Electron microscopy
Python Automation and Machine Learning for ICs: Chapter F
- 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



Filters (Kernels) in ML Introduction
K-means clustering and PCA for failure analysis Introduction
Four/five V of big data Introduction
Apache Flink Introduction
Facets Overview Introduction
Trade-offs between fairness and performance Introduction
Principles of ethical and responsible ML (selection bias, confirmation bias, automation bias, model fairness) Introduction
Feature engineering in semiconductor manufacturing ML Introduction
FastText for ML Introduction
HDFS (Hadoop Distributed File System) Introduction
Ingesting data in Hadoop (Sqoop, Flume, Kafka, NiFi) Introduction
Context-free grammar (CFG) Introduction
Formal grammar Introduction
Feedforward neural network Introduction
tf.keras.datasets (e.g. MNIST, CIFAR-10, CIFAR-100, Fashion MNIST) Introduction
Guess and check algorithm with a combination of a for loop and an if statement Introduction
Function approximation Introduction
Fit and Smooth Plotted Curves Introduction
Precision, Recall, False Positive Rate, and False Negative Rate (Miss Rate or False Negative Proportion) Introduction
First-order logic (FOL) Introduction
Analyzing the impact of fabrication conditions on semiconductor wafer fail rates Introduction
Feature engineering Introduction
Breadth-First Search (BFS) Introduction
Depth-first search (DFS) Introduction
Cost/loss function versus reward function Introduction
Optimal value function in Markov Decision Process (MDP) Introduction
Finite-horizon MDP (Markov Decision Process) Introduction
Q-Learning with Function Approximation (Deep Q-Network - DQN) Introduction
Model-Free RL and Model-based RL (reinforcement learning) Introduction
State transition function (probability) in reinforcement learning Introduction
Locate/find the center of a bright (maximum/highest intensity) spot in an image Introduction
Machine learning example step-by-step (wafer fail analysis) Introduction
Find nearest white pixel to a given pixel location on an binary image Introduction
Extract the least/most frequency/duplicate/occurrence element in a list Introduction
Functional API to create a Keras model with TensorFlow Introduction
Find duplicate items in a list Introduction
Draw smiling face emoji Introduction
Machine learning for few things Introduction
Least squares fit Introduction
Convert strings to number (integers/float) Introduction
.find_element() Introduction
find_element(By.XPATH, " ") Introduction
find_element(CSS_SELECTOR, " ") Introduction
tf.function Introduction
Try a number of times before exception or fail Introduction
Formats of datasets for classification Introduction
Put most code into a function or class code
Create a function called main() to contain the code you want to run code
Positions and colors of mouse/cursor and features Introduction
Change the font size for selected text Introduction
Launch Replace menu from Find menu Introduction
Move the cursor/mouse to the found, similar spots one-by-one Introduction
Copy and apply formatting in Word and PowerPoint Introduction
Find the values of the keys Introduction
Bring/activate an application/window to most front/foreground Introduction
Bind Python functions and methods to events (similar to if loops) Introduction
Term Frequency-Inverse Document Frequency (TF-IDF) Introduction
Find/predict the best word similarity, e.g. car Introduction
Immediately invoked function expression code
Swap the order of arguments in a function Introduction
Access variable inside and outside a function 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 frames Introduction
Find a similar feature and then click it Introduction
Find minimum and maximum values in a list Introduction
Computing equations and formulas with Python Examples. Numerical integration at 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.
Convert jpg format to tif format code
Find edges of images code, Introduction
Find the greatest of three numbers code1, code2, code3
Get the list of the methods for a function Introduction
Find elements on a webpage Introduction
Stability/reliability of find_element(By.xxx, "") Introduction
Text Format in pptx Introduction
Find latitude and longitude of a place in a map Introduction
Find a specific word in a webpage and count occurrences Introduction
matplotlib.pyplot axis/text color (xticks, rotation, xlabel, ylabel, title, fontsize, grid(), legend(), show()) Introduction
Find common elements/items between two lists Introduction
Fill in closed curves Introduction
Find contours in an image and their areas and coordinates Introduction
Global access to a variable inside a function from outside of the function Introduction
Search/extract/find text on an image Introduction
Break/exit/skip a function/code line after a certain time Introduction
Change/capitalize the case of the first letter of a string Introduction
Patterns on wafers commonly reflecting specific process failure information Introduction
Fault analysis/PFA (Physical Failure Analysis) time and efficiency Introduction
Wafer map failure pattern recognition (WMFPR) and similarity ranking (SR) Introduction
Histogram for wafer analysis (e.g. percentage and frequency of grey level in the image) Introduction
Laplacian filtering Introduction
Canny filter Introduction
Few-shot learning Introduction
Pass variables between functions/from one to another Introduction
Methods of physical failure analysis (PFA) of ICs Introduction
Cost (expense) and speed (fastest and slowest) of computation in ML Introduction
Cumulative Distribution Function (CDF) Introduction
Mixture of Gaussians (MoG) versus Factor Analysis (FA) Introduction
Factor Analysis Model Introduction
Isolation forest algorithm Introduction
ML example: face recognition algorithm Introduction
Finding a correct loss (risk, objective) function for a specific problem Introduction
Number (size) of features in ML Introduction
(Forward and backward) propagation equations Introduction
Holidays/Festivals/Vocations (Thanksgiving/Christmas) Introduction
Finance analysis Introduction
True Function Introduction
Deterministic function Introduction
No Free Lunch Theorems Introduction
Frequentist approach versus Bayesian approach Introduction
Kernel tricks and kernel function Introduction
Mathematical equations, formulas and inequalities used in machine learning Introduction
Geometric Margin versus Functional Margin Introduction
Functional margin in ML Introduction
Feature vector and number of features Introduction
Fully Connected Layers (FC) in Deep Learning Introduction
Indicator function Introduction
Parameters, features and examples in ML Introduction
Canonical response function/canonical link function Introduction
Logistic function/sigmoid function Introduction
Latent features and latent variables Introduction
Comparison between mean squared error (MSE), absolute error (L1 Loss) and fourth-power loss
Exponential Family: Parameter, Sufficient Statistic, Natural Parameter, Base Measure and Log-Partition Function (Bernoulli distribution and Gaussian distribution) Introduction
Probability density function (PDF): comparisons between (normal (gaussian) distribution, uniform distribution, exponential distribution and poisson distribution)
Input data (feature) in ML Introduction
Feature analysis/feature importance analysis/weight of feature Introduction
Feature importance for Multinomial Naive Bayes algorithm Introduction
Feature extractions from wafers Introduction
  Feature extraction using radon transform Introduction
Categorical features preprocessing layers Introduction
feature_extraction.text (code). (code). (code)
Feature ingestion Introduction
Vertex AI Feature Store Introduction
  tf.feature_column.bucketized_column Introduction
  tf.feature_column.categorical_column_with_identity Introduction
Feature and feature vector/Featurization Introduction
Outlier of feature (code)
Feature selection Introduction
  Forward Search method of feature selection Introduction
  Feature selection: removing unnecessary (constant & quasi constant) features Introduction
  Feature selection: removing multicollinearity Introduction
  Feature selection: Univariate feature selection Introduction
  Feature Selection: Chi Square to select dependent and independent variables Introduction
Convex optimization, convexity of loss functions, convex functions and convex sets Introduction
Loss (risk, cost, objective) function Introduction
Finite Hypothesis Class versus Infinite Hypothesis Class Introduction
Natural Language Processing (NLP) approaches in addressing the Failure Analysis (FA) search problem Introduction
Percentages of information received through different senses (eye, nose, ear and hand feeling) Introduction
Variance Inflation Factors (VIFs) Introduction
Hypothesis class/hypothesis family/predictor class/model class/hypothesis family/predictor family/model family (h) Introduction
Parameterized family and model parameter Introduction
Brute force discretization Introduction
Finite Hypothesis Class/finite Hypothesis Analysis Introduction
Combine multiple images into a single multi-page/frame image or vice versa (split a single multi-page image to multiple images) Introduction
Read outlook messages in .msg format Introduction
Summary/templates of plotting graphs/figures Introduction
Fréchet Inception Distance (FID) coefficient Introduction
Format strings Introduction
Send a variable from one script (back) to another script with a function Introduction
Extract the first or last N letters from a string Introduction
String template class for formating strings (F-strings (for calculation) (f"{}"), format() method ({}), %s, %d, Template ($)) Introduction
Remove/reload/unload an imported module/function/script Introduction
Plot multiple images on the same figure by hiding x- and y-labels Introduction
Insert paragraphs of texts into Python script (f"""/f''') Introduction
Automatically restart script execution after it breaks/fails/error Introduction
Count the number of the pages in a single multi-page/frame image Introduction
Random (Bootstrap) Forests Introduction
K-Fold Cross-Validation Introduction
Fairness Analysis and Python Libraries Introduction
Summary/templates/examples of pptx and PowerPoint format Introduction
Continue script execution no matter whether some try fails or not (finally)
Calculating the area fraction of each circle overlapped by a square grid and build wafer map Introduction
Get the frequency of occurrence of a string in a column DataFrame Introduction
Trick: return True and return False Introduction
Get the current directory/folder path Introduction
Count how many (number) files and folders in a directory 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 (layer by layer) from its directory/path Introduction
Monitor multiple changed of folder and files Introduction
Monitor the current folder Introduction
Move(remove) all files from original folder in a directory to a new directory Introduction
Delete the entire directory and/or all the files in the directory/folder 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
Randomly open an image/file in a specific folder Introduction
Work with (e.g. open) all/every files and subfolders/subdirectory in a folder (does not include any files or include all files in subfolders) Introduction
Open folders and explorer Introduction
Copy images to a different folder/Save an image in a new folder code, code. code. code.
Select/input a folder/directory/path for later to be called to use Introduction
Folder for saving images from screenshot obtained by Snipping Tool on windows Introduction
Get/list immediate subdirectories/subfolders Introduction
Create a temporary file or directory/folder Introduction
Cheatsheet of folder and file Introduction
Time and date used as a file/folder name stamp (e.g. duplicate a file in the same folder) Introduction
Modify file path/directory by changing folder names by merging a list Introduction
Check/find/get a file name/all file names or the last folder name (e.g. from a path/directory) Introduction
Check 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
Close file after reading a file: avoid file locking Introduction
With a default folder code1 and code2
Delete a single file Introduction
Creat a file name, folder name. {}{}....format. Manipulation of file and folder names (rename file name and folder name): i) 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
Get the latest/newest/most recent file in a folder Introduction
Collect the file list in a folder into a csv file Introduction
Check updated new files in a folder Introduction
(Find) file size/find the largest file in a directory/folder Introduction
Write special/certain rows (row-by-row) of one csv file to another csv file Introduction
FileExistsError Introduction
Check existence of phrase on text file line-by-line Introduction
Check if a file exists again (double check) Introduction
Remove duplicate cell values from a csv file/dataframe (keeping the first/top one) Introduction
Create a Batch File to Run a Python Script Introduction
Read a frequently updated file periodically (similar to watchdog) Introduction
Read a frequently updated file periodically (similar to watchdog) Introduction
Plot graph/figure/image from CSV file Introduction
Plot graph/figure/image from CSV file/DataFrame by removing/hiding blank/empty cells with axis range (plt.xlim()) Introduction
Plot data into the same graph/figure/image from different csv files Introduction
Plot data into the same graph/figure/image from different csv files Introduction
Compare/check if two text files have the same contents Introduction
Compute the similarity between two text documents/files Introduction
Print the files and keyword occurrence which have been searched from a ppt file Introduction
Extract text/check specific text from multiple powerpoint/pptx files Introduction
Merge/combine two pptx files into one Introduction
Count number of lines in a text file Introduction
Count occurrence/nubmer of words/phrase in a text file Introduction
Convert a text file to a string Introduction
Convert PDF file to text file Introduction
Write/save content to a text file/append a string into a text file. Introduction
Extract pdf pages to form new pdf files Introduction
Generate text file with the bank of collecting all words, characters and strings from news Introduction
Remove \n in string or new line in txt/text file Introduction
Find and remove duplicate/same lines in a text file Introduction
Add/insert a column into an existing csv file Introduction
Copy a file or all files (with os.mkdir) to save to somewhere (create a directory first if it does not exist) Introduction
Monitor specific new files and execute the file Introduction
Watchdog for monitoring specific file or files with specific extension, and then run another file from watchdog Introduction
Find and convert the file time/date and compare with the current time Introduction
Check if both files are the same file, e.g. symbolic link, shortcut Introduction
Check whether a file is empty or not Introduction
Run multiple Python files/scripts one after another Introduction
Create CSV files (e.g. with headers only) Introduction
Remove duplicate cell values from a csv file CSV: Introduction
Upload files to webpages Introduction
Open and close specific files Introduction
Move file(s) from one directory to another Introduction
Watchdog for monitoring specific file or files with specific extension Introduction
Simple ways to execute another python file when a new file has been uploaded Introduction
List all files and directories which has specific files or files with specific extensions Introduction
Sort a text file Introduction
Call and then run your own functions and modules in different/other Python files; Python run another Python script Introduction
Keyword search function/check whether or not a string is within another string (a space is included as a string character) Introduction
Merge/combine two text files into a new text file, add a new line to the beginning of a text file Introduction
watchdog to look for filesystem changes Introduction
Launch file menu Introduction
Save files Introduction
Create an executable (.exe) file from a Python script Introduction
Set default programs by file extensions and by file types and programs on Windows Introduction
Open any files, e.g. text (.txt), image files, and so on. .txt file will be opened in Python program, e.g. IDLE shell code
Open an image to file from URL (webpage), then it can be saved in PC code, code. open with color changed
Choose a file with simple dialog Code. code. code
Copy text into clipboard and then you can paste it a webpage, text/txt, word or powerpoint file automatically code
Save the image in clipboard to an image file Introduction
Save the text in clipboard to a txt file Introduction
Save contents (download pdf files) in the webpages obtained by Google search into a text file 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.  
DataFrame.from_csv(): from_csv(path, header=0, sep=', ', index_col=0, parse_dates=True, encoding=None, tupleize_cols=False, infer_datetime_format=False): Read CSV file. It is preferable to use the more powerful pandas.read_csv() for most general purposes, but from_csv makes for an easy roundtrip to and from a file, especially with a DataFrame of time series data.  
class watchdog.events.FileSystemEvent(src_path): Immutable type that represents a file system event that is triggered when a change occurs on the monitored file system. event_type = None :: The type of the event as a string. is_directory = False :: True if event was emitted for a directory; False otherwise. src_path :: Source path of the file system object that triggered this event.  
g.fileopenbox Code
filepath_or_buffer: Either a string path to a file, URL (including http, ftp, and S3 locations), or any object with a read method (such as an open file or StringIO).  

class watchdog.events.FileSystemMovedEvent(src_path, dest_path): watchdog.events.FileSystemEvent :: File system event representing any kind of file system movement. dest_path :: The destination path of the move event.


Fabric: Is a command-line tool for streamlining the use of SSH for application deployment or systems administration tasks. With it, you can execute local or remote shell commands, upload/download files, and even prompt running user for input, or abort execution.

FileSystemEventHandler. (code).  
class watchdog.events.FileMovedEvent(src_path, dest_path): Bases: watchdog.events.FileSystemMovedEvent :: File system event representing file movement on the file system.  
class watchdog.events.FileModifiedEvent(src_path): Bases: watchdog.events.FileSystemEvent :: File system event representing file modification on the file system.  
class watchdog.events.FileCreatedEvent(src_path): Bases: watchdog.events.FileSystemEvent :: File system event representing file creation on the file system.  
class watchdog.events.FileDeletedEvent(src_path): Bases: watchdog.events.FileSystemEvent :: File system event representing file deletion on the file system.  
class watchdog.events.FileSystemEventHandler: Base file system event handler that you can override methods from.  
watchdog.events.FileSystemEventHandler: Matches given patterns with file paths associated with occurring events.  
watchdog.events.FileSystemEventHandler: Matches given regexes with file paths associated with occurring events.  
files_created: List of files that were created.  
files_deleted: List of files that were deleted.  
files_modified: List of files that were modified.  
import fileinput code
Automatically run a file in an application Introduction
PermissionError (E.g. file cannot be written when it is open/locked) Introduction
FileNotFoundError Introduction
File names  
Get directory/path/file name partially Introduction
Print and set file path as a variable (e.g. convert all characters in the pathname to lowercase) Introduction
Find/search birthyear by name . code. Check file existence with partial filename Introduction
Find files with a specific file extension/type or with file names ending with specific characters Introduction
Find the file names of the images in a pptx, (and then save/extract the image as a file) Introduction
split(): Split a string by dots, split a file name by dots, split a file name from its extension. code. (code). Introduction
from tkinter.filedialog import askopenfilename code. code
filename (format) code
Get the name of the current/most front window Introduction
FindWindow(): FindWindow(className, windowName ): Searches for the specified top-level window. Parameters: className is a string, which is the window class name to find, else None; The windowName is also a string, which is the window name (ie, title) to find, else None Introduction
Truth (True) and False Table Introduction
Change date/month/year format Introduction
Check if a string can be converted to float 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
.find(sub[start, [end]]): find the index. code. "-1" is given if the letter is not found. Introduction
FloatingPointError Introduction
find_element_by_id(): Use this when the id attribute of the element known, e.g.:
      <form id="loginForm">
      <input name="username" type="text" />
      <input name="password" type="password" />
      <input name="Submit" type="Submit" value="Login" />
Then, the form element can be:
      MyLogin = driver.find_element_by_id('loginForm')
find_element_by_name(): Use this when the name attribute of the element is known, e.g.:
      <form id="loginForm">
      <input name="username" type="text" />
      <input name="password" type="password" />
      <input name="continue" type="submit" value="Login" />
      <input name="continue" type="button" value="Clear" />
Then, the username & password elements can be:
      username = driver.find_element_by_name('username')
      password = driver.find_element_by_name('password')
      continue = driver.find_element_by_name('continue')
find_element_by_xpath(): XPath is the language used for locating nodes in an XML document. One of the main reasons of using XPath is when you don’t have a suitable id or name attribute for the element.
      <form id="loginForm">
      <input name="username" type="text" />
      <input name="password" type="password" />
      <input name="continue" type="submit" value="Login" />
      <input name="continue" type="button" value="Clear" />
The first form elements can like this:
      login_form = driver.find_element_by_xpath("/html/body/form[1]")
      login_form = driver.find_element_by_xpath("//form[1]")
      login_form = driver.find_element_by_xpath("//form[@id='loginForm']")
The second element (username) can be like this:
      username = driver.find_element_by_xpath("//form[input/@name='username']")
      username = driver.find_element_by_xpath("//form[@id='loginForm']/input[1]")
      username = driver.find_element_by_xpath("//input[@name='username']")
The third element (Clear Button) can be like this:
      clear_button = driver.find_element_by_xpath("//input[@name='continue'][@type='button']")
      clear_button = driver.find_element_by_xpath("//form[@id='loginForm']/input[4]")
find_element_by_tag_name()Use this, when you want to locate an element by tag name, e.g.:
      <p>Site content goes here.</p>
The heading (h1) element can be like this:
      heading1 = driver.find_element_by_tag_name('h1')
find_element_by_class_name(): Use this, when you want to locate an element by class name, e.g.:
      <p class="content">Site content goes here.</p>
The “p” element can be like this:
      content = driver.find_element_by_class_name('content')
files_moved: Each event is a two-tuple the first item of which is the path that has been renamed to the second item in the tuple.  
driver.findElement(By.name("q")).sendKeys ("")
dom =document.getElementById("")
driver.findElement(By.partialLinkText(" NextP")).click()
driver.find_element(By.name("q")).sendKeys ("")
dom =document.getElementById("")
driver.find_element(By.partialLinkText(" NextP")).click()
Plot a figure with a colored arrow between text lines/steps Introduction
Estimate the file size in memory before saving to PC Introduction
Built-in functions in Python Introduction
filter() Introduction
Check if all/any values are true or false in a range of data Introduction
Optimizing failure analysis processes in semiconductor labs using machine learning Introduction
ML for failure analysis in the semiconductor industry Introduction
Convert a floating-point number (decimal) to exponential format Introduction
Apply a formatting function to all cells in a DataFrame Introduction
Font size of tick labels in plot Introduction
Font size of a (single/multiple) cell in table in PowerPoint Introduction
Only use the first 4 characters in the headers of the table for pptx/dataframe 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
Fail rate generator in wafers Introduction
Overcoming automation challenges and forward-looking suggestions Introduction






CSV: 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.

.fillna() Replace empty cells with anything. (code)
from typing import List, Dict CSV: code.
false_values CSV: list of strings to recognize as False
float_format CSV: Format string for floating point numbers
f1_score()/F1-score() f1_score: float or array of float, shape = [n_unique_labels]
F1 score of the positive class in binary classification or weighted average of the F1 scores of each class for the multiclass task. Introduction.
fit_transform() Introduction
.fit() Introduction.




Use those when the link text used within an anchor tag is known: the first element with the link text matching the provided value will be returned, e.g.:
      <p>Are you sure you want to do this?</p>
      <a href="continue.html">Continue</a>
      <a href="cancel.html">Cancel</a>
The continue.html link can be like this:
      continue_link = driver.find_element_by_link_text('Continue')
      continue_link = driver.find_element_by_partial_link_text('Conti')
(code) (code). Introduction.
find_element_by_css_selector() Use this, when you want to locate an element using CSS selector syntax, e.g.:
      <p class="content">Site content goes here.</p>
The “p” element can be like this:
      content = driver.find_element_by_css_selector('p.content')
(code). Introduction.
F1 (Code)
F2 (Code)
F3 (Code)
F4 Introduction
F5 Introduction
F6 (Code)
F7 (Code)
F8 (Code)
F9 (Code)
F10 (Code)
F11 (Code)
F12 (Code)
from <library nmae> import <function number> This is used if only one part of the fuctions in the library is needed.
from ... import * Import everything from ... module. code.
for for loop. The for construct is generally used for lists, tuples, strings, etc. E.g. repeating a Process with for, code, code. With for, you generally get a fixed number of loops, one for each item in a range or one for each item in a list. With a while loop, the loop keeps going as long as (while) some condition is true. copy method. for loop can be rewitten as a while loop with a counter.
for ... in ... Introduction. Print all iterms in a list in a seperate line: string list1. string list2. string list3. dictionary. remove items/keys/values in a dictionary. for row in csvreader. code.
for ... in rang() rang (a), rang (a, b), rang (a, b, c). code, code. code. repeating action.
for ... in rang() in csv Introduction
for(;;) Introduction
Loop through a list (e.g. for loop) Introduction
Loops (e.g. for loop) for 2D (two-dimensional) plot Introduction
textwrap.fill code.
Frame() Introduction
iframe in webpage <Introduction>
fill=tk.X (code)
from functools import reduce Reduce the list sign to "|". Introduction
format() Formats the specified value(s) and insert them inside the string's placeholder. code.
.font (code)
'{:,}'.format >>> x = '{:,}'.format(188899660)
>>> print (x)
Output: 188,899,660

In F-string (f"), all you need is a lowercase or uppercase f followed immediately by some text or expressions enclosed in quotation marks. Introduction. Code.

math.floor() The floor of a given number is the nearest integer smaller than or equal to that number. For example the floor of 4.68 is 4 and that of 4 is also 4.
math.factorial() Factorial: The factorial of a number x is defined as the continued product of the numbers from 1 to that value. code.
math.fabs() The absolute value of a number
sys.stderr.flush() (code)
Integer/fractions/round /decimal/digits/floating
It does not have any fractional part. Introduction. int: Example code.
Floating Point It can store number with a fractional part
float() Returns a floating point number constructed from a number or string
datetime.__format__(format) Same as datetime.strftime() (code)
scipy.linalg.fiedler Create a symmetric Fiedler matrix.
scipy.linalg.fiedler_companion Create a Fiedler companion matrix.
ft.fft2d Code.
ft.fftshift Code.
skimage.measure.find_contours(array, level) Find iso-valued contours in a 2D array for a given level value.
fillna() (code)
figsize Size of figure to create as tuple
**fig_kw Additional keywords to subplots are used when creating the figure, such as plt.subplots(2, 2, figsize=(8, 6))
np.full_like Return a full array with the same shape and type as a given array. Code.
Flask A web framework, Flask is built with a small core and many extensions.



.forward() (code)
.fillcolor() “fillcolor”: color-string or color-tuple. (code)
.activate() Activate a window: with the active cursor in the window and the window is brought to the most front on the monitor. (code)


FLAT, RAISED, SUNKEN, GROOVE and RIDGE in Tkinter button relief styles Introduction
foreground= (code)
warnings.filterwarnings (code).
FlexForm() (code).
driver.navigate().forward() Navigate forwards in browser history
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.
.format.line (code)
soup.find() and soup.find_all() Introduction
.fromtimestamp() (code)
os.path.normpath(): Normalize/format the path string into a proper string for the OS (code)
go.Figure (Code)
.filter2D() (Code)