file_object = open("alice.txt", "r")
contents = file_object.read()
print(contents[:71])
file_object.close()

"""
Explanation: Lecture 12: Saving to and loading from the file system
CSCI 1360E: Foundations for Informatics and Analytics
Overview and Objectives
So far, all the data we've worked with have either been manually instantiated as NumPy arrays or lists of strings, or randomly generated. Here we'll finally get to go over reading to and writing from the filesystem. By the end of this lecture, you should be able to:

Identify some of the primary data storage formats
Implement a basic file reader / writer using built-in Python tools
Explain how to use other tools for some of the more exotic data types
Use exception handlers to make your interactions with the filesystem robust to failure

Part 1: Interacting with text files
Text files are probably the most common and pervasive format of data. They can contain almost anything: weather data, stock market activity, literary works, and raw web data.
Text files are also convenient for your own work: once some kind of analysis has finished, it's nice to dump the results into a file you can inspect later.
Reading an entire file
So let's jump into it! Let's start with something simple; say...the text version of Lewis Carroll's Alice in Wonderland?
End of explanation
"""


file_object = open("alice.txt", "r")

"""
Explanation: Yep, I went there.
Let's walk through the code, line by line. First, we have a call to a function open() that accepts two arguments:
End of explanation
"""


contents = file_object.read()

"""
Explanation: The first argument is the file path. It's like a URL, except to a file on your computer. It should be noted that, unless you specify a leading forward slash "/", Python will interpret this path to be relative to wherever the Python script is that you're running with this command.


The second argument is the mode. This tells Python whether you're reading from a file, writing to a file, or appending to a file. We'll come to each of these.


These two arguments are part of the function open(), which then returns a file descriptor. You can think of this kind of like the reference / pointer discussion we had in our prior functions lecture: file_object is a reference to the file.
The next line is where the magic happens:
End of explanation
"""


print(contents[:71])

"""
Explanation: In this line, we're calling the method read() on the file reference we got in the previous step. This method goes into the file, pulls out everything in it, and sticks it all in the variable contents. One big string!
End of explanation
"""


file_object.close()

"""
Explanation: ...of which I then print the first 71 characters, which contains the name of the book and the author. Feel free to print the entire string contents; it'll take a few seconds, as you're printing the whole book!
Finally, the last and possibly most important line:
End of explanation
"""


with open("alice.txt", "r") as file_object:
    contents = file_object.read()
    print(contents[:71])

"""
Explanation: This statement explicitly closes the file reference, effectively shutting the valve to the file. 
Do not underestimate the value of this statement. There are weird errors that can crop up when you forget to close file descriptors. It can be difficult to remember to do this, though; in other languages where you have to manually allocate and release any memory you use, it's a bit easier to remember. Since Python handles all that stuff for us, it's not a force of habit to explicitly shut off things we've turned on.
Fortunately, there's an alternative we can use!
End of explanation
"""


with open("alice.txt", "r") as file_object:
    num_lines = 0
    for line_of_text in file_object:
        print(line_of_text)

        num_lines += 1
        if num_lines == 5: break

"""
Explanation: This code works identically to the code before it. The difference is, by using a with block, Python intrinsically closes the file descriptor at the end of the block. Therefore, no need to remember to do it yourself! Hooray!
Let's say, instead of Alice in Wonderland, we had some behemoth of a piece of literature: something along the lines of War and Peace or even an entire encyclopedia. Essentially, not something we want to read into Python all at once. Fortunately, we have an alternative:
End of explanation
"""


with open("alice.txt", "r") as file_object:
    lines_of_text = file_object.readlines()
    print(lines_of_text[0])

"""
Explanation: We can use a for loop just as we're used to doing with lists. In this case, at each iteration, Python will hand you exactly 1 line of text from the file to handle it however you'd like.
Of course, if you still want to read in the entire file at once, but really like the idea of splitting up the file line by line, there's a function for that, too:
End of explanation
"""


data_to_save = "This is important data. Definitely worth saving."
with open("outfile.txt", "w") as file_object:
    file_object.write(data_to_save)

"""
Explanation: By using readlines() instead of plain old read(), we'll get back a list of strings, where each element of the list is a single line in the text file. In the code snippet above, I've printed the first line of text from the file.
Writing to a file
We've so far seen how to read data from a file. What if we've done some computations and want to save our results to a file?
End of explanation
"""


data_to_save = "This is ALSO important data. BOTH DATA ARE IMPORTANT."
with open("outfile.txt", "a") as file_object:
    file_object.write(data_to_save)

"""
Explanation: You'll notice two important changes from before:

Switch the "r" argument in the open() function to "w". You guessed it: we've gone from Reading to Writing.
Call write() on your file descriptor, and pass in the data you want to write to the file (in this case, data_to_save).

If you try this using a new notebook on JupyterHub (or on your local machine), you should see a new text file named "outfile.txt" appear in the same directory as your script. Give it a shot!
Some notes about writing to a file:


If the file you're writing to does NOT currently exist, Python will try to create it for you. In most cases this should be fine (but we'll get to outstanding cases in Part 3 of this lecture).


If the file you're writing to DOES already exist, Python will overwrite everything in the file with the new content. As in, everything that was in the file before will be erased.


That second point seems a bit harsh, doesn't it? Luckily, there is recourse.
Appending to an existing file
If you find yourself in the situation of writing to a file multiple times, and wanting to keep what you wrote to the file previously, then you're in the market for appending to a file.
This works exactly the same as writing to a file, with one small wrinkle:
End of explanation
"""


data_to_save = "This is important data. Definitely worth saving.\n"
with open("outfile.txt", "w") as file_object:
    file_object.write(data_to_save)

data_to_save = "This is ALSO important data. BOTH DATA ARE IMPORTANT."
with open("outfile.txt", "a") as file_object:
    file_object.write(data_to_save)

with open("outfile.txt", "r") as file_object:
    contents = file_object.readlines()
    print("LINE 1: {}".format(contents[0]))
    print("LINE 2: {}".format(contents[1]))

"""
Explanation: The only change that was made was switching the "w" in the open() method to "a" for, you guessed it, Append. If you look in outfile.txt, you should see both lines of text we've written.
Some notes on appending to files:


If the file does NOT already exist, then using "a" in open() is functionally identical to using "w".


You only need to use append mode if you closed the file descriptor to that file previously. If you have an open file descriptor, you can call write() multiple times; each call will append the text to the previous text. It's only when you close a descriptor, but then want to open up another one to the same file, that you'd need to switch to append mode.


Let's put together what we've seen by writing to a file, appending more to it, and then reading what we wrote.
End of explanation
"""


import csv

with open("eggs.csv", "w") as csv_file:
    file_writer = csv.writer(csv_file)
    row1 = ["Sunny-side up", "Over easy", "Scrambled"]
    row2 = ["Spam", "Spam", "More spam"]
    file_writer.writerow(row1)
    file_writer.writerow(row2)
    
with open("eggs.csv", "r") as csv_file:
    print(csv_file.read())

"""
Explanation: Part 2: Other file formats
We've discussed text formats: each line of text in a file can be treated as a string in a list of strings. What else might we encounter in our data science travels?
CSV files
Easily the most common text file format is the CSV, or comma-separated values format. This is pretty much what it sounds like: if you have (semi-) structured data, you can delineate spaces between data using commas (or, to generalize, other characters like tabs).
As an example, we could represent a matrix very easily using the CSV format. The file storing a 3x3 matrix would look something like this:
<pre>
1,2,3
4,5,6
7,8,9
</pre>

Each row is on one line by itself, and the columns are separated by commas.
How do we read CSV files? Python has a core csv module built-in:
End of explanation
"""


with open("eggs.csv", "r") as csv_file:
    file_reader = csv.reader(csv_file)
    for csv_row in file_reader:
        print(csv_row)

"""
Explanation: Notice that you first create a file reference, just like before. The one added step, though, is passing that reference to the csv.writer() function.
Once you've created the file_writer object, you can call its writerow() function and pass in a list to the function, and it is automatically written to the file in CSV format!
The CSV readers let you do the opposite: read a line of text from a CSV file directly into a list.
End of explanation
"""


person = """
{"name": "Wes",
"places_lived": ["United States", "Spain", "Germany"],
"pet": null,
"siblings": [{"name": "Scott", "age": 25, "pet": "Zuko"},
            {"name": "Katie", "age": 33, "pet": "Cisco"}]
}
"""

"""
Explanation: You can use a for loop to iterate over the rows in the CSV file. In turn, each row is a list, where each element of the list was separated by a comma.
JSON files
"JSON", short for "JavaScript Object Notation", has emerged as more or less the de facto standard format for interacting with online services. Like CSV, it's a text-based format, but is much more flexible than CSV.
Here's an example: an object in JSON format that represents a person.
End of explanation
"""


import json

"""
Explanation: It looks kind of a like a Python dictionary, doesn't it? You have key-value pairs, and they can accommodate almost any data type. In fact, when JSON objects are converted into native Python data structures, they are usually represented using dictionaries.
For reading and writing JSON objects, we can use the built-in json Python module.
End of explanation
"""


python_dict = json.loads(person)
print(python_dict)

"""
Explanation: There are two functions of interest: dumps() and loads(). One of them takes a JSON string and converts it to a native Python object, while the other does the opposite.
First, we'll take our JSON string and convert it into a Python dictionary:
End of explanation
"""


json_string = json.dumps(python_dict)
print(json_string)

"""
Explanation: And if you want to take a Python dictionary and convert it into a JSON string--perhaps you're about to save it to a file, or send it over the network to someone else--we can do that.
End of explanation
"""


import pickle

# We'll use the `python_dict` object from before.
binary_object = pickle.dumps(python_dict)
print(binary_object)

"""
Explanation: At first glance, these two print-outs may look the same, but if you look closely you'll see some differences. Plus, if you tried to index json_string["name"] you'd get some very strange errors. python_dict["name"], on the other hand, should nicely return "Wes".
XML files
AVOID AT ALL COSTS.
...but if you have to interact with XML data (e.g., you're manually parsing a web page!), Python has a built-in xml library.
It's a bit beyond the scope of this course, but feel free to check it out.
Binary files
What happens when we're not dealing with text? After all, images and videos are most certainly not encoded using text. Furthermore, if memory is an issue, converting text into binary formats can help save space.
There are two primary options for reading and writing binary files.

pickle, or "pickling", is native in Python and very flexible.
NumPy's binary format, which works very well for NumPy arrays but not much else.

Pickle has some similarities with JSON. In particular, it uses the same method names, dumps() and loads(), for converting between native Python objects and the raw data format. There are several differences, however.


Most notably, JSON is text-based whereas pickle is binary. You could open up a JSON file and read the text yourself. Not the case with pickled files.


While JSON is used widely outside of Python, pickle is specific to Python and its objects. Consequently, JSON only works on a subset of Python data structures; pickle, on the other hand, works on just about everything.


Here's an example of saving (or "serializing") a dictionary using pickle instead of JSON:
End of explanation
"""


import numpy as np

# Generate some data and save it.
some_data = np.random.randint(10, size = (3, 3))
print(some_data)
np.save("my_data.npy", some_data)

"""
Explanation: You can kinda see some English in there--mainly, the string constants. But everything else has been encoded in binary. It's much more space-efficient, but complete gibberish until you convert it back into a text format (e.g. JSON) or native Python object (e.g. dictionary).
If, on the other hand, you're using NumPy arrays, then you can use its own built-in binary format for saving and loading your arrays.
End of explanation
"""


my_data = np.load("my_data.npy")
print(my_data)

"""
Explanation: Now we can load it back:
End of explanation
"""


with open("alicee.txt", "r") as file_object:
    contents = file_object.readlines()
    print(contents[0])

"""
Explanation: This is by far the easiest format to work with when you're dealing exclusively with NumPy arrays; don't bother with CSV or pickling. You don't even need to set up file descriptors with the NumPy interface.
Part 3: Preventing errors
This aspect of programming hasn't been very heavily emphasized--that of error handling--because for the most part, data science is about building models and performing computations so you can make inferences from your data.
...except, of course, from nearly every survey that says your average data scientist spends the vast majority of their time cleaning and organizing their data.

Data is messy and computers are fickle. Just because that file was there yesterday doesn't mean it'll still be there tomorrow. When you're reading from and writing to files, you'll need to put in checks to make sure things are behaving the way you expect, and if they're not, that you're handling things gracefully.
We're going to become good friends with try and except whenever we're dealing with files. For example, let's say I want to read again from that Alice in Wonderland file I had:
End of explanation
"""


filename = "alicee.txt"
try:
    with open(filename, "r") as file_object:
        contents = file_object.readlines()
        print(contents[0])
except FileNotFoundError:
    print("Sorry, the file '{}' does not seem to exist.".format(filename))

"""
Explanation: Whoops. In this example, I simply misnamed the file. In practice, maybe the file was moved; maybe it was renamed; maybe you're getting the file from the user and they incorrectly specified the name. Maybe the hard drive failed, or any number of other "acts of God." Whatever the reason, your program should be able to handle missing files.
You could probably code this up yourself:
End of explanation
"""


