not found")

False

File not Found

Awesome! Because the file does not exist, getsize() will never be called and our error message will be printed instead.

We saw earlier how to manipulate files
with functions like OS.rename and OS.remove, and how to check whether the
file exists using OS.path.exist. We can get a lot more info about our
files using functions in OS.path module. For example, to check how big a file
is, we can use the getsize function which returns the file size in bytes. To check when the file was last
modified, the getmtime function comes in really handy. Let’s check out how this
works. What’s that long number? It doesn’t look like time, does it? That’s because it’s a timestamp. In this case specifically, it’s a Unix
timestamp. It represents the number of seconds
since January 1st, 1970. Seems a bit random, but there’s
actually a really good reason behind this date. This was adopted years ago
to store the times associated to files in computers. Since that’s when they
started publishing Unix operating systems, Unix uses that date because
there couldn’t be any file created before that time. While Unix timestamps
have a 50-year history, they’re still very much present today. They’re used by file systems to show
when a file was created, accessed, or modified. They are also used in other
systems like databases. As an IT specialist, you’re bound to
run into them in your day to day. But despite all of that, the number is
pretty hard to make sense of. We can use the datetime module to make
it easier for us humans to read, like this. Here, we’re using the fromtimestamp
method of the datetime class inside the datetime module. It makes the date far
easier for us to understand. Remember, the functions and the OS.path
module take the info provided by the operating system so that we can use it
in our scripts no matter what OS we’re running. We can check a file size or
last modification date without having to know the operating system, the machines
running or the type of file system that the file stored in. Nice, right? Another cool feature of the functions
is that we can work with both relative and absolute paths. In our examples,
we’ve been using the relative file names without having to specify their full
paths. In some cases, we may need to specify
exactly where the file is to work with it in our script. This is where the
abspath function can help. The abspath function takes a filename
and turns it into an absolute path. Python uses the current working
directory which is where the script is being run to start looking for the file
and construct the full path that identifies it. This is useful if you
want to store at the file’s full path or access a file no matter what the
current directory is. There is a ton more functions in the OS
and OSpath modules that let us work with files. But don’t worry, you don’t have
to learn them all by heart. Whenever you need to do something with
files, it’s a good idea to check the documentation and research what
functions are available to find the ones that you need. You’ll find a link to
that documentation in our next reading. Wow, we’re steaming right along now. We’ve seen how to read, iterate
through, and write files. We then learned how to manage files
using lots of useful methods. Now is a good time to try our examples
on your computer or maybe even come up with your own to really get a grasp of
it all. So no rush, you’ll find me in the next
video whenever you’re ready. There, we’ll dive into how to work with
directories in our scripts.

What's the purpose of the os.path.join function?

It creates a string containing cross-platform concatenated directories.

Right on! By using os.path.join we can concatenate directories in a way that can be used with other os.path() functions.

If you need to work with files in your
scripts, odds are you’ll have to handle directory suite. For example, you might
need to process all the files that are in a specific directory or generate a
directory with the results of your data analysis. As with files, Python
provides a bunch of different functions that let us create, delete and browse
the contents of directories. You might remember when we talked about
the current working directory and how important it is to know this when using
a relative path to determine the location of a file. To check which
current directory your Python program is executing in, you can use the getcwd
method. If you use a unix-like system, you
might remember that the name of the command that prints the working
directory is called pwd. In this case, the current directory is
our user’s home directory which is a default directory we start on when
opening a terminal in Linux. To create a directory, we use the mkdir
function. This function has the same name as both
the Windows and Linux commands that do the same exact thing. So we’ve just created a directory
called newdir and it’s located in the current working directory. You can also
change directories in your program by using the chdir function and passing
the directory you’d like to change to as a parameter. Just like the other
functions we’ve seen, we can use relative or absolute paths to do that. As you can see, we’ve changed the
current working directory to the new directory that we created inside of the
user’s home directory. We use mkdir to create directories and
we can use our rmdir to remove them like this but the rmdir function will only work
if the directory is empty. If we try to remove a directory that
has files in it, we get an error. We need to first delete all the files
and sub-directories in that directory before we can actually remove it but
how can we find out what contents are in that directory? Well, there are a few
techniques that we can use to do this. The os.listdir function returns a list
of all the files and sub-directories in a given directory. Let’s see how this
looks for our website directory. So we’ve got a list of strings. Since they’re just strings, we don’t
know if they’re directories or files. To find out what they are, we can use
functions like os.path.isdir but before we look at how that works. See how the list contains just file
names. If we want to know whether one of these
files is a directory, we need to use os.path.join to create the full path. Let’s see all of this in action now. This code is doing a bunch of
interesting stuff but let’s go through it step-by-step. First, we’re defining
a dir variable with the name of the directory that we want to check. This makes our code more readable and
more usable. Then we’re iterating through the file
names returned by the os.listdir. We know from our previous execution of
this function that these are just the names of the files without directory. So using os.path.join, we join the
directory to each of those file names and create a String with a valid full
name. Finally, we use that full name to call
os.path.isdir to check if it’s a directory or a file. Maybe you’re wondering what’s up with
that join function? It seems to just add a slash between
two strings. Well, the join function lets us be
independent from the operating system again. In Linux and MacOS, the portions
of a file are split using a forward slash. On Windows, they’re split using
a backslash. By using the os.path.join function
instead of explicitly adding a slash, we can make sure that our scripts work
with all operating systems. It’s another one of those handy little
tools that help us avoid errors when working on different platforms. As we called out earlier, there’s a lot
of Functions to manage directories in Python and we only covered a handful
here. We’ll learn some others throughout the
course as we need them. This stuff sure is a big complex. Great job sticking with it. Persistence and determination are key
qualities for both IT jobs and learning the program and you’re showing me that
you’ve got both by the bucket load. The more comfortable you are with what
you’ve learned so far, the easier it’ll be to learn new stuff coming up. So take the time now, to review and
practice everything before moving on. You’ll find a cheat sheet in the next
reading to help you do just that. So be sure to check that out and then
head on over to the quiz and checking in on how much you’ve learned.

Tutorial: Reading Data in Different Formats with Python

Data comes in all shapes and sizes, and Python offers powerful tools to unlock its potential. This tutorial explores how to tackle various data formats using Python, equipping you to handle real-world data challenges.

Understanding Data Formats:

Imagine data as ingredients—their structure determines how you cook with them. Common formats include:

• Text: Lines of text, useful for configuration files, logs, and simple data storage.
• HTML: Markup language structuring webpages, often containing valuable data.
• JSON: Lightweight format for data exchange, common in APIs and web applications.
• CSV: Comma-separated values, popular for tabular data like spreadsheets.

Parsing the Ingredients: The Role of Libraries

Python’s standard library provides tools for common formats:

• csv module: Read and write CSV files, a crucial skill for many tasks.
• html.parser and BeautifulSoup: Parse HTML content and extract specific data.
• json module: Work with JSON data, often encountered in web development.

For less common formats or advanced needs, explore external libraries like Pandas for advanced data manipulation or NumPy for scientific computing.

Hands-on: Conquering CSV Files

Let’s dive into CSV files, a versatile format often exported from spreadsheets:

1. Import the csv module:

import csv

1. Open the CSV file:

with open("my_data.csv", "r") as csvfile:
    reader = csv.reader(csvfile)

1. Process each row:

for row in reader:
    # Access data using indexing: row[0] for first column, etc.
    print(row)

Beyond CSV: Expanding Your Toolkit

This tutorial focused on CSV, but remember:

• The same core principles apply to other formats.
• Utilize appropriate libraries and modules for diverse data structures.
• Practice with different formats to solidify your skills.

Bonus Challenges:

• Try parsing a simple HTML file to extract specific information.
• Explore libraries like Pandas for more advanced data analysis tasks.
• Remember, the possibilities are endless with Python’s rich data handling capabilities!

Ready to explore the world of data in Python? Start with mastering these core concepts and expand your toolkit with each new format you encounter. Happy coding!

If we have data in a format we understand, then we have what we need to parse the information from the file. What does parsing really mean?

Using rules to understand a file or datastream as structured data.

Right on! If we know the format of the data, we can separate it into understandable parts.

[MUSIC] When we first talked
about how to read files, we looked at files with lines of text,
one after the other. This is useful in a bunch of different
situations since lots of programs store their state and text files. And we can also have configuration
files and log files as text. But data comes in a bunch of
different formats besides text. And you may need to deal with
some of these in your scripts. Formats give data structure. And remember that computers
love structure and precision. To be able to process a data set, it helps to know ahead of time how
that data set will be arranged. If you can expect data to be
represented in a certain way, it’s easier to extract meaning from it. Let’s look at a very simple example. If we have a file that contains
one line per machine and details the users are logged
into that machine, then when we read the file we know how to parse
it to get the information that we want. Parsing a file means analyzing its
content to correctly structure the data. We use a bunch of different file formats
to structure, store, and transport data. You might be familiar with some already. For example, HTML is a markup format
which defines the content of a webpage. JSON is a data interchange format
commonly used to pass data between computers on networks,
especially the internet. CSV or comma separated values is a very
common data format used to store data as segment of text separated by commas. In the Python standard library,
you’ll find classes and modules for working with many of these data formats,
including CSV and HTML. For less common file formats or
more advanced manipulation techniques, you’ll find more libraries available
as additional Python modules. In the next few videos, we’ll check out how we can use
a CSV module to process CSV files. This not only shows how we can use Python
to work with a specific data format. Knowing how to work with CSV files
is a pretty useful skill to know. This format lets us easily store and
retrieve information that we might need for our scripts like employees in our
company or computer’s inner network. In my job as a system administrator,
I create CSV files when I want to convert the output of a command into a format
that will be easier to parse later on. For example, the df command
prints the currently used disk space in a format that’s
easy to read by human eyes. By turning the info into a CSV makes
it much easier to work with the data in my scripts. A lot of programs are capable
of exporting data as CSV files, such as spreadsheet applications like
Microsoft Excel or Google Sheets. It can actually be helpful to think of
a CSV file like it’s a spreadsheet, where each line corresponds to a row and each comma separated field
corresponds to a column. So, now that we know what CSV files are,
let’s learn how to read them.

Which of the following lines would correctly interpret a CSV file called "file" using the CSV module? Assume that the CSV module has already been imported.

data=csv.reader(file)

Right on! The reader() function of the CSV module will interpret the file as a CSV.

Working with CSV Files in Python: A Tutorial

Comma-separated values (CSV) files are a common format for storing tabular data. Their simplicity and widespread use make them valuable tools for data analysis and exchange. Here’s a tutorial to guide you through working with CSV files in Python:

1. Importing the CSV module:

Start by importing the csv module from the Python standard library:

Python

import csv

2. Reading a CSV file:

• Open the file: Use the open() function to open the CSV file in read mode (“r”).

Python

with open("my_data.csv", "r") as csvfile:
    # ... your processing here ...

• Create a reader object: Use the csv.reader() function to create a reader object that parses the file contents.

Python

reader = csv.reader(csvfile)

• Iterate through rows: Use a for loop to iterate through each row in the CSV file. Each row is represented as a list containing the individual field values.

Python

for row in reader:
    # Access data in each row
    name = row[0]
    phone_number = row[1]
    role = row[2]
    # Process the data
    print(f"Name: {name}, Phone: {phone_number}, Role: {role}")

3. Unpacking values:

For cleaner code, unpack the row list into individual variables instead of using indices:

Python

for name, phone_number, role in reader:
    # Process data using named variables
    print(f"Name: {name}, Phone: {phone_number}, Role: {role}")

4. Writing to a CSV file:

• Open the file: Open the file in write mode (“w”) or append mode (“a”).

Python

with open("new_data.csv", "w") as csvfile:
    # ... your writing here ...

• Create a writer object: Use csv.writer() to create a writer object.

Python

writer = csv.writer(csvfile)

• Write data: Use the writer.writerow() method to write each row as a list.

Python

writer.writerow(["Name", "Phone Number", "Role"])
writer.writerow(["John Doe", "123-456-7890", "Manager"])
writer.writerow(["Jane Smith", "987-654-3210", "Developer"])

5. Additional considerations:

• Delimiter: By default, csv.reader() uses a comma (,) as the delimiter. You can specify a different delimiter using the delimiter argument.
• Quotes: Use the quotechar argument to specify the character used to enclose quoted fields.
• Custom formats: For more advanced control, explore other attributes and methods of the csv module.

Remember: Always close files after reading or writing using file.close().

Practice: Use this tutorial as a starting point to:

• Write more complex data processing scripts.
• Generate your own CSV files from diverse data sources.
• Combine your CSV skills with other Python functionalities for comprehensive data analysis.

By mastering CSV handling, you unlock the potential to work effectively with various data formats, a crucial skill for many Python applications.

[MUSIC] Like we said, CSV stands for
Comma Separated Values. CSV is a pretty simple format. These files are stored in plaintext. And each line in a CSV file generally
represents a single data record. Each field in that record
is separated by a comma, with the contents of the field
stored between the commas. For example, if we are storing information
about employees at our company, we might store the data like this. Looking at this example, the line that
starts with Sabrina is a data record. And the name Sabrina Green represents
a name field followed by a phone number field and a role field. Python standard library includes
a module which lets us read, create and manipulate CSV files. Do you want to guess what
that module’s called? If you guessed CSV, you’re right. All these self-explanatory names
makes life a lot easier, right? So, we’ll be using the CSV module. And to do that, we’ll need to import it like we’ve
been doing with the other modules. Now, before we can parse a CSV file, we need to open the file
the same way as before. And now we can parse this
file using the CSV module. Okay, that has given us an instance
of the CSV reader class. We can now iterate through its contents
and access information that it parsed. Now, the row variable hold
each row in the CSV file. This variable is a list with each
field in the CSV corresponding to one element in the list. We know from the before that the first
field is a name, the second one, the phone number, and the third, the role. So we can unpack the values so that we
can use variables to refer to them. Remember that for this to work we need to
have the exact same amount of variables on the left side of the equal sign as the
length of the sequence on the right side. Now that we’ve unpacked these values,
let’s print them to the screen. As we mentioned, we unpack the row so that we don’t have to use indexes to
access each element in that list. For example, we could have used row[0]
to access the name of the employee. This is valid but it can be hard to
follow when reading a lot of code. Unpacking the list into name variables
makes the code easier to understand later on. And before we forget, let’s close this
file now that we’re done with it. Pretty useful, right? With that, we’ve now learned how to
read and iterate through CSV files. Up next, we’ll talk about how you
can generate your own CSV files.

Writing Data to a CSV File in Python

This tutorial guides you through writing data to a CSV (comma-separated values) file in Python using the csv module. CSV files are commonly used for storing and transferring tabular data due to their simple format and widespread compatibility.

Prerequisites:

• Basic understanding of Python programming
• Python installed on your system (download from https://www.python.org/)

Steps:

Import the csv module:

import csv

Prepare your data:

• Create a list of lists, where each sub-list represents a row in the CSV file.Each element within a sub-list represents a value in a specific column.

data = [
    ["Machine Name", "IP Address"],
    ["Server1", "192.168.1.100"],
    ["Client2", "10.0.0.5"],
    ["Printer", "192.168.1.200"],
]

Open the CSV file in write mode:

with open("my_data.csv", "w", newline="") as file:
    # Optional: Add "newline='' to avoid issues with extra \n characters on Windows
    writer = csv.writer(file)
    writer.writerows(data)

open function opens the file in write mode ("w").

with block ensures the file is automatically closed.

newline='' is optional and prevents extra newline characters on Windows systems.

csv.writer() creates a CSV writer object.

Write data to the CSV file:

• Use the writerows() method to write all rows at once.

    writer.writerows(data)

Verify the output:

Open the generated “my_data.csv” file in a text editor or spreadsheet program.

Confirm that your data is accurately represented in the format you intended.

Additional notes:

• You can use writerow() instead of writerows() to write each row individually.
• For more complex data structures, consider using a csv.DictWriter object to write dictionaries directly to the CSV file.
• Explore documentation for advanced options like custom delimiters, quoting styles, and error handling: https://docs.python.org/3/library/csv.html

By following these steps, you can effectively write data to CSV files in Python, enabling data exchange and storage for various purposes.

Which of the following must we do before using the csv.writer() function?

Open the file with write permissions.

Nice work! The file must be open, preferably using with open() as, and write permissions must be given.

In the last video, we used the reader function from a CSV module to read the
contents of the CSV file. Similarly, we can use the writer function to
generate contents to a file. This can be really helpful
if you process some data in your script and you
must store it in a file. Maybe you want to import it into a spreadsheet or use it
later on in your script. We’ll start by storing
the data that we want to write into a list. We’ve created a list of lists. This is the data that we want
to store in the CSV file, representing the names
of the machines in our network and
their IP addresses. All right, with that data
ready to be written, let’s open the file
in write mode. We’ll use the with
block that we saw before so we don’t forget
to close the file. Okay. Now that we have the
file opened for writing, let’s call the writer function of the CSV module with this
file as a parameter. The writer variable is now an instance of a
CSV writer class. There are two functions
that we can use: write row, which we’ll write
one row at a time; and write rows, which we’ll
write all of them together. In this case, we already have all the data that
we want to write. So we’ll call writerows. Nice. With that, we’ve run
our data to the CSV file. Before we move on,
let’s see how this looks when we use a
tool outside of Python, like the cat command. All right. Our code
worked and we generated a CSV file with the data
that we wanted. Nice work. We’re on a roll. So
let’s keep going. In the next video, we’ll learn how to read and write CSV files with dictionaries.
I’ll see you there.

DictReader() allows us to convert the data in a CSV file into a standard dictionary. DictWriter() \ allows us to write data from a dictionary into a CSV file. What’s one parameter we must pass in order for DictWriter() to write our dictionary to CSV format?

The fieldnames parameter of DictWriter() requires a list of keys

Right on! This will help DictWriter() organize the CSV rows properly.

In our earlier examples, we saw how we
can read and write CSV files, and we use list as datatype on the Python side. This works when we know what the fields
are going to be, but it can be pretty cumbersome when we have a lot of
columns, and we need to remember which is which. Imagine if your lists of
employees not only had name, phone number and role but also start date,
username, office location, department, preferred pronouns and so on. It would soon get hard to keep track of
which column corresponds to which position in the row. For cases like this, it’s common for
CSVs to include the names of the columns as a first line in the file, like in
this example; this CSV file list a bunch of
internally developed programs used at the company including the latest
version, the current development status and the number of people using it. Check out how the first line of the
file includes the names of each of the fields. We can profit from this
additional information by using DictReader, a slightly different reader
that’s also provided by the CSV module. This reader turns each row of the data
in a CSV file into a dictionary. We can then access the data by using
the column names instead of the position in the row. Let’s see how that looks. So here we’re opening the file and
creating a DictReader to process our CSV data, then when going through the rows
we can access information in each row using the keys just like we would when
accessing data in the dictionary. See how we use row, name and row users
to get the name of the number of users. Let’s execute this and see what happens
next. As you can see, we’ve successfully
printed the contents of the two fields that we wanted. Two important things to
call out here. One, the order of the fields in the
file doesn’t matter. We can just use the name of the field
instead, and two, chatty chicken is still an alpha, so only it has four
users but you know the name like that, it’s going to be a hit. So we can use
DictWriter in a similar way to generate a CSV file from the contents of a list
of dictionaries. This means that each element in the
list will be a row in the file, and the values of each field will come out of
each of the dictionaries. For this to work, we’ll also need to
pass a list of the keys that we want to be stored in the file when creating the
writer. Let’s see this in action. First we need
a list of dictionaries with the data that we want to store. For this
example, we want to store data about the users in our company and the
departments that they work in. So here we have our list of
dictionaries and each contain the keys, name, username and department. We now want to write this HTML file and
the code will look like this. So we first define the list of keys
that we want to write to the file, then we open the file for writing. Next we created the DictWriter passing
the keys that we had identified before, and then we call two different methods
on the writer. The right header method will create the
first line of the CSV based on keys that we passed, and the right rows method
will turn the list of dictionaries into lines in that file. Let’s check if this
worked correctly. All right, we successfully generated
CSV from our dictionary. Nice, right? Let’s take a moment to
acknowledge everything you’ve learned about managing files over the last few
videos. You can read, write and work with files
and directories, and now, you can also read and write CSV files. You started this course with
fundamental knowledge of Python, and you’ve progressed so much in just a few
videos. Remember, we don’t expect you to learn
everything by heart, you’ll pick it all up with practice which is why up next,
we’ve got another cheat sheet for you. In it you’ll find a rundown of
everything we just covered so you can refer to it whenever you need a little
refresher on working with CSV files. After you check that out, head on over
to the quiz and put your new knowledge to the test.

Awesome. You made it to the end of the
module. You now know a whole lot more about how
to manage files with Python. Take a sec to congratulate yourself. Some of these concepts might seem
difficult now, but you’ll see how quickly your confidence develops once
you start managing files on your own. These are super useful skills that
you’re picking up. In the past few videos, we checked out
how to read and write to both texts and CSV files and how to manage files and
directories with Python. We’ve covered a lot, but really we’ve
only scratched the surface of all the different things that we can do with
files and directories. Keep in mind that files are one of the
most important ways of storing and transmitting information in IT. There are more file formats, more ways
of iterating through directories, and more tools to help us get things done. As we call the earlier, the Python
standard library includes modules for manipulating only some data formats out
there. There’s a bunch more data types the
standard library doesn’t cover. If you need to do work with a different
format or do something more advanced than what the standard library
provides, you can always use one of the many external Python modules available
on PyPi. Throughout the rest of the course,
we’ll be using what we’ve learned so far and checking out more tools and
functionality that are available to operate with files and directories in
Python. But before moving on to our next
subject, we’ve got a graded assessment for you to tackle. Again, you’ll be
using Qwiklabs to simulate a real life scenario. We’ll give you some data
files that you’ll need to process and then you’ll write a script that takes
info and uses it to generate a report. Nervous? Don’t be. You got this, and if you need to go
back and review any of the videos, then take your time. I’ll be here waiting
for you after you finish your assessment. Now go, get after it.