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

=================================================================================

 

 
                                                       
"Extract, Transform, Load" (ETL) and " Extract, Load, Transform" (ELT) processes Introduction
Tungsten in Spark Introduction
Building an effective machine learning team Introduction
Trade-offs between fairness and performance Introduction
Decision threshold in ML  Introduction
Text summarization for ML Introduction
FastText for ML Introduction
Timeseries prediction in ML Introduction
Analytics and Technology Automation (ATA) Introduction
Transformer in ML Introduction
Pre-training in ML Introduction
GPT (Generative Pre-trained Transformer) Introduction
Google Translate Introduction
TensorFlow Playground Introduction
Python Tutor Introduction
Trade-off between minimizing loss and minimizing complexity Introduction
Evaluation (Precision and Recall) in Text classification with Naive Bayes Introduction
Precision and Recall Tradeoff Introduction
Text classification with Naive Bayes Introduction
Search patterns in a text file Introduction
Theorem Proving Introduction
Goal State and Goal Test in ML Introduction
Transition model Introduction
Stationary and Non-Stationary State Transitions in Markov Decision Process (MDP) Introduction
Trade-off between exploration and exploitation, and epsilon(ε-) greedy exploration Introduction
State transition function (probability) in reinforcement learning Introduction
Open datasets, and open-source tools and libraries for ML practice Introduction
Time of training a ML algorithm Introduction
Hyperbolic tangent (Tanh) function Introduction
Comparison among sigmoid, hyperbolic tangent (tanh) and rectified linear unit (ReLU) functions Introduction
Holidays/Festivals/Vocations (Thanksgiving/Christmas) Introduction
Regular decision trees and decision trees with bagging Introduction
Data parallelism in distributed training Introduction
Misclassification loss in decision trees Introduction
Hyperparameter tuning (model tuning) 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
 Introduction
True Function Introduction
True Distribution Introduction
No Free Lunch Theorems Introduction
Margin Theory Introduction
Learning theory Introduction
Tricks for learning three dimensional geometry Introduction
Splitting a training dataset into different subsets Introduction
Choice of parameters for training models Introduction
Training score/training error Introduction
Training error versus model complexity Introduction
Kernel tricks and kernel function Introduction
L2 regularization/Ridge/ridge regularization/Tikhonov regularization Introduction
Representer theorem and its derivation
 Introduction
Training set Introduction
Transpose of vector and matrix and their equations Introduction
Trace of a square matrix Introduction
Binary trees Introduction
Kendall Tau Rank Correlation Coefficient Introduction
Threading Introduction
Generalization Error/Generalization Loss/Test Error/Expected Error of Hypothesis/Risk Introduction
True Mean in ML Introduction
Deviation Threshold in ML Introduction
True risk in ML Introduction
Two-sample t-test Introduction
t-SNE (t-distributed stochastic neighbor embedding, from sklearn.manifold import TSNE) Introduction
try and except Introduction
Save the text in clipboard to a txt file Introduction
Color in table obtained by matplotlib.pyplot/change background color of cells in table/colored with threshold Introduction
Try a number of times before exception or fail Introduction
Diversity prediction theorem Introduction
Mirror/reflect image from left to right/from top to bottom Introduction
Three dimensional shapes/structures Introduction
Modify/replace the line in a text file if a line contains specific string Introduction
except TypeError Introduction
Hide/turn on/off axes/axis on matplotlib Introduction
Common values in two pandas series Introduction
Crop/snip (without opening the image) part of a image with definition by a pixel line (with transparent added portion) Introduction
Convert a number type to another Introduction
.norm() (Taxicab Norm, Manhattan Norm, Euclidian Norm and Vector Max Norm) Introduction
Script execution limited by retry time Introduction
Predictive/predict model (with "best"-option table) Introduction
Microsoft Teams Introduction
Clean clipboard and/or check if clipboard is empty, text or image Introduction
Tracking process in ML Introduction
Tensors and vectors Introduction
Output (target variable, y, Y) Introduction
   
Training process in ML (with "best"-option table) Introduction
◆ Trained model and automatic model selection Introduction
◆ Metrics to monitor during training and testing Introduction
◆ Train-dev-test split (training-validation-testing split: Ratio for splitting dataset into training, validation and test sets Introduction
◆ Typical training setup in AI and comparisons of different training libraries Introduction
◆ Text/keyword classification/sort/prediction, training/test e.g. Youtube spam Introduction
◆ Empirical loss/training loss Introduction
◆ Train/Test versus Model Accuracy Introduction
◆ Training Exmaple (x, y) Introduction
   
TensorFlow Introduction
◆ 3 ways to create a Keras model with TensorFlow  
  ✔ Sequential API to create a Keras model with TensorFlow Introduction
  ✔ Functional API to create a Keras model with TensorFlow Introduction
  ✔ TensorFlow Data Validation (TFDV) Introduction
  ✔ Model Subclassing to create a Keras model with TensorFlow Introduction
◆ Supervised learning with tensorFlow Introduction
◆ Comparison between scikit-learn and tensorflow Introduction
◆ Comparison between TensorFlow, PyTorch, Theano and OpenCV Introduction
◆ Comparison between steps and epochs in TensorFlow Introduction
◆ Commands  
  ✔ train_and_evaluate Introduction
  ✔ RunConfig Introduction
  ✔ save_checkpoints_steps Introduction
  ✔ model_fn Introduction
  ✔ train_op Introduction
  ✔ end( ) Introduction
  ✔ get_shape() Introduction
  ✔ export_savedmodel Introduction
  ✔ tf.saved_model.save() Introduction
  ✔ serving_input_fn Introduction
  ✔ checkpoint_path Introduction
  ✔ input_fn Introduction
  ✔ tf.keras.optimizers.Adam Introduction
  ✔ tf.Variable Introduction
  ✔ tf.distribute.Strategy Introduction
  ✔ tf.Graph() Introduction
  ✔ .as_default() Introduction
  ✔ tf.constant() Introduction
  ✔ tf.Session Introduction
  ✔ tf.function Introduction
  ✔ tf.data  
    X tf.data API Introduction
    X tf.data.Dataset Introduction
      • tf.data.TextLineDataset() (Code)
      • tf.data.TFRecordDataset() (Code)
      • tf.data.Dataset.from_tensor_slices (Code)
      • tf.data.FixedLengthRecordDataset (Code)
  ✔ tf.feature_column.categorical_column_with_identity Introduction
  ✔ tf.feature_column.bucketized_column Introduction
◆ Basics of tensors  
  ✔ Rank/ranking tensors Introduction
  ✔ Shape of tensor Introduction
  ✔ Flow of tensor Introduction
  ✔ Size in tensor Introduction
  ✔ Axis/dimension of tensor Introduction
◆ Neural networks with TensorFlow Introduction
◆ TensorBoard Introduction
◆ Variables in TensorFlow Introduction
◆ Hook Introduction
◆ after_run function Introduction
◆ stop_if_no_decrease_hook Introduction
◆ early_stopping.stop_if_no_decrease_hook Introduction
◆ Comparison between Keras and Estimators (tf.estimators) Introduction
  ✔ Keras Introduction
    X Stateful preprocessing layers (adapt() method)  
      • TextVectorization  
      • StringLookup  
      • IntegerLookup  
      • Normalization  
      • Discretization  
    X Categorical features preprocessing layers Introduction
      • tf.keras.layers.CategoryEncoding (categoryEncoding layer) Introduction
      • tf.keras.layers.Hashing (hashing layer) Introduction
      • tf.keras.layers.StringLookup (stringLookup layer) Introduction
      • tf.keras.layers.IntegerLookup (IntegerLookup layer) Introduction
    X Trainable layers Introduction
      • tf.keras.layers.Discretization Introduction
      • tf.keras.layers.normalization Introduction
      • tf.keras.layers.StringLookup (stringLookup layer) Introduction
    X Non-trainable layers Introduction
      • tf.keras.layers.Hashing (hashing layer) Introduction
    X tf.keras.layers.TextVectorization Introduction
    X tf.keras API Introduction
    X tf.keras.datasets (e.g. MNIST, CIFAR-10, CIFAR-100, Fashion MNIST) Introduction
  ✔ tf.estimator Introduction
    X  tf.estimator.evaluate() (code)
    X tf.estimator.EstimatorSpec (code)
    X tf.estimator.ModeKeys.TRAIN (code)
    X tf.estimator.Estimator (code)
    X tf.estimator.Estimator.get_variable_value (code)
    X tf.estimator.experimental.stop_if_no_decrease_hook (code)
    X tf.estimator.Estimator.get_variable_names() (code)
    X tf.estimator.Estimator.get_variable_value(name) (code)
    X tf.estimator.Estimator.evaluate (code)
    X tf.estimator.Estimator.predict (code)
◆ Training in TensorFlow Introduction
◆ tensorflow_ranking (TensorFlow Ranking) Introduction
◆ Typical training setup in AI and comparisons of different training libraries Introduction
◆ TensorFlow APIs Introduction
   
   
History/hot topics of machine learning Introduction
   
Transform process in ML Introduction
◆ Feature extraction using radon transform Introduction
   
Save the webpages obtained by Google search into a text file Introduction
Troubleshooting/debugging and problem solving in Python programming page4806
Thresholding with Match Template: 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 Introduction
Open a new tab in an application window Introduction
Open task manager window Introduction
Table of powerPoint shortcut hotkeys Introduction
Table of PC/computer/Windows shortcut hotkeys Introduction
Table of Word shortcut hotkeys Introduction
Table of Excel shortcut hotkeys Introduction
Table of Chrome shortcut hotkeys Introduction
Loops (e.g. for loop) for 2D (two-dimensional) plot (or map) Introduction
Table of digital micrograph (DM) shortcut hotkeys Introduction
Go to the pointed tab on an app Introduction
Type capital letters Introduction
Check to see if or get a window with a name containing specific titles or texts Introduction
stopwatch and timing/countering a process Introduction
Turn on and off with mouse press or a process/button switch, button state Introduction
Copy text to clipboard Introduction
Tricks in Python Programming and principles and practices in good programming Introduction
Term Frequency-Inverse Document Frequency (TF-IDF) Introduction
Print colored text in Python IDLE's terminal Introduction
Find the best word/text similarity Introduction
Copy text into clipboard and then use it immediately (one time clipboard) Introduction
API (Application Programming Interface), e.g. weather, temperature Introduction
Detection of tables from an image Introduction
type() Introduction
Send emails in HTML and text formats Introduction
matplotlib.pyplot axis/text color 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
Copy text into clipboard and then you can paste it anywhere Introduction
Merge/combine two text files (and add a new line to the beginning of a text file) Introduction
Highlight texts or make selection Introduction
File name, folder name. {}{}....format. Manipulation of file and folder names (rename file name and folder name): i) Create a new folder and then copy all files from a folder to the new folder and rename the file, and then open the file. If the folder exists, then no file will be copied, but the file will still be opened. ii) Print and export the folder names and file names (with or without extensions) from a folder into a text file. iii) csv2image filename. Introduction
Data structures (Data science, and comparison between list, tuple, set, dictionary) Introduction
Set default programs by file extensions and by file types and programs on Windows Introduction
Numpy: Access the element at the second row, the third entry, access a specific row or a column, access some elements (submatrix), or replace/modify an element in the array, print a transfer of an array, access array under conditions or filtering Introduction
.T (Transfer of array in Python) Introduction
Copy text into clipboard and then you can paste it a webpage, text/txt, word or powerpoint file automatically (code)
Count the times of repeated excutions code
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
Swap two numbers code
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
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 CSV: Introduction. code.
Compute the difference between two images by using Structural Similarity Index with "pip install --upgrade imutils" code. code
Subtract (minus) two images after resizing them code, code.
Sum two images after resizing them code
Create images with global, adaptive mean, adaptive Gaussian, binary, trunc, Tozero, and tozero thresholds. code
Bail out/terminate of a loop code
Find the greatest of three numbers code1, code2, code3.
Create slides with text only Introduction
Text Format in pptx Introduction
Find and convert the file time/date Introduction
Read its nth character in a text file  Introduction
Transparency of marker (e.g. for plots) Introduction
matplotlib.pyplot axis/text color (xticks, rotation, xlabel, ylabel, title, fontsize, grid(), legend(), show()) Introduction
Find files with a specific file extension/type or with file names ending with specific characters Introduction
Simple watchdog to monitor file/folder changes (monitor file creation and then write the file paths into the text file) Introduction
Convert a CSV file to a TXT file Introduction
Execute scheduled jobs (time-schedule) Introduction
Find common elements/items between two lists Introduction
top and left for pptx Introduction
Time and date used as a file/folder name stamp (e.g. duplicate a file in the same folder) Introduction
matplotlib.pyplot axis/text color (title, xticks, rotation, xlabel, ylabel, title, fontsize, grid(), legend(), show()) Introduction
Sort a text file Introduction
Search text on an image Introduction
Search/extract/find text on an image Introduction
Extract text/check specific text from multiple powerpoint files Introduction
Take a screenshot using a mouse click and drag method. For instance, take a screenshot, and then insert the image and/or a text into a ppt file. Introduction
Top (ranking) Python libraries/modules Introduction
Resize and then sum/mix/overlap two images Introduction
Shift/translate image along x-axis/y-axis Introduction
Resize and then sum/mix/overlap two images (w/o transparency) Introduction
Break/exit/skip a function/code line after a certain time Introduction
Decision tree learning Introduction
Textrank Introduction
DataFrame workflow: Drop/delete rows with empty cells in a column, Sort DataFrame by time/date order
Count occurrence/nubmer of words/phrase in a text file Introduction
Convert a text file to a string Introduction
Ranking/most popular programming languages/tools for data analysts Introduction
Convert PDF file to text file 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
Remove \n in string or new line in txt/text file Introduction
Critical thinking in data science Introduction
Remove duplicate/same lines in a text file Introduction
Ranking/most popular automation testing tools Introduction
Ranking/most popular IT automation software tools Introduction
Extract a mask from an image with a threshold Introduction
Mask an image with a threshold or with a color as a threshold Introduction
Add/insert text to an image Introduction
Fault analysis/PFA (Physical Failure Analysis) time and efficiency Introduction
Plot table Introduction
Read/print a text/txt file Introduction
Extract a table from a webpage or text file Introduction
Convert DataFrame to a HTML Table and save as a HTML webpage Introduction
Write contents of DataFrame/memory into text file Introduction
Various names or terms that describe similar concepts or techniques in ML Introduction
Taylor expansion Introduction
"True label" ("observed label") in machine learning Introduction
BERTScore/BERT (Bidirectional Encoder Representations from Transformer) Introduction
Natural Language Processing (NLP) versus Text Introduction
Tokenization Introduction
Two-dimensional neural network Introduction
Classification of groups of texts Introduction
Common Words for Classification of Groups of Texts Introduction
Bayes' theorem (Bayes rule or Bayes law) in machine learning Introduction
Test process in machine learning Introduction
NLTK (Natural Language Toolkit) Introduction
Regression tree/decision tree for regression Introduction
Classification tree/decision tree for classification Introduction
Various names or terms that describe similar concepts or techniques in ML Introduction
   
Good research topics in the field of semiconductor manufacturing and computer vision Introduction
AI/machine learning algorism for text analysis Introduction
   
Trick: return True and return False Introduction
Clustering versus Classification of texts and documents Introduction
Clustering of texts Introduction
Classification of texts Introduction
   
Multimodal text and image similarity Introduction
Get the latest/newest/most recent file in a folder within certain time/days Introduction
Multimodal text and image search 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
Text search Introduction
Sentence, text and document embeddings Introduction
(Text and image) contrastive learning Introduction
Overfitting and underfitting Introduction
Bias and variance, and bias-variance trade-off in ML Introduction
Compare/check if two text files have the same contents Introduction
Count number of lines in a text file Introduction
Check existence of phrase on text file line-by-line Introduction
Modify HTML webpage (e.g. with graph network by adding/inserting text/hyperlink in) Introduction
Insert paragraphs of texts into Python script Introduction
igraph for clustering, network and tree Introduction
String template class for formating strings (F-strings (for calculation) (f"{}"), format() method ({}), %s, %d, Template ($)) Introduction
Check if all the (and how many, length of a string) characters in the text are digits/numbers Introduction
Summary/templates of plotting graphs/figures Introduction
Continue script execution no matter whether some try fails or not (finally)
 Introduction
Remove duplicate cell values from a csv file/dataframe (keeping the first/top one) Introduction
Trick: pd.concat() for merging/adding (two) columns Introduction
   
Get the last line in a text file Introduction
Read line-by-line from a text file Introduction
Plot multiple images on the same figure by hiding x- and y-(tick) labels on axis Introduction
Find the same elements in columns in two separate dataframes and then merge them Introduction
Trick: generic code/script templates for complex automation 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
◆    
   
Avoid duplicates when creating text file Introduction
matplotlib.pyplot to plot/generate images (with axis/colored text or annotation) Introduction
Check if two lists have the same elements Introduction
Compute the similarity between two text documents/files (with heatmap) Introduction
BERTScore (Bidirectional Encoder Representations from Transformer) for text Introduction
Nearest/most similar lyrics of a sentence/text to a CSV file Introduction
SentenceTransformers Introduction
Transfer data from Python module to a YAML file Introduction
Automatically restart script execution, for xyz times, after it breaks/fails/error Introduction
Avoid two or multiple plots being wrongly/incorrectly/unnecessarily mixed/overlap Introduction
Summary/templates/examples of pptx and PowerPoint format Introduction
Create a temporary file or directory/folder Introduction
RegEx (Regular Expression) (characters to check if a string contains a specified search pattern, remove double spaces, and clean texts) Introduction
Recall (Sensitivity or True Positive Rate) in machine learning Introduction
CSV column transposer (rows/columns) Introduction
Filters the rows based on the condition of being within n days of today's date Introduction
Plot a figure with a colored arrow between text lines/steps Introduction
Truth (True) and False Table Introduction
Check if all/any values are true or false in a range of data Introduction
Tableau integration with Python Introduction
tableauhyperapi module Introduction
Convert a sentence/text to a list Introduction
Replace the lines between two lines “xx” and “yy” in a text file with new lines 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
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
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
Populate the table with logarithmic format in pptx Introduction
Perform transpose operations (x-axis to y-axis, and vice versa) on a given dataframe Introduction
Cheatsheet of .txt file manipulation Introduction
.apply(tuple, axis=1) Introduction
Cheatsheet of tuples Introduction
HTTP(Hypertext Transfer Protocol)/URL (Uniform Resource Locator) Introduction

 

 

   
true_values CSV: list of strings to recognize as True
thousands CSV: 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.
pandas.DataFrame.to_csv() CSV: DataFrame.to_csv(path_or_buf=None, sep=',', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, mode='w', encoding=None, compression='infer', quoting=None, quotechar='"', line_terminator=None, chunksize=None, date_format=None, doublequote=True, escapechar=None, decimal='.', errors='strict', storage_options=None). Write the contents of the Frame into a CSV file: code.
.T (Transfer of array in Python) Introduction
tupleize_cols CSV: 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
tupleize_cols CSV: If False (default), write as a list of tuples, otherwise write in an expanded line format suitable for read_csv
Series.to_csv() CSV: Is a 1-D ndarray with axis labels and writes the given series object to a comma-separated values (csv) file/format. code.
train_test_split (code)
transform() (code)
Open tast manager (code)
.tripleClick() .tripleClick(x=moveToX, y=moveToY) (Code)
.typewrite() E.g. .typewrite('Hello world!\n', interval=secs_between_keys); .typewrite(['a', 'b', 'c', 'left', 'backspace', 'enter', 'f1'], interval=secs_between_keys). (Code)
Tuples Introduction, and data structures. Are very similar to lists. A tuple contains elements which can be treated individually or as a group. The difference from lists is that tuples are immutable, meaning that after they’re defined, you code can’t change anything about them. E.g. can neither set value nor sort tuples. A tuple may also contain heterogeneous elements (e.g. a string and an integer): Example code. Declaration: tup = tuple(); tup = (); tup = (2, 5, 10); tup = (2004, "Python", 3) and tup = (8,). Tuples are useful in operations like swapping etc.: swap two numbers, Example code2.
threading.Thread() Convenience class for creating stoppable threads. The Thread() accepts many parameters. The main ones are: target: specifies a function (fn) to run in the new thread. args: specifies the arguments of the function (fn). The args argument is a tuple. Introduction.
Thread(target=fn,args=args_tuple)
ternary The ternary operator performs the same task as the if-else construct.
Syntax:
<Output variable> = <The result when the condition is true>
if <condition> else <The result when the condition is not true>
code
textwrap.fill code.
tkinter
tkinter popup windows Introduction
import tkinter as tk code
import tkinter.messagebox Import messagebox from tkinter module. code.
from tkinter.filedialog import askopenfilename code. code.
.Label() (code)
.iconbitmap() (code)
.destroy() (code)
.config() (code)
justify='center', justify='right', justify='left' (code)
global   (code)
PhotoImage() (code)
Frame() (code)
pady= (code)
borderwidth= (code)
Toplevel() (code)
fill=tk.X (code)
padx= (code)
ipadx= (code)
FLAT, RAISED, SUNKEN, GROOVE and RIDGE in Tkinter button relief styles Introduction
Widget in Tkinter Introduction
entry.delete(0, tk.END) Use the special constant tk.END for the second argument of .delete() to remove all text in an Entry
relwidth The relative width of the background. (code)
 
try This block lets you test a block of code for errors. Introduction. code.
shapes.title (code)
.text_frame() (code)
__truediv__  
__trunc__  
timeout Blocking timeout for reading events.
timeout

Event queue block timeout.
threading.Thread() Convenience class for creating stoppable threads. The Thread() accepts many parameters. The main ones are: target: specifies a function (fn) to run in the new thread. args: specifies the arguments of the function (fn). The args argument is a tuple. Introduction.
Thread(target=fn,args=args_tuple)
Text (txt) files in Python (Two types of files can be handled in python, normal text (.txt) files and binary files)
.read() .read([number of characters = optional]). Read the entire .txt file in Python. read([n]): Returns the read bytes in form of a string. Reads n bytes, if no n specified, reads the entire file. Introduction. .txt to read the entire file. .txt to read a specific number of characters. code. code
.readline() Read the first line and the first lines in a .txt file in Python. Introduction. txt.
.readlines() Read all the lines in a .txt file in Python. txt.
Sort txt files Introduction
import fileinput (code)
rstrip() (code)
os.path.splitext() Introduction.
replace() (code) (code)
Remove lines Remove all the lines before the starting text. Introduction.
Replace contents/letter Replace contents and change letter cases in text files. Introduction
Insert/add text or line Insert text or new text lines to a specific position, at the end of a line, in a file. Introduction
Codes Details
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 CSV: Introduction. code.
'r'
 Introduction. Read Only: Open text file for reading. The handle is positioned at the beginning of the file. code
'r+' Introduction. Read and Write: Open the file for reading and writing. The handle is positioned at the beginning of the file. code
'w' Introduction. Write Only: Open the file for writing. For existing file, the data is truncated and over-written.
'w+' Introduction. Write and Read: Open the file for reading and writing. For existing file, data is truncated and over-written.
'a' Introduction. Append Only () : Open the file for writing. The file is created if it does not exist. The handle is positioned at the end of the file. The data being written will be inserted at the end, after the existing data.
'a+' Introduction. Open the file in append & read mode, e.g. append and Read: Open the file for reading and writing. The file is created if it does not exist. The handle is positioned at the end of the file. The data being written will be inserted at the end, after the existing data. (code)
 
math.tan(x) Returns the tangent of x radians.
math.tau() Returns the mathematical constant tau (6.283185 . . .).
title code. code. code.
to_bytes  
text code. code. code.
mss.tools.to_png (code)
datetime.time An idealized time, independent of any particular day, assuming that every day has exactly 24*60*60 seconds. (code)
datetime.timedelta A duration expressing the difference between two date, time, or datetime instances to microsecond resolution. (code)
datetime.tzinfo An abstract base class for time zone information objects. (code)
datetime.timezone A class that implements the tzinfo abstract base class as a fixed offset from the UTC. (code)
.date.today() Get current date: code (in different formats) and code. (code)
time.time() Introduction. code
title() Make the first letter in each word upper case. code. code.
scipy.linalg.toeplitz Create a Toeplitz matrix.
tensordot(a, b[, axes]) Compute tensor dot product along specified axes.
trace(a[, offset, axis1, axis2, dtype, out]) Return the sum along diagonals of the array.
linalg.tensorsolve(a, b[, axes]) Solve the tensor equation a x = b for x.
linalg.tensorinv(a[, ind]) Compute the ‘inverse’ of an N-dimensional array.
__closure__/tuple name/type: the function closure, i.e. bindings for free variables (often is None)
__defaults__/tuple name/type: default values for the formal parameters
Text I/O f = open("myfile.txt", "r", encoding="utf-8"), f = io.StringIO("some initial text data").
Python Twisted Is an event-driven networking engine, Twisted is written in Python, and licensed under the open-source MIT license.
cv2.TM_SQDIFF() method=CV_TM_SQDIFF
cv2.TM_SQDIFF_NORMED method=CV_TM_SQDIFF_NORMED
cv2.TM_CCORR method=CV_TM_CCORR
cv2.TM_CCORR_NORMED method=CV_TM_CCORR_NORMED
cv2.TM_CCOEFF_NORMED() method=CV_TM_CCOEFF_NORMED
The third parameter here is the method used for matching. code, code. code.
cv2.TM_CCOEFF method=CV_TM_CCOEFF
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
to_clipboard([excel, sep]) Attempt to write text representation of object to the system clipboard. This can be pasted into Excel, for example.
to_dict(*args, **kwargs) Convert DataFrame to dictionary.
to_html([buf, columns, col_space, colSpace, ...]) Render a DataFrame as an HTML (www, webpage) table.
to_sql(name, con[, flavor, schema, ...]) Write records stored in a DataFrame to a SQL database.
to_timestamp([freq, how, axis, copy]) Cast to DatetimeIndex of timestamps, at beginning of period
to_records([index, convert_datetime64]) Convert DataFrame to record array.
to_string([buf, columns, col_space, ...]) Render a DataFrame to a console-friendly tabular output.
to_dense() Return dense representation of NDFrame (as opposed to sparse)
to_excel(excel_writer[, sheet_name, na_rep, ...]) Write DataFrame to a excel sheet
to_gbq(destination_table[, project_id, ...]) Write a DataFrame to a Google BigQuery table.
to_hdf(path_or_buf, key, **kwargs) activate the HDFStore
to_json([path_or_buf, orient, date_format, ...]) Convert the object to a JSON string.
to_latex([buf, columns, col_space, ...]) Render a DataFrame to a tabular environment table.
to_msgpack([path_or_buf]) msgpack (serialize) object to input file path
to_panel() Transform long (stacked) format (DataFrame) into wide (3D, Panel) format.
to_period([freq, axis, copy]) Convert DataFrame from DatetimeIndex to PeriodIndex with desired
to_pickle(path) Pickle (serialize) object to input file path
to_sparse([fill_value, kind]) Convert to SparseDataFrame
to_stata(fname[, convert_dates, ...]) A class for writing Stata binary dta files from array-like objects
title Plot title as string
numpy.transpose() Reverse or permute the axes of an array; returns the modified array. For an array a with two axes, transpose(a) gives the matrix transpose of swapping. code.
.title() (code)
.text() Introduction
Merge two csv files CSV: Introduction
Theano Introduction
top and left for locateOnScreen() Introduction
t.shape() “classic”, “arrow”, “turtle”, “circle”, “square” and “triangle”. (code)
t.speed() Is used to change the speed of the turtle by the value of the argument that it takes. E.g. .speed(speed=None). The turtle’s speed lies in the range 0-10. If input is a number greater than 10 or smaller than 0.5, speed is set to 0. ‘fastest’ : 0; ‘fast’ : 10; ‘normal’ : 6; ‘slow’ : 3; ‘slowest’ : 1. (code)
t.Screen() Is used to set the size and position of the main window. (code)
t.getscreen() Return the TurtleScreen object the turtle is drawing on. (code)
t.getcanvas() Return the Canvas of this TurtleScreen. (code)
winfo_height() Contain the dimensions of the current turtle graphics window. (code)
t.screensize() turtle.screensize(canvwidth=None, canvheight=None, bg=None). If there is no parameter, then it is getting the actual screen size. (code)
.right() (code)
random.choice() Randomly select from options. (code)
randint(start, end) randint() is an inbuilt function of the random module in Python3. (code)
t.penup()/t.pu()/t.up() Pull the pen up – no drawing when moving. (code)
t.pendown()/t.pd()/t.down() Pull the pen down – drawing when moving. (code)
t.circle()

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)
.goto()

.goto(x, y=None) x – a number or a pair/vector of numbers; y – a number or None. (code)

.getcanvas().postscript(file="") Picture saved in current working directory. (code)
.forward() (code)
.fillcolor() “fillcolor”: color-string or color-tuple. (code)
.backward() (code)
t.begin_fill() To be called just before drawing a shape to be filled. (code)
t.end_fill() turtle.end_fill(). Fill the shape drawn after the last call to begin_fill(). (code)
.left() (code)
t.hideturtle()/t.ht() Make the turtle invisible while you’re in the middle of doing some complex drawing, because hiding the turtle speeds up the drawing observably. (code)
 
hotkey('t') Introduction
hotkey('tab') 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
.getWindowsWithTitle() Introduction
.topleft (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)
.GetWindowText() (code)
highlightthickness= (code)
textvariable= (code)
.tags= (code)
.top/.bottom/.left/.right Introduction
feature_extraction.text (code)
TfidfVectorizer() (code)
.get_feature_names_out() (code)
.toarray() (code)
TfidfTransformer (code).
.query() (code).
  (code).
skipgram model (for training) (code)
CBW model (for training) (code)
text_color= (code).
.terminate() (code).
driver.navigate().to(“http://globalsino.com”)
 Navigate to URL
.DataFrame() Introduction. .drop(), index, columns, axes, dtypes, size, shape, ndim, empty, T (swap between column and row), values
.set_xticks() (code)
.set_xticklabels() (code)
   
   

 

 

 

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