Electron microscopy
Python Automation and Machine Learning for ICs: Chapter C
- Python Automation and Machine Learning for ICs -
- An Online Book: Python Automation and Machine Learning for ICs by Yougui Liao -
Python Automation and Machine Learning for ICs                                                       http://www.globalsino.com/ICs/        

Table of Contents/Index 
Chapter/Index: Introduction | 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 | Appendix



K-means clustering and PCA for failure analysis Introduction
ML model complexity versus dataset size Introduction
Clusters (Kubernetes, Apache Mesos, Spark Standalone, Apache Hadoop YARN) in Apache Spark Introduction
Leveraging precision, speed, and automation: Integrating Mask R-CNN and YOLOv8 Introduction
Feature selection: removing unnecessary (constant & quasi constant) features Introduction
Comparison between Clouds (Amazon, IBM, Google ...) Introduction
Feature Selection: Chi Square to select dependent and independent variables Introduction
Comparison between CNN, CNN with Attention and Autoencoder Introduction
Mask R-CNN (Mask Region-based Convolutional Neural Network)  Introduction
Configuring Spark Introduction
Deploy modes for driver process in Apache Spark: client mode and cluster mode Introduction
Apache Spark applications to a Kubernetes cluster Introduction
Apache Spark on IBM Cloud Introduction
Catalyst in Spark Introduction
Coarsening data Introduction
Principles of ethical and responsible ML (selection bias, confirmation bias, automation bias, model fairness) Introduction
Image classification with ML Introduction
Google Cloud Shell Introduction
Drawbacks of Coursera classes Introduction
Spark Core of Apache Spark Introduction
Parallel computing and distributed computing Introduction
Calculation of Principal Component Analysis (PCA) Introduction
Squared Pearson correlation coefficient Introduction
Covariance Introduction
Covariance versus Covariance Matrix Introduction
Principal Component Analysis (PCA) versus Uniform Manifold Approximation and Projection (UMAP) Introduction
Impact of corpus narrowness on language model training Introduction
Context-free grammar (CFG) Introduction
CaptionBot Introduction
tf.keras.datasets (e.g. MNIST, CIFAR-10, CIFAR-100, Fashion MNIST) Introduction
Image convolution Introduction
Code blocks Introduction
Python Conference Introduction
Computer hardware architecture Introduction
Labor cost of data analysis with and without automation and ML techniques Introduction
Computer vision Introduction
Clustering in computer vision Introduction
Trade-off between minimizing loss and minimizing complexity Introduction
Nearest-neighbor (NN) classification Introduction
Replaces a symbol/character/letter in a string Introduction
Analyzing the impact of fabrication conditions on semiconductor wafer fail rates Introduction
Plot with letters/words/character as x-/y-axis Introduction
Fit and Smooth Plotted Curves Introduction
Maintaining arc-consistency Introduction
Constraint Satisfaction Problems (CSPs) as Search Problems Introduction
Arc consistency Introduction
Binary constraint Introduction
Node consistency Introduction
Unary constraint Introduction
Soft constraints and hard constraints Introduction
Constraint satisfaction problem Introduction
Hill Climbing Introduction
Evaluation (Precision and Recall) in Text classification with Naive Bayes Introduction
Text classification with Naive Bayes Introduction
Markov chain Introduction
Unconditional probability Introduction
Path and Path Cost in ML Introduction
Cost/loss function versus reward function Introduction
Cost (expense) and speed (fastest and slowest) of computation in ML Introduction
Credit assignment problem in reinforcement learning Introduction
Electroencephalogram cap (EEG cap) for brain Introduction
Nonlinear extensions of Independent Component Analysis (ICA) Introduction
Cumulative Distribution Function (CDF) Introduction
Formats of datasets for classification Introduction
Finding a correct loss (risk, objective) function for a specific problem Introduction
Cache Introduction
Logistic regression as a one-neuron/single-layer neural network (connection between linear & activation parts) Introduction
Softmax regression (multinomial logistic regression)/softmax multi-class network/softmax classifier Introduction
Extract/confirm any substrings with any pattern (e.g. dot (.)) Introduction
Dependently Identically Distributed/Correlated Identically Distributed Introduction
kfp.dsl package versus pipelines and components Introduction
Probably Approximately Correct (PAC) learning Introduction
Sample Complexity Introduction
Convergence and Optimization Introduction
Model Complexity Introduction
Leave-One-Out Cross-Validation (LOOCV) Introduction
Plot pixel intensity (histogram) along a line (row/column/x-axis/y-axis) of an image Introduction
K-Fold Cross-Validation Introduction
CIFAR (Canadian Institute for Advanced Research) (CIFAR-10 and CIFAR-100) Introduction
Linear correlation between two variables with Pearson Correlation Coefficient, Spearman Rank Correlation Coefficient, Kendall's Tau, Linear Regression, Coefficient of Determination and Correlation Ratio
Choice of parameters for training models Introduction
Training error versus model complexity Introduction
Cross-Validation in ML Introduction
Comparison among classifier, hyperplane and decision boundary Introduction
Optimal margin classifier/maximum margin separator Introduction
Conditional probability Introduction
Custom AI/ML chips/ICs Introduction
Energy consumption in computation of machine learning Introduction
Convolutional Layers (CONV) in Deep Learning Introduction
Fully Connected Layers (FC) in Deep Learning Introduction
Laplace smoothing/Laplace correction/add-one smoothing Introduction
Categorical distribution Introduction
Canonical response function Introduction
Conferences on machine learning Introduction
Linear regression versus classification Introduction
Comparison between mean squared error (MSE), absolute error (L1 Loss) and fourth-power loss
Comparison between L1 Regularization and L1 Loss (absolute loss or mean absolute error (MAE)) Introduction
Close() and close opened file  
Close file after reading a file: avoid file locking Introduction
cv2.waitKey(1): Will display a frame for 1 ms, after which display will be automatically closed. Since the OS has a minimum time between switching threads, the function will not wait exactly 1 ms, it will wait at least 1 ms, depending on what else is running on your computer at that time.

close(): It closes the file or webpage, and frees the memory space acquired by that file. (code)
win32clipboard.CloseClipboard() code.
Open and close any type of files with default programs/apps (e.g. word, excel, dm3, dm4, Digital Micrograph, powerpoint, internet explorer, chrome, and so on) in windows Introduction
Minimize/maximize/restore/activate/resize/move/close Window objects Introduction
Open and close specific files Introduction
Fill in closed curves Introduction
Methods to open google chrome (problems: Google chrome closes immediately after being launched with selenium) (with close-browser and quit-driver function) (Introduction)
Convolution and convolutional layers Introduction
Comparison of regression classes Introduction
Independent Component Analysis (ICA) Introduction
Convex optimization, convexity of loss functions, convex functions and convex sets Introduction
Cocktail party problem Introduction
Lipschitzness/Lipschitz continuity Introduction
Point-Biserial Correlation Introduction
Kendall Tau Rank Correlation Coefficient Introduction
Pearson Correlation Coefficient/Pearson's r/Correlation Coefficient Introduction
Spearman Rank Correlation/Spearman's rho/Spearman correlation Introduction
Epsilon cover/ε-cover/epsilon-net Introduction
Infinite Hypothesis Class Introduction
Finite Hypothesis Class/finite Hypothesis Analysis Introduction
Complementary inequality Introduction
Core Steps/Procedure/Designing of Machine Learning Introduction
Finite Hypothesis Class versus Infinite Hypothesis Class Introduction
Various names or terms that describe similar concepts or techniques in ML Introduction
Hypothesis class/hypothesis family/predictor class/model class/hypothesis family/predictor family/model family (h) Introduction
Concentration inequality Introduction
Central Limit Theorem (CLT) Introduction
Covariance matrix Introduction
Modify/replace the line in a text file if a line contains specific string Introduction
Well-specified case Introduction
Uniform convergence Introduction
Consistency in Statistics Introduction
Defect Detection and Classification by using Machine Learning Introduction
Cross entropy (log loss/logistic loss) Introduction
Trick: generic code/script templates for complex automation Introduction
Loss (risk, cost, objective) function Introduction
Confusion matrix heatmap Introduction
Common Words for Classification of Groups of Texts Introduction
Good research topics in the field of semiconductor manufacturing and computer vision Introduction
Autonomous vehicles/cars and machine learning Introduction
Classification tree/decision tree for classification Introduction
Fréchet Inception Distance (FID) coefficient Introduction
Misclassification rate (classification error rate or error rate) in machine learning Introduction
Corpus Introduction
Class Activation Mapping (CAM) Introduction
Similarity-based clustering method (SCM) Introduction
Comparison between supervised and unsupervised learning Introduction
Convert a list to a matrix Introduction
Execute a command on Command Prompt of Windows Introduction
Continue script execution no matter whether some try fails or not (finally)
Create a temporary file or directory/folder Introduction
Remove unwanted/unnecessary parts from strings in a column of dataframe Introduction
Convert dataframe row/column into a comma separated string Introduction
Compare string entries/cells/elements of columns in different dataframes
Candidate keywords Introduction
Built-ins/Builtins Commands in Python Introduction
Build own/customized keyword candidates Introduction
Electrical characteristics of MOS capacitor Introduction
Count how many empty strings in a list Introduction
Semantic clustering Introduction
Clustering versus Classification of texts and documents Introduction
Clustering of texts Introduction
Classification of texts Introduction
Classification of groups of texts Introduction
(Text and image) contrastive learning Introduction
String template class for formating strings (F-strings (for calculation) (f"{}"), format() method ({}), %s, %d, Template ($)) Introduction
webdriver.Chrome() (Instruction)
ODBC (Open Database Connectivity) 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
Python drivers for SQL server (pyodbc, pymssql, PyMySQL, cx_Oracle) 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
Change the current directory to any directory/path (e.g. with os.chdir) Introduction
Count number of lines in a text file Introduction
Find common/different elements/items between two lists/sets Introduction
Plot confidence bands Introduction
Convert between numpy array and string Introduction
igraph for clustering and network Introduction
Call/run/execute JMP from Python Introduction
Count the number of the pages in a single multi-page/frame image Introduction
"@echo off" and "pause" in Command Prompt Window Introduction
Create a log (log.log) file to monitor script execution Introduction
Combine multiple images into a single multi-page image or vice versa Introduction
Convert/change the case of all letters/word into uppercase (capital) or lowercase in a list of strings Introduction
Compare dates (x days after or before a date), and difference between two dates in days Introduction
Convert set into a list and vice versa Introduction
Merge dictionaries (update(), **, chain(), ChainMap(), |, |=) Introduction
Calculating the area fraction of each circle overlapped (filled with color) by a square grid and build wafer map with integration Introduction
Convert strings to number (integers/float) Introduction
Trick: Get coordinate difference between mouse positions Introduction
Select/skip columns by index in DataFrame without changing the DataFrame itself, and change the order of of the selected columns Introduction
Convert all elements of specific column or in entire dataframe into strings Introduction
Find repeating patterns in columns, group them as cycles, and column correlations Introduction
Find the same elements in columns in two separate dataframes and then merge them Introduction
Remove the substring after the first or last character "::" in a given string, or extract the substring between the first and last "::" Introduction
Codes: Automation of Mouse Movements and Clicks, and keyboard control (comparison among pyautogui, pygetwindow, pydirectinput, autoit, Quartz, platform, 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
Create table on pptx with certain rows and columns of strings Introduction
Create table on pptx with certain rows and columns in DataFrame Introduction
Click a menus of an application Introduction
exception KeyboardInterrupt, Raised when the user hits the interrupt key (normally Control-C/ctrl-c or Delete). code. 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
checkpoint_path Introduction
Check if two circles intersect or overlap Introduction
Check if rectangles overlap Introduction
Check if two lists are same/identical Introduction
Check all the imported/current modules/libraries Introduction
Model checking Introduction
Model Checking Algorithms and Modus Ponens Algorithms Introduction
save_checkpoints_steps Introduction
RegEx (Regular Expression) (characters to check if a string contains a specified search pattern, remove double spaces, and clean texts) Introduction
Check ... empty  
  Check if an item/element is in a list or not Introduction
  Check if a list is empty or not Introduction
  Check file existence with partial filename Introduction
  Check if a file/folder exists or not (Cannot find a specific file/folder? a specific folder in the path? select specific folders to form a string, split a dos path into its components, and then print the list, or check files with extension) Introduction
  Check if Windows/PC screen is locked Introduction
  Check if two lists have the same elements Introduction
  Check whether a file is empty or not Introduction
  Check if a variable does exist/is assigned/defined Introduction
  Check if a string is empty or space only Introduction
  Check if a key exists in a dictionary Introduction
  'xyz'.isalpha(): Check if string is alphabet (letter, or one type of character)  
  Check all the imported/current modules/libraries Introduction
Check if one list is subset of another (partially (part of)) Introduction
Check if a variable is a number or string Introduction
Check if an element in a sublist of a list Introduction
Check if a file exists again (double check) Introduction
Clean clipboard and/or check if clipboard is empty, text or image Introduction
Check updated new files in a folder Introduction
Check if all the (and how many, length of a string) characters in the text are digits/numbers Introduction
Check to see if or get a window with a name containing specific titles or texts Introduction
Check the letters and symbols starting and ending with code
Keyword search function/check whether or not a string is within another string (a space is included as a string character) Introduction
Check if a popup dialog is a window or not for Selenium app Introduction
checkbox Introduction
Check/find/get a file name or the last folder name (e.g. from a path/directory) Introduction
Check if both files are the same file, e.g. symbolic link, shortcut Introduction
Check if a letter is in a string Introduction
Check if a string can be converted to float Introduction
Check if a letter/character is in a string Introduction
Extract text/check specific text from multiple powerpoint files Introduction
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
Compare/check if two text files have the same contents Introduction
Check existence of phrase on text file line-by-line Introduction
from keyboard import is_pressed (Esc, check pressed key) Introduction
Check and drop negative from dataframe pandas Introduction
Check (difference) whether or not a cell value in a column of a CSV file Matchs a value in a column of another CSV file, then do something: e.g. add a value to another column of a csv file  Introduction
File name, folder name. {}{}....format. Manipulation of file and folder names (rename file name and folder name): i) Check and 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
Check if CSV cell value is NaN Introduction
Check if a CSV file contains all of the specified strings Introduction
Convert between rows and columns from csv: Convert resulting row from CSV search into a column. Introduction, CSV
Convert CSV to images, row-by-row, with pixel values: each row is an image code.
Heatmap with input from a csv/pkl file Introduction
Print all values cell by cell in order of row and column in the csv file Introduction
Replace/change to new headers in a csv file Introduction
Remove duplicate cell values from a csv file/dataframe (e.g. keeping the first/top one; drop_duplicates(); .duplicated(); .get_option(); .set_option(); keep='first'/keep='last'; display.max_rows; display.max_columns) Introduction
Skip rows and/or columns in csv Introduction.
Data cleaning examples in csv files Introduction
Skip/remove empty rows (row-by-row) in DataFrame/csv Introduction
Convert a CSV file to a JSON file Introduction
csv workflow: Read into dataframeSelect a specific column from DataFrame, Select several specific columns to plot 
Convert a CSV file to an image with one column and another column as x-axis and y-axis, respectively. code.
Change/convert a colored image to a grey image(, and then show pixel values). cv2, cv2, cv2/skimage. cv2/skimage. PIL. matplotlib.
Convert a CSV file to a TXT file Introduction
Multiple headers in a csv file: Count the number of header rows first and then split a single csv file to multiple csv files Introduction
Change/rename a column name/header in a CSV file Introduction
Delete the column/row in a CSV file if they are empty or less than a number (or header/index only) Introduction
Read specific cells (cell by cell) in csv file Introduction
Create CSV files (e.g. with headers only) Introduction
CSV column transposer (rows/columns) Introduction
Input a sentence and then output a sentence based on a dictionary obtained from csv Introduction
Creates a dictionary from a csv file Introduction
Skip/replace empty cells/NaN value from DataFrame/CSV file Introduction
Trick: Output a portion of rows and columns from a csv file cell by cell into another csv file Introduction
Nearest/most similar lyrics of a sentence/text to a CSV file Introduction
Write special/certain rows (row-by-row) of one csv file to another csv file Introduction
Convert csv/dataframe column to a list or vice versa Introduction
Plot graph/figure/image from CSV file/DataFrame by removing/hiding blank/empty cells with axis range (plt.xlim()) Introduction
Plot graph/figure/image from CSV file Introduction
Creates a dictionary from a csv file Introduction
Correlations/similarity/dissimilarity/pair/match of two columns in csv data Introduction
Put the keywords in a grouped string into the first available cells in the corresponding columns in a csv file Introduction
Separately plot data into the same graph/figure/image from different csv files for each category (import multiple CSV files and concatenate into one DataFrame): append row-by-row or column-by-column Introduction
Convert a csv column to a string seperated by comma Introduction
Summary and cheatsheet of command for csv file Introduction
Collect the file list in a folder into a csv file Introduction
pandas for CSV Introduction
Write cvs cells  
  .to_csv()/.writerow() Instruction
  pandas.DataFrame.to_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
  writerow()/.writerows():: Save data into a CSV file. code. delete all items Introduction
  Write cell by cell with .loc  Instruction
  cv2.imwrite:: Save images code. code
Search/print/output the rows: 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. code. Introduction
Sort a csv file with column: used "key = operator.itemgetter()" Instruction
Calculation and data extraction in csv under conditions: Compares with the ones which cannot be used for math calculation, find the maximum in a column Introduction
Replace/change to new headers in a csv file Introduction
Skip rows and/or columns in csv Introduction
Merge/combine two csv files Introduction
Split columns and merge in csv: Split columns and then merge the splits in a csv file. Introduction
Count the number of lines (rows) and columns in a txt (and a csv) file, count different numbers in each region in a column, count missing or not available values. code Introduction
Sort a csv file with column: used "key = operator.itemgetter()" Instruction
Calculation in csv: Compares with the ones which cannot be used for math calculation, find the maximum in a column Introduction
Replace/change to new headers in a csv file Introduction
Skip rows and/or columns in csv Introduction
Filters the rows based on the condition of being within n days of today's date Introduction
.axes[0] and .axes [1]. for csv. (code)
.sum(): sum and percentage for csv Introduction
Count duplicates/occurrence and show unique values in csv files Introduction
Search in csv file: code. code Instruction
Split columns and merge in csv: Split columns and then merge the splits in a csv file Introduction
Count the number of lines (rows) and columns in a txt (and a csv) file, count different numbers in each region in a column, count missing or not available values. code. Introduction
Search/print/output the rows: 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. code. Introduction
for ... in rang() in csv Introduction
Guess and check algorithm with a combination of a for loop and an if statement Introduction
Sort a csv file with column: used "key = operator.itemgetter()" Instruction
Calculation in csv: Compares with the ones which cannot be used for math calculation, find the maximum in a column Introduction
datatable.Frame.to_csv(): Write the contents of the Frame into a CSV file. If no path is given, then the Frame will be serialized into a string, and that string will be returned  
csv.Sniffer(): The Sniffer class is used to deduce the format of a CSV file. It expects a sample string, not a file code
next(): Skip headers in CSV code
info(): Print information in CSV (code)
Series.to_csv(): Is a 1-D ndarray with axis labels and writes the given series object to a comma-separated values (csv) file/format code
line: Print line by line from a CSV file code
names: Names in headers of csv files code. code
DataFrame.equals(): Confirm if the two csv files are the same or not code
.groupby(): sort/group columns. (code). CSV: (code)
quoting: Set quoting rules as in csv module (default csv.QUOTE_MINIMAL)  
dialect: string or csv.Dialect instance to expose more ways to specify the file format. To expose more ways to specify the file format code
Create a file (e.g. csv, pptx files): See open().  
csv.writer(). Return a writer object responsible for converting the user’s data into delimited strings on the given file-like object. csvfile can be any object with a write() method.. code. delete all items. Instruction
Count duplicates/occurrence and show unique values in csv files Introduction
Split columns and merge in csv: Split columns and then merge the splits in a csv file Introduction
Count the number of lines (rows) and columns in a txt (and a csv) file, count different numbers in each region in a column, count missing or not available values Introduction. code.
Split columns and merge in csv: Split columns and then merge the splits in a csv file. Introduction
Skip rows and/or columns in csv Introduction.
Print/obtain a specific digit in a number, e.g. in a cell value in csv Introduction
Write to a specific cell in a csv file Introduction
Mean (average, .mean())/.sum()/maximum(.max())/minimum(.min())/number of non-null values(.count())/.median()/variance(.var())/standard deviation(.std()/pstdev()) Introduction
Get the csv/pandas cell value with certain condition Introduction
Add/insert a column into an existing csv file Introduction
Change csv cell value under conditions (e.g. if a cell value is equal to another cell value, then compare the third cell value; if both cell value is the same, then change its value to a value) Introduction
csv.reader(): Return a reader object which will iterate over lines in the given csvfile Introduction
tupleize_cols: If False (default), write as a list of tuples, otherwise write in an expanded line format suitable for read_csv  
DictReader() Introduction
pandas.read_table(): code Introduction
pandas.read_csv() 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
Read columns with numeric values/numbers only in dataframe Introduction
cv2.imread(/path/to/image, flag): A method loads an image from the specified file. If the image cannot be read (because of missing file, improper permissions, unsupported or invalid format) then this method returns an empty matrix. imread() decodes the image into a matrix with the color channels stored in the order of Blue, Green, Red and A (Transparency) respectively: (:, :, 0) represents Blue channel; (:, :, 1) represents Green channel; (:, :, 2) represents Red channel; (:, :, 3) represents Transparency channel. The flag is optional. code. code. code. code.
cv2.IMREAD_GRAYSCALE: Reads the image as grey image. If the source image is color image, grey value of each pixel is calculated by taking the average of color channels. = 0, code.
DataFrame workflow: Drop/delete rows with empty cells in a column, Sort DataFrame by time/date order
Electrical circuit simulations  
Switches simulations  
Diodes simulations  
Electrical characteristics of the MOS capacitor Introduction
Change date/month/year format Introduction
Get header/column names from DataFrame Introduction
Get the frequency of occurrence of a string in a column DataFrame Introduction
Difference/comparison between real mouse click and click from script/program, e.g. Pyautogui
Plot images with certain image size and in color Introduction
Plot confidence bands Introduction
.bat (batch) files for Command Prompt Windows Introduction
Trick: pd.concat() for merging/adding (two) columns Introduction
CycleGAN (Cycle-Consistent Adversarial Networks) Introduction
Copy a file or all files (with os.mkdir) to save to somewhere (create a directory first if it does not exist) Introduction
Remove decimal part in a string with comma Introduction
Form a list of strings from an old string with all the 6 digits by removing all special characters or spaces Introduction
Class Introduction
self and __init__ method in Class Introduction
Find the computer name Introduction
__str__ method for a class Introduction
__add__, __call__, __contains__, __delitem__, __delattr__, __eq__, __enter__, __ge__, __getattribute__, __getnewargs__, __getattr__, __getitem__, __gt__, __hash__, __reduce__, __iadd__, __imul__, __init_subclass__, __index__, __int__, __invert__, __new__, __neg__, __reduce_ex__, __reversed__, __rmul__, __radd__, __rand__, __rdivmod__, __rfloordiv__, __rlshift__, __rmod__, __ror__, __round__,__rpow__, __rrshift__, __rshift__, __rsub__, __rtruediv__, __rxor__, __dir__, __doc__, __divmod__, __iter__, __le__, __lt__, __len__, __ne__, __repr__, __setattr__, __setitem__, __sizeof__, __lshift__, __sub__, __subclasshook__, __str__ Introduction
Safely use credentials (username and password) in Python project Introduction
Convert a sentence/text to a list Introduction
Merge columns which contain specific strings Introduction
Merge rows/columns of a csv file into an old csv file if the rows/columns are not in the old csv file Introduction
Select specific columns from a DataFrame to form a new DataFrame Introduction
Output the row into dataframe if the value of the cell in a column contains a specific substring in a csv file (with headers) Introduction
Add letter/commas/numbers/characters to the end/beginning of strings in a list Introduction
Word cloud visualization Introduction
Extract the last column as subdataframe Introduction
Convert a floating-point number to exponential format Introduction
Apply a formatting function to all cells in a DataFrame Introduction
Color the Tables in pptx (PowerPoint) Introduction
Color and rotate/vertical text in pptx Introduction
Selecting only numeric/number columns, and then select two specific columns for plot Introduction
Merge columns with character/symbol Separation Introduction
Plot workflow: Create new empty column in DataFrameMove the cells in a column to another column under certain conditionSelect specific columns for scatter plot
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
Change the width of the cell in ppt Introduction
Highlight the plotted dots uncer certain condition Introduction
Only use the first 4 characters in the headers of the table for pptx/dataframe Introduction
Embed/hide codes or markers into HTML files Introduction
Cheatsheet about headers (column names) in DataFrame Introduction
Plot a heatmap with three columns of data Introduction
Aggregate duplicates in columns of data 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
Overcoming automation challenges and forward-looking suggestions Introduction





.create_sheet('') (code)
from typing import List, Dict code.
inplace=True Change the items permanently. (code). (code).
.str.split() (code).
expand=True (code).
rename() (code).
columns= (code). (code).
regex=True (code).
delim_whitespace Parse whitespace-delimited (spaces or tabs) file (much faster than using a regular expression)
compression decompress ’gzip’ and ’bz2’ formats on the fly. Set to ’infer’ (the default) to guess a format based on the file extension.
dtype A data type name or a dict of column name to data type. If not specified, data types will be inferred.
(Unsupported with engine=’python’)
header= Whether to write out the column names (default True). Introduction. (code). Row number(s) to use as the column names, and the start of the data. Defaults to 0 if no names passed, otherwise None. Explicitly pass header=0 to be able to replace existing names. The header can be a list of integers that specify row locations for a multi-index on the columns E.g. [0,1,3]. Intervening rows that are
not specified will be skipped (e.g. 2 in this example are skipped). Note that this parameter ignores commented lines and empty lines if skip_blank_lines=True (the default), so header=0 denotes the first line of data rather than the first line of the file.
skip_blank_lines whether to skip over blank lines rather than interpreting them as NaN values
skiprows A collection of numbers for rows in the file to skip. Can also be an integer to skip the first n rows
index_col column number, column name, or list of column numbers/names, to use as the index (row labels) of the resulting DataFrame. By default, it will number the rows without using any column, unless there is one more data column than there are headers, in which case the first column is taken as the index.
names List of column names to use as column names. To replace header existing in file, explicitly pass header=0.


true_values list of strings to recognize as True
false_values list of strings to recognize as False
keep_default_na whether to include the default set of missing values in addition to the ones specified in na_values
parse_dates 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’
keep_date_col if True, then date component columns passed into parse_dates will be retained in the output (False by default).
date_parser function to use to parse strings into datetime objects. If parse_dates is True, it defaults to the very robust dateutil.parser. Specifying this implicitly sets parse_dates as True. You can also use functions from community supported date converters from date_converters.py
.to_datetime() Change data format. (code)
dayfirst if True then uses the DD/MM international/European date format (This is False by default)
thousands specifies the thousands separator. If not None, this character will be stripped from numeric dtypes. However, if it is the first character in a field, that column will be imported as a string. In the PythonParser, if not None, then parser will try to look for it in the output and parse relevant data to numeric dtypes. Because it has to essentially scan through the data again, this causes a significant performance hit so only use if necessary.
lineterminator string (length 1), default None, Character to break file into lines. Only valid with C parser
quotechar string, The character to used to denote the start and end of a quoted item. Quoted items can include the delimiter and it will be ignored.


skipinitialspace boolean, default False, Skip spaces after delimiter
escapechar string, to specify how to escape quoted data

Indicates remainder of line should not be parsed. If found at the beginning of a line, the line will be ignored altogether. This parameter must be a single character. Like empty lines, fully commented lines are ig-
nored by the parameter header but not by skiprows. For example, if comment=’#’, parsing ‘#emptyn1,2,3na,b,c’ with header=0 will result in ‘1,2,3’ being treated as the header.

.value_counts() .value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True). (code) (code)
skip_footer number of lines to skip at bottom of file (default 0) (Unsupported with engine=’c’)
confirm() confirm(text='', title='', buttons=['OK', 'Cancel']) (code)
converters a dictionary of functions for converting values in certain columns, where keys are either integers
or column labels
encoding a string representing the encoding to use for decoding unicode data, e.g. ’utf-8‘ or ’latin-1’. Full list of Python standard encodings
verbose show number of NA values inserted in non-numeric columns
squeeze if True then output with only one column is turned into Series
error_bad_lines if False then any lines causing an error will be skipped bad lines
usecols skip column. Only store the columns which are need so that a subset of columns is returned, resulting in much faster parsing time and lower memory usage. code. code. code. code.
mangle_dupe_cols boolean, default True, then duplicate columns will be specified as ‘X.0’...’X.N’, rather than ‘X’...’X’
tupleize_cols boolean, default False, if False, convert a list of tuples to a multi-index of columns, otherwise, leave the column index as a list of tuples

string, default None. Specifies which converter the C engine should use for floating-point values. The options are None for the ordinary converter, ‘high’ for the high-precision converter, and ‘round_trip’ for the round-trip converter.

path_or_buf A string path to the file to write or a StringIO
sep Field delimiter for the output file (default ”,”)
na_rep A string representation of a missing value (default ‘’)
float_format Format string for floating point numbers
cols Columns to write (default None)
index whether to write row (index) names (default True)

Column label(s) for index column(s) if desired. If None (default), and header and index are True, then the index names are used. (A sequence should be given if the DataFrame uses MultiIndex).

mode Python write mode, default ‘w’
encoding a string representing the encoding to use if the contents are non-ASCII, for python versions prior to 3
line_terminator Character sequence denoting line end (default ‘\n’)
quotechar Character used to quote fields (default ‘”’)
doublequote Control quoting of quotechar in fields (default True)
escapechar Character used to escape sep and quotechar when appropriate (default None)
chunksize Number of rows to write at a time
date_format Format string for datetime objects


lineterminator code.
describe() Print statistic summary of the data. (code)
import pandas as pd code.
skiprows code. code. code.
skipfooter skip rows from bottom. code.
header skip header. code. code. code. code.
engine code.
skiprows = lambda x: code. code. code.
index_col Skip column index. code. code. code.
.fillna() Replace empty cells with anything. (code)
sort_values(by=...) Introduction
drop() (code)(code)
head() (code)
dropna() Allows the user to analyze and drop Rows/Columns with Null values in different ways. code.
reset_index() code.
unique() (code)
nunique() (code)
left_index=, right_index= (code)
classification_report (code)
confusion_matrix (code). (code)
click() .click() function is just a convenient wrapper around these two .mouseDown() and .mouseUp() function calls. click() without parameters gives a single, left-button mouse click at the mouse’s current position; click(x, y) calls moveTo() before the click. 'left', 'middle', and 'right' specify a different mouse button. (code)
Delete all the dictionary's key-value pairs. General.
.config() (code)
continue Introduction
from pptx.dml.color import RGBColor (code)
.color. (code)
math.ceil() The ceiling of a given number is the nearest integer greater than or equal to that number. For example, the ceiling of 4.568 is 5. Code
math.copysign(x, y) Copy sign: The sign of the second argument is returned along with the result on the execution of this function. x: Integer value to be converted, y: Integer whose sign is required. Example code
case_sensitive=True (code)
Integer/fractions/round /decimal/digits/floating
It does not have any fractional part. Introduction. int: Example code.
Complex It can store real and imaginary parts
Cast, or convert a variable from one type to another. Input() always stores a string, even if the value inputted is a number. Casting = temporarily converting a value to another type. There could be loss of precision: i.e. int(1.5) turns it into 1. code.
int() Converts a float number or a string to an integer, cast the number. code1
float() Returns a floating point number constructed from a number or string
str() Introduction. Returns a string which is fairly human readable. code.
"str()" and "," difference (code)
chr() Convert an integer to a string of one character whose ASCII code is same as the integer. Introduction.
complex() Print a complex number with the value real + imag*j or convert a string or number to a complex number
ord() Returns an integer representing Unicode code point for the given Unicode character. code. code
hex() Convert an integer number (of any size) to a lowercase hexadecimal string prefixed with “0x”
oct() Convert an integer number (of any size) to an octal string prefixed with “0o”
.convert() Image.convert(mode=None, matrix=None, dither=None, palette=0, colors=256). Dither: Dithering method, used when converting from mode “RGB” to “P” or from “RGB” or “L” to “1”. Available methods are NONE or FLOYDSTEINBERG (default). code. code.
capitalize() (Code) Returns a copy of the original string and converts the first character of the string to a capital (uppercase) letter while making all other characters in the string lowercase letters. code. code.
os.chdir code
ctypes Code. Code.
ctypes.windll.user32.MessageBoxW Code. Code.
shutil.copyfileobj code.
shutil.copy() (code)
.correlate2d code. code.
Correlate1D() Performs a 1D correlation using Fourier transforms.
Correlate2D Performs a 2D correlation using Fourier transforms and uses ft.fft2d and ft.ifft2d.
Correlate2DF Can be is used in cases where the input and filter are already in Fourier space, and can also be used to finish the correlation computation between the inputs and
filter. Code.
conjugate( ) Code.
ConfigParser Manipulate data and manage user-editable configuration files for an application. The configuration files are organized into sections, and each section can contain name-value pairs for configuration dat
scipy.linalg.circulant Create a circulant matrix.
scipy.linalg.companion Create a companion matrix.
scipy.linalg.convolution_matrix Create a convolution matrix.
linalg.cholesky(a) Cholesky decomposition.
linalg.cond(x[, p]) Compute the condition number of a matrix.
command Introduction. code. code.
cmap=plt.cm.gray code.
__call__/method-wrapper name/type: implementation of the () operator; a.k.a. the callable object protocol
__closure__/tuple name/type: the function closure, i.e. bindings for free variables (often is None)
__code__/code name/type: function metadata and function body compiled into bytecode
skimage.measure.compare_mse(im1, im2) Compute the mean-squared error between two images.
skimage.measure.compare_nrmse(im_true, im_test) Compute the normalized root mean-squared error (NRMSE) between two images.
skimage.measure.compare_psnr(im_true, im_test) Compute the peak signal to noise ratio (PSNR) for an image.
skimage.measure.correct_mesh_orientation(...) Correct orientations of mesh faces.
skimage.measure.CircleModel() Total least squares estimator for 2D circles.
compare_ssim(X, Y[, ...]), or skimage.measure.compare_ssim(X, Y[, ...])
Is a function from scikit-image, a score (structural similarity index between the two input images. Compute the mean structural similarity index between two images. This value can fall into the range [-1, 1] with a value of one being a “perfect match”) and difference image can be calculated. code.
import clipboard (code)

Is a competitor to Hebel. It aims at increasing the flexibility of deep learning models. The three key focus areas of chainer include :
a. Transportation system: The makers of Chainer have consistently shown an inclination towards automatic driving cars and they have been in talks with Toyota Motors about the same.
b. Manufacturing industry: From object recognition to optimization, Chainer has been used effectively for robotics and several machine learning tools.
c. Bio-health care: To deal with the severity of cancer, the makers of Chainer have invested in research of various medical images for early diagnosis of cancer cells.

v2.rectangle(image, start_point, end_point, color of border line, border thickness) border. Compute the bounding box of the contour and then draw the bounding box on an image to represent where the ROI is. code. code. code.
cv2.circle() cv2.circle(image, center_coordinates, radius, color, thickness). (code). code. code.
cv2.EVENT_FLAG_LBUTTON Mouse callback function with a single left click. code.
cv2.EVENT_LBUTTONDOWN Mouse callback function with a single left click. code. (code)
cv2.EVENT_FLAG_MBUTTON Mouse callback function with a single left click. code.
cv2.EVENT_LBUTTONUP Mouse callback function with a single left click. code.
cv2.EVENT_LBUTTONDBLCLK Mouse callback function with double left clicks. code.
cv2.EVENT_RBUTTONDOWN Mouse callback function with a single right click. code.
cv2.EVENT_RBUTTONUP Mouse callback function with a single right click. code.
cv2.EVENT_FLAG_RBUTTON Mouse callback function with a single right click. code.
cv2.EVENT_FLAG_CTRLKEY Mouse callback function with double right clicks. code.
cv2.EVENT_MBUTTONDOWN Mouse callback function with the single middle mouse click. code.
cv2.EVENT_MBUTTONUP Mouse callback function with the single middle mouse click. code.
cv2.EVENT_MOUSEMOVE Mouse move. code.
cv2.matchTemplate Introduction. Returns a correlation map, essentially a grayscale image. Other than contour filtering, matching keypoints, contour detection and processing (with thresholding, edge detection, etc. to generate a binary image), template matching is arguably one of the most simple forms of object detection (only 2-3 lines of code), which can detect multiple instances of the same/similar object in an input image. This method quickly fails when there are unknown changes of rotation, scale, viewing angle, etc. In those cases, you should use dedicated object detectors including HOG + Linear SVM, Faster R-CNN, SSDs, YOLO, etc. code. code. code. code. code.
Limitations: The matching can fail (if there is no special treatments in the script) if the size of the template is substantially smaller than the feature in the image being searched.
cv2.Canny()/Canny filter Introduction. Canny Edge Detection is a popular edge detection algorithm. This detection is susceptible to noise in the image, so that first step is to remove the noise in the image with a 5x5 Gaussian filter. Code. code. code.
The third parameter here is the method used for matching. code, code. code.
cv2.add code.
cv2.resize Resizing does only change the width and height of the image. code.
cv2.waitKey(0) Will display the window infinitely until any keypress (it is suitable for image display). Therefore, if you use waitKey(0) you see a still image until you actually press something. code. code. code. code.
cv2.setMouseCallback Mouse clicks: introduction. code. code.
cv2.destroyAllWindows() Simply destroys all the windows we created. code. code.
cv2.destroyWindow() To destroy any specific window with the exact window name.
cv2.imshow A method is used to display an image in a window. The window automatically fits to the image size. First argument is a window name which is a string. Second argument is our image. code. code. Code.
cv2.cvtColor() Is used to convert an image from one color space to another. code.
cv2.COLOR_BGR2GRAY code.
cv2.THRESH_BINARY If pixel intensity is greater than the set threshold, value set to 255, else set to 0 (black). code. code
cv2.THRESH_BINARY_INV Inverted or Opposite case of cv2.THRESH_BINARY. code. code
cv2.line() Draw a line in a image. code
cv2.arrowedLine() cv2.arrowedLine(image, start_point, end_point, color[, thickness[, line_type[, shift[, tipLength]]]]) is used to draw arrow segment pointing from the start point to the end point. The parameters of the cv2.arrowedLine function are the same as those for cv2.line. code.

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)

cv2.namedWindow code.
cv2.moveWindow Set the position (coordinates) of the opened window. code.
cv.THRESH_TRUNC If pixel intensity value is greater than threshold, it is truncated to the threshold. The pixel values are set to be the same as the threshold. All other values remain the same. code. code
cv.THRESH_TOZERO Pixel intensity is set to 0, for all the pixels intensity, less than the threshold value. code. code
cv.THRESH_TOZERO_INV Inverted or Opposite case of cv2.THRESH_TOZERO. code. code
v2.rectangle(image, start_point, end_point, color of border line, border thickness) border. Compute the bounding box of the contour and then draw the bounding box on an image to represent where the ROI is. code. code. code.
Get dimensions (sizes) of image:
dimensions = img.shape
Get height, width, number of channels in image
height = img.shape[0]
width = img.shape[1]
channels = img.shape[2]
General, code. code. code. code. code. code.
Split a list into columns Introduction

cmaps['Perceptually Uniform Sequential'] = ['viridis', 'plasma', 'inferno', 'magma', 'cividis']
cmaps['Sequential'] = ['Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds', 'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu', 'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn']
cmaps['Sequential (2)'] = ['binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink', 'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia', 'hot', 'afmhot', 'gist_heat', 'copper']
cmaps['Diverging'] = [ 'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu', 'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic']
cmaps['Cyclic'] = ['twilight', 'twilight_shifted', 'hsv']
cmaps['Qualitative'] = ['Pastel1', 'Pastel2', 'Paired', 'Accent', 'Dark2', 'Set1', 'Set2', 'Set3',
'tab10', 'tab20', 'tab20b', 'tab20c']
cmaps['Miscellaneous'] = ['flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern', 'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg', 'gist_rainbow', 'rainbow', 'jet', 'turbo', 'nipy_spectral', 'gist_ncar']. code

matplotlib.cbook (code) (code)
va='center' code.
Caffe2 Is a Lightweight, Modular, and Scalable Deep Learning Framework. It aims to provide an easy and straightforward way for you to experiment with deep learning.
random.choice() Randomly select from options. (code)

turtle.circle(radius, extent=None, steps=None). radius – a number; extent – a number (or None); steps – an integer (or None). (code)

.color() (code)
Collections of geometric shape (code)
GetKeyState(VK_CAPITAL) Caps Lock, .press("capslock"). (code).
from pynput.mouse import Controller (code)
.click(Button.left, x) x clicks of mouse. (code)
.locateCenterOnScreen() x, y = MySearch_img to get the x- and y-coordinates of centers of the feature. (code)
ctrlleft Introduction
ctrlright Introduction
ctrl Introduction
from selenium.webdriver.common.keys import Keys (code)
from selenium.webdriver.common.by import By (code)
.center() (code).
hotkey('c') Introduction
Type capital letters Introduction
.getAllTitles() Get all the Python program windows, *IDLE Shell window, e.g. *IDLE Shell 3.9.5, the most front window on Dreamweaver, the most front webpage on each Chrome window, the name of each opened applications, e.g. DigitalMicrograph. Introduction
confidence= (code), (code), (code).
.configure() (code)
.copy() Copy text. (code)
CountVectorizer() Introduction. It is the bag of words technique, which means counting how many times each word appears and puts them into a vector.
cross_val_score (code).
from collections import defaultdict (code).
CBW model (for training) (code)
driver.current_window_handle <Instruction>
Cm Inches, Emu, Cm, Mm, Pt, and Px are base class for length classes, providing properties for converting length values to convenient units.
.pixelMatchesColor() Introduction
from pptx.chart.data import ChartData: ChartData() (code)
.categories (code)
.chart (code)

The circle() function in plotting module of bokeh library is used to Configure and add Circle glyphs to a figure.
Syntax: circle(x, y, *, angle=0.0, angle_units=’rad’, fill_alpha=1.0, fill_color=’gray’, line_alpha=1.0, line_cap=’butt’, line_color=’black’, line_dash=[], line_dash_offset=0, line_join=’bevel’, line_width=1, name=None, radius=None, radius_dimension=’x’, radius_units=’data’, size=4, tags=[], **kwargs)
Parameters: This method accept the following parameters that are described below:
x: This parameter is the x-coordinates for the center of the markers.
y: This parameter is the y-coordinates for the center of the markers.
angle: This parameter is the angles to rotate the markers.
fill_alpha: This parameter is the fill alpha values for the markers.
fill_color: This parameter is the fill color values for the markers.
line_alpha: This parameter is the line alpha values for the markers with default value of 1.0 .
line_cap: This parameter is the line cap values for the markers with default value of butt.
line_color: This parameter is the line color values for the markers with default value of black.
line_dash: This parameter is the line dash values for the markers with default value of [].
line_dash_offset: This parameter is the line dash offset values for the markers with default value of 0.
line_join: This parameter is the line join values for the markers with default value of bevel.
line_width: This parameter is the line width values for the markers with default value of 1.
mode: This parameter can be one of three values : [“before”, “after”, “center”].
name: This parameter is the user-supplied name for this model.
tags: This parameter is the user-supplied values for this model.
radius: This parameter is the radius values for circle markers .
radius_dimension: This parameter is the dimension to measure circle radii along.
size: This parameter is the size (diameter) values for the markers in screen space units.
alpha: This parameter is used to set all alpha keyword arguments at once.
color: This parameter is used to to set all color keyword arguments at once.
legend_field: This parameter is the name of a column in the data source that should be used or the grouping.
legend_group: This parameter is the name of a column in the data source that should be used or the grouping.
legend_label: This parameter is the legend entry is labeled with exactly the text supplied here.
muted: This parameter contains the bool value.
name: This parameter is the optional user-supplied name to attach to the renderer.
source: This parameter is the user-supplied data source.
view: This parameter is the view for filtering the data source.
visible: This parameter contains the bool value.
x_range_name: This parameter is the name of an extra range to use for mapping x-coordinates.
y_range_name: This parameter is the name of an extra range to use for mapping y-coordinates.
level: This parameter specify the render level order for this glyph.

plt.colorbar() (code)
sklearn.cluster.KMeans() Introduction
.DataFrame() Introduction. .drop(), index, columns, axes, dtypes, size, shape, ndim, empty, T (swap between column and row), values
Image overlap. Code. copy method
CNTK Introduction
Crowd’s error Introduction
cosine similarity/distance Introduction
Add padding/black/colored edge to images Introduction
Save the text in clipboard to a txt file Introduction
Common values in two pandas series Introduction
Save contents in the webpages obtained by Google search into a text file Introduction
Color in table obtained by matplotlib.pyplot/change background color of cells in table Introduction
Locate/find the center/coordinates of a bright (maximum/highest intensity) spot in an image Introduction
Median blurring and cv2.medianBlur() Introduction
Three dimensional (3D) shapes/structures (e.g. cylinder) Introduction
Graphlab Create Introduction
Categorical variables Introduction
Save the image in clipboard to an image file Introduction
Compile model Introduction
Colab Introduction
Categorical features preprocessing layers Introduction
GPUs/CPUs Introduction
googlecoursera/console.cloud.google Introduction
Categorical bins Introduction
tf.constant() Introduction
find_element(CSS_SELECTOR, " ") Introduction
tf.feature_column.categorical_column_with_identity Introduction
Convert a number type to another Introduction
Typical training setup in AI and comparisons of different training libraries Introduction
Comparison between Python and C/C++ Introduction
Data structures (Data science, and comparison between list, tuple, set, dictionary) Introduction
Comparison between steps and epochs in TensorFlow Introduction
Comparison between iteration algorithm and recursive algorithm: a function repeat itself Introduction
Comparison of qualifications and skills between data science manager, engineering and scientist Introduction
Comparison between machine learning and human beings Introduction
Comparison between strings code
Comparison with classical wafer map inspection algorithms Introduction
Speed comparison with and without numpy Introduction
Comparison between =, ==, .copy() and copy.copy() for "list": changes of "list" Introduction
Convert capital alphabet letters/characters to number Introduction
Extract three blue, green, red images from a color image or grey image, or convert RGB (color) image into three blue, green, red images code
Convert jpg format to tif format code
Print and set file path as a variable (e.g. convert all characters in the pathname to lowercase) Introduction
Convert a text file to a string Introduction
Find and convert the file time/date and compare with the current time Introduction
Libraries used to convert incident documents into numerical vectors Introduction
Convert PDF file to text file Introduction
Put most code into a function or class code
Use __name__ to control execution of the code code

Match on images to find and to highlight unsimilar (threshold=0) to identical (threshold=1) regions of an image that match a template with a cross-correlation method

Holidays/Festivals/Vocations (Thanksgiving/Christmas) Introduction
Call and then run your own functions and modules in different/other Python files Introduction
Automatically review, scroll, click webpage and its link Introduction
Positions and colors of mouse/cursor and features Introduction
while True, e.g. with the "while True" loop, e,g. it constantly/continuously refreshes actions. Introduction
Coordinate at center of features, e.g. images Introduction
Skip, remove, extract, use specific columns Introduction
Automation of mouse movements and clicks Introduction
Create a new presentation Introduction
Move the cursor/mouse to the found, similar spots one-by-one Introduction
Table of PC/computer/Windows shortcut hotkeys Introduction
Table of Chrome shortcut hotkeys Introduction
Selection between choices or options Introduction
Copy and apply formatting in Word and PowerPoint Introduction
Work (read, write, insert and delete rows and columns, and merge and unmerge cells, shift/move cell values) in Excel sheets Introduction
Calculation in an Excel Sheet, Style, Bold, and Color Introduction
Move the mouse/cursor to the left or right Introduction
Get the name of the current/most front window Introduction
Bind/link/combined multiple commands to buttons Introduction
stopwatch and timing/countering a process Introduction
Copy and then store it into memory and it can be pasted for use later (multiple clipboard) Introduction
Copy text to Clipboard Introduction
Courses/classes for Computer Science Introduction
Text classification/sort/prediction, train/test e.g. Youtube spam Introduction
Print colored text in Python IDLE's terminal Introduction
Copy text into clipboard and then use it immediately (one time clipboard) Introduction
matplotlib.pyplot axis/text color (xticks, rotation, xlabel, ylabel, title, fontsize, grid(), legend(), show()) Introduction
Template matching: a technique for finding areas of an image that are similar to a patch (template). Patch are small images with a certain feature. The goal of template matching is to find and/or highlight the patch/template in an image.
Image matching with cross correlation and overlap of template edge. In this matching process, Normalized cross-correlation with those edge images is performed. code.
Cross correlation between two images code
Cross correlation between two images in any sizes. Multiscaling is used to avoid the issue caused by the different sizes of the template and original image, in order to find match in a original image, namely, the size of template is larger than the original image. code
watchdog to look for filesystem changes Introduction
Skip, remove, extract, use specific columns Introduction
Copy methods: b = [*a]; c = a * 1; d = a[:]; "e = []; e.extend(a)"; f = a[0:len(a)]; *g, = a; h = list(a); i = [z for z in a]; "j = [] for item in a: j.append(item)"; a.copy(); "import copy l = copy.copy(a)". code. code.
Calibrate and put a scale bar, and draw a line segment on an image Introduction
Matrix conversion to image image.
Calculator of length accuracy in 3D structure 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
Merge/combine two text files into a new text file, add a new line to the beginning of a text file Introduction
Create an executable (.exe) file from a Python script Introduction
Mixing of using numbers and strings by conversions Introduction
Calculators 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
Calculations in DataFrame: Add a column, calculate for a new column, delete a column, all the rows with values greater than 30 in "Score A" column Introduction
Calculations in DataFrame: Add a column, calculate for a new column, delete a column, all the rows with values greater than 30 in "Score A" column Introduction
Pint summary of the statistic data, change data format, sort/group columns Introduction
Handle NaN value in DataFrame, replace empty cells with ...
Copy text into clipboard and then you can paste it a webpage, text/txt, word or powerpoint file automatically (code)
Count the numbers of uppercase letters, lowercase letters and spaces in a string and then swap the cases of the letters.
Count the times of repeated excutions code
Markers (e.g. color cross, scatter, and circles) at specific coordinates with x- and y-axis Matplotlib
Find a similar feature and then click it Introduction
Computing equations and formulas with Python Examples. Numerical integration at code.
Prevent other applications to modify the content until other Python script runs code.
Choose a file with simple dialog. Code. code. code. With a default folder: code1 and code2. .
Open webpages in internet browsers (Chrome, Microsoft edge, IE browser) Introduction
Open an image to file from URL (webpage), then it can be saved in PC (computer) code, code. open with color changed.
Copy images to a different folder/Save an image in a new folder code, code. code. code.
Set the output image to zero everywhere except my mask (color filter), and display red, green, and blue (RGB) channels of an image. code, code.
Remove letters or characters on either side (both left and right sides) and stops when neither such letters no characters on either side code
Creates an image from an colored images after remove the grey components (image conversion from color to gray involved) code
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

Create images with global, adaptive mean, adaptive Gaussian, binary, trunc, Tozero, and tozero thresholds.

Load/launch images and ColorMixing in DigitalMicrograph Introduction
Automation of mouse movements and clicks, and keyboard control Introduction
dropdown box/option/selection/choice Introduction
Add a new slide into an existing ppt or a created ppt file Introduction
Draw lines, elbow connectors and arrows in a ppt Introduction
Find the color of a pixel on the screen Introduction
Plot curve/chart in pptx Introduction
Count and delete slides from ppt Introduction
Infinite loops (e.g. stop infinite cycling of opening the same images) Introduction
Get maximum and minimum value of column and its index Introduction
Find a specific word in a webpage and count occurrences Introduction
Web Scraping (save contents from a webpage) Introduction
Copy a file to save to somewhere 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
Read its nth character in a text file  Introduction
Circular dependencies in Python execution Introduction
Launch script from another script using subprocess.run/subprocess.call Introduction
Monitor multiple changed of folder and files Introduction
Monitor the current folder Introduction
Find files with a specific file extension/type or with file names ending with specific characters Introduction
Move/copy all files from original folder in a directory to a new directory Introduction
top and left 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
Invert the contrasts of black and white images (Code)
watchdog with conditioning break Introduction
Count how many (number) files and folders in a directory Introduction
Find contours in an image and their areas and coordinates Introduction
Take a screenshot using a mouse click and drag method Introduction
Global access to a local variable inside a function/class from outside of the function/class externally
Calculation with combinations of variables from lists
Get mouse position/coordinates on click Introduction
Measure length/distance on an image w/o calibrated bar Introduction
Get pixel location/coordinates on an image using mouse click/events Introduction
Crop/snip (without opening the image) part of a image with definition by a pixel line (with transparent added portion) Introduction
Change/swap values in a list Introduction
Modify file path/directory by changing folder names by merging a list Introduction
Merge/combine two pptx files into one Introduction
Plot distance between points calculated by coordinates Introduction
Break/exit/skip a function/code line after a certain time Introduction
Classification and algorithms for classification Introduction
Binary classifiers
Clustering Introduction
Count occurrence/nubmer of words/phrase in a text file Introduction
Remove/repace (part) character(s) from string Introduction
Change/capitalize the case of the first letter of a string Introduction
Write/save content to a text file Introduction
Generate text file with the bank of collecting all words, characters and strings from news Introduction
Critical thinking in data science Introduction
Support-vector clustering (SVC) Introduction
Model-based clustering Introduction
Nearest-neighbor (NN) cluster removal Introduction
K-Means clustering for images Introduction
Image segmentation ("clustering") in color Introduction
Clustering of Laplacian Introduction
Mask an image with a threshold or with a color as a threshold Introduction
Convolutional neural networks (CNN) Introduction
Wafer map failure pattern recognition (WMFPR)/wafer failure pattern detection (WFPD)/defect classification Introduction
Convert images between Cartesian and Polar forms Introduction
Draw circles/lines on images Introduction
Detection and classification of defective dies/chips in wafer map
Convolutional Autoencoder (CAE) Introduction
Plot a list of x, y coordinates to an image Introduction
Store images in pandas dataframe column Introduction
Write contents of DataFrame/memory into text file Introduction
Convert DataFrame to a HTML Table and save as a HTML webpage Introduction