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Merge branch 'main' into NET-6

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Marcello 2021-09-23 15:35:55 +02:00
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6
DotNet/ASP.NET/REST API.md
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@ -27,7 +27,7 @@ namespace <Namespace>
{
services.AddControllers(); // controllers w/o views
//or
sevices.AddControllersWithViews(); // MVC Controllers
services.AddControllersWithViews(); // MVC Controllers
}
// This method gets called by the runtime. Use this method to configure the HTTP request pipeline.
@ -117,7 +117,7 @@ public void ConfigureServices(IServiceCollection services)
- Mirroring of model in the DB.
- Will create & update DB Schema if necessary
In Packge Manager Shell:
In Package Manager Shell:
```ps1
add-migrations <migration_name>
@ -294,7 +294,7 @@ namespace <App>.Controllers
if(result != null)
{
return Ok(_mapper.Map<EntityCrudOp>(result)); // transfrom entity to it's DTO
return Ok(_mapper.Map<EntityCrudOp>(result)); // transform entity to it's DTO
}
return NotFound(); // ActionResult NOT FOUND (404)

6
Python/Jupyter/Jupiter Notebook.md
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@ -4,7 +4,7 @@
`%quickref` Display the IPython Quick Reference Card
`%magic` Display detailed documentation for all of the available magic commands
`%debug` Enter the interactive debugger at the bottom of the last exception traceback
`%debug` Enter the interactive debugger at the bottom of the last exception trace-back
`%hist` Print command input (and optionally output) history
`%pdb` Automatically enter debugger after any exception
`%paste` Execute pre-formatted Python code from clipboard
@ -14,7 +14,7 @@
`%run` script.py Run a Python script inside IPython
`%prun` statement Execute statement with cProfile and report the profiler output
`%time` statement Report the execution time of single statement
`%timeit` statement Run a statement multiple times to compute an emsemble average execution time. Useful for timing code with very short execution time
`%timeit` statement Run a statement multiple times to compute an ensemble average execution time. Useful for timing code with very short execution time
`%who`, `%who_ls`, `%whos` Display variables defined in interactive namespace, with varying levels of information / verbosity
`%xdel` variable Delete a variable and attempt to clear any references to the object in the IPython internals
@ -23,7 +23,7 @@
`!cmd` Execute cmd in the system shell
`output = !cmd args` Run cmd and store the stdout in output
`%alias alias_name cmd` Define an alias for a system (shell) command
`%bookmark` Utilize IPython’s directory bookmarking system
`%bookmark` Utilize IPython's directory bookmarking system
`%cd` directory Change system working directory to passed directory
`%pwd` Return the current system working directory
`%pushd` directory Place current directory on stack and change to target directory

4
Python/Libs/CLI/click.md
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@ -37,7 +37,7 @@ cli.add_command(dropdb)
The `group()` decorator works like the `command()` decorator, but creates a Group object instead which can be given multiple subcommands that can be attached with `Group.add_command()`.
For simple scripts, it’s also possible to automatically attach and create a command by using the `Group.command()` decorator instead.
For simple scripts, it's also possible to automatically attach and create a command by using the `Group.command()` decorator instead.
The above script can instead be written like this:
```py
@ -54,7 +54,7 @@ def dropdb():
click.echo('Dropped the database')
```
You would then invoke the Group in your setuptools entry points or other invocations:
You would then invoke the Group in your setup-tools entry points or other invocations:
```py
if __name__ == '__main__':

66
Python/Libs/GUI/Tkinter.md
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@ -10,27 +10,27 @@ from tkinter import ttk # import Themed Widgets
## GEOMETRY MANAGEMENT
Putting widgets on screen
master widget --> toplevel window, frame
master widget --> top-level window, frame
slave widget --> widgets contained in master widget
geometry managers determine size and oder widget drawing properties
## EVENT HANDLING
event loop recives events from the OS
event loop receives events from the OS
customizable events provide a callback as a widget configuration
```py
widget.bind('event', function) # method to capture any event and than execute an arbitrary piece of code (generally a functio or lambda)
widget.bind('event', function) # method to capture any event and than execute an arbitrary piece of code (generally a function or lambda)
```
VIRTUAL EVENT --> hig level event genearted by widget (listed in widget docs)
VIRTUAL EVENT --> hig level event generated by widget (listed in widget docs)
## WIDGETS
"idgets are objects and all things on screen. All widgets are children of a window.
Widgets are objects and all things on screen. All widgets are children of a window.
```py
widget_name = tk_object(parent_window) # widget is insetred into widget hierarchy
widget_name = tk_object(parent_window) # widget is inserted into widget hierarchy
```
## FRAME WIDGET
@ -40,7 +40,7 @@ Displays a single rectangle, used as container for other widgets
```py
frame = ttk.Frame(parent, width=None, height=None, borderwidth=num:int)
# BORDERWIDTH: sets frame border width (default: 0)
# width, height MUST be specified if frame is empty, otherwise determinded by parent geometry manager
# width, height MUST be specified if frame is empty, otherwise determined by parent geometry manager
```
### FRAME PADDING
@ -49,7 +49,7 @@ Extra space inside widget (margin).
```py
frame['padding'] = num # same padding for every border
frame['padding'] = (horizzontal, vertical) # set horizontal THEN vertical padding
frame['padding'] = (horizontal, vertical) # set horizontal THEN vertical padding
frame['padding'] = (left, top, right, bottom) # set left, top, right, bottom padding
# RELIEF: set border style, [flat (default), raised, sunken, solid, ridge, groove]
@ -58,7 +58,7 @@ frame['relief'] = border_style
## LABEL WIDGET
Display tetx or image without interactivity.
Display text or image without interactivity.
```py
label = ttk.Label(parent, text='label text')
@ -67,7 +67,7 @@ label = ttk.Label(parent, text='label text')
### DEFINING UPDATING LABEL
```py
var = StringVar() # variable containing text, watchs for changes. Use get, set methods to interact with the value
var = StringVar() # variable containing text, watches for changes. Use get, set methods to interact with the value
label['textvariable'] = var # attach var to label (only of type StringVar)
var.set("new text label") # change label text
```
@ -105,7 +105,7 @@ label['anchor'] = compass_direction #compass_direction: n, ne, e, se, s, sw, w,
```py
# use \n for multi line text
label['wraplength'] = size # max line lenght
label['wraplength'] = size # max line length
```
### CONTROL TEXT JUSTIFICATION
@ -114,8 +114,8 @@ label['wraplength'] = size # max line lenght
label['justify'] = value #value: left, center, right
label['relief'] = label_style
label['foreground'] = color # color pased with name or HEX RGB codes
label['background'] = color # color pased with name or HEX RGB codes
label['foreground'] = color # color passed with name or HEX RGB codes
label['background'] = color # color passed with name or HEX RGB codes
```
### FONT STYLE (use with caution)
@ -129,7 +129,7 @@ Fonts:
- TkDefaultFont -- default for all GUI items
- TkTextFont -- used for entry widgets, listboxes, etc
- TkFixedFont -- standar fixed-width font
- TkFixedFont -- standard fixed-width font
- TkMenuFont -- used for menu items
- TkHeadingFont -- for column headings in lists and tables
- TkCaptionFont -- for window and dialog caption bars
@ -159,7 +159,7 @@ button.invoke() # button activation in the program
### BUTTON STATE
Activate (interagible) or deactivate (not interagible) the widget.
Activate or deactivate the widget.
```py
button.state(['disabled']) # set the disabled flag, disabling the button
@ -172,7 +172,7 @@ button.instate(['!disabled'], cmd) # execute 'cmd' if the button is not disable
## CHECKBUTTON
Button with binary value of some kind (e.g a toggle) and also invokes a comman callback
Button with binary value of some kind (e.g a toggle) and also invokes a command callback
```py
checkbutton_var = TkVarType
@ -182,9 +182,9 @@ check = ttk.Checkbutton(parent, text='button text', command=action_performed, va
### WIDGET VALUE
Variable option holds value of button, updated by widget toggle.
EFAULT: 1 (while checked), 0 (while unchecked)
`onvalue`, `offvalue` are used to personalize cheched and uncheched values
if linked variable is empry or different from on-offvalue the state flag is set to alternate
DEFAULT: 1 (while checked), 0 (while unchecked)
`onvalue`, `offvalue` are used to personalize checked and unchecked values
if linked variable is empty or different from on-off value the state flag is set to alternate
checkbutton won't set the linked variable (MUST be done in the program)
### CONFIG OPTIONS
@ -245,7 +245,7 @@ radio.instate(['flag'])
## COMBOBOX
Drop-down list of avaiable options.
Drop-down list of available options.
```py
combobox_var = StringVar()
@ -253,7 +253,7 @@ combo = ttk.Combobox(parent, textvariable=combobox_var)
combobox.get() # return combobox current value
combobox.set(value) # set combobox new value
combobox.current() # returns which item in the predefined values list is selected (0-based index of the provided list, -1 if not in the list)
#combobox will generate a bind-able <ComboxboSelected> virtual event whenever the value changes
#combobox will generate a bind-able <ComboboxSelected> virtual event whenever the value changes
combobox.bind('<<ComboboxSelected>>', function)
```
@ -271,7 +271,7 @@ Display list of single-line items, allows browsing and multiple selection (part
```py
lstbx = Listbox(parent, height=num, listvariable=item_list:list)
# listvariable links a variable (MUST BE a list) to the listbox, each elemenmt is a item of the listbox
# listvariable links a variable (MUST BE a list) to the listbox, each element is a item of the listbox
# manipulation of the list changes the listbox
```
@ -292,7 +292,7 @@ lstbx.curselection() # returns list of indices of selected items
scroll = ttk.Scrollbar(parent, orient=direction, command=widget.view)
# ORIENT: VERTICAL, HORIZONTAL
# WIDGET.VIEW: .xview, .yview
# NEEDS ASSOCITED WIDGET SCROLL CONFIG
# NEEDS ASSOCIATED WIDGET SCROLL CONFIG
widget.configure(xscrollcommand=scroll.set)
widget.configure(yscrollcommand=scroll.set)
```
@ -324,10 +324,10 @@ txt.delete(start, end) # delete range of text
Feedback about progress of lenghty operation.
```py
progbar = ttk.Progressbar(parent, orient=direction, lenght=num:int, value=num, maximum=num:float mode=mode)
progbar = ttk.Progressbar(parent, orient=direction, length=num:int, value=num, maximum=num:float mode=mode)
# DIRECTION: VERTICAL, HORIZONTAL
# MODE: determinate (relative progress of completion), indeterminate (no estimate of completion)
# LENGHT: dimension in pixel
# LENGTH: dimension in pixel
# VALUE: sets the progress, updates the bar as it changes
# MAXIMUM: total number of steps (DEFAULT: 100)
```
@ -342,7 +342,7 @@ progbar.step(amount) # increment value of given amount (DEFAULT: 1.0)
```py
progbar.start() # starts progressbar
progbar.stop() #stopos progressbar
progbar.stop() #stoops progressbar
```
## SCALE
@ -350,7 +350,7 @@ progbar.stop() #stopos progressbar
Provide a numeric value through direct manipulation.
```py
scale = ttk.Scale(parent, orient=DIR, lenght=num:int, from_=num:float, to=num:float, command=cmd)
scale = ttk.Scale(parent, orient=DIR, length=num:int, from_=num:float, to=num:float, command=cmd)
# COMMAND: calls cmd at every scale change, appends current value to func call
scale['value'] # set or read current value
scale.set(value) # set current value
@ -364,9 +364,9 @@ Choose numbers. The spinbox choses item from a list, arrows permit cycling lits
```py
spinval = StringVar()
spin = Spinbox(parent, from_=num, to=num, textvariable=spinval, increment=num, value=lst, wrap=boolean)
# INCREMENT specifies incremend\decremenment by arrow button
# INCREMENT specifies increment\decrement by arrow button
# VALUE: list of items associated with the spinbox
# WRAP: boolean value determining if value shuld wrap around if beyond start-end value
# WRAP: boolean value determining if value should wrap around if beyond start-end value
```
## GRID GEOMETRY MANAGER
@ -424,7 +424,7 @@ tlw = Toplevel(parent) # parent of root window, no need to grid it
window.destroy()
# can destroy every widget
# destroing parent also destroys it's children
# destroying parent also destroys it's children
```
### CHANGING BEHAVIOR AND STYLE
@ -436,7 +436,7 @@ window.title('new title') # sets title
# SIZE AND LOCATION
window.geometry(geo_specs)
'''full geomtry specification: width * height +- x +- y (actual coordinates of screen)
'''full geometry specification: width * height +- x +- y (actual coordinates of screen)
+x --> x pixels from left edge
-x --> x pixels from right edge
+y --> y pixels from top edge
@ -454,7 +454,7 @@ window.lift(otherwin) # relative to other window
window.lower() # absolute position
window.lower(otherwin) # relative to other window
# RESIZION BEHAVIOR
# RESIZE BEHAVIOR
window.resizable(boolean, boolean) # sets if resizable in width (1st param) and width (2nd param)
window.minsize(num, num) # sets min width and height
window.maxsize(num, num) # sets max width and height
@ -470,7 +470,7 @@ window.deiconify() # deiconifies window
### STANDARD DIALOGS
```py
# SLECTING FILE AND DIRECTORIES
# SLEETING FILE AND DIRECTORIES
# on Windows and Mac invokes underlying OS dialogs directly
from tkinter import filedialog
filename = filedialog.askopenfilename()

4
Python/Libs/Image/Pillow.md
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@ -1,4 +1,4 @@
# Pillow Library Cheat Sheet
# Pillow
## Standard Imports
@ -16,7 +16,7 @@ image = Image.open(filepath, mode) # open image file (returns Image object)
image.format # image file extension
image.size # 2-tuple (width, height) in pixels
image.mode # defines number and name of bands in image, pixeld type and depth
image.mode # defines number and name of bands in image, pixel type and depth
```
## SAVING IMAGE FILE

12
Python/Libs/Image/PyCairo.md
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@ -1,4 +1,4 @@
# PyCarto Cheat Sheet
# PyCairo
## Definitions
@ -11,7 +11,7 @@ A `Path` is a collection of points used to create primitive shapes such as lines
In a closed path, starting and ending points meet. In an open path, starting and ending point do not meet. In PyCairo, we start with an empty path.
First, we define a path and then we make them visible by stroking and/or filling them. After each `stroke()` or `fill()` method call, the path is emptied.
We have to define a new path. If we want to keep the existing path for later drawing, we can use the `stroke_preserve()` and `fill_preserve()` methods.
A path is made of subpaths.
A path is made of sub-paths.
A `Source` is the paint we use in drawing. We can compare the source to a pen or ink that we use to draw the outlines and fill the shapes.
There are four kinds of basic sources: colors, gradients, patterns, and images.
@ -83,7 +83,7 @@ context.set_source_rgb(red, green, blue)
`context.new_sub_path()` begins a new sub-path. Note that the existing path is not affected. After this call there will be no current point.
In many cases, this call is not needed since new sub-paths are frequently started with `Context.move_to()`.
A call to `new_sub_path()` is particularly useful when beginning a new sub-path with one of the `Context.arc()` calls.
This makes things easier as it is no longer necessary to manually compute the arc’s initial coordinates for a call to `Context.move_to()`.
This makes things easier as it is no longer necessary to manually compute the arc's initial coordinates for a call to `Context.move_to()`.
### Stroke
@ -99,7 +99,7 @@ After `fill()`, the current path will be cleared from the Context.
`context.set_fill_rule(fill_rule)` set a FILL RULE to the cairo context.
For both fill rules, whether or not a point is included in the fill is determined by taking a ray from that point to infinity and looking at intersections with the path.
The ray can be in any direction, as long as it doesn’t pass through the end point of a segment or have a tricky intersection such as intersecting tangent to the path.
The ray can be in any direction, as long as it doesn't pass through the end point of a segment or have a tricky intersection such as intersecting tangent to the path.
(Note that filling is not actually implemented in this way. This is just a description of the rule that is applied.)
* `cairo.FILL_RULE_WINDING` (default):
@ -138,9 +138,9 @@ Font Weights:
## Creating the image
```py
surface.show_page() # Emits and clears the current page for backends that support multiple pages. Use copy_page() if you don’t want to clear the page.
surface.show_page() # Emits and clears the current page for backends that support multiple pages. Use copy_page() if you don't want to clear the page.
surface.copy_page() # Emits the current page for backends that support multiple pages, but doesn’t clear it, so that the contents of the current page will be retained for the next page. Use show_page() if you want to get an empty page after the emission.
surface.copy_page() # Emits the current page for backends that support multiple pages, but doesn't clear it, so that the contents of the current page will be retained for the next page. Use show_page() if you want to get an empty page after the emission.
surface.write_to_png("filename") # Writes the contents of Surface to filename as a PNG image
```

48
Python/Libs/Math/NumPy.md
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@ -16,7 +16,7 @@ Unless explicitly specified `np.array` tries to infer a good data type for the a
The data type is stored in a special dtype object.
```py
var = np.array(sequence) # createa array
var = np.array(sequence) # creates array
var = np.asarray(sequence) # convert input to array
var = np.ndarray(*sequence) # creates multidimensional array
var = np.asanyarray(*sequence) # convert the input to an ndarray
@ -53,12 +53,12 @@ The numerical `dtypes` are named the same way: a type name followed by a number
| complex64, complex128, complex256 | c8, c16, c32 | Complex numbers represented by two 32, 64, or 128 floats, respectively |
| bool | ? | Boolean type storing True and False values |
| object | O | Python object type |
| string_ | `S<num>` | Fixed-length string type (1 byte per character), `<num>` is string lenght |
| unicode_ | `U<num>` | Fixed-length unicode type, `<num>` is lenght |
| string_ | `S<num>` | Fixed-length string type (1 byte per character), `<num>` is string length |
| unicode_ | `U<num>` | Fixed-length unicode type, `<num>` is length |
## OPERATIONS BETWEEN ARRAYS AND SCALARS
Any arithmetic operations between equal-size arrays applies the operation elementwise.
Any arithmetic operations between equal-size arrays applies the operation element-wise.
array `+` scalar --> element-wise addition (`[1, 2, 3] + 2 = [3, 4, 5]`)
array `-` scalar --> element-wise subtraction (`[1 , 2, 3] - 2 = [-2, 0, 1]`)
@ -73,7 +73,7 @@ array_1 `/` array_2 --> element-wise division (`[1, 2, 3] / [3, 2, 1] = [0.33, 1
## SHAPE MANIPULATION
```py
np.reshape(array, newshape) # changes the shape of the array
np.reshape(array, new_shape) # changes the shape of the array
np.ravel(array) # returns the array flattened
array.resize(shape) # modifies the array itself
array.T # returns the array transposed
@ -87,7 +87,7 @@ np.swapaxes(array, first_axis, second_axis) # interchange two axes of an array
```py
np.vstack((array1, array2)) # takes tuple, vertical stack of arrays (column wise)
np.hstack((array1, array2)) # takes a tuple, horizontal stack of arrays (row wise)
np.dstack((array1, array2)) # takes a tuple, depth wise stack of arrays (3rd dimesion)
np.dstack((array1, array2)) # takes a tuple, depth wise stack of arrays (3rd dimension)
np.stack(*arrays, axis) # joins a sequence of arrays along a new axis (axis is an int)
np.concatenate((array1, array2, ...), axis) # joins a sequence of arrays along an existing axis (axis is an int)
```
@ -95,32 +95,32 @@ np.concatenate((array1, array2, ...), axis) # joins a sequence of arrays along a
## SPLITTING ARRAYS
```py
np.split(array, indices) # splits an array into equalli long sub-arrays (indices is int), if not possible raises error
np.split(array, indices) # splits an array into equall7 long sub-arrays (indices is int), if not possible raises error
np.vsplit(array, indices) # splits an array equally into sub-arrays vertically (row wise) if not possible raises error
np.hsplit(array, indices) # splits an array equally into sub-arrays horizontally (column wise) if not possible raises error
np.dsplit(array, indices) # splits an array into equally sub-arrays along the 3rd axis (depth) if not possible raises error
np.array_split(array, indices) # splits an array into sub-arrays, arrays can be of different lenghts
np.array_split(array, indices) # splits an array into sub-arrays, arrays can be of different lengths
```
## VIEW()
```py
var = array.view() # creates a new array that looks at the same data
# slicinga returnas a view
# view shapes are separated but assignement changes all arrays
# slicing returns a view
# view shapes are separated but assignment changes all arrays
```
## COPY()
```py
var = array.copy() # creates a deepcopy of the array
var = array.copy() # creates a deep copy of the array
```
## INDEXING, SLICING, ITERATING
1-dimensional --> sliced, iterated and indexed as standard
n-dimensinal --> one index per axis, index given in tuple separated by commas `[i, j] (i, j)`
dots (`...`) represent as meny colons as needed to produce complete indexing tuple
n-dimensional --> one index per axis, index given in tuple separated by commas `[i, j] (i, j)`
dots (`...`) represent as many colons as needed to produce complete indexing tuple
- `x[1, 2, ...] == [1, 2, :, :, :]`
- `x[..., 3] == [:, :, :, :, 3]`
@ -134,7 +134,7 @@ iteration on first index, use .flat() to iterate over each element
## UNIVERSAL FUNCTIONS (ufunc)
Functions that performs elemen-wise operations (vectorization).
Functions that performs element-wise operations (vectorization).
```py
np.abs(array) # vectorized abs(), return element absolute value
@ -151,7 +151,7 @@ np.ceil(array) # vectorized ceil()
np.floor(array) # vectorized floor()
np.rint(array) # vectorized round() to nearest int
np.modf(array) # vectorized divmod(), returns the fractional and integral parts of element
np.isnan(array) # vectorized x == NaN, return bollean array
np.isnan(array) # vectorized x == NaN, return boolean array
np.isinf(array) # vectorized test for positive or negative infinity, return boolean array
np.isfineite(array) # vectorized test fo finiteness, returns boolean array
np.cos(array) # vectorized cos(x)
@ -163,7 +163,7 @@ np.tanh(array) # vectorized tanh(x)
np.arccos(array) # vectorized arccos(x)
np.arcsinh(array) # vectorized arcsinh(x)
np.arctan(array) # vectorized arctan(x)
np.arccosh(array) # vectorized arccos(x)
np.arccosh(array) # vectorized arccosh(x)
np.arcsinh(array) # vectorized arcsin(x)
np.arctanh(array) # vectorized arctanh(x)
np.logical_not(array) # vectorized not(x), equivalent to -array
@ -246,13 +246,13 @@ np.setxor1d() # Set symmetric differences; elements that are in either of the a
## FILE I/O WITH ARRAYS
```py
np.save(file, array) # save array to binary file in .npy fromat
np.savez(file, *array) # saveseveral arrays into a single file in uncompressed .npz format
np.save(file, array) # save array to binary file in .npy format
np.savez(file, *array) # save several arrays into a single file in uncompressed .npz format
np.savez_compressed(file, *args, *kwargs) # save several arrays into a single file in compressed .npz format
# *ARGS: arrays to save to the file. arrays will be saved with names “arr_0”, “arr_1”, and so on
# *ARGS: arrays to save to the file. arrays will be saved with names "arr_0", "arr_1", and so on
# **KWARGS: arrays to save to the file. arrays will be saved in the file with the keyword names
np.savetxt(file, X, fmt="%.18e", delimiter=" ") # save arry to text file
np.savetxt(file, X, fmt="%.18e", delimiter=" ") # save array to text file
# X: 1D or 2D
# FMT: Python Format Specification Mini-Language
# DELIMITER: {str} -- string used to separate values
@ -272,14 +272,14 @@ np.diag(array, k=0) # extract a diagonal or construct a diagonal array
np.dot(x ,y) # matrix dot product
np.trace(array, offset=0, dtype=None, out=None) # return the sum along diagonals of the array
# OFFSET: {int} -- offest of the diagonal from the main diagonal
# OFFSET: {int} -- offset of the diagonal from the main diagonal
# dtype: {dtype} -- determines the data-type of the returned array
# OUT: {ndarray} -- array into which the output is placed
np.linalg.det(A) # compute the determinant of an array
np.linalg.eig(A) # compute the eigenvalues and right eigenvectors of a square array
np.linalg.inv(A) # compute the (multiplicative) inverse of a matrix
# Ainv satisfies dot(A, Ainv) = dor(Ainv, A) = eye(A.shape[0])
# A_inv satisfies dot(A, A_inv) = dor(A_inv, A) = eye(A.shape[0])
np.linalg.pinv(A) # compute the (Moore-Penrose) pseudo-inverse of a matrix
np.linalg.qr() # factor the matrix a as qr, where q is orthonormal and r is upper-triangular
@ -304,13 +304,13 @@ np.random.Generator.beta(a, b, size=None) # draw samples from a Beta distributi
np.random.Generator.binomial(n, p, size=None) # draw samples from a binomial distribution
# N: {int, array ints} -- parameter of the distribution, >= 0
# P: {float, attay floats} -- Parameter of the distribution, >= 0 and <= 1
# P: {float, arrey floats} -- Parameter of the distribution, >= 0 and <= 1
np.random.Generator.chisquare(df, size=None)
# DF: {float, array floats} -- degrees of freedom, > 0
np.random.Generator.gamma(shape, scale=1.0, size=None) # draw samples from a Gamma distribution
# SHAPE: {flaot, array floats} -- shape of the gamma distribution, != 0
# SHAPE: {float, array floats} -- shape of the gamma distribution, != 0
np.random.Generator.normal(loc=0.0, scale=1.0, Size=None) # draw random samples from a normal (Gaussian) distribution
# LOC: {float, all floats} -- mean ("centre") of distribution

144
Python/Libs/Math/Pandas.md
View file

@ -1,4 +1,4 @@
# Pandas Lib
# Pandas
## Basic Pandas Imports
@ -25,8 +25,8 @@ s = Series(dict) # Series created from python dict, dict keys become index valu
```py
s['index'] # selection by index label
s[condition] # return slice selected by condition
s[ : ] # slice endpoin included
s[ : ] = *value # modifi value of entire slice
s[ : ] # slice endpoint included
s[ : ] = *value # modify value of entire slice
s[condition] = *value # modify slice by condition
```
@ -35,8 +35,8 @@ s[condition] = *value # modify slice by condition
Missing data appears as NaN (Not a Number).
```py
pd.isnull(array) # retunn a Series index-bool indicating wich indexes dont have data
pd.notnull(array) # retunn a Series index-bool indicating wich indexes have data
pd.isnull(array) # return a Series index-bool indicating which indexes don't have data
pd.notnull(array) # return a Series index-bool indicating which indexes have data
array.isnull()
array.notnull()
```
@ -53,18 +53,18 @@ s.index.name = "index name" # renames index
### SERIES METHODS
```py
pd.Series.isin(self, values) # boolean Series showing whether elements in Series matcheselements in values exactly
pd.Series.isin(self, values) # boolean Series showing whether elements in Series matches elements in values exactly
# Conform Series to new index, new object produced unless the new index is equivalent to current one and copy=False
pd.Series.reindex(delf, index=None, **kwargs)
pd.Series.reindex(self, index=None, **kwargs)
# INDEX: {array} -- new labels / index
# METHOD: {none (dont fill gaps), pad (fill or carry values forward), backfill (fill or carry values backward)}-- hole filling method
# METHOD: {none (don't fill gaps), pad (fill or carry values forward), backfill (fill or carry values backward)}-- hole filling method
# COPY: {bool} -- return new object even if index is same -- DEFAULT True
# FILLVALUE: {scalar} --value to use for missing values. DEFAULT NaN
pd.Series.drop(self, index=None, **kwargs) # return Series with specified index labels removed
# INPLACE: {bool} -- if true do operation in place and return None -- DEFAULT False
# ERRORS: {ignore, raise} -- If ‘ignore’, suppress error and existing labels are dropped
# ERRORS: {ignore, raise} -- If "ignore", suppress error and existing labels are dropped
# KeyError raised if not all of the labels are found in the selected axis
pd.Series.value_counts(self, normalize=False, sort=True, ascending=False, bins=None, dropna=True)
@ -72,7 +72,7 @@ pd.Series.value_counts(self, normalize=False, sort=True, ascending=False, bins=N
# SORT: {bool} -- sort by frequency -- DEFAULT True
# ASCENDING: {bool} -- sort in ascending order -- DEFAULT False
# BINS: {int} -- group values into half-open bins, only works with numeric data
# DROPNA: {bool} -- dont include counts of NaN
# DROPNA: {bool} -- don't include counts of NaN
```
## DATAFRAME
@ -124,19 +124,19 @@ df.T # transpose
### DATAFRAME METHODS
```py
pd.DataFrame.isin(self , values) # boolean DataFrame showing whether elements in DataFrame matcheselements in values exactly
pd.DataFrame.isin(self , values) # boolean DataFrame showing whether elements in DataFrame matches elements in values exactly
# Conform DataFrame to new index, new object produced unless the new index is equivalent to current one and copy=False
pd.DataFrame.reindex(self, index=None, columns=None, **kwargs)
# INDEX: {array} -- new labels / index
# COLUMNS: {array} -- new labels / columns
# METHOD: {none (dont fill gaps), pad (fill or carry values forward), backfill (fill or carry values backward)}-- hole filling method
# METHOD: {none (don't fill gaps), pad (fill or carry values forward), backfill (fill or carry values backward)}-- hole filling method
# COPY: {bool} -- return new object even if index is same -- DEFAULT True
# FILLVALUE: {scalar} --value to use for missing values. DEFAULT NaN
pd.DataFrame.drop(self, index=None, columns=None, **kwargs) # Remove rows or columns by specifying label names
# INPLACE: {bool} -- if true do operation in place and return None -- DEFAULT False
# ERRORS: {ignore, raise} -- If ‘ignore’, suppress error and existing labels are dropped
# ERRORS: {ignore, raise} -- If "ignore", suppress error and existing labels are dropped
# KeyError raised if not all of the labels are found in the selected axis
```
@ -147,7 +147,7 @@ Holds axis labels and metadata, immutable.
### INDEX TYPES
```py
pd.Index # immutable ordered ndarray, sliceable. stortes axis labels
pd.Index # immutable ordered ndarray, sliceable. stores axis labels
pd.Int64Index # special case of Index with purely integer labels
pd.MultiIndex # multi-level (hierarchical) index object for pandas objects
pd.PeriodINdex # immutable ndarray holding ordinal values indicating regular periods in time
@ -169,27 +169,27 @@ pd.Index.hasnans # Return True if the index has NaNs
pd.Index.append(self, other) # append a collection of Index options together
pd.Index.difference(self, other, sort=None) # set difference of two Index objects
# SORT: {None (attempt sorting), False (dont sort)}
# SORT: {None (attempt sorting), False (don't sort)}
pd.Index.intersection(self, other, sort=None) # set intersection of two Index objects
# SORT: {None (attempt sorting), False (dont sort)}
# SORT: {None (attempt sorting), False (don't sort)}
pd.Index.union(self, other, sort=None) # set union of two Index objects
# SORT: {None (attempt sorting), False (dont sort)}
# SORT: {None (attempt sorting), False (don't sort)}
pd.Index.isin(self, values, level=None) # boolean array indicating where the index values are in values
pd.Index.insert(self, loc, item) # make new Index inserting new item at location
pd.Index.delete(self, loc) # make new Index with passed location(-s) deleted
pd.Index.drop(self, labels, errors='raise') # Make new Index with passed list of labels deleted
# ERRORS: {ignore, raise} -- If ‘ignore’, suppress error and existing labels are dropped
# ERRORS: {ignore, raise} -- If 'ignore', suppress error and existing labels are dropped
# KeyError raised if not all of the labels are found in the selected axis
pd.Index.reindex(self, target, **kwargs) # create index with target’s values (move/add/delete values as necessary)
# METHOD: {none (dont fill gaps), pad (fill or carry values forward), backfill (fill or carry values backward)}-- hole filling method
pd.Index.reindex(self, target, **kwargs) # create index with target's values (move/add/delete values as necessary)
# METHOD: {none (don't fill gaps), pad (fill or carry values forward), backfill (fill or carry values backward)}-- hole filling method
```
## ARITMETHIC OPERATIONS
## ARITHMETIC OPERATIONS
NumPy arrays operations preserve labels-value link.
Arithmetic operations automatically align differently indexed data.
@ -199,10 +199,10 @@ Missing values propagate in arithmetic computations (NaN `<operator>` value = Na
```py
self + other
pd.Series.add(self, other, fill_value=None) # add(), supports substituion of NaNs
pd,Series.radd(self, other, fill_value=None) # radd(), supports substituion of NaNs
pd.DataFrame.add(self, other, axis=columns, fill_value=None) # add(), supports substituion of NaNs
pd.DataFrame.radd(self, other, axis=columns, fill_value=None) # radd(), supports substituion of NaNs
pd.Series.add(self, other, fill_value=None) # add(), supports substitution of NaNs
pd,Series.radd(self, other, fill_value=None) # radd(), supports substitution of NaNs
pd.DataFrame.add(self, other, axis=columns, fill_value=None) # add(), supports substitution of NaNs
pd.DataFrame.radd(self, other, axis=columns, fill_value=None) # radd(), supports substitution of NaNs
# OTHER: {scalar, sequence, Series, DataFrame}
# AXIS: {0, 1, index, columns} -- whether to compare by the index or columns
# FILLVALUE: {None, float} -- fill missing value
@ -212,10 +212,10 @@ pd.DataFrame.radd(self, other, axis=columns, fill_value=None) # radd(), support
```py
self - other
pd.Series.sub(self, other, fill_value=None) # sub(), supports substituion of NaNs
pd.Series.radd(self, other, fill_value=None) # radd(), supports substituion of NaNs
ps.DataFrame.sub(self, other, axis=columns, fill_value=None) # sub(), supports substituion of NaNs
pd.DataFrame.rsub(self, other, axis=columns, fill_value=None) # rsub(), supports substituion of NaNs
pd.Series.sub(self, other, fill_value=None) # sub(), supports substitution of NaNs
pd.Series.radd(self, other, fill_value=None) # radd(), supports substitution of NaNs
ps.DataFrame.sub(self, other, axis=columns, fill_value=None) # sub(), supports substitution of NaNs
pd.DataFrame.rsub(self, other, axis=columns, fill_value=None) # rsub(), supports substitution of NaNs
# OTHER: {scalar, sequence, Series, DataFrame}
# AXIS: {0, 1, index, columns} -- whether to compare by the index or columns
# FILLVALUE: {None, float} -- fill missing value
@ -225,10 +225,10 @@ pd.DataFrame.rsub(self, other, axis=columns, fill_value=None) # rsub(), support
```py
self * other
pd.Series.mul(self, other, fill_value=None) # mul(), supports substituion of NaNs
pd.Series.rmul(self, other, fill_value=None) # rmul(), supports substituion of NaNs
ps.DataFrame.mul(self, other, axis=columns, fill_value=None) # mul(), supports substituion of NaNs
pd.DataFrame.rmul(self, other, axis=columns, fill_value=None) # rmul(), supports substituion of NaNs
pd.Series.mul(self, other, fill_value=None) # mul(), supports substitution of NaNs
pd.Series.rmul(self, other, fill_value=None) # rmul(), supports substitution of NaNs
ps.DataFrame.mul(self, other, axis=columns, fill_value=None) # mul(), supports substitution of NaNs
pd.DataFrame.rmul(self, other, axis=columns, fill_value=None) # rmul(), supports substitution of NaNs
# OTHER: {scalar, sequence, Series, DataFrame}
# AXIS: {0, 1, index, columns} -- whether to compare by the index or columns
# FILLVALUE: {None, float} -- fill missing value
@ -238,14 +238,14 @@ pd.DataFrame.rmul(self, other, axis=columns, fill_value=None) # rmul(), support
```py
self / other
pd.Series.div(self, other, fill_value=None) # div(), supports substituion of NaNs
pd.Series.rdiv(self, other, fill_value=None) # rdiv(), supports substituion of NaNs
pd.Series.truediv(self, other, fill_value=None) # truediv(), supports substituion of NaNs
pd.Series.rtruediv(self, other, fill_value=None) # rtruediv(), supports substituion of NaNs
ps.DataFrame.div(self, other, axis=columns, fill_value=None) # div(), supports substituion of NaNs
pd.DataFrame.rdiv(self, other, axis=columns, fill_value=None) # rdiv(), supports substituion of NaNs
ps.DataFrame.truediv(self, other, axis=columns, fill_value=None) # truediv(), supports substituion of NaNs
pd.DataFrame.rtruediv(self, other, axis=columns, fill_value=None) # rtruediv(), supports substituion of NaNs
pd.Series.div(self, other, fill_value=None) # div(), supports substitution of NaNs
pd.Series.rdiv(self, other, fill_value=None) # rdiv(), supports substitution of NaNs
pd.Series.truediv(self, other, fill_value=None) # truediv(), supports substitution of NaNs
pd.Series.rtruediv(self, other, fill_value=None) # rtruediv(), supports substitution of NaNs
ps.DataFrame.div(self, other, axis=columns, fill_value=None) # div(), supports substitution of NaNs
pd.DataFrame.rdiv(self, other, axis=columns, fill_value=None) # rdiv(), supports substitution of NaNs
ps.DataFrame.truediv(self, other, axis=columns, fill_value=None) # truediv(), supports substitution of NaNs
pd.DataFrame.rtruediv(self, other, axis=columns, fill_value=None) # rtruediv(), supports substitution of NaNs
# OTHER: {scalar, sequence, Series, DataFrame}
# AXIS: {0, 1, index, columns} -- whether to compare by the index or columns
# FILLVALUE: {None, float} -- fill missing value
@ -255,10 +255,10 @@ pd.DataFrame.rtruediv(self, other, axis=columns, fill_value=None) # rtruediv(),
```py
self // other
pd.Series.floordiv(self, other, fill_value=None) # floordiv(), supports substituion of NaNs
pd.Series.rfloordiv(self, other, fill_value=None) # rfloordiv(), supports substituion of NaNs
ps.DataFrame.floordiv(self, other, axis=columns, fill_value=None) # floordiv(), supports substituion of NaNs
pd.DataFrame.rfloordiv(self, other, axis=columns, fill_value=None) # rfloordiv(), supports substituion of NaNs
pd.Series.floordiv(self, other, fill_value=None) # floordiv(), supports substitution of NaNs
pd.Series.rfloordiv(self, other, fill_value=None) # rfloordiv(), supports substitution of NaNs
ps.DataFrame.floordiv(self, other, axis=columns, fill_value=None) # floordiv(), supports substitution of NaNs
pd.DataFrame.rfloordiv(self, other, axis=columns, fill_value=None) # rfloordiv(), supports substitution of NaNs
# OTHER: {scalar, sequence, Series, DataFrame}
# AXIS: {0, 1, index, columns} -- whether to compare by the index or columns
# FILLVALUE: {None, float} -- fill missing value
@ -268,10 +268,10 @@ pd.DataFrame.rfloordiv(self, other, axis=columns, fill_value=None) # rfloordiv(
```py
self % other
pd.Series.mod(self, other, fill_value=None) # mod(), supports substituion of NaNs
pd.Series.rmod(self, other, fill_value=None) # rmod(), supports substituion of NaNs
ps.DataFrame.mod(self, other, axis=columns, fill_value=None) # mod(), supports substituion of NaNs
pd.DataFrame.rmod(self, other, axis=columns, fill_value=None) # rmod(), supports substituion of NaNs
pd.Series.mod(self, other, fill_value=None) # mod(), supports substitution of NaNs
pd.Series.rmod(self, other, fill_value=None) # rmod(), supports substitution of NaNs
ps.DataFrame.mod(self, other, axis=columns, fill_value=None) # mod(), supports substitution of NaNs
pd.DataFrame.rmod(self, other, axis=columns, fill_value=None) # rmod(), supports substitution of NaNs
# OTHER: {scalar, sequence, Series, DataFrame}
# AXIS: {0, 1, index, columns} -- whether to compare by the index or columns
# FILLVALUE: {None, float} -- fill missing value
@ -281,10 +281,10 @@ pd.DataFrame.rmod(self, other, axis=columns, fill_value=None) # rmod(), support
```py
other ** self
pd.Series.pow(self, other, fill_value=None) # pow(), supports substituion of NaNs
pd.Series.rpow(self, other, fill_value=None) # rpow(), supports substituion of NaNs
ps.DataFrame.pow(self, other, axis=columns, fill_value=None) # pow(), supports substituion of NaNs
pd.DataFrame.rpow(self, other, axis=columns, fill_value=None) # rpow(), supports substituion of NaNs
pd.Series.pow(self, other, fill_value=None) # pow(), supports substitution of NaNs
pd.Series.rpow(self, other, fill_value=None) # rpow(), supports substitution of NaNs
ps.DataFrame.pow(self, other, axis=columns, fill_value=None) # pow(), supports substitution of NaNs
pd.DataFrame.rpow(self, other, axis=columns, fill_value=None) # rpow(), supports substitution of NaNs
# OTHER: {scalar, sequence, Series, DataFrame}
# AXIS: {0, 1, index, columns} -- whether to compare by the index or columns
# FILLVALUE: {None, float} -- fill missing value
@ -299,7 +299,7 @@ NumPy ufuncs work fine with pandas objects.
```py
pd.DataFrame.applymap(self, func) # apply function element-wise
pd.DataFrame.apply(self, func, axis=0, args=()) # apllay a function along an axis of a DataFrame
pd.DataFrame.apply(self, func, axis=0, args=()) # apply a function along an axis of a DataFrame
# FUNC: {function} -- function to apply
# AXIS: {O, 1, index, columns} -- axis along which the function is applied
# ARGS: {tuple} -- positional arguments to pass to func in addition to the array/series
@ -309,7 +309,7 @@ pd.Series.sort_values(self, ascending=True, **kwargs) # sort series by the valu
# ASCENDING: {bool} -- if True, sort values in ascending order, otherwise descending -- DEFAULT True
# INPALCE: {bool} -- if True, perform operation in-place
# KIND: {quicksort, mergesort, heapsort} -- sorting algorithm
# NA_POSITION {first, last} -- ‘first’ puts NaNs at the beginning, ‘last’ puts NaNs at the end
# NA_POSITION {first, last} -- 'first' puts NaNs at the beginning, 'last' puts NaNs at the end
pd.DataFrame.sort_index(self, axis=0, ascending=True, **kwargs) # sort object by labels along an axis
pd.DataFrame.sort_values(self, axis=0, ascending=True, **kwargs) # sort object by values along an axis
@ -317,7 +317,7 @@ pd.DataFrame.sort_values(self, axis=0, ascending=True, **kwargs) # sort object
# ASCENDING: {bool} -- if True, sort values in ascending order, otherwise descending -- DEFAULT True
# INPALCE: {bool} -- if True, perform operation in-place
# KIND: {quicksort, mergesort, heapsort} -- sorting algorithm
# NA_POSITION {first, last} -- ‘first’ puts NaNs at the beginning, ‘last’ puts NaNs at the end
# NA_POSITION {first, last} -- 'first' puts NaNs at the beginning, 'last' puts NaNs at the end
```
## DESCRIPTIVE AND SUMMARY STATISTICS
@ -332,7 +332,7 @@ pd.DataFrame.count(self, numeric_only=False) # count non-NA cells for each colu
### DESCRIBE
Generate descriptive statistics summarizing central tendency, dispersion and shape of dataset’s distribution (exclude NaN).
Generate descriptive statistics summarizing central tendency, dispersion and shape of dataset's distribution (exclude NaN).
```py
pd.Series.describe(self, percentiles=None, include=None, exclude=None)
@ -350,7 +350,7 @@ pd.Series.min(self, skipna=None, numeric_only=None) # minimum of the values for
pd.DataFrame.max(self, axis=None, skipna=None, numeric_only=None) # maximum of the values for the requested axis
pd.DataFrame.min(self, axis=None, skipna=None, numeric_only=None) # minimum of the values for the requested axis
# SKIPNA: {bool} -- exclude NA/null values when computing the result
# NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not immplemented for Series
# NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not implemented for Series
```
### IDXMAX - IDXMIN
@ -381,7 +381,7 @@ pd.Series.sum(self, skipna=None, numeric_only=None, min_count=0) # sum of the v
pd.DataFrame.sum(self, axis=None, skipna=None, numeric_only=None, min_count=0) # sum of the values for the requested axis
# AXIS: {0, 1, index, columns} -- axis for the function to be applied on
# SKIPNA: {bool} -- exclude NA/null values when computing the result
# NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not immplemented for Series
# NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not implemented for Series
# MIN_COUNT: {int} -- required number of valid values to perform the operation. if fewer than min_count non-NA values are present the result will be NA
```
@ -392,7 +392,7 @@ pd.Series.mean(self, skipna=None, numeric_only=None) # mean of the values
pd.DataFrame.mean(self, axis=None, skipna=None, numeric_only=None) # mean of the values for the requested axis
# AXIS: {0, 1, index, columns} -- axis for the function to be applied on
# SKIPNA: {bool} -- exclude NA/null values when computing the result
# NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not immplemented for Series
# NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not implemented for Series
```
### MEDIAN
@ -402,7 +402,7 @@ pd.Series.median(self, skipna=None, numeric_only=None) # median of the values
pd.DataFrame.median(self, axis=None, skipna=None, numeric_only=None) # median of the values for the requested axis
# AXIS: {0, 1, index, columns} -- axis for the function to be applied on
# SKIPNA: {bool} -- exclude NA/null values when computing the result
# NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not immplemented for Series
# NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not implemented for Series
```
### MAD (mean absolute deviation)
@ -422,7 +422,7 @@ pd.DataFrame.var(self, axis=None, skipna=None, ddof=1, numeric_only=None) # un
# AXIS: {0, 1, index, columns} -- axis for the function to be applied on
# SKIPNA: {bool} -- exclude NA/null values. if an entire row/column is NA, the result will be NA
# DDOF: {int} -- Delta Degrees of Freedom. divisor used in calculations is N - ddof (N represents the number of elements) -- DEFAULT 1
# NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not immplemented for Series
# NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not implemented for Series
```
### STD (standard deviation)
@ -433,7 +433,7 @@ pd.Dataframe.std(self, axis=None, skipna=None, ddof=1, numeric_only=None) # sam
# AXIS: {0, 1, index, columns} -- axis for the function to be applied on
# SKIPNA: {bool} -- exclude NA/null values. if an entire row/column is NA, the result will be NA
# DDOF: {int} -- Delta Degrees of Freedom. divisor used in calculations is N - ddof (N represents the number of elements) -- DEFAULT 1
# NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not immplemented for Series
# NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not implemented for Series
```
### SKEW
@ -443,19 +443,19 @@ pd.Series.skew(self, skipna=None, numeric_only=None) # unbiased skew Normalized
pd.DataFrame.skew(self, axis=None, skipna=None, numeric_only=None) # unbiased skew over requested axis Normalized by N-1
# AXIS: {0, 1, index, columns} -- axis for the function to be applied on
# SKIPNA: {bool} -- exclude NA/null values when computing the result
# NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not immplemented for Series
# NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not implemented for Series
```
### KURT
Unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.
Unbiased kurtosis over requested axis using Fisher's definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.
```py
pd.Series.kurt(self, skipna=None, numeric_only=None)
pd.Dataframe.kurt(self, axis=None, skipna=None, numeric_only=None)
# AXIS: {0, 1, index, columns} -- axis for the function to be applied on
# SKIPNA: {bool} -- exclude NA/null values when computing the result
# NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not immplemented for Series
# NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not implemented for Series
```
### CUMSUM (cumulative sum)
@ -471,7 +471,7 @@ pd.Dataframe.cumsum(self, axis=None, skipna=True) # cumulative sum over request
```py
pd.Series.cummax(self, skipna=True) # cumulative maximum
pd.Series.cummin(self, skipna=True) # cumulative minimumm
pd.Series.cummin(self, skipna=True) # cumulative minimum
pd.Dataframe.cummax(self, axis=None, skipna=True) # cumulative maximum over requested axis
pd.Dataframe.cummin(self, axis=None, skipna=True) # cumulative minimum over requested axis
# AXIS: {0, 1, index, columns} -- axis for the function to be applied on
@ -499,7 +499,7 @@ pd.DataFrame.diff(self, periods=1, axis=0)
# AXIS: {0, 1, index, columns} -- Take difference over rows or columns
```
### PCT_CAHNGE
### PCT_CHANGE
Percentage change between the current and a prior element.
@ -541,7 +541,7 @@ pd.DataFrame.fillna(self, value=None, method=None, axis=None, inplace=False, lim
## HIERARCHICAL INDEXING (MultiIndex)
Enables storing and manupulation of data with an arbitrary number of dimensions.
Enables storing and manipulation of data with an arbitrary number of dimensions.
In lower dimensional data structures like Series (1d) and DataFrame (2d).
### MULTIIINDEX CREATION
@ -565,7 +565,7 @@ pd.MultiIndex.get_level_values(self, level)
### PARTIAL AND CROSS-SECTION SELECTION
Partial selection “drops” levels of the hierarchical index in the result in a completely analogous way to selecting a column in a regular DataFrame.
Partial selection "drops" levels of the hierarchical index in the result in a completely analogous way to selecting a column in a regular DataFrame.
```py
pd.Series.xs(self, key, axis=0, level=None, drop_level=True) # cross-section from Series
@ -608,7 +608,7 @@ pd.MultiIndex.sortlevel(self, level=0, ascending=True, sort_remaining=True) # s
pd.read_fwf(filepath, colspecs='infer', widths=None, infer_nrows=100) # read a table of fixed-width formatted lines into DataFrame
# FILEPATH: {str, path object} -- any valid string path is acceptable, could be a URL. Valid URLs: http, ftp, s3, and file
# COLSPECS: {list of tuple (int, int), 'infer'} -- list of tuples giving extents of fixed-width fields of each line as half-open intervals { [from, to) }
# WIDTHS: {list of int} -- list of field widths which can be used instead of ‘colspecs’ if intervals are contiguous
# WIDTHS: {list of int} -- list of field widths which can be used instead of "colspecs" if intervals are contiguous
# INFER_ROWS: {int} -- number of rows to consider when letting parser determine colspecs -- DEFAULT 100
pd.read_excel() # read an Excel file into a pandas DataFrame
@ -635,7 +635,7 @@ pd.DataFrame.to_csv(self, path_or_buf, sep=',', na_rep='', columns=None, header=
# COLUMNS: {sequence} -- colums to write
# HEADER: {bool, list of str} -- write out column names. if list of strings is given its assumed to be aliases for column names
# INDEX: {bool, list of str} -- write out row names (index)
# ENCODING: {str} -- string representing encoding to use -- DEFAULT ‘utf-8’
# ENCODING: {str} -- string representing encoding to use -- DEFAULT "utf-8"
# LINE_TERMINATOR: {str} -- newline character or character sequence to use in the output file -- DEFAULT os.linesep
# DECIMAL: {str} -- character recognized as decimal separator (in EU ,)

30
Python/Libs/Math/seaborn.md
View file

@ -17,18 +17,18 @@ sns.set(style='darkgrid')
```python
sns.replot(x='name_in_data', y='name_in_data', hue='point_color', size='point_size', style='point_shape', data=data)
# HUE, SIZE and STYLE: {name in data} -- used to differenciate points, a sort-of 3rd dimention
# HUE, SIZE and STYLE: {name in data} -- used to differentiate points, a sort-of 3rd dimension
# hue behaves differently if the data is categorical or numerical, numerical uses a color gradient
# SORT: {False, True} -- avoid sorting data in function of x
# CI: {None, sd} -- avoid comuting confidence intervals or plot standard deviation
# CI: {None, sd} -- avoid computing confidence intervals or plot standard deviation
# (aggregate multiple measurements at each x value by plotting the mean and the 95% confidence interval around the mean)
# ESTIMATOR: {None} -- turn off aggregation of multiple observations
# MARKERS: {True, False} -- evidetiate observations with dots
# DASHES: {True, False} -- evidetiate observations with dashes
# MARKERS: {True, False} -- evidenziate observations with dots
# DASHES: {True, False} -- evidenziate observations with dashes
# COL, ROW: {name in data} -- categorical variables that will determine the grid of plots
# COL_WRAP: {int} -- “Wrap” the column variable at this width, so that the column facets span multiple rows. Incompatible with a row facet.
# COL_WRAP: {int} -- "Wrap" the column variable at this width, so that the column facets span multiple rows. Incompatible with a row facet.
# SCATTERPLOT
# depicts the joint distibution of two variables usinga a cloud of points
# depicts the joint distribution of two variables using a cloud of points
# kind can be omitted since scatterplot is the default for replot
sns.replot(kind='scatter') # calls scatterplot()
sns.scatterplot() # underlying axis-level function of replot()
@ -45,16 +45,16 @@ sns.lineplot() # underlying axis-level function of replot()
## CATPLOT (categorical)
Categorical: dicided into discrete groups.
Categorical: divided into discrete groups.
```python
sns.catplot(x='name_in_data', y='name_in_data', data=data)
# HUE: {name in data} -- used to differenciate points, a sort-of 3rd dimention
# HUE: {name in data} -- used to differenziate points, a sort-of 3rd dimension
# COL, ROW: {name in data} -- categorical variables that will determine the grid of plots
# COL_WRAP: {int} -- “Wrap” the column variable at this width, so that the column facets span multiple rows. Incompatible with a row facet.
# ORDER, HUE_ORDER: {list of strings} -- oreder of categorical levels of the plot
# COL_WRAP: {int} -- "Wrap" the column variable at this width, so that the column facets span multiple rows. Incompatible with a row facet.
# ORDER, HUE_ORDER: {list of strings} -- order of categorical levels of the plot
# ROW_ORDER, COL_ORDER: {list of strings} -- order to organize the rows and/or columns of the grid in
# ORIENT: {'v', 'h'} -- Orientation of the plot (can also swap x&y assignement)
# ORIENT: {'v', 'h'} -- Orientation of the plot (can also swap x&y assignment)
# COLOR: {matplotlib color} -- Color for all of the elements, or seed for a gradient palette
# CATEGORICAL SCATTERPLOT - STRIPPLOT
# adjust the positions of points on the categorical axis with a small amount of random “jitter”
@ -85,7 +85,7 @@ sns.boxplot()
Combines a boxplot with the kernel density estimation procedure.
```py
sns.catplot(kind='violon')
sns.catplot(kind='violin')
sns.violonplot()
```
@ -113,7 +113,7 @@ sns.pointplot()
# JOIN: {bool} -- if True, lines will be drawn between point estimates at the same hue level
# SCALE: {float} -- scale factor for the plot elements
# ERRWIDTH: {float} -- thickness of error bar lines (and caps)
# CAPSIZE: {float} -- width of the “caps” on error bars
# CAPSIZE: {float} -- width of the "caps" on error bars
```
### CATEGORICAL ESTIMATE - BARPLOT
@ -126,7 +126,7 @@ sns.barplot()
# CI: {float, sd} -- size of confidence intervals to draw around estimated values, sd -> standard deviation
# ERRCOLOR: {matplotlib color} -- color for the lines that represent the confidence interval
# ERRWIDTH: {float} -- thickness of error bar lines (and caps)
# CAPSIZE: {float} -- width of the “caps” on error bars
# CAPSIZE: {float} -- width of the "caps" on error bars
# DODGE: {bool} -- whether elements should be shifted along the categorical axis if hue is used
```
@ -173,7 +173,7 @@ sns.rugplot(a=data) # -> axes obj with plot on it
Fit and plot a univariate or bivariate kernel density estimate.
```py
# DATA: {1D array-like} -- inpoy data
# DATA: {1D array-like} -- input data
sns.kdeplot(data=data)
# DATA2 {1D array-like} -- second input data. if present, a bivariate KDE will be estimated.
# SHADE: {bool} -- if True, shade-in the area under KDE curve (or draw with filled contours is bivariate)

28
Python/Libs/Web/BeautifulSoup.md
View file

@ -8,7 +8,7 @@ from bs4 import BeautifulSoup
import requests
import lxml # better html parser than built-in
response = requests.get("url") # retuire a web page
response = requests.get("url") # retrieve a web page
soup = BeautifulSoup(response.text, "html.parser") # parse HTML from response w/ python default HTML parser
soup = BeautifulSoup(response.text, "lxml") # parse HTML from response w/ lxml parser
@ -32,7 +32,7 @@ type(tag) # <class 'bs4.element.Tag'>
print(tag) # <b class="boldest">Extremely bold</b>
tag.name # tag name
tag["attribute"] # access to ttag attribute values
tag["attribute"] # access to tag attribute values
tag.attrs # dict of attribue-value pairs
```
@ -48,21 +48,21 @@ A string corresponds to a bit of text within a tag. Beautiful Soup uses the `Nav
soup.<tag>.<child_tag> # navigate using tag names
<tag>.contents # direct children as a list
<tag>.children # direct children as a genrator for iteration
<tag>.descendats # iterator over all childered, recusive
<tag>.children # direct children as a generator for iteration
<tag>.descendants # iterator over all children, recursive
<tag>.string # tag contents, does not have further children
# If a tag’s only child is another tag, and that tag has a .string, then the parenttag is considered to have the same .string as its child
# If a tag contains more than one thing, then it’s not clear what .string should refer to, so .string is defined to be None
# If a tag's only child is another tag, and that tag has a .string, then the parent tag is considered to have the same .string as its child
# If a tag contains more than one thing, then it's not clear what .string should refer to, so .string is defined to be None
<tag>.strings # generattor to iterate over all children's strings (will list white space)
<tag>.stripped_strings # generattor to iterate over all children's strings (will NOT list white space)
<tag>.strings # generator to iterate over all children's strings (will list white space)
<tag>.stripped_strings # generator to iterate over all children's strings (will NOT list white space)
```
### Going Up
```py
<tag>.parent # tags direct parent (BeautifleSoup has parent None, html has parent BeautifulSoup)
<tag>.parent # tags direct parent (BeautifulSoup has parent None, html has parent BeautifulSoup)
<tag>.parents # iterable over all parents
```
@ -94,7 +94,7 @@ soup.<tag>.<child_tag> # navigate using tag names
soup.find_all("tag") # by name
soup.find_all(["tag1", "tag2"]) # multiple tags in a list
soup.find_all(function) # based on a bool function
sopu.find_all(True) # Match everyting
soup.find_all(True) # Match everything
```
## Methods
@ -102,10 +102,10 @@ sopu.find_all(True) # Match everyting
Methods arguments:
- `name` (string): tag to search for
- `attrs` (dict): attributte-value pai to search for
- `attrs` (dict): attribute-value pai to search for
- `string` (string): search by string contents rather than by tag
- `limit` (int). limit number of results
- `**kwargs`: be turned into a filter on one of a tag’s attributes.
- `**kwargs`: be turned into a filter on one of a tag's attributes.
```py
find_all(name, attrs, recursive, string, limit, **kwargs) # several results
@ -140,7 +140,7 @@ soup.select("css_selector") # search for CSS selectors of HTML tags
<tag>["attribute"] = "value" # modify the attribute value
del <tag>["attribute"] # remove the attribute
soup.new_tag("name", <attribute> = "value") # creat a new tag with specified name and attributes
soup.new_tag("name", <attribute> = "value") # create a new tag with specified name and attributes
<tag>.string = "new content" # modify tag text content
<tag>.append(item) # append to Tag content
@ -161,7 +161,7 @@ soup.new_tag("name", <attribute> = "value") # creat a new tag with specified na
<tag>.replace_with(item) # remove a tag or string from the tree, and replaces it with the tag or string of choice
<tag>.wrap(other_tag) # wrap an element in the tag you specify, return the new wrapper
<tag>.unwrap() # replace a tag with whatever’s inside, good for stripping out markup
<tag>.unwrap() # replace a tag with whatever's inside, good for stripping out markup
<tag>.smooth() # clean up the parse tree by consolidating adjacent strings
```

6
Python/Libs/Web/Flask/Flask.md
View file

@ -34,14 +34,14 @@ Converter Type | Accepts
`uuid` | UUID strings
```python
@app.route("/user/<string:username>") # hanle URL at runtime
@app.route("/user/<string:username>") # handle URL at runtime
def profile(username):
return f"{escape(username)}'s profile'"
```
## Redirection
`url_for(endpoint, **values)` is used to redirect passing keyeworderd arguments. It can be used in combination with `@app.route("/<value>")` to accept the paassed arguments.
`url_for(endpoint, **values)` is used to redirect passing keyworded arguments. It can be used in combination with `@app.route("/<value>")` to accept the passed arguments.
```py
from flask import Flask, redirect, url_for
@ -137,7 +137,7 @@ In `parent_template.html`:
The content of the block will be filled by the child class.
In `child_template.hmtl`:
In `child_template.html`:
```html
{% extends "parent_template.html" %}

2
Python/Libs/Web/Flask/requests.md
View file

@ -27,7 +27,7 @@ In `login.html`:
```html
<html>
<!-- action="#" goes to page itsef but with # at the end of the URL -->
<!-- action="#" goes to page itself but with # at the end of the URL -->
<form action="#" method="post">
<input type="text" name="field name">
</html>

8
Python/Libs/Web/requests.md
View file

@ -21,7 +21,7 @@ The response message consists of:
```text
1xx -> INFORMATIONAL RESPONSE
2xx -> SUCCESS
200 OK -> request succesful
200 OK -> request successful
3xx -> REDIRECTION
4xx -> CLIENT ERRORS
404 NOT FOUND -> resource not found
@ -85,7 +85,7 @@ requests.post('URL', json={'key': 'value'})
### INSPECTING THE REQUEST
```py
# requests lib prepares the requests nefore sending it
# requests lib prepares the requests before sending it
response = requests.post('URL', data={'key':'value'})
response.request.something # inspect request field
```
@ -93,7 +93,7 @@ response.request.something # inspect request field
## AUTHENTICATION
```py
requests.get('URL', auth=('uesrname', 'password')) # use implicit HTTP Basic Authorization
requests.get('URL', auth=('username', 'password')) # use implicit HTTP Basic Authorization
# explicit HTTP Basic Authorization and other
from requests.auth import HTTPBasicAuth, HTTPDigestAuth, HTTPProxyAuth
@ -101,7 +101,7 @@ from getpass import getpass
requests.get('URL', auth=HTTPBasicAuth('username', getpass()))
```
### PERSOANLIZED AUTH
### PERSONALIZED AUTH
```py
from requests.auth import AuthBase

194
Python/Python.md
View file

@ -21,12 +21,12 @@ dir(object) # return an alphabetized list of names comprising (some of) the att
import sys # importa modulo
from sys import argv # importa singolo elemento da un modulo
from sys import * # importa tutti gli elementi di un modulo (non cecessaria sintassi modulo.metodo)
from sys import * # importa tutti gli elementi di un modulo (non necessaria sintassi modulo.metodo)
import sys as alias # importa il modulo con un alias, utilizzo alias.metodo
# SET CARATTERI
import string
string.ascii_lowecase = 'abcdefghijklmnopqrstuvwxyz'
string.ascii_lowercase = 'abcdefghijklmnopqrstuvwxyz'
string.asci_uppercase = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
string.asci_letters = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'
string.digits = '0123456789'
@ -43,14 +43,14 @@ string.whitespace
```py
"""istruzioni a dx di = eseguite prima di istruzioni a sx di ="""
variabile = espressione # il tipo della variabile viene deciso dianmicamente da python in base al contenuto
variabile = espressione # il tipo della variabile viene deciso dinamicamente da python in base al contenuto
var_1, var_2 = valore1, valore2 # assegnamento parallelo
var_1, var_2 = var_2, var_1 # swap valori
# conditional assignement
# conditional assignment
x = a if condition else b
x = a or b # If bool(a) returns False, then x is assigned the value of b
# a series of OR expressions has the effect of returning the first item that evaluates True, or the last item (last item sould be a literal).
# a series of OR expressions has the effect of returning the first item that evaluates True, or the last item (last item should be a literal).
```
### Conversione Tipo Variabile
@ -60,7 +60,7 @@ x = a or b # If bool(a) returns False, then x is assigned the value of b
### Assegnamento Espressioni
```py
(var := expressione) # assegna ad una variabile un espressione per evitare di ripetere l'espressione
(var := expression) # assegna ad una variabile un espressione per evitare di ripetere l'espressione
```
### Confronto Variabili (`==` vs `is`)
@ -88,19 +88,19 @@ print(f'{name=}, {marks=}') # OUTPUT: name=Alex, marks=94.5
# USO DEI PLACEHOLDER
print('Name is %s, Marks are %3.2f' % (name, marks)) # metodo ereditato da C. La variabile viene sostituita al posto di %..
print("Name is {}, Marks are {}".format(name, marks))
print("Name is {1}, Marks are {2}".format(marks, name)) # indici in parentesi odrinano elementi in .format
print("Name is {n}, Marks are {m}".format(m = '94.5', n = 'Alex')) # indici in parentesi odrinano elementi in .format
print("Name is {1}, Marks are {2}".format(marks, name)) # indici in parentesi ordinano elementi in .format
print("Name is {n}, Marks are {m}".format(m = '94.5', n = 'Alex')) # indici in parentesi ordinano elementi in .format
print(f'Name is {name}, Marks are {marks}') # formattazione con f-strings
```
### Format Specification Mini-Language
`{value:width.precision symbol}`
`width.precision` => numeroCifreTottali.numeroCifreDecimali
`width.precision` => numeroCifreTotali.numeroCifreDecimali
Format: `[[fill]align] [sign] [#] [width] [grouping] [.precidion] [type]`
Format: `[[fill]align] [sign] [#] [width] [grouping] [.precision] [type]`
OVVERRIDE __format__()
OVERRIDE __format__()
{!a} | chiama ascii() sulla variabile
{!r} | chiama repr() sulla variabile
{!s} | chiama str() sulla variabile
@ -108,10 +108,10 @@ OVVERRIDE __format__()
RIEMPIMENTO [fill]
{<qualsiasi_carattere>}
| `[align]` | Allinemanto |
| `[align]` | Allineamento |
| --------- | ---------------------- |
| `:<` | allinemaneto sininstra |
| `:>` | allinemento destra |
| `:<` | allineamento sinistra |
| `:>` | allineamento destra |
| `:=` | padding dopo il segno |
| `:^` | centrato |
@ -139,14 +139,14 @@ RIEMPIMENTO [fill]
| `:e` | output è notazione esponenziale (precisione base 6 cifre) |
| `:E` | output è notazione esponenziale (precisione base 6 cifre) separatore maiuscolo |
| `:f` | output è float (precisione base 6 cifre) |
| `:%` | output è percentuale (moltiplica * 100, diplay come :f) |
| `:%` | output è percentuale (moltiplica * 100, display come :f) |
### Input Da Tastiera
```py
# input ritorna sempre una STRINGA
s = input() # richiesta input senza messaggio
s = input('Prompt') # richiesta imput
s = input('Prompt') # richiesta input
i = int(input('prompt')) # richiesta input con conversione di tipo
# INPUT MULTIPLI
@ -158,7 +158,7 @@ lista = [int(x) for x in input('prompt').split('separatore')]
```py
a = 77
b = 1_000_000 # underscore può essere usato per seoarare gruppi di cifre
b = 1_000_000 # underscore può essere usato per separare gruppi di cifre
c = -69
# float numbers
@ -184,7 +184,7 @@ bin(3616544)
hex(589)
oct(265846)
# COVERSIONE UNICODE
# CONVERSIONE UNICODE
ord(c) # Given a string representing one Unicode character, return an integer representing the Unicode code point of that character
chr(i) # Return the string representing a character whose Unicode code point is the integer i
@ -196,21 +196,21 @@ round(num, precisione) # arrotonda il numero alla data precisione, non converte
### Confronto Numeri Decimali
Non usare `==` o `!=` per confrontare numeri in virgola mobile. Essi sono approssiamazioni o hanno parecchie cifre.
Non usare `==` o `!=` per confrontare numeri in virgola mobile. Essi sono approssimazioni o hanno parecchie cifre.
Conviene verificare se la differenza tra i numeri è sufficientemente piccola.
## Stringhe
```py
stringa = 'contenuto stringa' # asegnazione e creazione variabile stringa
stringa = 'contenuto stringa' # assegnazione e creazione variabile stringa
stringa = '''multi
line
string'''
stringa3 = stringa1 + stringa2 # concatenazione stringhe (polimorfismo operatore +)
# INDEXING (selezione di un carattere nella srtinga)
# INDEXING (selezione di un carattere nella stringa)
stringa[0]
stringa[2]
stringa[-3] # selezione partendo dal fondo (indice negativo)
@ -220,7 +220,7 @@ print(stringa * n)
len(stringa) # mostra la lunghezza di una stringa
# SLICING (estrazione di sottostringhe, non include la posizione dell'ultimo indice)
# SLICING (estrazione di sotto-stringhe, non include la posizione dell'ultimo indice)
stringa[0:5]
stringa[:6]
stringa[-3:-1]
@ -257,7 +257,7 @@ stringa.capitalize()
# SEPARAZIONE IN ELEMENTI LISTA
stringa.split()
stringa.split('separatore') # separa usando il separatore (separatore omesso nella lista)
stringa.partition('char') # -> tuple # sepra la stringa i 3 parti alla prima occorrenza di separatore
stringa.partition('char') # -> tuple # separa la stringa i 3 parti alla prima occorrenza di separatore
# METODI IS_CHECK --> bool
stringa.isalnum()
@ -284,18 +284,18 @@ lista = [9, 11, 'WTC', -5.6, True] # le liste possono contenere dati di tipo div
lista[3] # indexing
lista[3:5] # slicing
lista * 3 # repetition
len(lista) # lenght
len(lista) # length
lista3 = lista1 + lista2 # concatenazione liste (polimorfismo operatore +)
lista[indice] = valore # modifica elemento lista
del(lista[1]) # rimozione per indice (INBUILT IN PYTHON)
# modifica la lista tra gli indici start e stop riasegnando gli elementi dell'iterabile
# modifica la lista tra gli indici start e stop riassegnando gli elementi dell'iterabile
lista[start:stop] = iterabile
# METODI LISTE
lista.append(oggetto) # aggiunge oggetto al fondo
lista.count(item) # conta il numero di occorrenze di item
lista.extend(sequenza) # aggiunge gli elementi di sequenza alla lista
lista.insert(posizione, oggetto) # inserise oggetto in lista[posizione]
lista.insert(posizione, oggetto) # inserisce oggetto in lista[posizione]
lista.index(item) # restituisce l'indice di item
lista.remove(item) # rimuove item
lista.pop(item) # elimina item e lo restituisce
@ -354,7 +354,7 @@ var = [(exp_1, exp_2) for item_1 in seq_1 for item_2 in seq_2] # --> [(..., ...
```py
# LE TUPLE NON POSSONO ESSERE MODIFICATE
tuple = (69, 420, 69, 'abc') # assegnazione tuple
tuple = (44, ) # tuple di signolo elemento necessitano di una virgola
tuple = (44, ) # tuple di singolo elemento necessitano di una virgola
tuple[3] # indexing
tuple * 3 # repetition
@ -376,7 +376,7 @@ var_1, (var_2, var_3) = tup
#OPERATORE *VAR (tuple unpacking)
var_1, var_2, *rest = sequenza # var_1 = seq[0], var_2 = seq[1], rest = seq[2:]
var_1, *body, var_2, var_3 = sequeza # var_1 = seq[0], body = seq[1:-2], var_2 = sequenza[-2], var_3 = seq[-1]
var_1, *body, var_2, var_3 = sequenza # var_1 = seq[0], body = seq[1:-2], var_2 = sequenza[-2], var_3 = seq[-1]
# *var recupera gli item in eccesso, se in assegnamento parallelo usabile max una volta ma in posizione qualsiasi
```
@ -411,14 +411,14 @@ set.remove(item) # rimuove item dal set se presente, altrimenti solleva KeyErro
set.discard(item) #rimuove item dal set se presente, altrimenti fa nulla
set.difference(*sets) # -> set # restituisce elementi in set che sono assenti in *sets
set.difference_update(*sets) # rimuove le differenze dal set_2
set.union(*sets) # -> set # restituisce tutti gli elemnti dei set
set.union(*sets) # -> set # restituisce tutti gli elementi dei set
set.update(*sets) # aggiunge elementi *sets a set
set.intersection(*sets) # -> set # restituisce gli elementi comuni ai set
set.intersection_update(*sets) # rimuove tutti gli elementi tranne quelli comuni ai set
set.symmetric_difference(*sets) # -> set # restituisce gli elementi non comuni ai set
set.symmetric_difference_update(*sets) # rimuove tutti gli elementi comuni ai set (lasci solo gli elementi non comuni)
set_1.isdisjoint(set_2) # -> bool # True se non ci sono elementi comunni (intersezione è vuota)
set_1.isdisjoint(set_2) # -> bool # True se non ci sono elementi comuni (intersezione è vuota)
set_1.issubset(set_2) # -> bool # True se ogni elemento di set_1 è anche in set_2
set_1.issuperset(set_2) # -> bool # True se ogni elemento di set_2 è anche in set_1
@ -461,7 +461,7 @@ bytearray.count(subseq, start, end) # restituisce conteggio apparizioni subseq
BYTE LITERALS
ASCII --> stesso carattere
tab, newline, carriage return, escape sequence --> \t, \n, \r, \\
altro --> escape sequence exadeciamle (null byte --> \x00)
altro --> escape sequence esadecimale (null byte --> \x00)
Unicode Literals:
@ -485,11 +485,11 @@ stringa.encode('utf-8', errors='replace') # -> b'byte literals'
# DECODING
# trasforma byte literal in stringa
# error='replace' sostituisce gli errori (byte literal non appartenentia formato di decodifica) con U+FFFD "REPLACEMENT CHARARCTER"
# error='replace' sostituisce gli errori (byte literal non appartenenti a formato di decodifica) con U+FFFD "REPLACEMENT CHARACTER"
bytes.decode('utf-8', errors='replace') # -> str
# NORMALIZZAZIONE UNICODE
# gestione equivalenti canconici unicode (e.g. é, e\u0301 sono equivalenti per unicode)
# gestione equivalenti canonici unicode (e.g. é, e\u0301 sono equivalenti per unicode)
import unicodedata
unicodedata.normalize(form, unicode_string) # FORM: NFC,NFD, NFCK, NFDK
# NFC --> "Normalization Form C" --> produce la stringa equivalente più corta
@ -588,9 +588,9 @@ d | e # {'spam': 1, 'eggs': 2, 'cheese': 'cheddar', 'aardvark': 'Ethel'}
e | d # {'aardvark': 'Ethel', 'spam': 1, 'eggs': 2, 'cheese': 3}
d |= e # {'spam': 1, 'eggs': 2, 'cheese': 'cheddar', 'aardvark': 'Ethel'}
# DIZIONARI ANNIDATI (possibile annidare dizioanari all'interno di dizionari)
# DIZIONARI ANNIDATI (possibile annidare dizionari all'interno di dizionari)
my_dict = {'key_1':123, 'key_2':[12, 23, 33], 'key_3':['item_0', 'item_1', 'item_2']}
my_dict['key'][0] # restituisce elemento annnidato
my_dict['key'][0] # restituisce elemento annidato
# DICT COMPREHENSIONS
var = {key : value for elemento in sequenza}
@ -598,7 +598,7 @@ var = {key : value for elemento in sequenza}
## Operators
### Methematical Operators
### Mathematical Operators
| Operator | Operation |
| -------- | ------------------------------ |
@ -621,7 +621,7 @@ var = {key : value for elemento in sequenza}
| x `==` y | equality |
| x `!=` y | inequality |
### Assignement
### Assignment
| Operator | Operation |
| --------- | ---------- |
@ -646,7 +646,7 @@ var = {key : value for elemento in sequenza}
| x `^` y | bitwise XOR |
| x `|` y | bitwise OR |
| x `<<` y | left bit shift |
| x `>>` y | rigth bit shift |
| x `>>` y | right bit shift |
### Logical Operators
@ -667,7 +667,7 @@ var = {key : value for elemento in sequenza}
| Operator | Operation |
| -------- | ---------------------- |
| `in` | item in cooection |
| `in` | item in collection |
| `not in` | item not in collection |
### Precedenza Operatori
@ -691,16 +691,7 @@ built-in objects considered *false*:
- zero of any numeric type: `0`, `0.0`, `0j`, `Decimal(0)`, `Fraction(0, 1)`
- empty sequences and collections: `''`, `()`, `[]`, `{}`, `set()`, `range(0)`
### `if-else` semplice
```py
if (condizione):
# code here
else:
# code here
```
### `if-else` multiramo
### `if-else`
```py
if (condizione):
@ -726,7 +717,7 @@ contextmanager.__enter__(self)
# restituisce exc_type, exc_value, traceback
contextmanager.__exit__(self, exc_type, exc_value, traceback)
# exc_type: exception class
# exc_value: exception istance
# exc_value: exception instance
# traceback: traceback object
# NO EXCEPTION -> restituisce None, None, None
# SOPPRESSIONE ECCEZIONE: necessario restituire valore True
@ -765,7 +756,7 @@ for key, value in dict.items():
### Istruzioni `break` & `continue`
`break`: causa l'uscita immediata dal ciclo senza l'esecuzione delle successive iterazioni
`continue`: salte le restanti istruzioni del'iterazione e prosegue il ciclo
`continue`: salta le restanti istruzioni del'iterazione e prosegue il ciclo
### Istruzione `range`
@ -785,7 +776,7 @@ list(enumerate(iterabile)) # restituisce lista di tuple [(1, iterabile[0]), (2,
```py
list_1 = [1, 2, 3, 4, 5]
lsit_2 = ['a', 'b', 'c', 'd', 'e']
list_2 = ['a', 'b', 'c', 'd', 'e']
zip(list_1, list_2) # restituisce oggetto zip
list(zip(list_1, list_2)) # restituisce lista di tuple fondendo la lista [(list_1[0], list_2[0]), (list_1[1], list_2[1]), ...]
@ -802,12 +793,12 @@ randint(inizio, fine) # restituisce un intero random compreso tra inizio e fine
### Istruzione `in`
```py
item in iterabile # controlla presenza di intem in iterabile (restituisce True o False)
item in iterabile # controlla presenza di item in iterabile (restituisce True o False)
```
## Funzioni
### Definizone Funzione
### Definizione Funzione
```py
def nome_funzione (parametri):
@ -820,14 +811,14 @@ def nome_funzione (parametri):
`nome_funzione(parametri)`
### Specificare Tipo Paremetri In Funzioni
### Specificare Tipo Parametri In Funzioni
- parametri prima di `/` possono essere solo *posizionali*
- parametri tra `/` e `*` possono essere *posizionali* o *keyworded*
- parametri dopo `*` possono essere solo *keyworded*
```py
def funz(a, b, /, c, d, *, e, f):
def func(a, b, /, c, d, *, e, f):
# code here
```
@ -865,17 +856,17 @@ def funzione (parametro1 = valore1, parametro2 = valore3): # valori di default i
# code here
return espressione
funzione(parametro2 = valore2, parametro1 = valore1) # argometi passati con keyword per imporre l'ordine di riferimento
funzione(parametro2 = valore2, parametro1 = valore1) # argomenti passati con keyword per imporre l'ordine di riferimento
```
### VARIABILI GLOBALI E LOCALI
```py
# global scope
def funz_esterna():
def func_esterna():
# enclosing local scope
# code here
def funz_interna():
def func_interna():
# local scope
# code here
```
@ -898,11 +889,11 @@ def funzione():
# code here
```
### Iteratabili, Iteratori E Generatori
### Iterabili, Iteratori E Generatori
**Iterabile**: oggetto implementante `__iter__()`, sequenze e oggetti supportanti `__getitem__` con index `0`
**Iteratore**: oggetto implementante `__next__` e `__iter__` (**protocollo iteratore**), quando interamente consumato da next() diventa inutilizablie.
**Iteratore**: oggetto implementante `__next__` e `__iter__` (**protocollo iteratore**), quando interamente consumato da next() diventa inutilizzabili.
Gli iteratori sono iterabili, viceversa non vero. Restituisce `StopIteration` quando `next()` ha restituito tutti gli elementi.
**Funzione Generatore**: funzione con keyword `yield` (se presente anche `return` causa `StopIteration`), restituisce un generatore che produce i valori uno alla volta.
@ -913,7 +904,7 @@ Funzionamento `iter()`:
- chiama __iter__()
- in assenza di esso python sfrutta __getitem__() (se presente) per creare un iteratore che tenta di recuperare gli item in ordine, partendo dall'indice `0`
- in caso di fallimento resituisce `TypeError "obj_cls is not iterable"`
- in caso di fallimento restituisce `TypeError "obj_cls is not iterable"`
**Note**: `abc.Iterable` non controlla la presenza di `__getitem__` per decidere se un sotto-oggetto è membro conseguentemente il miglior test per l'iterabilità è usare `iter()` e gestirne le eccezioni.
@ -923,7 +914,7 @@ Funzionamento `iter()`:
next(iterabile) # prossimo item dell'iterabile o errore StopIteration
iter(oggetto) # ottiene un iteratore a partire da un oggetto
# chiama callable_onj.next() senza argomenti finche esso restituisce valori diversi da sentinella
# chiama callable_onj.next() senza argomenti finché esso restituisce valori diversi da sentinella
iter(callable_obj, sentinella)
```
@ -948,7 +939,7 @@ for item in custom_generator(parametri):
# solleva eccezione al punto di sospensione e restituisce valore del generatore
# se il generatore termina senza restituire valori solleva StopIteration
# se un eccezione non viene gestita viene propagata al chiamante
generator.throw(ExceptionType, exceptio_value, traceback)
generator.throw(ExceptionType, exception_value, traceback)
# solleva GeneratorExit al punto si sospensione
# se generatore restituisce un valore -> RuntimeError
@ -962,7 +953,7 @@ generator.close()
# sequenza di lunghezza zero (valori generati sul momento)
var = (espressione for iterabile in sequenza if condizione)
# ISTRUZIONE ENUMERATE()
# restituisce una lista di tuple associando ad ogni elemendo della sequenza un indice di posizione
# restituisce una lista di tuple associando ad ogni elemento della sequenza un indice di posizione
# [(0, sequenza[0]), (1, sequenza[1]), (2, sequenza[2]), ...)
enumerate(sequenza) # -> oggetto enumerate
```
@ -1004,7 +995,7 @@ def coroutine(func):
# TERMINAZIONE COROUTINE E EXCEPTION HANDLING
# eccezioni in coroutine non gestite si propagano alle iterazioni successive
# un eccezione causa la tarminazione della coroutine che non puo riprendere
# un eccezione causa la terminazione della coroutine che non puo riprendere
# yield solleva eccezione, se gestita ciclo continua
# throw() restituisce valore del generatore
@ -1019,7 +1010,7 @@ coroutine.close()
### `yield from <iterabile>`
**Note**: auto-priming generators incomplatible with `yield from`
**Note**: auto-priming generators incompatible with `yield from`
**DELEGATING GENERATOR**: funzione generatore contenente yield from
**SUBGENERATOR**: generatore ottenuto da `yield from <iterabile>`
@ -1034,8 +1025,8 @@ La funzione principale di `yield from` è aprire una canale bidirezionale tra il
- Any values that the subgenerator yields are passed directly to the caller of the delegating generator (i.e., the client code).
- Any values sent to the delegating generator using `send()` are passed directly to the subgenerator.
- If the sent value is `None`, the subgenerator’s `__next__()` method is called.
- If the sent value is not `None`, the subgenerator’s `send()` method is called.
- If the sent value is `None`, the subgenerator's `__next__()` method is called.
- If the sent value is not `None`, the subgenerator's `send()` method is called.
- If the call raises `StopIteration`, the delegating generator is resumed.
- Any other exception is propagated to the delegating generator.
@ -1067,7 +1058,7 @@ def delegating_gen(var):
def client():
# code here
result = delegating_gen() # use delegating_gen
result.send(None) # termina istanza sub_gen (IMPORATNTE)
result.send(None) # termina istanza sub_gen (IMPORTANTE)
```
## Ricorsione
@ -1085,9 +1076,6 @@ def factorial(n):
else:
result = (n*factorial(n-1))
return result
num = int(input('Enther a number to calculate it\'s factorial:'))
print(factorial(num))
```
## Funzioni Avanzate
@ -1103,31 +1091,31 @@ def funzione(parametri):
# chiamata funzione senza parentesi
funzione # restituisce oggetto funzione
var = funzione # assegna (per riferimento) la funzione ad una variabile.
# la chiamata funziona anche se la funz originale viene eliminata (del funzione)
# la chiamata funziona anche se la func originale viene eliminata (del funzione)
var() # chiama la funzione tramite la variabile (usando le parentesi tonde)
```
### Funzioni Annidate
```py
# funz_interna locale viene ritornata per riferimento
def funz_esterna(args):
# func_interna locale viene ritornata per riferimento
def func_esterna(args):
def funz_interna(): # funz_interna ha accesso a scope funzione esterna (aka Closure)
def func_interna(): # func_interna ha accesso a scope funzione esterna (aka Closure)
# code here
return funz_interna # restituisce funz_interna
return func_interna # restituisce func_interna
funz_esterna() # chiamata funz_esterna che chiama funz_interna (risultato funz_esterna è riferimento funz_interna)
func_esterna() # chiamata func_esterna che chiama func_interna (risultato func_esterna è riferimento func_interna)
```
### Funzioni Passate Per Argomento
```py
# funzione passata come argomento viene eseguita (chiamata) all'esecuzione della funzione a cui viene passata
def funz_esterna(funzione):
def func_esterna(funzione):
funzione() # chiama funzione passata come argomento
funz_esterna() # esecuzione funz_esterna chiama funz_interna
func_esterna() # esecuzione func_esterna chiama func_interna
```
### Funzioni Incapsulanti Funzioni (Argomenti Wrapper = Argomenti Wrapped)
@ -1138,7 +1126,7 @@ def wrapped(*args):
def wrapper(*args):
# instructions
wrapped(*args) # wrapped chiamata con gli argomeni passati a wrapper AS-IS (args = tupla) senza incapsulamento in una tupla
wrapped(*args) # wrapped chiamata con gli argomenti passati a wrapper AS-IS (args = tuple) senza incapsulamento in una tuple
```
## LAMBDA Functions
@ -1154,28 +1142,28 @@ var(args) # invocazione lambda
## Decoratori
Entita' chiamabile che prende in input una funzione (come argomento).
Entità' chiamabile che prende in input una funzione (come argomento).
Eventualmente effettua operazioni con la funzione decorata e la restituisce o sostituisce.
vengono eseguiti all'importazione, prima di ogni altra istruzione.
```py
# STRUTTURA DECORATORE PARZIALE (SOSTITUISCE FUNZIONE IMPUT)
# STRUTTURA DECORATORE PARZIALE (SOSTITUISCE FUNZIONE INPUT)
def decorator(funzione): # prende in input una funzione
def wrapper(): # funzione decoratrice
# code here
return wrapper # restituisce wrapper (chiamata a funz_decorata chiama wrapper)
return wrapper # restituisce wrapper (chiamata a func_decorata chiama wrapper)
# STRUTTURA DECORATORE COMPLETA (MODOFICA FUNZIONE INPUT)
# STRUTTURA DECORATORE COMPLETA (MODIFICA FUNZIONE INPUT)
def decorator(funzione): # prende in input una funzione da decorare
@functools.wraps(funzione) # keep code inspection avaiable
@functools.wraps(funzione) # keep code inspection available
def wrapper(*args, **kwargs): # funzione decoratrice (args, kwargs sono argomenti della funzione decorata)
# do something before
var_funz = funzione(*args, **kwargs)
var_func = funzione(*args, **kwargs)
# do something after
return var_funz # restituisce il risultato della decorazione della funzione
return var_func # restituisce il risultato della decorazione della funzione
return wrapper # restituisce wrapper (chiamata a funz_decorata chiama wrapper)
return wrapper # restituisce wrapper (chiamata a func_decorata chiama wrapper)
@decorator # applicazione del decoratore alla funzione
def funzione(): # funzione da decorare
@ -1190,9 +1178,9 @@ def decorator(*dec_args, **dec_kwargs): # prende in input argomenti del decorat
def inner_wrapper(*args, **kwargs):
# do something before
var_funz = funzione(*args, **kwargs)
var_func = funzione(*args, **kwargs)
# do something after
return var_funz
return var_func
return inner_wrapper
@ -1213,14 +1201,14 @@ def funzione(): # funzione da decorare
class NomeClasse:
# creazione variabile statica (o di classe; condivisa da tutte le istanze)
# ovverride possinile tramite subclassing o assegnazine diretta (oggetto.static_var =)
# override possibile tramite subclassing o assegnazione diretta (oggetto.static_var =)
# NomeClasse.static_var = cambia l'attributo di classe in tutte le istanze
static_var = espressione
def __init__(self, valore_1, valore_2): # costruttore di default parametrizzato
# attributi sono alias degli argomenti (modifiche in-place cambiano argomenti)
self.variabile = valore_1 # creazione variabili di istanza
self.__variabile = valore_2 # variabile appartenente ad oggetti della classe e non ai figli, accesso tarmite NAME MANGLING
self.__variabile = valore_2 # variabile appartenente ad oggetti della classe e non ai figli, accesso tramite NAME MANGLING
@classmethod # metodo agente sulla classe e non sull'oggetto (utile x costruttori alternativi)
@ -1234,14 +1222,14 @@ class NomeClasse:
return espressione
@staticmethod # indica un metodo statico (NECESSARIO)
def metodo_statico(parametri): # metodi statici non influenzano variabili di istanza (SELF non necessatio)
def metodo_statico(parametri): # metodi statici non influenzano variabili di istanza (SELF non necessario)
instruction
return espressione
oggetto = NomeClasse(parametri) # creazione di un oggetto
oggetto.variabile = espressione # modifica variabile pubblica
oggetto.metodo(parametri) # invocazione metodo di istanza
NomeClasse.metodo(parametri) #ivocazione metodo statico
NomeClasse.metodo(parametri) # invocazione metodo statico
oggetto._NomeClasse__var_privata # accesso a variabile specificando la classe di appartenenza (NAME MANGLING)
```
@ -1260,7 +1248,7 @@ class NomeClasse:
return self.__parametro
@parametro.setter
def parameto(self, valore):
def parametro(self, valore):
self.__parametro = valore
@parametro.deleter # metodo deleter
@ -1271,12 +1259,12 @@ class NomeClasse:
### `__slots__`
L'attributo `__slots__` implementa **Flyweight Design Pattern**:
salva gli attributi d'istanza in una tupla e può essere usato per diminuire il costo in memoria inserendovi solo le variabili di istanza (sopprime il dizionario dell'istanza).
salva gli attributi d'istanza in una tuple e può essere usato per diminuire il costo in memoria inserendovi solo le variabili di istanza (sopprime il dizionario dell'istanza).
**Default**: attributi salvati in un dizionario (`oggetto.__dict__`)
**Uso**: `__slots_ = [attributi]`
`__slots__` non viene ereditato dalle sottoclassi, impedisce l'aggiunta dinamica delgi attributi.
`__slots__` non viene ereditato dalle sottoclassi, impedisce l'aggiunta dinamica degli attributi.
### Fluent Interface
@ -1324,7 +1312,7 @@ I metodi speciali sono definiti dall'uso di doppi underscore; essi permettono l'
class NomeClasse():
def __init__(self, parametri):
istuzioni
istruzioni
# usato da metodo str() e print()
# gestisce le richieste di rappresentazione come stringa
@ -1334,8 +1322,8 @@ class NomeClasse():
def __len__(self):
return espressione # necessario return in quanto len richiede una lunghezza/dimensione
def __del__(self): # elimina l'istanda della classe
instruction # eventuali istruzionni che avvengono all'eliminazione
def __del__(self): # elimina l'istanza della classe
instruction # eventuali istruzioni che avvengono all'eliminazione
oggetto = NomeClasse()
len(oggetto) # funzione speciale applicata ad un oggetto
@ -1363,7 +1351,7 @@ __xor__(self, other) # ^
__or__(self, other) # |
# operatori aritmetici riflessi
# [se self.__dunde__(other) fallisce viene chiamato other.__dunder__(self)]
# [se self.__dunder__(other) fallisce viene chiamato other.__dunder__(self)]
__radd__(self, other) # reverse +
__rsub__(self, other) # reverse -
__rmul__(self, other) # reverse *

6
Python/Standard Library/argparse.md
View file

@ -45,7 +45,7 @@ ArgumentParser.add_argument("name_or_flags", nargs="...", action="...")
### Actions
`store`: This just stores the argument’s value. This is the default action.
`store`: This just stores the argument's value. This is the default action.
```py
>>> parser = argparse.ArgumentParser()
@ -159,7 +159,7 @@ Namespace(bar='XX', foo='c')
Namespace(bar='d', foo='d')
```
`*`: All command-line arguments present are gathered into a list. Note that it generally doesn’t make much sense to have more than one positional argument with `nargs='*'`, but multiple optional arguments with `nargs='*'` is possible.
`*`: All command-line arguments present are gathered into a list. Note that it generally doesn't make much sense to have more than one positional argument with `nargs='*'`, but multiple optional arguments with `nargs='*'` is possible.
```py
>>> parser = argparse.ArgumentParser()
@ -170,7 +170,7 @@ Namespace(bar='d', foo='d')
Namespace(bar=['1', '2'], baz=['a', 'b'], foo=['x', 'y'])
```
`+`: All command-line args present are gathered into a list. Additionally, an error message will be generated if there wasn’t at least one command-line argument present.
`+`: All command-line args present are gathered into a list. Additionally, an error message will be generated if there wasn't at least one command-line argument present.
```py
>>> parser = argparse.ArgumentParser(prog='PROG')

2
Python/Standard Library/collections.md
View file

@ -6,7 +6,7 @@
# sottoclasse dizionario per contare oggetti hash-abili
from collections import Counter
Counter(sequenza) # -> oggetto Counter
# {item: num comparese in sequenza, ...}
# {item: num comprese in sequenza, ...}
var = Counter(sequenza)
var.most_common(n) # produce lista degli elementi più comuni (n più comuni)

10
Python/Standard Library/csv.md
View file

@ -29,10 +29,10 @@ fieldnames
# elimina il dialect associato a name
.unregister_dialect()
# restituisce il dialet associato a name
# restituisce il dialetto associato a name
.get_dialect(name)
# elenco dialec associati a name
# elenco dialetti associati a name
.list_dialect(name)
# restituisce (se vuoto) o setta il limite del campo del csv
@ -75,8 +75,8 @@ class csv.Sniffer
.has_header(campione) --> bool # True se prima riga è una serie di intestazioni di colonna
#COSTANTI
csv.QUOTE_ALL # indica a writer di citere (" ") tutti i campi
csv.QUOTE_MINIMAL # indica a write di citare solo i campi contenenti caratteri speciali come delimiter, quotechar ...
csv.QUOTE_NONNUMERIC # indica al vriter di citare tutti i campi non numerici
csv.QUOTE_ALL # indica a writer di citare (" ") tutti i campi
csv.QUOTE_MINIMAL # indica a write di citare solo i campi contenenti caratteri speciali come delimiter, quote char ...
csv.QUOTE_NONNUMERIC # indica al writer di citare tutti i campi non numerici
csv.QUOTE_NONE # indica a write di non citare mai i campi
```

33
Python/Standard Library/ftplib.md
View file

@ -1,14 +1,13 @@
# Ftplib Module Cheat Sheet
# Ftplib Module
## FTP CLASSES
```py
# restiuisce istanza classe FTP
ftplib.FTP(host="", user="", password="", acct="")
# se HOST fornito esegue connect(host)
# SE USER fornito esegue login(user, password, acct)
# if HOST => connect(host)
# if USER => login(user, password, acct)
# sottoclasse FTP con TLS
ftplib.FTP_TLS(host="", user="", password="", acct="")
```
@ -18,7 +17,7 @@ ftplib.FTP_TLS(host="", user="", password="", acct="")
ftplib.error_reply # unexpected error from server
ftplib.error_temp # temporary error (response codes 400-499)
ftplib.error_perm # permanent error (response codes 500-599)
ftplib.error_proto # error not in ftp specs
ftplib.error_proto # error not in ftp specs
ftplib.all_errors # tuple of all exceptions
```
@ -29,43 +28,43 @@ ftplib.all_errors # tuple of all exceptions
# method on binary files: -binary
# CONNECTION
FTP.connect(host="", port=0) # used unce per instance
# DONT CALL if host was supplied at instance creation
FTP.connect(host="", port=0) # used once per instance
# DON'T CALL if host was supplied at instance creation
FTP.getwelcome() # return welcome message
FTP.login(user='anonymous', password='', acct='')
# called unce per instance after connection is established
# DEAFAULT PASSWORD: anonymous@
# DONT CALL if host was supplied at instance creation
# called once per instance after connection is established
# DEFAULT PASSWORD: anonymous@
# DON'T CALL if host was supplied at instance creation
FTP.sendcmd(cmd) # send command string and return response
FTP.voidcmd(cmd) # send command string and return nothing if successful
# FILE TRANSFER
FTP.abort() # abort in progress file transfer (can fail)
FTTP.transfercmd(cmd, rest=None) # returns socket for connection
# CMD avtive mode: send EPRT or PORT command and CMD and accept connection
# CMD active mode: send EPRT or PORT command and CMD and accept connection
# CMD passive mode: send EPSV or PASV and start transfer command
FTP.retrbinary(cmd, callback, blocksize=8192, rest=None) # retrieve file in binary mode
# CMD: appropriate RETR comkmand ('RETR filename')
# CMD: appropriate RETR command ('RETR filename')
# CALLBACK: func called on every block of data received
FTP.rertlines(cmd, callback=None)
# retrieve file or dir list in ASCII transfer mode
# CMD: appropriate RETR, LSIT (list and info of files), NLST ( list of file names)
# CMD: appropriate RETR, LSIT (list and info of files), NLST (list of file names)
# DEFAULT CALLBACK: sys.stdout
FTP.set_pasv(value) # set passive mode if value is true, otherwise disable it
# passive mode on by deafultù
# passive mode on by default
FTP.storbinary(cmd, fp, blocksize=8192, callback=None, rest=None) # store file in binary mode
# CMD: appropriate STOR command ('STOR filename')
# FP: {file object in binary mode} read until EOF in blocks of blocksize
# CLABBACK: func called on each bloak after sending
# CALLBACK: func called on each block after sending
FTP.storlines(cmd, fp, callback=None) # store file in ASCII transfer mode
# CMD: appropriate STOR command ('STOR filename')
# FP: {file object} read until EOF
# CLABBACK: func called on each bloak after sending
# CALLBACK: func called on each block after sending
```

42
Python/Standard Library/functools.md
View file

@ -1,42 +0,0 @@
# Functools Module Cheat Sheet
Hiegher-order functions and operations on callable objects.
```py
# crea nuova funzione con argomenti (*args, **kwarg) parzialmente fissati
new_func = partial(func, *args, **kwargs)
# crea nuovo metodo con argomenti (*args, **kwarg) parzialmente fissati
new_method = partialmethod(func, *args, **kwargs)
# applica ripetutamente funzione( , ) all'iterabile per creare un output singolo
# funzione applicata ai primi due elementi
# restituisce inizzializzatore se l'iterabile è vuoto (dipendente dalla funzione)
reduce(funzione((arg_1, arg_2), iterabile, inizzializzatore) # -> singolo output
# decoratore che salva maxsixe:int chiamate recenti in cache
# utilizza dizionario per memorizzazione, argomenti (posizionali e keyworded devono essere hashabili)
# se maxsixe=None cache cresce indefinitivamente e feature LRU è disattivata
# LRU --> Least Recent Used. Elementi poco usati rimossi dalla cache
# per efficienza maxsize=2**n
@lru_cache(maxsize=128, typed=False)
# decoratore che trasforma la funzione in una single-dispatch generic function
# generic function --> singola funzione implementa la stessa operazione per tipi diversi (ALTERNATIVA A METHOD OVERLOAD)
# single dispatch --> forma di generic function in cui l'implementazione è decissa in base ad un singolo argomento
# ATTENZIONE: single dispatch deciso dal primo argomento
@singledispatch # crea decorated_func.register per raggruppare funzioni in una generic function
@decorated_func.register() # decide implementazione basandosi su type annotation
@decorated_func.register(type) # decide implementazione secondo argomento type (da usare se non è presente type annotation)
# il nome di decorated_func è irrilevante
# è utile usare register(type) su ABC per supportare classi più generiche e classi future
# decoratore per aggiornare wrapper function per apparire come wrapperd function
# funz_decoratrice mantiene argomenti e docstring della funzione decorata
def decorator(funzione):
@wraps(funzione)
def wrapper(): #funz_decoratrice dentro decorator
# crea operatori uguaglianza se classe ne implementa almeno uno e __eq__()
@total_ordering
```

67
Python/Standard Library/itertools.md
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@ -1,56 +1,51 @@
# Itertools Module
```py
# iteratore restituisce somma cumulativa, se presente viene applicata func( , )
# accumulate([1,2,3,4,5]) -> 1, 3(1+2), 6(1+2+3), 10(1+2+3+6), 15(1+2+3+4+5)
# accumulate(iter, func( , )) -> iter[0], func(iter[0] + iter[1]) + func(ris_prec + iter[2]), ...
accumulate(iterabile, func(_, _))
# accumulate(iter, func( , )) -> iter[0], func(iter[0] + iter[1]) + func(prev + iter[2]), ...
accumulate(iterable, func(_, _))
# iteratore restituisce elemenmti dal primo iterabile,
# iteratore restituisce elementi dal primo iterable,
# poi procede al successivo fino alla fine degli iterabili
# non funziona se l'iterabile è uno solo
# non funziona se l'iterable è uno solo
chain(*iterabili)
ChainMap(*maps) # A ChainMap groups multiple dicts or other mappings together to create a single, updateable view. Lookups search the underlying mappings successively until a key is found. A ChainMap incorporates the underlying mappings by reference. So, if one of the underlying mappings gets updated, those changes will be reflected in ChainMap
# concatena elementi del singolo iterable anche se contiene sequenze
chain.from_iterable(iterable)
# concatena elementi del singolo iterabile anche se contiene sequenze
chain.from_iterable(iterabile)
# restituisce sequenze di lunghezza r a partire dall'iterabile
# restituisce sequenze di lunghezza r a partire dall'iterable
# elementi trattati come unici in base al loro valore
combinations(iterabile, r)
combinations(iterable, r)
# # restituisce sequenze di lunghezza r a partire dall'iterabile permettendo la ripetizione degli elementi
combinations_with_replacement(iterabile, r)
# # restituisce sequenze di lunghezza r a partire dall'iterable permettendo la ripetizione degli elementi
combinations_with_replacement(iterable, r)
# iteratore filtra elementi di data restituenso solo quelli che hanno
# iteratore filtra elementi di data restituendo solo quelli che hanno
# un corrispondente elemento in selectors che ha valore vero
compress(data, selectors)
# iteratore restituiente valori equidistanti a partire da start
#! ATTENZIONE: sequenza numerica infinita
count(start, step)
# iteratore restituiente valori in sequenza infinita
cycle(iterabile)
# iteratore restituente valori in sequenza infinita
cycle(iterable)
# iteratore droppa elementi dell'iterabile finchè il predicato è vero
dropwhile(predicato, iterabile)
# iteratore scarta elementi dell'iterable finché il predicato è vero
dropwhile(predicato, iterable)
# iteratore restituiente valori se il predicato è falso
filterfalse(predicato, iterabile)
# iteratore restituente valori se il predicato è falso
filterfalse(predicato, iterable)
# iteratore restituisce tuple (key, group)
# key è il criterio di raggruppamento
# group è un generatore restituiente i membri del gruppo
groupby(iterabile, key=None)
# group è un generatore restituente i membri del gruppo
groupby(iterable, key=None)
# iteratore restituisce slice dell'iterabile
# iteratore restituisce slice dell'iterable
isslice(iterable, stop)
isslice(iterable, start, stop, step)
# restituisce tutte le permutazioni di lunghezza r dell'iterabile
permutations(iterabile, r=None)
# restituisce tutte le permutazioni di lunghezza r dell'iterable
permutations(iterable, r=None)
# prodotto cartesiano degli iterabili
# cicla iterabili in ordine di input
@ -61,17 +56,17 @@ product(*iterabili, ripetizioni=1)
# restituisce un oggetto infinite volte se ripetizioni non viene specificato
repeat(oggetto, ripetizioni)
# iteratore computa func(iterabile)
# usato se iterabile è sequenza pre-zipped (seq di tuple raggruppanti elementi)
starmap(func, iterabile)
# iteratore computa func(iterable)
# usato se iterable è sequenza pre-zipped (seq di tuple raggruppanti elementi)
starmap(func, iterable)
# iteratore restituiente valori da iterabile finchè predicato è vero
takewhile(predicato, iterabile)
# iteratore restituente valori da iterable finché predicato è vero
takewhile(predicato, iterable)
# restituisce n iteratori indipendenti dal singolo iterabile
tee(iterabile, n=2)
# restituisce n iteratori indipendenti dal singolo iterable
tee(iterable, n=2)
# produce un iteratore che aggrega elementi da ogni iterabile
# produce un iteratore che aggrega elementi da ogni iterable
# se gli iterabili hanno lunghezze differenti i valori mancanti sono riempiti secondo fillervalue
zip_longest(*iterabile, fillvalue=None)
zip_longest(*iterable, fillvalue=None)
```

10
Python/Standard Library/json.md
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@ -65,7 +65,7 @@ json.loads(s, cls=None)
## Default Decoder (`json.JSONDecoder()`)
Convertions (JSON -> Python):
Conversions (JSON -> Python):
- object -> dict
- array -> list
@ -78,7 +78,7 @@ Convertions (JSON -> Python):
## Default Encoder (`json.JSONEncoder()`)
Convertions (Python -> Json):
Conversions (Python -> Json):
- dict -> object
- list, tuple -> array
@ -96,7 +96,7 @@ import json
class ComplexEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, complex):
return [obj.real, obj.imag]
return [obj.real, obj.image]
# Let the base class default method raise the TypeError
return json.JSONEncoder.default(self, obj)
```
@ -105,6 +105,6 @@ class ComplexEncoder(json.JSONEncoder):
```python
data = json.loads(json)
data["key"] # retieve the value associated with the key
data["outer key"]["nested key"] # nested key value retireval
data["key"] # retrieve the value associated with the key
data["outer key"]["nested key"] # nested key value retrieval
```

18
Python/Standard Library/logging.md
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@ -1,4 +1,4 @@
# Logging Module Cheat Sheet
# Logging Module
## Configuration
@ -41,24 +41,24 @@ logging.disable(level=LOG_LEVEL)
| Directive | Meaning |
|-----------|------------------------------------------------------------------------------------------------------------------------------|
| `%a` | Locale’s abbreviated weekday name. |
| `%A` | Locale’s full weekday name. |
| `%b` | Locale’s abbreviated month name. |
| `%B` | Locale’s full month name. |
| `%c` | Locale’s appropriate date and time representation. |
| `%a` | Locale's abbreviated weekday name. |
| `%A` | Locale's full weekday name. |
| `%b` | Locale's abbreviated month name. |
| `%B` | Locale's full month name. |
| `%c` | Locale's appropriate date and time representation. |
| `%d` | Day of the month as a decimal number [01,31]. |
| `%H` | Hour (24-hour clock) as a decimal number [00,23]. |
| `%I` | Hour (12-hour clock) as a decimal number [01,12]. |
| `%j` | Day of the year as a decimal number [001,366]. |
| `%m` | Month as a decimal number [01,12]. |
| `%M` | Minute as a decimal number [00,59]. |
| `%p` | Locale’s equivalent of either AM or PM. |
| `%p` | Locale's equivalent of either AM or PM. |
| `%S` | Second as a decimal number [00,61]. |
| `%U` | Week number of the year (Sunday as the first day of the week) as a decimal number [00,53]. |
| `%w` | Weekday as a decimal number [0(Sunday),6]. |
| `%W` | Week number of the year (Monday as the first day of the week) as a decimal number [00,53]. |
| `%x` | Locale’s appropriate date representation. |
| `%X` | Locale’s appropriate time representation. |
| `%x` | Locale's appropriate date representation. |
| `%X` | Locale's appropriate time representation. |
| `%y` | Year without century as a decimal number [00,99]. |
| `%Y` | Year with century as a decimal number. |
| `%z` | Time zone offset indicating a positive or negative time difference from UTC/GMT of the form +HHMM or -HHMM [-23:59, +23:59]. |

57
Python/Standard Library/operator.md
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@ -9,14 +9,14 @@ __ne__(a, b), ne(a, b) # a != b
__ge__(a, b), ge(a, b) # a >= b
__gt__(a, b), gt(a, b) # a > b
# OPERATOTI LOGICI
not_(obj) # not obj
truth(obj) # True o Flase ini base a valore verità oggetto (come costruttore bool)
is_(a, b) # return a is b
is_not(a, b) # return a is not b
# OPARATORI BINARI E MATEMATICI
__abs__(a, b), abs(obj) # valore assoluto di obj
not_(obj)
truth(obj)
is_(a, b)
is_not(a, b)
__abs__(a, b), abs(obj)
__add__(a, b), add(a, b) # a + b
__sub__(a, b), sub(a, b) # a - b
__mul__(a,b), mul(a,b) # a * b
@ -25,37 +25,20 @@ __truediv__(a, b), truediv(a, b) # a / b
__floordiv__(a, b), floordiv(a, b) # return a // b
__mod__(a, b), mod(a, b) # a % b
__neg__(obj), neg(obj) # -obj
__index__(a), index(a) # converte a in intero
__index__(a), index(a)
__and__(a, b), and_(a, b) # a and b binario (a & b)
__or__(a, b), or_(a, b) # a or b binario (a | b)
__xor__(a, b), xor(a, b) # a xor b binario (a ^ b)
__and__(a, b), and_(a, b) # a & b
__or__(a, b), or_(a, b) # a | b
__xor__(a, b), xor(a, b) # a ^ b
__inv__(obj), inv(obj), __inverse__(obj), inverse(obj) # inverso binario di obj, ~obj
__lshift__(obj), lshift(a, b) # restituisce a spostato a sinistra di b
__inv__(obj), inv(obj), __inverse__(obj), inverse(obj) # ~obj
__lshift__(obj), lshift(a, b)
__concat__(a, b), concat(a, b) # a + b per sequenze (CONCATENZIONE)
__contains__(a, b), contains(a, b) # return b in a
countOf(a, b) # numero occorrenze b in a
indexOF(a, b) # restituisce prima occorrenza di b in a
__concat__(a, b), concat(a, b)
__contains__(a, b), contains(a, b)
countOf(a, b)
indexOf(a, b)
__delitem__(a, b), delitem(a, b) # rimuove valore a in posizione b
__getitem__(a, b), getitem(a, b) # restituisce valore a in posizione b
__setitem__(a, b), setitem(a, b) # setta valore a in psoizione b
# ATTRGETTER
# restituisce oggetto chiamabile che recupera attributo attr da oggetto
funz = attrgetter(*attr)
funz(var) # restituisce var.attr
# ITEMGETTER
# restituisce oggetto chiamabile che recupera item dall'oggetto
# implementa __getitem__
funz = itemgetter(*item)
funz(var) # restituisce var[item]
# METHODCALLER
# restutiusce oggetto che chiama metodo method sull'oggetto
var = methodcaller(method, args)
var(obj) # return obj.method()
```
__delitem__(a, b), delitem(a, b)
__getitem__(a, b), getitem(a, b)
__setitem__(a, b), setitem(a, b)

60
Python/Standard Library/os.md
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@ -1,60 +0,0 @@
# OS Cheat Sheet
```python
os.curdir # stringa identificante cartella corrente ("." per WIN)
os.pardir # stringa identificante cartella genitore (".." per WIN)
os.sep # carattere separatore in path (\\ per WIN, / per POSIX)
os.extsep # carattere separatore estensione file (".")
os.pathsep # carattere separatore in ENVVAR PATH (";" per WIN, ":" per POSIX)
os.linesp # stringa usata per separare linee (\r\n per WIN, \n per POSIX)
os.system("command") # execute command in shell
os.remove(path) # cancella il file indicato da path
os.rmdir(path) # cancella cartella indicata da path
os.listdir(path) # restituisce lista nomi dei contenuti cartella
os.path.exists(path) # True se path si riferisce ad elemento esistente
os.path.split() # divide path in (head, tail), head + tail == path
os.path.splitdrive(path) # divide path in (drive, tail), drive + tail == path
os.path.splitext() # divide path in (root, ext), root + ext == path
os.path.dirname(path) # restituisce nome cartella (path head)
os.path.getatime(path) # restituisce data ultimo accesso
os.path.getmtime(path) # restituisce data ultima modifica
os.path.getsize(path) # restituisce dimesione in bytes (OSError se file inaccessibile o inesistente)
os.path.isfile(path) # True se path è file esistente
os.path.isdir(path) # True se path è cartella esistente
os.path.join(path, *paths) # concatena vari path
os.path.realpath(path) # Return the canonical path of the specified filename, eliminating symbolic links
os.path.relpath(path, start=os.curdir) # restituisce path relativo (start opzionale, default os.curdir)
os.path.abspath(path) # return a normalized absolutized version of the pathname path
# collapses redundant separators and up-level references so that A//B, A/B/, A/./B and A/foo/../B all become A/B
os.walk(top)
# Generate the file names in a directory tree by walking the tree either top-down or bottom-up.
# For each directory in the tree rooted at directory top (including), it yields a 3-tuple (dirpath, dirnames, filenames).
# dirpath is a string, the path to the directory.
# dirnames is a list of the names of the subdirectories in dirpath (excluding '.' and '..').
# filenames is a list of the names of the non-directory files in dirpath.
```
## Folder Operations
```python
os.getcwd() # Return a string representing the current working directory
os.chdir(path) # change the current working directory to path
os.mkdir(path, mode=0o777) # Create a directory named path with numeric mode MODE.
os.makedirs(name, mode=0o777) # Recursive directory creation
```
## Exceptions
```python
IsADirectoryError # file operation requested on directory
NotADirectoryError # directory operation requested on file
```

55
Python/Standard Library/re.md
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@ -1,55 +0,0 @@
# Regex Module Cheat Sheet
Compile a regular expression pattern into a regular expression object, which can be used for matching.
```py
regex_obj = re.compile(r"") # raw string doesn't escapes special caracters
# cerca la corrispondenza con REGEX_OBJ nella stringa
match_obj = regex_obj.search(stringa) # -> oggetto Match
# cerca la corrispondenza con REGEX_OBJ all'inizio della stringa
# se non vi sono corrispondenze l'oggetto match ha valore NONE
match_obj = regex_obj.match(stringa) # -> oggetto Match
# l'intera stringa eve corrispondere a REGEX_OBJ
# se non vi sono corrispondenze l'oggetto match ha valore NONE
match_obj = regex_obj.fullmatch(stringa) # -> oggetto Match
# restituisce tutte le sottostringhe corrispondenti a REGEX_OBJ in una lista.
# In caso non ve ne siano la lista è vuota
# se nel pattern sono presenti due o più gruppi verrà restituita una lista di tuple.
# Solo il contenuto dei gruppi vine restituito.
regex_obj.findall(stringa)
# suddivide le stringhe in base a REGEX_OBJ, caratteri cercati non riportati nella lista
regex_obj.split(pattern, stringa)
# sostituisce ogni sottostringa corrispondente a REGEX_OBJ con substringa
regex_obj.sub(substringa, stringa)
```
## Match Objects
L'oggetto match contiene True/None, info sul match, info su string, REGEX usata e posizione della corrispondenza
```python
match_obj.group([number]) # restituisce la stringa individuata, [number] selezione il sottogurppo della REGEX
match_obj.groups() # Return a tuple containing all the subgroups of the match
match_obj.start() # posizione inizio corrispondenza
match_obj.end() # posizione fine corrispondenza
```
## Regex Configuration
```python
re.compile(r"", re.OPTION_1 | re.OPTION_2 | ...) # specify options
# Allows regex that are more readable by allowing visually separate logical sections of the pattern and add comments.
re.VERBOSE
# Make the '.' special character match any character at all, including a newline. Corresponds to the inline flag (?s).
re.DOTALL
re.IGNORECASE
re.MULTILINE
```

32
Python/Standard Library/shelve.md
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@ -1,32 +0,0 @@
# Shelve Module Cheat Sheet
```python
import shelve
# open a persistent dictionary, returns a shelf object
shelf = shelve.open("filename", flag="c", writeback=False)
```
FLAG:
- r = read
- w = read & write
- c = read, wite & create (if doesen't exist)
- n = always create new
If `writeback` is `True` all entries accessed are also cached in memory, and written back on `sync()` and `close()`.
This makes it handier to mutate mutable entries in the persistent dictionary, but, if many entries are accessed, it can consume vast amounts of memory for the cache, and it can make the close operation very slow since all accessed entries are written back.
```python
# key is a string, data is an arbitrary object
shelf[key] = data # store data at key
data = shelf[key] # retrieve a COPY of data at key
shelf.keys() # list of all existing keys (slow!)
shelf.values() # lsit of all existing values
del shelf[key] # selete data stored at key
shelf.close() # Synchronize and close the persistent dict object.
# Operations on a closed shelf will fail with a ValueError.
```

12
Python/Standard Library/shutil.md
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@ -1,4 +1,4 @@
# Shutil Module Cheat Sheet
# Shutil Module
High-level file operations
@ -18,7 +18,7 @@ shutil.copytree(src, dst, dirs_exist_ok=False)
# or any missing parent directory already exists
# delete an entire directory tree
shutil.rmtree(apth, ignore_errors=False, onerror=None)
shutil.rmtree(path, ignore_errors=False, onerror=None)
# IGNORE_ERROR: {bool} -- if true errors (failed removals) will be ignored
# ON_ERROR: handler for removal errors (if ignore_errors=False or omitted)
@ -37,15 +37,15 @@ shutil.chown(path, user=None, group=None)
# create archive file and return its name
shutil.make_archive(base_name, format, [root_dir, base_dir])
# BASE_NAME: {string} -- name of the archive, including path, excluding extension
# FROMAT: {zip, tar, gztar, bztar, xztar} -- archive fromat
# FROMAT: {zip, tar, gztar, bztar, xztar} -- archive format
# ROOT_DIR: {path} -- root directory of archive (location of archive)
# BASE_DIR: {path} -- directory where the archivation starts
# BASE_DIR: {path} -- directory where the archiviation starts
# unpack an archive
shutil.unpack_archive(filename, [extarct_dir, format])
shutil.unpack_archive(filename, [extract_dir, format])
# FILENAME: full path of archive
# EXTRACT_DIR: {path} -- directory to unpack into
# FROMAT: {zip, tar, gztar, bztar, xztar} -- archive fromat
# FORMAT: {zip, tar, gztar, bztar, xztar} -- archive format
# return disk usage statistics as Namedtuple w/ attributes total, used, free
shutil.disk_usage(path)

7
Python/Standard Library/smtplib.md
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@ -21,8 +21,8 @@ SMTP.verify(address) # Check the validity of an address on this server using SM
SMTP.login(user="full_user_mail", password="user_password") # Log-in on an SMTP server that requires authentication
SMTP.SMTPHeloError # The server didn’t reply properly to the HELO greeting
SMTP.SMTPAuthenticationError # The server didn’t accept the username/password combination.
SMTP.SMTPHeloError # The server didn't reply properly to the HELO greeting
SMTP.SMTPAuthenticationError # The server didn't accept the username/password combination.
SMTP.SMTPNotSupportedError # The AUTH command is not supported by the server.
SMTP.SMTPException # No suitable authentication method was found.
@ -41,6 +41,3 @@ SMTP.send_message(msg, from_addr=None, to_addrs=None, **kwargs)
# msg: {email.message.Message object} -- message string
SMTP.quit() # Terminate the SMTP session and close the connection. Return the result of the SMTP QUIT command
```
In general, use the email package’s features to construct an {email.message.EmailMEssage} message to send via send_message()
EMAIL EXAMPLES --> {https:\\docs.python.org\3\library\email.examples.html#email-examples}

6
Python/Standard Library/socket.md
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@ -12,7 +12,7 @@ In this context, sockets are assumed to be associated with a specific socket add
```python
import socket
# socket ovet the intenet, socket is a stream of data
# socket over the internet, socket is a stream of data
socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
socket.connect = (("URL", port: int)) # connect to socket
@ -23,9 +23,9 @@ socket.close() # close connection
```python
import socket
HTTP_Method = "GET hhtp://url/resource HTTP/version\n\n".encode() # set HTTP request (encoded string from UTF-8 to bytes)
HTTP_Method = "GET http://url/resource HTTP/version\n\n".encode() # set HTTP request (encoded string from UTF-8 to bytes)
socket.send(HTTP_Method) # make HTTP request
data = socket.recv(buffer_size) # recieve data from socket
data = socket.recv(buffer_size) # receive data from socket
decoded = data.decode() # decode data (from bytes to UTF-8)
```

11
Python/Standard Library/sqllite.md
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@ -26,9 +26,10 @@ The data saved is persistent and is available in subsequent sessions.
### Query Construction
Usually your SQL operations will need to use values from Python variables.
You shouldn’t assemble your query using Python’s string operations because doing so is insecure: it makes your program vulnerable to an [SQL injection attack](https://en.wikipedia.org/wiki/SQL_injection)
You shouldn't assemble your query using Python's string operations because doing so is insecure:
it makes your program vulnerable to an [SQL injection attack](https://en.wikipedia.org/wiki/SQL_injection)
Put `?` as a placeholder wherever you want to use a value, and then provide a _tuple of values_ as the second argument to the cursor’s `execute()` method.
Put `?` as a placeholder wherever you want to use a value, and then provide a _tuple of values_ as the second argument to the cursor's `execute()` method.
```python
# Never do this -- insecure!
@ -58,7 +59,7 @@ connection.commit()
### Multiple SQL Instructions
```python
conection = sqlite3.connect("file.db")
connection = sqlite3.connect("file.db")
cur = con.cursor()
cur.executescript("""
QUERY_1;
@ -74,7 +75,7 @@ con.close()
```python
# Fetches the next row of a query result set, returning a single sequence.
# Reruens None when no more data is available.
# Returns None when no more data is available.
cursor.fetchone()
# Fetches all (remaining) rows of a query result, returning a list.
@ -86,7 +87,7 @@ cursor.fetchall()
fetchmany(size=cursor.arraysize)
```
The number of rows to fetch per call is specified by the `size` parameter. If it is not given, the cursor’s `arraysize` determines the number of rows to be fetched.
The number of rows to fetch per call is specified by the `size` parameter. If it is not given, the cursor's `arraysize` determines the number of rows to be fetched.
The method should try to fetch as many rows as indicated by the size parameter.
If this is not possible due to the specified number of rows not being available, fewer rows may be returned.

36
Python/Standard Library/string.md
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@ -1,36 +0,0 @@
# String Module Cheat Sheet
## TEMPLATE STRINGS
Template strings support $-based substitutions, using the following rules:
`$$` is an escape; it is replaced with a single `$`.
`$identifier` names a substitution placeholder matching a mapping key of "identifier".
By default, "identifier" is restricted to any case-insensitive ASCII alphanumeric string (including underscores) that starts with an underscore or ASCII letter.
The first non-identifier character after the $ character terminates this placeholder specification.
`${identifier}` is equivalent to `$identifier`.
It is required when valid identifier characters follow the placeholder but are not part of the placeholder.
Any other appearance of `$` in the string will result in a `ValueError` being raised.
The string module provides a Template class that implements these rules.
```python
from string import Template
# The methods of Template are:
string.Template(template) # The constructor takes a single argument which is the template string.
substitute(mapping={}, **kwargs)
# Performs the template substitution, returning a new string.
# mapping is any dictionary-like object with keys that match the placeholders in the template.
# Alternatively, you can provide keyword arguments, where the keywords are the placeholders.
# When both mapping and kwds are given and there are duplicates, the placeholders from kwds take precedence.
safe_substitute(mapping={}, **kwargs)
# Like substitute(), except that if placeholders are missing from mapping and kwds,
# instead of raising a KeyError exception, the original placeholder will appear in the resulting string intact.
# Also, unlike with substitute(), any other appearances of the $ will simply return $ instead of raising ValueError.
# While other exceptions may still occur, this method is called “safe” because it always tries to return a usable string instead of raising an exception.
# In another sense, safe_substitute() may be anything other than safe, since it will silently ignore malformed templates containing dangling delimiters, unmatched braces, or placeholders that are not valid Python identifiers.
```

24
Python/Standard Library/time_datetime.md
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@ -1,4 +1,4 @@
# Time & Datetime Modules Cheatsheet
# Time & Datetime
## Time
@ -6,7 +6,7 @@
# epoch: tempo in secondi trascorso (in UNIX parte da 01-010-1970)
import time # UNIX time
variabile = time.time() # restituisce il tempo (In secondi) trascorso da 01-01-1970
variabile = time.ctime(epochseconds) # traforma l'epoca in data
variabile = time.ctime(epochseconds) # trasforma l'epoca in data
var = time.perf_counter() # ritorna il tempo di esecuzione attuale
# tempo di esecuzione = tempo inizio - tempo fine
@ -16,24 +16,24 @@ var = time.perf_counter() # ritorna il tempo di esecuzione attuale
| Format | Data |
|--------|------------------------------------------------------------------------------------------------------------|
| `%a` | Locale’s abbreviated weekday name. |
| `%A` | Locale’s full weekday name. |
| `%b` | Locale’s abbreviated month name. |
| `%B` | Locale’s full month name. |
| `%c` | Locale’s appropriate date and time representation. |
| `%a` | Locale's abbreviated weekday name. |
| `%A` | Locale's full weekday name. |
| `%b` | Locale's abbreviated month name. |
| `%B` | Locale's full month name. |
| `%c` | Locale's appropriate date and time representation. |
| `%d` | Day of the month as a decimal number `[01,31]`. |
| `%H` | Hour (24-hour clock) as a decimal number `[00,23]`. |
| `%I` | Hour (12-hour clock) as a decimal number `[01,12]`. |
| `%j` | Day of the year as a decimal number `[001,366]`. |
| `%m` | Month as a decimal number `[01,12]`. |
| `%M` | Minute as a decimal number `[00,59]`. |
| `%p` | Locale’s equivalent of either AM or PM. |
| `%p` | Locale's equivalent of either AM or PM. |
| `%S` | Second as a decimal number `[00,61]`. |
| `%U` | Week number of the year (Sunday as the first day of the week) as a decimal number `[00,53]`. |
| `%w` | Weekday as a decimal number `[0(Sunday),6]`. |
| `%W` | Week number of the year (Monday as the first day of the week) as a decimal number `[00,53]`. |
| `%x` | Locale’s appropriate date representation. |
| `%X` | Locale’s appropriate time representation. |
| `%x` | Locale's appropriate date representation. |
| `%X` | Locale's appropriate time representation. |
| `%y` | Year without century as a decimal number `[00,99]`. |
| `%Y` | Year with century as a decimal number. |
| `%z` | Time zone offset indicating a positive or negative time difference from UTC/GMT of the form +HHMM or -HHMM |
@ -48,14 +48,14 @@ today = datetime.date.today() # restituisce data corrente
today = datetime.datetime.today() # restituisce la data e l'ora corrente
# esempio di formattazione
print('Curent Date: {}-{}-{}' .format(today.day, today.month, today.year))
print('Current Date: {}-{}-{}' .format(today.day, today.month, today.year))
print('Current Time: {}:{}.{}' .format(today.hour, today.minute, today.second))
var_1 = datetime.date(anno, mese, giorno) # crea oggetto data
var_2 = datetime.time(ora, minuti, secondi, micro-secondi) # crea oggetto tempo
dt = datetime.combine(var_1, var_2) # combina gli oggetti data e tempo in un unico oggetto
date_1 = datetieme.date('year', 'month', 'day')
date_1 = datetime.date('year', 'month', 'day')
date_2 = date_1.replace(year = 'new_year')
#DATETIME ARITHMETIC

8
Python/Standard Library/unittest.md
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@ -1,16 +1,12 @@
# Unittest Module
Permette di testare il propio codice e controllare se l'output corrisponde a quello desiderato.
```py
import unittest
import modulo_da_testare
import module_under_test
class Test(unittest.TestCase): # eredita da unittest.TestCase
class Test(unittest.TestCase):
# testa se l'output è corretto con un asserzione
def test_1(self):
# code here
self.assert*(output, expected_output)
if __name__ == '__main__':

43
Python/Standard Library/urllib.md
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@ -1,43 +0,0 @@
# Urllib Module Cheatsheet
## Module Structure
`urllib` is a package that collects several modules for working with URLs:
- `urllib.request` for opening and reading URLs
- `urllib.error` containing the exceptions raised by urllib.request
- `urllib.parse` for parsing URLs
- `urllib.robotparser` for parsing robots.txt files
## urllib.request
### Opening an URL
```python
import urllib.request
# HTTP request header are not returned
response = urllib.request.urlopen(url)
data = response.read().decode()
```
### Readign Headers
```python
response = urllib.request.urlopen(url)
headers = dict(response.getheaders()) # store headers as a dict
```
## urllib.parse
### URL Encoding
Encode a query in a URL
```python
url = "http://www.addres.x/_?"
# encode an url with passed key-value pairs
encoded = url + urllib.parse.encode( {"key": value} )
```

26
Python/Standard Library/xml.md
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@ -1,26 +0,0 @@
# XML Module CheatSheet
## Submodules
The XML handling submodules are:
- `xml.etree.ElementTree`: the ElementTree API, a simple and lightweight XML processor
- `xml.dom`: the DOM API definition
- `xml.dom.minidom`: a minimal DOM implementation
- `xml.dom.pulldom`: support for building partial DOM trees
- `xml.sax`: SAX2 base classes and convenience functions
- `xml.parsers.expat`: the Expat parser binding
## xml.etree.ElementTree
```python
import xml.etree.ElementTree as ET
data = "<xml/>"
tree = ET.fromstring(data) # parse string containing XML
tree.find("tag").text # return data contained between <tag></tag>
tree.find("tag").get("attribute") # return value of <tag attrubute="value">
tree.findall("tag1/tag2") # list of tag2 inside tag1
```