07_Input_Output.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Input and Output\n",
    "\n",
    "[Python 3 Tutorial](https://docs.python.org/3/tutorial/inputoutput.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several ways to present the output of a program; data can be printed in a human-readable form, or written to a file for future use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Facier Output Formatting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two ways to format your output; \n",
    "* The first way is to do all the string handling yourself; using string slicing and concatenation operations you can create any layout you can imagine. The string type has some methods that perform useful operations for padding strings to a given column width; these will be discussed shortly. \n",
    "* The second way is to use `formatted string literals`, or the `str.format()` method.\n",
    "\n",
    "The `string` module contains a `Template` class which offers yet another way to substitute values into strings.\n",
    "\n",
    "One question remains, of course: how do you convert values to strings? Luckily, Python has ways to convert any value to a string: pass it to the `repr()` or `str()` functions.\n",
    "\n",
    "The `str()` function is meant to return representations of values which are fairly human-readable, while `repr()` is meant to generate representations which can be read by the interpreter (or will force a `SyntaxError` if there is no equivalent syntax). For objects which don’t have a particular representation for human consumption, `str()` will return the same value as `repr()`. Many values, such as numbers or structures like lists and dictionaries, have the same representation using either function. Strings, in particular, have two distinct representations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 'Hello, world.'\n",
    "str(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "repr(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(repr(s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "str(1/7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "1/7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 10 * 3.25\n",
    "y = 200 * 200\n",
    "s = 'The value of x is ' + repr(x) + ', and y is ' + repr(y) + '...'\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The repr() of a string adds string quotes and backslashes:\n",
    "hello = 'hello, world\\n'\n",
    "hellos = repr(hello)\n",
    "print(hellos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The argument to repr() may be any Python object:\n",
    "repr((x, y, ('spam', 'eggs')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are two ways to write a table of squares and cubes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in range(1, 11):\n",
    "    print(x, x*x, x*x*x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in range(1, 11):\n",
    "    print(x, x*x, end=' ')\n",
    "    # Note use of 'end' on previous line\n",
    "    print(x*x*x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for x in range(1, 11):\n",
    "    print(repr(x).rjust(2), repr(x*x).rjust(3), end=' ')\n",
    "    # Note use of 'end' on previous line\n",
    "    print(repr(x*x*x).rjust(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in range(1, 11):\n",
    "    print('{0:2d} {1:3d} {2:4d}'.format(x, x*x, x*x*x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that one space between each column was added by the way `print()` works: it always adds spaces between its arguments.\n",
    "\n",
    "This example demonstrates the `str.rjust()` method of string objects, which right-justifies a string in a field of a given width by padding it with spaces on the left. There are similar methods `str.ljust()` and `str.center()`. These methods do not write anything, they just return a new string. If the input string is too long, they don’t truncate it, but return it unchanged; this will mess up your column lay-out but that’s usually better than the alternative, which would be lying about a value. (If you really want truncation you can always add a slice operation, as in `x.ljust(n)[:n]`.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is another method, `str.zfill()`, which pads a numeric string on the left with zeros. It understands about plus and minus signs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'12'.zfill(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'-3.14'.zfill(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'3.14159265359'.zfill(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic usage of the `str.format()` method looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('We are the {} who say \"{}!\"'.format('knights', 'Ni'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The brackets and characters within them (called format fields) are replaced with the objects passed into the `str.format()` method. A number in the brackets can be used to refer to the position of the object passed into the `str.format()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('{0} and {1}'.format('spam', 'eggs'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('{1} and {0}'.format('spam', 'eggs'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If keyword arguments are used in the `str.format()` method, their values are referred to by using the name of the argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('This {food} is {adjective}.'.format(food='spam', adjective='absolutely horrible'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Positional and keyword arguments can be arbitrarily combined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('The story of {0}, {1}, and {other}.'.format('Bill', 'Manfred', other='Georg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`'!a'` (apply ascii()), `'!s'` (apply str()) and `'!r'` (apply `repr()`) can be used to convert the value before it is formatted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "contents = 'eels'\n",
    "print('My hovercraft is full of {}.'.format(contents))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('My hovercraft is full of {!r}.'.format(contents))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An optional `':'` and format specifier can follow the field name. This allows greater control over how the value is formatted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('The value of PI is approximately {0:.3f}.'.format(math.pi))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Passing an integer after the `':'` will cause that field to be a minimum number of characters wide. This is useful for making tables pretty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 7678}\n",
    "for name, phone in table.items():\n",
    "    print('{0:10} ==> {1:10d}'.format(name, phone))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have a really long format string that you don’t want to split up, it would be nice if you could reference the variables to be formatted by name instead of by position. This can be done by simply passing the dict and using square brackets `'[]'` to access the keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 8637678}\n",
    "print('Jack: {0[Jack]:d}; Sjoerd: {0[Sjoerd]:d}; '\n",
    "      'Dcab: {0[Dcab]:d}'.format(table))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This could also be done by passing the table as keyword arguments with the `‘**’` notation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 8637678}\n",
    "print('Jack: {Jack:d}; Sjoerd: {Sjoerd:d}; Dcab: {Dcab:d}'.format(**table))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is particularly useful in combination with the built-in function `vars()`, which returns a dictionary containing all local variables.\n",
    "\n",
    "For a complete overview of string formatting with `str.format()`, see [Format String Syntax](https://docs.python.org/3/library/string.html#formatstrings)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading and Writing Files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`open()` returns a file object, and is most commonly used with two arguments: `open(filename, mode)`."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "f = open('workfile', 'w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first argument is a string containing the filename. The second argument is another string containing a few characters describing the way in which the file will be used. \n",
    "\n",
    "`mode` can be \n",
    "* `'r'` when the file will only be read, \n",
    "* `'w'` for only writing (an existing file with the same name will be erased), and \n",
    "* `'a'` opens the file for appending; any data written to the file is automatically added to the end.\n",
    "* `'r+'` opens the file for both reading and writing. The mode argument is optional; `'r'` will be assumed if it’s omitted.\n",
    "\n",
    "Normally, files are opened in `text mode`, that means, you read and write strings from and to the file, which are encoded in a specific encoding. If encoding is not specified, the default is platform dependent (see [open()](https://docs.python.org/3/library/functions.html#open)). `'b'` appended to the mode opens the file in `binary mode`: now the data is read and written in the form of bytes objects. This mode should be used for all files that don’t contain text.\n",
    "\n",
    "In text mode, the default when reading is to convert platform-specific line endings (`\\n` on Unix, `\\r\\n` on Windows) to just `\\n`. When writing in text mode, the default is to convert occurrences of `\\n` back to platform-specific line endings. This behind-the-scenes modification to file data is fine for text files, but will corrupt binary data like that in `JPEG` or `EXE` files. Be very careful to use binary mode when reading and writing such files.\n",
    "\n",
    "It is good practice to use the `with` keyword when dealing with file objects. The advantage is that the file is properly closed after its suite finishes, even if an exception is raised at some point. Using `with` is also much shorter than writing equivalent `try-finally` blocks:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "with open('workfile') as f:\n",
    "    read_data = f.read()\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you’re not using the `with` keyword, then you should call `f.close()` to close the file and immediately free up any system resources used by it. If you don’t explicitly close a file, Python’s garbage collector will eventually destroy the object and close the open file for you, but the file may stay open for a while. Another risk is that different Python implementations will do this clean-up at different times."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After a file object is closed, either by a `with` statement or by calling `f.close()`, attempts to use the file object will automatically fail."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Methods of File Objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To read a file’s contents, call `f.read(size)`, which reads some quantity of data and returns it as a string (in text mode) or bytes object (in binary mode). `size` is an optional numeric argument. When `size` is omitted or negative, the entire contents of the file will be read and returned; it’s your problem if the file is twice as large as your machine’s memory. Otherwise, at most `size` bytes are read and returned. If the end of the file has been reached, `f.read()` will return an empty string (`''`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  `read()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "read_data = []\n",
    "with open('wine.csv') as f:\n",
    "    read_data = f.read()\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "read_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for line in read_data:\n",
    "    print(line, end='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `readline()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`f.readline()` reads a single line from the file; a newline character (`\\n`) is left at the end of the string, and is only omitted on the last line of the file if the file doesn’t end in a newline. This makes the return value unambiguous; if `f.readline()` returns an empty string, the end of the file has been reached, while a blank line is represented by `'\\n'`, a string containing only a single newline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('wine.csv', 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.readline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.readline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For reading lines from a file, you can loop over the file object. This is memory efficient, fast, and leads to simple code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_data = []\n",
    "with open('wine.csv') as f:\n",
    "    for line in f:\n",
    "        file_data.append(line.rstrip('\\n'))\n",
    "f.closed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['14.23,1.71,2.43,15.6,127,2.8,3.06,0.28,2.29,5.64,1.04,3.92,1065',\n",
       " '13.2,1.78,2.14,11.2,100,2.65,2.76,0.26,1.28,4.38,1.05,3.4,1050',\n",
       " '13.16,2.36,2.67,18.6,101,2.8,3.24,0.3,2.81,5.68,1.03,3.17,1185',\n",
       " '14.37,1.95,2.5,16.8,113,3.85,3.49,0.24,2.18,7.8,0.86,3.45,1480',\n",
       " '13.24,2.59,2.87,21,118,2.8,2.69,0.39,1.82,4.32,1.04,2.93,735']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`f.write(string)` writes the contents of string to the file, returning the number of characters written."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_line = '9.99,9.99,9.99,9.99,9.99,9.99,9.99,9.99,9.99,9.99,9.99,9.99,9.99'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('test_file.csv', 'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.write(new_line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other types of objects need to be converted – either to a string (in text mode) or a bytes object (in binary mode) – before writing them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving structured data with `json`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Strings can easily be written to and read from a file. Numbers take a bit more effort, since the read() method only returns strings, which will have to be passed to a function like `int()`, which takes a string like `'123'` and returns its numeric value 123. When you want to save more complex data types like nested lists and dictionaries, parsing and serializing by hand becomes complicated.\n",
    "\n",
    "Rather than having users constantly writing and debugging code to save complicated data types to files, Python allows you to use the popular data interchange format called `[JSON](http://json.org/) (JavaScript Object Notation)`. The standard module called [`json`](https://docs.python.org/3/library/json.html#module-json) can take Python data hierarchies, and convert them to string representations; this process is called `serializing`. Reconstructing the data from the string representation is called deserializing. Between serializing and `deserializing`, the string representing the object may have been stored in a file or data, or sent over a network connection to some distant machine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have an object `x`, you can view its JSON string representation with a simple line of code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "json.dumps([1, 'simple', 'list'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another variant of the `dumps()` function, called `dump()`, simply serializes the object to a `text file`. So if `f` is a `text file` object opened for writing, we can do this:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "json.dump(x, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To decode the object again, if `f` is a `text file` object which has been opened for reading:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "x = json.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This simple serialization technique can handle lists and dictionaries, but serializing arbitrary class instances in JSON requires a bit of extra effort. The reference for the [`json`](https://docs.python.org/3/library/json.html#module-json) module contains an explanation of this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[pickle](https://docs.python.org/3/library/pickle.html#module-pickle) - the pickle module\n",
    "\n",
    "Contrary to JSON, `pickle` is a protocol which allows the serialization of arbitrarily complex Python objects. As such, it is specific to Python and cannot be used to communicate with applications written in other languages. It is also insecure by default: deserializing pickle data coming from an untrusted source can execute arbitrary code, if the data was crafted by a skilled attacker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = json.dumps([1, 'simple', 'list'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('json_test_file.csv', 'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.write(x_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('json_test_file.csv', 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References:\n",
    "\n",
    "* [Python Tutorial](https://docs.python.org/3.6/tutorial/index.html)\n",
    "* [Geeks for Geeks](https://www.geeksforgeeks.org/python/)\n",
    "* [Python for Econometrics](https://www.kevinsheppard.com/Python_for_Econometrics)\n",
    "* [Python Course](https://www.kevinsheppard.com/Python_Course)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  }
 ],
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