Python Automation and Machine Learning for EM and ICs

Linear correlation between two variables with Pearson Correlation Coefficient, Spearman Rank Correlation Coefficient, Kendall's Tau, Linear Regression, Coefficient of Determination and Correlation Ratio

Introduction

Model = architecture + parameters

Introduction

Structure of PowerPoint reports

Introduction

Data parallelism in distributed training

Introduction

Linear model versus polynomial model

Introduction

Posterior distribution

Introduction

Distribution of θ (parameter distribution) in ML

Introduction

Population Distribution in ML

Introduction

Updating Hypothesis (ĥ) and/or Parameter θ^ in ML

Introduction

Probably Approximately Correct (PAC) learning

Introduction

Polynomial models in ML

Introduction

Choice of parameters for training models

Introduction

Minimum A Priori (MAP)

Introduction

Mean Average Precision (MAP)

Introduction

Maximum A Posteriori (MAP)

Introduction

"Norm" of parameters, and L1 Norm (Manhattan Norm) and L2 Norm (Euclidean Norm)

Introduction

Conditional probability

Introduction

Posterior probability and prior probability

Introduction

Comparison between Poisson distribution, Gaussian (normal) distribution and logistic regression

Introduction

Single parameter estimation versus multiple parameter estimation

Introduction

Parameterizations in ML

Introduction

Exponential Family: Parameter, Sufficient Statistic, Natural Parameter, Base Measure and Log-Partition Function (Bernoulli distribution and Gaussian distribution)

Introduction

Bayesian Probability, Bayesian Statistics (Distribution Over a Distribution), versus Bayesian Inference

Introduction

Parameters, features and examples in ML

Introduction

Comparison between mean squared error (MSE), absolute error (L1 Loss) and fourth-power loss

Introduction

Practice ML projects for beginners

Introduction

Perceptron algorithm and logistic regression

Introduction

Actual Probability of Deviation

Introduction

Deviation Probability (Hoeffding Bound)

Introduction

Core Steps/Procedure/Designing of Machine Learning

Introduction

Pearson Correlation Coefficient/Pearson's r/Correlation Coefficient

Introduction

Point-Biserial Correlation

Introduction

Types of predictions with Supervised Learning

Introduction

Cocktail party problem

Introduction

Hypothesis (predicted output (h(x)))

Introduction

Parametric learning algorithm

Introduction

Non-parametric learning algorithm

Introduction

Update parameters θj using gradient of the loss function

Introduction

Perceptron algorithm

Introduction

Bandwidth parameter (τ) in LWR and KDE

Introduction

Probability density function (PDF): comparisons between (normal (gaussian) distribution, uniform distribution, exponential distribution and poisson distribution)

Introduction

PySpice: Provides the interface to circuit simulators like Ngspice or Xyce.

code.

import PySpice.Logging.Logging as Logging

(code)

from PySpice.Spice.Netlist import Circuit

(code)

from PySpice.Unit import *

(code)

.simulator()

(code)

NgSpice/PySpice for electrical circuit analysis

Coding introduction

Save contents (download pdf files) in the webpages obtained by Google search into a text file

Introduction

Analysis of ROI (region of interest)/portion of an image

Introduction

Find nearest white pixel to a given/specifical pixel location on an binary image

Introduction

Diversity prediction theorem

Introduction

Categorical features preprocessing layers

Introduction

Reasons of using Python for automation

Introduction

Machine learning example step-by-step (prediction of house price)

Introduction

.pack(side=LEFT)/.pack(side=RIGHT)/.place(x=, y=) --- position of the buttons

(Code)

Add padding/black/colored edge to images

Introduction

.pad()

(code)

.paste()

Introduction

Common values in two pandas series

Introduction

Keras preprocessing layers

Introduction

pywinauto

Introduction

Machine learning workflow/procedure

Introduction

Predictive/predict model (with "best"-option)

Introduction

Pipelines in ML and Data Science

Introduction

Pipelines in ML

Introduction

Pipelines in Data Science

Introduction

kfp.dsl package versus pipelines and components

Introduction

Learning algorithm (ensemble learning) and pipeline

Introduction

preprocessing.

Introduction

PyTorch

Introduction

Comparison between TensorFlow, PyTorch, Theano and OpenCV

Introduction

Troubleshooting and problem solving in Python programming

page4806

Heatmap with input from a csv/pkl file

Introduction

Positions and colors of mouse/cursor and features

Introduction

Left click a specific position

Introduction

Right click a specific position

Introduction

Double click a specific position

Introduction

Print document

Introduction

Search in a powerpoint file

Introduction

Save files (e.g. powerpoint/ppt)

Introduction

Summary of working on ppt

Introduction

Analysis of papers/publications/literature in machine learning and Python applications

Introduction

Plot workflow: Create new empty column in DataFrame, Move the cells in a column to another column under certain condition, Select specific columns for scatter plot

Add a new slide into an existing ppt, or work on existing slides, check the existence of a pptx file, if does not exist then create it

Introduction

Table of powerPoint shortcut hotkeys

Introduction

Table of PC/computer/Windows shortcut hotkeys

Introduction

Comparison between Python and C/C++

Introduction

stopwatch and timing a process

Introduction

Turn on and off with mouse press or a process

Introduction

Quit/exit/stop a process (including by pressing a letter)

Introduction

Periodic Table

code

(Single and multiple enter/input) box for pop-up window

Introduction

Copy and then store it into memory and it can be pasted for use later

Introduction

Tricks in Python Programming and principles and practices in good programming

Introduction

Text classification/sort/prediction, train/test e.g. Youtube spam

Introduction

Print colored text in Python IDLE's terminal

Introduction

API (Application Programming Interface), e.g. weather, temperature

Introduction

Find/predict the best word similarity, e.g. car

Introduction

Principal Component Analysis (PCA)

Introduction

Select/input a folder/directory/path for later to be called to use

Introduction

Pop-up windows/messages

tkinter, ctypes, easygui

Convert CSV to images, row by row, with pixel values: each row is an image

code.

Calibrate and put a scale bar, and draw a line segment on an image

Introduction

Option/selection/choice methods ("pop-up windows of Yes and No ")

Introduction

Copy text into clipboard and then you can paste it anywhere

Introduction

Calculate/pass the arbitrary (any) number of variables or input arguments

Introduction

File name, folder name. {}{}....format. Manipulation of file and folder names (rename file name and folder name): i) Create a new folder and then copy all files from a folder to the new folder and rename the file, and then open the file. If the folder exists, then no file will be copied, but the file will still be opened. ii) Print and export the folder names and file names (with or without extensions) from a folder into a text file. iii) csv2image filename.

Introduction

Set default programs by file extensions and by file types and programs on Windows

Introduction

Numpy: Access the element at the second row, the third entry, access a specific row or a column, access some elements (submatrix), or replace/modify an element in the array, print a transfer of an array, access array under conditions or filtering

Introduction

Pint summary of the statistic data, change data format, sort/group columns

Introduction

Popup windows with inputs

Introduction

Popup windows with frames

Introduction

Copy text into clipboard and then you can paste it a webpage, text/txt, word or powerpoint file automatically

(code)

Draw an arrow segment pointing from the start point to the end point in an image.

code.

Prevent other applications to modify the content until other Python script runs

code.

Open an image to file from URL (webpage), then it can be saved in PC

code, code. open with color changed.

Calculate the coordinates of a point in a given rectangle and the distance of a given point to a line

code

Compute the difference between two images by using Structural Similarity Index with "pip install --upgrade imutils"

code. code

Search position of numbers

code

Reference list items by position

code1, code2

Repeated printing the same string

code

Palindrome repeat

code

Selenium and pop-up windows

Introduction

Check if a popup dialog is a window or not for Selenium app

Introduction

Draw lines and arrows in a ppt

Introduction

Find the color of a pixel on the screen

Introduction

Create slides with text only

Introduction

Draw lines, elbow connectors and arrows (shapes) in a pptx

Introduction

Count and delete slides from ppt

Introduction

Insert all the images into a ppt file (one image per slide)

(Introduction)

Check file existence with partial filename

Introduction

Get/list immediate subdirectories/subfolders; get only the last part of a path/folder/drive; split a dos path into its components, and then print the list

Introduction

Move file(s) from one directory to another

Introduction

Get maximum and minimum value of column and its index

Introduction

Get the csv/pandas cell value with certain condition

Introduction

Find latitude and longitude of a place in a map

Introduction

Check/find/get a file name or the last folder name (e.g. from a path/directory)

Introduction

Print and set file path as a variable (e.g. convert all characters in the pathname to lowercase)

Introduction

Check if a file/folder/path/directory exists or not (Cannot find a specific file/folder?, or check files with extension, select specific folders to form a string, split a dos path into its components, and then print the list)

Introduction

Check if a file/folder exists or not (Cannot find a specific file/folder? a specific folder in the path?)

Introduction

Check if a directory is empty; find the position/index of a particular file/folder in a file directory/path; remove folder or file level by level (or layer by laer) from its directory/path

Introduction

Placement/position of Python import statements

Introduction

Watchdog ignore/skip and take pattern in directory/path

Introduction

top and left, width and height for pptx (e.g. align the top-left corner of the image to the center of the slide no matter how the size of the images changes)

Introduction

Extract text/check specific text from multiple powerpoint files (Some methods can extract text from most of the document extensions such as pptx and pptm)

Introduction

Get mouse position/coordinates on click

Introduction

Measure (real or pixel) length/distance on an image w/o calibrated bar

Introduction

Resize (by real dimension or pixel) and then sum/mix/overlap two images

Introduction

Get pixel location/coordinates on an image using mouse click/events

Introduction

Crop/snip part of a image with definition by a pixel line

Introduction

Modify file path/directory by changing folder names by merging a list

Introduction

Email providers and their SMTP servers

Introduction

Merge/combine two pptx files into one

Introduction

Find the file names of the images in a pptx, (and then save/extract the image as a file)

Introduction

Count occurrence/nubmer of words/phrase in a text file

Introduction

Remove/repace (part) character(s) from string

Introduction

Ranking/most popular programming languages for data analysts

Introduction

Convert PDF file to text file

Introduction

Extract pdf pages to form new pdf files

Introduction

Ranking/most popular automation testing tools

Introduction

Take a screenshot/snipping using a mouse click and drag method. For instance, take a screenshot, and then insert the image and/or a text into a ppt file.

Introduction

Ranking/most popular IT automation software tools

Introduction

Comparison between Python, Blue Prism, UiPath, Automation Anywhere

Introduction

Robots and Robotic Process Automation (RPA)

Introduction

Ranking/most popular machine learning frameworks used by data scientists

Introduction

Wafer map failure pattern recognition (WMFPR)

Introduction

Histogram for wafer analysis (e.g. percentage and frequency of grey level in the image)

Introduction

Spatial defect patterns

Introduction

Probe data

Introduction

Non-zero pixel values from an image

Introduction

Pixel values on specific pixel in an image

Introduction

Public datasets for wafer map analysis

Introduction

Pass variables between functions/from one to another

Introduction

Methods of physical failure analysis (PFA)/root cause analysis of ICs

Introduction

Convert images between Cartesian and Polar forms

Introduction

Fault analysis/PFA (Physical Failure Analysis) time and efficiency

Introduction

Patterns on wafers commonly reflecting specific process failure information

Introduction

Print specific rows of a DataFrame

Introduction

Table of applications of Python and its libraries

Introduction

Detection procedures/processes of spatial defect patterns (bins) in wafers

Introduction

Read/print a text/txt file

Introduction

Store images in pandas dataframe column

Introduction

Pickling and unpickling of pandas DataFrame (.pkl)

Introduction

Probability bounds analysis (PBA)

Introduction

Print the files and keyword occurrence which have been searched from a ppt file

Introduction

Percentages of information received through different senses (eye, nose, ear and hand feeling)

Introduction

Expected risk (population risk, expected value of loss or error)

Introduction

Empericial loss versus population loss

Introduction

Parameterized family and model parameters

Introduction

Predicted label

Introduction

Keyword Module in Python

Introduction

Python drivers for SQL server (pyodbc, pymssql, PyMySQL, cx_Oracle and psycopg2)

Introduction

Remove unwanted/unnecessary parts from strings in a column of dataframe

Introduction

Iterate over rows in a DataFrame/read and print row by row (number of columns and rows, df.shape[0]/df.shape[1])

Introduction

Trick: Output a portion of rows and columns from a csv file cell by cell into another csv file

Introduction

Paraphrase mining

Introduction

pyodbc for bridging SQL to Python

Introduction

Create a Batch File to Run a Python Script

Introduction

Read a frequently updated file periodically (similar to watchdog)

Introduction

Codes: Automation of Mouse Movements and Clicks, and keyboard control (comparison among pyautogui, pygetwindow, pydirectinput, autoit, Quartz, platform, pynput, ctypes, uiautomation and Sikuli)

Introduction

Principle and troubleshooting: Automation of Mouse Movements and Clicks (comparison among pyautogui, pygetwindow, pydirectinput, autoit, Quartz, platform, ctypes, uiautomation and Sikuli)

Introduction

Trick: Get coordinate difference between mouse positions

Introduction

Evaluation of Precision in Machine Learning Process

Introduction

Probabilistic model

Introduction

pyodbc for bridging SQL to Python

Introduction

Access and use SQL Database on SSMS (Microsoft SQL Server Management Studio Express) with pyodbc: localhost, insert rows, update, count updated, delete rows, comparision between extract data by Python and SQL itself

Introduction

Insert data/row into SQL Database on SSMS with pyodbc

Introduction

Get directory/path/file name partially

Introduction

Call and run another script in a different/any (parent or children) directory/path/subfolder from a script

Introduction

Get the current directory/folder path

Introduction

Python modules to interact with the operating system (os, platform, subprocess, shutils, glob and sys)

Introduction

Change the current directory to any directory/path (e.g. with os.chdir)

Introduction

Check existence of phrase on text file line-by-line

Introduction

"@echo off" and "pause" in Command Prompt Window

Introduction

Last n days/weeks/months (.to_datetime(x), .set_index(y), .last(z), .reset_index(), and .max() in pandas)

Introduction

Check if Windows/PC screen is locked

Introduction

Combine multiple images into a single multi-page image or vice versa

Introduction

Count the number of the pages in a single multi-page/frame image

Introduction

Extract elements from a list (different way from removing elements to get part of the list)

Introduction

Lock a file to prevent deleting, and then release the file once job is done

Introduction

PermissionError (E.g. file cannot be written when it is open)

Introduction

Extract/confirm any substrings with any pattern (e.g. dot (.))

Introduction

Get username and encoded password with getpass or or base64

Introduction

Find repeating patterns in columns, group them as cycles, and column correlations

Introduction

Create table on pptx with certain rows and columns of strings

Introduction

Create table on pptx with certain rows and columns in DataFrame

Introduction

hotkey('h') in pyautogui

Introduction

Difference/comparison between real mouse click and click from script/program, e.g. Pyautogui

Introduction

Comparisons between JMP (John's Macintosh Project) and Python

Introduction

RegEx (Regular Expression) (characters to check if a string contains a specified search pattern, remove double spaces, and clean texts)

Introduction

Compare (pattern/ratio of) two different columns, check whether column values match in DataFrame

Introduction

Check whether one column contains number only and another column contains letters only or mixture of numbers and letters in DataFrame

Introduction

Check the difference between two columns in DataFrame

Introduction

csv workflow: Read into dataframe, Select a specific column from DataFrame, Select several specific columns to plot

from keyboard import is_pressed (Esc, check pressed key)

Introduction

Execute a command on Command Prompt of Windows

Introduction

Check if one list is subset of another (partially (part of))

Introduction

Insert paragraphs of texts into Python script (f"""/f''')

Introduction

Plot figures with date/month/year

Introduction

Summary/templates/examples of pptx and PowerPoint format

Introduction

.bat (batch) files for Command Prompt Windows

Introduction

pd.concat()

Introduction

Penalized regression (Lasso and Ridge)

Introduction

Joint probability distribution (p)

Introduction

Predicted label versus predictor (feature)

Introduction

Hypothesis class/hypothesis family/predictor class/model class/hypothesis family/predictor family/model family

Introduction

Predicted values (ŷ, y^)

Introduction

Phrase

Introduction

Summary/templates of plotting graphs/figures

Introduction

Plot from dictionary

Introduction

Data plot with labels

Introduction

Plot multiple images on the same figure by hiding x- and y-labels

Introduction

Plot table

Introduction

Plot graph/figure/image from CSV file/DataFrame

Introduction

Plot graph/figure/image from CSV file/DataFrame by removing/hiding blank/empty cells with axis range (plt.xlim())

Introduction

Plot images with certain image size and in color

Introduction

Plot multiple datasets on the same scatter graph with different x- and y-axis values

Introduction

Separately plot data into the same graph/figure/image from different (multiple) csv files for each category

Introduction

Plot a list of x, y coordinates to an image

Introduction

|Plot distance between points calculated by coordinates

Introduction

Plot pixel intensity (histogram) along a line (row/column/x-axis/y-axis) of an image

Introduction

Plot histogram

Introduction

Change/convert a colored image to a grey image(, and then show pixel values).

cv2, cv2, cv2/skimage. cv2/skimage. PIL. matplotlib.

Plot curve/chart in pptx

Introduction

Transparency of marker (e.g. for plots)

Introduction

Loops (e.g. for loop) for 2D (two-dimensional) plot (or map)

Introduction

plotly.graph_objects

Introduction

Commands/concepts of plot with matplotlib.pyplot to plot/generate images: e.g. axis/colored text or annotation, ScaleBar()

Introduction

plots

(code)

.xticks() and .yticks()

Introduction

code. code

code

plt.plot(): Combine multiple plots and plot continuous curve, solid green ('-g'), dashed green ('--g'), dashdot ('.g'), dotted (':g'). Plot by different grouping and summing. code.

Introduction

plt.scatter(): Plot scattered curves

Introduction

plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None), or plt.subplots_adjust(wspace=0, hspace=0): Adjusting the spacing around subplots

code

Plotly: Is used for plotting graphs easily. This works very well in interactive web applications. With this, we can make different types of basic charts like line, pie, scatter, heat maps, polar plots and so on. Whatever visualisation we can think of, we can easily plot a graph of it using Plotly.

from matplotlib import pyplot as plt: == import matplotlib.pyplot as plt.

(code)

bokeh.plotting.figure.circle()

Inside/outside edges/margins of plotted images

Introduction

Avoid two or multiple plots being wrongly/incorrectly/unnecessarily mixed/overlap

Introduction

Recall (Sensitivity or True Positive Rate) in machine learning

Introduction

Plot a figure with a colored arrow between text lines/steps

Introduction

Python IDLE restart during execution

Introduction

Box and Whisker plots

Introduction

Get the date and time (a past date) of N days ago

Introduction

std() and pstdev() standard deviation in csv/dataframe

Introduction

Waterfall plot

Introduction

Safely use credentials (username and password) in Python project

Introduction

Tableau integration with Python

Introduction

Percentage

Introduction

Color the Tables in pptx (PowerPoint)

Introduction

Define/measure the size of PowerPoint/pptx slides

Introduction

Color and rotate/vertical text in pptx

Introduction

Selecting only numeric/number columns, and then select two specific columns for plot

Introduction

Hide x-axis tick labels (only show some labels) where x values are under certain conditions

Introduction

Create table with merged cells on pptx

Introduction

Plot images from different DataFrame in a single row

Introduction

Cheatsheet of image plot

Introduction

plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None), or plt.subplots_adjust(wspace=0, hspace=0)

Introduction

Font size of tick labels in plot

Introduction

PowerPoint (pptx) cheatsheet

Introduction

Font size of a (single/multiple) cell in table in PowerPoint

Introduction

Change the width of the cell in ppt

Introduction

Only use the first 4 characters in the headers of the table for pptx/dataframe

Introduction

Populate the table with logarithmic format in pptx

Introduction

Cheatsheet of PySpark (for SparkSQL) and DataFrames

Introduction

Comparative overview of multivariate statistical methods (Correlation Analysis, Regression Analysis, Factor Analysis, Cluster Analysis, Principal Component Analysis (PCA), Canonical Correlation Analysis, Discriminant Analysis, Path Analysis, Structural Equation Modeling (SEM), Multivariate Analysis of Variance (MANOVA), Analysis of Covariance (ANCOVA) ): purposes, variables, and outputs

Introduction

HTTP(Hypertext Transfer Protocol)/URL (Uniform Resource Locator)

Introduction

Cheatsheet of Python programming

Introduction

Plotly.com

Introduction

Cheatsheet of Pandas and DataFrame

Introduction

Pandas

Introduction. Is a must for data-science since it is one of the most used packages for analyzing data, data exploration, and manipulation. It provides fast, expressive, and flexible data structures to easily (and intuitively) work with structured (tabular, multidimensional, potentially heterogeneous) and time-series data. Pandas can do well:
     Easy handling of missing floating point and non-floating point data
     Columns can be inserted and deleted from DataFrame and higher dimensional objects
     Automatic and explicit data alignment: objects can be aligned or be ignored to a set of labels
     Powerful, flexible grouping
     Convert ragged, differently-indexed data in other data structures into DataFrame objects
     Intelligent label-based slicing, fancy indexing, and subsetting of large data sets
     Intuitive merging and joining data sets
     Reshaping and pivoting of data sets
     Hierarchical labeling of axes
     Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases
     Compatible with ultrafast HDF5 format
     Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging

import pandas as pd

Introduction

pandas for CSV

Introduction

pandas.read_csv()

CSV: Both read_csv() and read_table() use the same parsing code to intelligently convert tabular data into a DataFrame. pandas.read_csv(filepath_or_buffer, sep=NoDefault.no_default, delimiter=None, header='infer', names=NoDefault.no_default, index_col=None, usecols=None, squeeze=False, prefix=NoDefault.no_default, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, encoding_errors='strict', dialect=None, error_bad_lines=None, warn_bad_lines=None, on_bad_lines=None, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None, storage_options=None). code.

pandas.DataFrame.to_csv()

CSV: DataFrame.to_csv(path_or_buf=None, sep=',', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, mode='w', encoding=None, compression='infer', quoting=None, quotechar='"', line_terminator=None, chunksize=None, date_format=None, doublequote=True, escapechar=None, decimal='.', errors='strict', storage_options=None). Write the contents of the Frame into a CSV file: code.

pandas.read_table()

CSV: Both read_csv() and read_table() use the same parsing code to intelligently convert tabular data into a DataFrame. Read general delimited file into DataFrame. pandas.read_table(filepath_or_buffer, sep=NoDefault.no_default, delimiter=None, header='infer', names=NoDefault.no_default, index_col=None, usecols=None, squeeze=False, prefix=NoDefault.no_default, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, dialect=None, error_bad_lines=None, warn_bad_lines=None, on_bad_lines=None, encoding_errors='strict', delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None)[source]. code.

loc[] and iloc[]

Introduction

describe()

Print statistic summary of the data. (code)

.fillna()

Replace empty cells with anything. (code)

.groupby()

sort/group columns. (code). CSV: (code)

info()

Print information in CSV. (code)

.to_datetime()

Change data format. (code)

del

Introduction

head()

(code)

path_or_buf

CSV: A string path to the file to write or a StringIO

inplace=True

(code)

.T (Transfer of array in Python)

Introduction

predict()

Introduction.

password()

password(text='', title='', default='', mask=''). (code)

parse_dates

CSV: if True then index will be parsed as dates (False by default). You can specify more complicated
options to parse a subset of columns or a combination of columns into a single date column (list of ints or names, list of lists, or dict) [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column [[1, 3]] -> combine columns 1 and 3 and parse as a single date column {‘foo’ : [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’

pop()

Removes the item at the given index from the list and returns the removed item. (code)

sys.platform

(code)

sys.path

Is a list of strings that specifies the search path for modules. Basically this tells Python what locations to look in when it tries to import a module. code.

sys.platform

Is a platform identifier. You can use this to append platform specific modules to sys.path, import different modules depending on platform or run different pieces of code. code.

from PIL import ImageTk

tkinter popup windows

Introduction

.place()

(code)

.pack()

(code). (code).

PhotoImage()

(code)

pady=

(code)

padx=

(code)

PyAutoGUI

Introduction

pyautogui.position()

(Code)

press()

Several key strings which will be pressed down in order, and then released in reverse order. Strings can be passed to this function: '\t', '\n', '\r', ' ', '!', '"', '#', '$', '%', '&', "'", '(', ')', '*', '+', ',', '-', '.', '/', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', ':', ';', '<', '=', '>', '?', '@', '[', '\\', ']', '^', '_', '`', 'a', 'b', 'c', 'd', 'e','f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', '{', '|', '}', '~', 'accept', 'add', 'alt', 'altleft', 'altright', 'apps', 'backspace', 'browserback', 'browserfavorites', 'browserforward', 'browserhome', 'browserrefresh', 'browsersearch', 'browserstop', 'capslock', 'clear', 'convert', 'ctrl', 'ctrlleft', 'ctrlright', 'decimal', 'del', 'delete', 'divide', 'down', 'end', 'enter', 'esc', 'escape', 'execute', 'f1', 'f10', 'f11', 'f12', 'f13', 'f14', 'f15', 'f16', 'f17', 'f18', 'f19', 'f2', 'f20', 'f21', 'f22', 'f23', 'f24', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8', 'f9', 'final', 'fn', 'hanguel', 'hangul', 'hanja', 'help', 'home', 'insert', 'junja', 'kana', 'kanji', 'launchapp1', 'launchapp2', 'launchmail', 'launchmediaselect', 'left', 'modechange', 'multiply', 'nexttrack', 'nonconvert', 'num0', 'num1', 'num2', 'num3', 'num4', 'num5', 'num6', 'num7', 'num8', 'num9', 'numlock', 'pagedown', 'pageup', 'pause', 'pgdn', 'pgup', 'playpause', 'prevtrack', 'print', 'printscreen', 'prntscrn', 'prtsc', 'prtscr', 'return', 'right', 'scrolllock', 'select', 'separator', 'shift', 'shiftleft', 'shiftright', 'sleep', 'space', 'stop', 'subtract', 'tab', 'up', 'volumedown', 'volumemute', 'volumeup', 'win', 'winleft', 'winright', 'yen', 'command', 'option', 'optionleft', 'optionright', 'spacebar'. (code)

hotkey()

keyDown()

keyUp()

winleft and winright

Introduction

.pixelMatchesColor()

Introduction

pyautogui.PAUSE = 1

Wait 1 second after performing an action

pyautogui.FAILSAFE

"pyautogui.FAILSAFE = True": moving the mouse cursor to the upper-left corner of the screen will cause PyAutoGUI to raise the pyautogui .FailSafeException exception. "pyautogui.FAILSAFE = False": disable this feature.

enter

Press the enter key or add "\n" in .typewrite function. Introduction

popup windows with pyautogui

Introduction

pyautogui.pixel(x,y)[]

e.g. pyautogui.pixel(30,60)[0] == 0, here, [0] = R (red) value, [1] = G (green) value, [2] = B (blue) value.

Mouse clicks

(code)

mouseUp()

Move the mouse and then release it. .mouseUp(x=moveToX, y=moveToY, button='left'). (code)

mouseDown()

Move the mouse and then release it. .mouseDown(x=moveToX, y=moveToY, button='left'). (code)

.middleClick()

.middleClick(x=moveToX, y=moveToY) (Code)

dragTo()

dragTo(x, y, duration=num_seconds) drags mouse to XY. (code).

dragRel()

dragRel(xOffset, yOffset, duration=num_seconds) drags mouse relative to its current position. (code).

displayMousePosition()

(code)

.moveRel()

.moveRel(xOffset, yOffset, duration=num_seconds). (Code).

.moveTo(x, y, t)

x and y: coordinates, and t: time. By default, pyautogui.MINIMUM_DURATION is 0.1. (Code)

.tripleClick()

.tripleClick(x=moveToX, y=moveToY) (Code)

.typewrite()

E.g. .typewrite('Hello world!\n', interval=secs_between_keys); .typewrite(['a', 'b', 'c', 'left', 'backspace', 'enter', 'f1'], interval=secs_between_keys). (Code)

.typewrite()

E.g. .typewrite('Hello world!\n', interval=secs_between_keys); .typewrite(['a', 'b', 'c', 'left', 'backspace', 'enter', 'f1'], interval=secs_between_keys). (Code)

.rjust()

Right-justify/align them so that they take up the same amount of space, whether the coordinate has one, two, three, or four letters or digits. (code).

alert()

alert(text='', tilte='', button='Ok'). (code)

confirm()

confirm(text='', title='', buttons=['OK', 'Cancel']) (code)

prompt()

prompt(text='', title='', default=''). (code)

.locateOnScreen()

.locateOnScreen('looksLikeThis.png') returns (left, top, width, height) on the image which the screenshot is taken from. Difference between pyautogui.locateOnScreen("anImage") and pyautogui.locateOnScreen("anImage", minSearchTime=): minSearchTime = amount of time in seconds to repeat taking screenshots and trying to locate a match. This function mostly is useless. Introduction. (code)

.locateCenterOnScreen()

Uses pyscreeze. x, y = MySearch_img to get the x- and y-coordinates of centers of the feature. Difference between pyautogui.locateOnScreen("anImage") and pyautogui.locateOnScreen("anImage", minSearchTime=): minSearchTime = amount of time in seconds to repeat taking screenshots and trying to locate a match. Introduction. (code)

.locateAllOnScreen()

Difference between pyautogui.locateOnScreen("anImage") and pyautogui.locateOnScreen("anImage", minSearchTime=): minSearchTime = amount of time in seconds to repeat taking screenshots and trying to locate a match. (code)

write()

(Code)

.screenshot()

(code)

PyKeyboard Attributes

'a', 'b', 'c', 'A', 'B', 'C', '1', '2', '3', '!', '@', '#', and so on

The keys for single characters

'enter' (or 'return' or '\n')

The enter key

'esc'

The esc key

'shiftleft', 'shiftright'

The left and right shift keys

'altleft', 'altright'

The left and right alt keys

'ctrlleft', 'ctrlright'

The left and right ctrl keys

'tab' (or '\t')

The tab key

'backspace', 'delete'

The backspace and delete keys

'pageup', 'pagedown'

The page up and page down keys

'home', 'end'

The home and end keys

'up', 'down', 'left', 'right'

The up, down, left, and right arrow keys

'f1', 'f2', 'f3', and so on

The F1 to F12 keys

'volumemute', 'volumedown', 'volumeup'

The mute, volume down, and volume up keys (some keyboards do not have these keys, but your operating system will still be able to understand these simulated keypresses)

'pause'

The pause key

'capslock', 'numlock', 'scrolllock'

The caps lock, num lock, and scroll lock keys

'insert'

The ins or insert key

'printscreen'

The prtsc or print screen key

'winleft', 'winright'

The left and right win keys (on Windows)

'command'

The Command (z) key (on OS X)

'option'

The option key (on OS X)

ppt

from pptx import Presentation

(code)

from pptx.util import Inches

(code)

from pptx.util import Pt

(code)

from pptx.dml.color import RGBColor

(code)

Add and work on a new slide

.slide_layouts[0]

.slide_layouts[1]

.slide_layouts[2]

.slide_layouts[3]

.slide_layouts[4]

.slide_layouts[5]

.slide_layouts[6]

Simple pptx slide generation. Introduction

.slide_layouts[7]

.slide_layouts[8]

.slide_layouts[9]

.slide_layouts[10]

.slide_layouts[11]

Presentation()

(code)

paragraphs[]

(code)

from pptx.enum.shapes import MSO_AUTO_SHAPE_TYPE

(code)

Quit from Powerpoint show

(code)

from pptx.chart.data import ChartData

(code)

from pptx.enum.chart import XL_CHART_TYPE

(code)

.categories

(code)

.add_series()

(code)

.add_chart()

(code)

.LINE

(code)

.chart

(code)

.series

(code)

.format.line

(code)

Image rotation in pptx (.rotation=)

Introduction.

Text Format (e.g. ALIGN) in pptx

PP_ALIGN has CENTER, DISTRIBUTE, JUSTIFY, JUSTIFY_LOW, LEFT, RIGHT, THAI_DISTRIBUTE, and MIXED, and MSO_VERTICAL_ANCHOR has TOP, MIDDLE, BOTTOM, MIXED. Introduction

MyPresentation.save()

(code)

from PIL import Image

(code)

.placeholders[]

(code)

pass()

Means "Do nothing": Tell Python that you’re just not quite ready to finish writing the class, so that it does't throw an error message. Instruction.

__pos__

__pow__

patterns

(Read-only) Patterns to allow matching event paths.

path

The path that this watch monitors.

class watchdog.observers.polling.PollingObserver(timeout=1)

class watchdog.observers.polling.PollingObserverVFS(stat, listdir, polling_interval=1)

path(id)

Returns path for id. None if id is unknown to this snapshot.

paths

Set of file/directory paths in the snapshot.

math.pi

Returns the mathematical constant pi (3.141592 . . .).

math.pow(x, y)

Returns x raised to the power y. code1, code2.

pow(x, y, z)

x raise to the power y and reminder by z.

patterns=['*.*']

(code)

.PatternMatchingEventHandler

(code)

os.path.join(path

Introduction. code. code. (code)

os.path.exists()

(code) (code)

path

code. (code)

.pack

code. code.

Floating Point

It can store number with a fractional part

property

The word are synonymous with attribute. code. code. Change a attribute, code.

os.path.splitext() method

Is used to split the path name into a pair root and ext. Here, ext stands for extension and has the extension portion of the specified path while root is everything except ext part. ext is empty if specified path does not have any extension. If the specified path has leading period (‘.’), it then will be ignored. Introduction. Examples are:

path name	root	ext
/Desktop/file.txt /home/User/Desktop/file .txt	/Desktop /home/User/Desktop	{empty}
file.py	file	.py
.txt	.txt	{empty}

pyperclip.paste()

(code). (code).

Py2exe

(code)

pyinstaller

(code)

scipy.linalg.pascal

Create a Pascal matrix.

linalg.pinv(a[, rcond, hermitian])

Compute the (Moore-Penrose) pseudo-inverse of a matrix.

Pyglet

Is an excellent choice for an object-oriented programming interface in developing games. In fact, it also finds use in developing other visually-rich applications for Mac OS X, Windows, and Linux. In the 90s, when people were bored, they resorted to playing Minecraft on their computers. Pyglet is the engine behind Minecraft.

PyGame

Provides an extremely easy interface to the Simple Directmedia Library (SDL) platform-independent graphic, audio, and input libraries.

Python Twisted

Is an event-driven networking engine, Twisted is written in Python, and licensed under the open-source MIT license.

PIL

Pillow is a friendly fork of PIL (Python Imaging Library), but is more user-friendly. If you work with images, then Pillow is your best friend. code1, code2.

from PIL import Image

code1, code2.

skimage.measure.perimeter(image[, neighbourhood])

Calculate total perimeter of all objects in binary image.

skimage.measure.points_in_poly

Test whether points lie inside a polygon.

skimage.measure.profile_line(img, src, dst)

Return the intensity profile of an image measured along a scan line.

import pyperclip

(code)

pywin32

Provides useful methods and class for interaction with Windows, as the name suggests.

PyGTK

Lets you easily create programs with a GUI (Graphical User Interface) with Python.

Pipenv

Is a production-ready tool that aims to bring the best of all packaging worlds to the Python world. The cardinal purpose is to provide users with a working environment which is easy to set up. Pipenv, the “Python Development Workflow for Humans” was created by Kenneth Reitz for managing package discrepancies.

Pydot

Is used for generating complex oriented and non-oriented graphs. It is specially used while developing algorithms based on neural networks and decision trees.

PyOD

As the name suggests, it is a Python toolkit for detecting outliers in multivariate data. It provides access to a wide range of outlier detection algorithms. Outlier detection, also known as anomaly detection, refers to the identification of rare items, events or observations which differ from the general distribution of a population.

Pint

Pynance

Work wonders for a stock market trader. It is an open-source python package that retrieves, analyses and visualizes the data from stock market derivatives. With this library in hand you can generate labels and features for machine learning models.

Pyfolio

Evaluates the trading performance with the help of Pyfolio. It provides risk analysis reports and performance results of financial portfolios based on the returns. This was developed by Quantopian and works well with Zipline which is a backtesting library (will discuss later). Pyfolio specializes in creating tear sheet models and bayesian analysis.

cv2.putText()

cv2.putText(image, text, org, font, fontScale, color[, thickness[, lineType[, bottomLeftOrigin]]]) is used to draw a text string on any image. The font types are FONT_HERSHEY_SIMPLEX = 0, FONT_HERSHEY_PLAIN = 1, FONT_HERSHEY_DUPLEX = 2, FONT_HERSHEY_COMPLEX = 3, FONT_HERSHEY_TRIPLEX = 4, FONT_HERSHEY_COMPLEX_SMALL = 5, FONT_HERSHEY_SCRIPT_SIMPLEX = 6, FONT_HERSHEY_SCRIPT_COMPLEX = 7, and FONT_ITALIC = 16. The thickness of the line is in pixel. lineType: This is an optional parameter.It gives the type of the line to be used. bottomLeftOrigin: This is an optional parameter. When it is true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner. code. (code)

.random.permutation()

code.

Search/print/output the rows

CSV: Print the rows if their cell values are greater than a specific value, in the csv file with numbers only; output the rows if the cell value is in a specific range. Introduction. code.

PyBrain

Contains algorithms for neural networks that can be used by entry-level students yet can be used for state-of-the-art research. The goal is to offer simple, flexible yet sophisticated and powerful algorithms for machine learning with many pre-determined environments to test and compare your algorithms.

t.penup()/t.pu()/t.up()

Pull the pen up – no drawing when moving. (code)

t.pendown()/t.pd()/t.down()

Pull the pen down – drawing when moving. (code)

.getcanvas().postscript(file="")

Picture saved in current working directory. (code)

.set_window_position()

(code)

.position

(code)

from pynput.mouse import Button

(code)

from pynput.mouse import Controller

(code)

.press(Button.left)

(code)

.press(Button.right)

(code)

space bar on keyboard

Press space bar. Introduction

PyScreeze

Is a simple, cross-platform screenshot module for Python 2 and 3.

hotkey('p')

Introduction

press(pagedown)

Introduction

press(pagedown)

Introduction

PyGetWindow

A simple, cross-platform module for obtaining GUI information on application's windows.

.Popen()

Opens a pipe from a command. This pipe allows the command to send its output to another command. Introduction

from time import perf_counter

(code)

.button_pressed()

(code)

os.path.getctime

(code)(code)

make_pipeline

(code).

PySimpleGUI27

(code).

.switch_to. and .switch_to.parent_frame()

Introduction

E.g. run.font.size = Pt(15). Inches, Emu, Cm, Mm, Pt, and Px are base class for length classes, providing properties for converting length values to convenient units.

Inches, Emu, Cm, Mm, Pt, and Px are base class for length classes, providing properties for converting length values to convenient units.

.slide_width

(code).

slide_height

(code).

os.path.basename()

(code).

os.path.abspath()

(code).

os.path.dirname()

(code).

os.path.expanduser()