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| Principles of ethical and responsible ML (selection bias, confirmation bias, automation bias, model fairness) |
Introduction |
| ResNet (Residual Network)
|
Introduction |
| Leveraging precision, speed, and automation: Integrating Mask R-CNN and YOLOv8 |
Introduction |
| Main reasons of a surge in ML usage across all industries recently but not earlier |
Introduction |
| Mask R-CNN (Mask Region-based Convolutional Neural Network) |
Introduction |
| Generate automated reports using Python |
Introduction |
| RDD (Resilient Distributed Dataset) |
Introduction |
| Comparison between RDBMS (Relational Database Management Systems) and Apache Hive |
Introduction |
| Martin Zinkevich's "Rule of Machine Learning": dataset quality |
Introduction |
| OLS (Ordinary Least Squares) regression model |
Introduction |
| Word representation |
Introduction |
| Regression evaluation metrics |
Introduction |
| Mean squared error (MSE) (L2 loss function, Euclidean loss) and root mean squared error (RMSE) |
Introduction |
| Handwritten digit recognition |
Introduction |
| Recursion in Python |
Introduction |
| Guido van Rossum |
Introduction |
| Generating random numbers and performing randomization tasks |
Introduction |
| Read specific cells (cell by cell) in csv file |
Introduction |
| Evaluation (Precision and Recall) in Text classification with Naive Bayes |
Introduction |
| Precision and Recall Tradeoff |
Introduction |
| Precision, Recall, False Positive Rate, and False Negative Rate (Miss Rate or False Negative Proportion) |
Introduction |
| Random variable |
Introduction |
| Resolution in ML |
Introduction |
| Theorem Proving |
Introduction |
| Rule-Based Systems |
Introduction |
| Modus ponens (a logical inference rule) |
Introduction |
| Inference and Inference Rules |
Introduction |
| Constraint ("limit"/"range") satisfaction |
Introduction |
| Knowledge base (repository) |
Introduction |
| Result(s, a) |
Introduction |
| Supervised, unsupervised and reinforcement learning |
Introduction |
| Policy search algorithms and "normal" reinforcement learning (RL) algorithms |
Introduction |
| Cost/loss function versus reward function |
Introduction |
| Linear Quadratic Regulation (LQR) |
Introduction |
| State-action rewards in Markov Decision Process (MDP) |
Introduction |
| Model-Free RL and Model-based RL (reinforcement learning) |
Introduction |
| Discretization in reinforcement learning |
Introduction |
| State transition function (probability) in reinforcement learning |
Introduction |
|
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| Replace |
|
| ◆ |
Skip/replace empty cells from DataFrame/CSV file |
Introduction |
| ◆ |
Replace/substitute a item in a list |
Introduction |
| ◆ |
Move/replace file(s) from one directory to another |
Introduction |
| ◆ |
Launch Replace menu from Find menu |
Introduction |
| ◆ |
Modify/replace the line in a text file if a line contains specific string |
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 |
| ◆ |
.replace() |
Introduction |
| ◆ |
Replace contents/letter: Replace contents and change letter cases in text files. |
Introduction |
| ◆ |
Replace/change to new headers in a csv file |
Introduction |
| ◆ |
Replaces a symbol/character/letter in a string |
Introduction |
| ◆ |
Handle NaN value in DataFrame, replace empty cells with ... |
Introduction |
|
|
| Robotics and machine learning |
Introduction |
| Credit assignment problem in reinforcement learning |
Introduction |
| ML example: face recognition algorithm |
Introduction |
| Finding a correct loss (risk, objective) function for a specific problem |
Introduction |
| Example of building robot (self-driving) systems with automated ML: helicopter |
Introduction |
| Logistic regression as a one-neuron/single-layer neural network (connection between linear & activation parts) |
Introduction |
| Batch Gradient Descent (BGD), Stochastic Gradient Descent (SGD), Mini-Batch Gradient Descent, Batch Stochastic Gradient Descent, Momentum, (Adagrad, Adadelta, RMSprop), and Adam (Adaptive Moment Estimation) |
Introduction |
| Plot pixel intensity (histogram) along a line (row/column/x-axis/y-axis) of an image |
Introduction |
| Comparison among sigmoid, hyperbolic tangent (tanh) and rectified linear unit (ReLU) functions |
Introduction |
| Structure of PowerPoint reports |
Introduction |
| Regularization techniques for decision trees |
Introduction |
| Comparison among Grid Search, Bayesian Optimization, Random Search and Manual Search |
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 |
| Generalization risk/generalization error versus empirical risk |
Introduction |
| Bayes error/Bayes risk/Bayes rate/irreducible error |
Introduction |
| Generalization Error/generalization risk/Generalization Loss/Test Error/Expected Error of Hypothesis/Risk |
Introduction |
| Logistic regression and Naive Bayes |
Introduction |
| L2 regularization/Ridge/ridge regularization/Tikhonov regularization |
Introduction |
Representer theorem and its derivation
|
Introduction |
| Support Vector Machines (SVM) and Logistic Regression |
Introduction |
| Comparison between Poisson distribution, Gaussian (normal) distribution and logistic regression |
Introduction |
| Logistic regression versus Gaussian discriminant analysis |
Introduction |
| Normalized ratios |
Introduction |
| Softmax regression (multinomial logistic regression)/softmax multi-class network/softmax classifier |
Introduction |
| Canonical response function/canonical link function |
Introduction |
| Learning rule in ML |
Introduction |
| Perceptron algorithm and logistic regression |
Introduction |
| Logistic regression |
Introduction |
| Close file after reading a file: avoid file locking |
Introduction |
| Logistic regression versus linear regression |
Introduction |
| Linear regression versus classification |
Introduction |
| Comparison between L1 Regularization and L1 Loss (absolute loss or mean absolute error (MAE)) |
Introduction |
| Spearman Rank Correlation/Spearman's rho/Spearman correlation |
Introduction |
| Learning rate |
Introduction |
| Kendall Tau Rank Correlation Coefficient |
Introduction |
| True risk in ML |
Introduction |
| Pearson Correlation Coefficient/Pearson's r/Correlation Coefficient |
Introduction |
| Recommender systems based on machine learning |
Introduction |
| Analysis of ROI (region of interest)/portion of an image |
Introduction |
| Mirror/reflect image from left to right/from top to bottom |
Introduction |
| RunConfig |
Introduction |
| Raw data |
Introduction |
| Retry a number of times/infinite retrying before exception or fail |
Introduction |
| Reasons of using Python for automation |
Introduction |
| .pack(side=LEFT)/.pack(side=RIGHT)/.place(x=, y=) --- position of the buttons |
(Code) |
| raise |
Introduction |
| from retrying import retry |
Introduction |
| Rank/ranking tensors |
Introduction |
| tensorflow_ranking (TensorFlow Ranking) |
Introduction |
| Regularization |
Introduction |
| Rectified Linear Units (ReLUs) |
Introduction |
| Reasons (benefits) of automation and how to start |
Introduction |
| Robots and Robotic Process Automation (RPA) |
Introduction |
| |
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| Regression introduction |
Introduction |
| ◆ |
Comparison of regression classes |
Introduction |
| ◆ |
Linear regression and its algorithm |
Introduction |
| |
✔ |
Multiple linear regression |
Introduction |
| ◆ |
Regression tree/decision tree for regression |
Introduction |
| ◆ |
Locally Weighted Regression (LWR) |
Introduction |
| |
|
| Penalized regression (Lasso and Ridge) |
Introduction |
| Adjusted R-squared values of two or more regression models |
Introduction |
| |
|
| Wafer map failure pattern recognition (WMFPR) |
Introduction |
| ◆ |
Feature extraction using radon transform |
Introduction |
| |
|
| Reasons of applications with Python |
Introduction |
| Feature extraction using radon transform |
Introduction |
| Use __name__ to control execution/run of the code |
code |
| Create a function called main() to contain the code you want to run/execution |
code |
| Automatically review, scroll, click webpage and its link |
Introduction |
| Mouse right single click |
Introduction |
| Righ click |
(Introduction) |
| Right click a specific position |
Introduction |
| while True, e.g. with the "while True" loop, e,g. it constantly/continuously refreshes actions. |
Introduction |
| Move the mouse/cursor to the left or right |
Introduction |
| Minimize/maximize/restore/activate/resize/move/close Window objects |
Introduction |
| Get the latest/newest/most recent file in a folder |
Introduction |
| Print colored text in Python IDLE's terminal |
Introduction |
| Skip, remove, extract, use specific columns |
Introduction |
| Convert CSV to images, row by row, with pixel values: each row is an image |
code. |
| 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 |
| 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 |
| Count the times of repeated excutions |
code |
| Prevent other applications to modify the content until other Python script runs |
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 |
| 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 |
| Calculate the coordinates of a point in a given rectangle and the distance of a given point to a line |
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. |
| Skip rows and/or columns in csv |
CSV: Introduction. |
| Remove duplicate cell values from a csv file |
CSV: Introduction |
| Subtract (minus) two images after resizing them |
code, code. |
| Sum two images after resizing them |
code |
| Reference list items by position |
code1, code2 |
| Loop through a string from left to right |
code1, code2 |
| Loop through numbers in a range |
code |
| Repeated printing the same string |
code |
| Palindrome repeat |
code |
| Reverse the digits of a given number |
code |
| Remove an/all/duplicate
item(s)/elements from a list |
Introduction |
| Comparison between iteration algorithm and recursive algorithm: a function repeat itself |
Introduction |
| DataFrame workflow: Drop/delete rows with empty cells in a column, Sort DataFrame by time/date order |
| Randomly open an image/file in a specific folder |
Introduction |
| Stability/reliability of locateCenterOnScreen() |
Introduction |
| Stability/reliability of find_element(By.xxx, "") |
Introduction |
| Read its nth character in a text file |
Introduction |
| Check if a directory is empty; find the position/index of a particular file/folder in a file directory/path; remove folder or file level by level (or layer by laer) from its directory/path |
Introduction |
| Call and then run your own functions and modules in different/other Python files; Python run another Python script |
Introduction |
| Run multiple Python files one after another |
Introduction |
| matplotlib.pyplot axis/text color (xticks, rotation, xlabel, ylabel, title, fontsize, grid(), legend(), show()) |
Introduction |
| Monitor specific new files, and execute the file or another file (and then restart the monitoring program itself to continue its monitoring by standby, with watchdog) |
Introduction |
| Launch script from another script using subprocess.run/subprocess.call |
Introduction |
| Move(remove) all files from original folder in a directory to a new directory |
Introduction |
| Top (ranking) Python libraries/modules |
Introduction |
| Resize (by real dimension or pixel) and then sum/mix/overlap two images |
Introduction |
| Rotate (alignment) an image by line along the x- or y-axis |
Introduction |
| Read/print a text/txt file |
Introduction |
| Print/remove/delete specific rows of a DataFrame |
Introduction |
| Modify a list (e.g. add/insert/remove an item between items, merge all items) |
Introduction |
| Split a sentence/string into list of words, remove all special characters from a sentence |
Introduction |
Reinforcement learning
|
Introduction |
| Reports |
Introduction |
| Rake_NLTK |
Introduction |
| Remove/repace (part) character(s) from string |
Introduction |
| Ranking/most popular programming languages for data analysts |
Introduction |
| Ranking and votes of essential/most important skills for data analysts |
Introduction |
| Remove \n in string or new line in txt/text file |
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 |
| Ranking/most popular machine learning frameworks used by data scientists |
Introduction |
| Wafer map similarity ranking (WMSR) |
Introduction |
| Denoising/remove noise in images |
Introduction |
| Methods of physical failure analysis (PFA)/root cause analysis of ICs |
Introduction |
| Root Cause Deconvolution (RCD) |
Introduction |
| Print specific rows of a DataFrame |
Introduction |
| DataFrame.drop()/delete column/row in DataFrame |
Introduction |
| RSquare (R^2) versus RASE (Root Average Squared Error) |
Introduction |
| Expected risk (population risk, expected value of loss or error) |
Introduction |
| Excess risk |
Introduction |
| Empirical Risk Minimization (ERM) |
Introduction |
Train-dev-test split (training-validation-testing split: Ratio for splitting dataset into training, validation and test set
|
Introduction |
| Recall (Sensitivity or True Positive Rate) in machine learning |
Introduction |
| Trick: return True and return False |
Introduction |
| Change/rename a column name/header in a CSV file |
Introduction |
| Misclassification rate (classification error rate or error rate) in machine learning |
Introduction |
| Loss (risk, cost, objective) function |
Introduction |
| Module import and execution/run are skipped during script execution |
Introduction |
| |
|
| Good research topics in the field of semiconductor manufacturing and computer vision |
Introduction |
| BERTScore/BERT (Bidirectional Encoder Representations from Transformer) |
Introduction |
| Recurrent Neural Networks (RNN) |
Introduction |
| Read a frequently updated file periodically (similar to watchdog) |
Introduction |
| Remove duplicate cell values from a csv file/dataframe (keeping the first/top one) |
Introduction |
| Remove the substring after the first or last character "::" in a given string, or extract the substring between the first and last "::" |
Introduction |
| Convert dataframe row/column into a comma separated string |
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 |
| Find repeating patterns in columns, group them as cycles, and column correlations |
Introduction |
| Remove unwanted/unnecessary parts from strings in a column of dataframe |
Introduction |
| Rapid Automatic Keyword Extraction (RAKE) |
Introduction |
| Remove rows if (multiple) NaN is more than a number in DataFrame |
Introduction |
| Call/run/execute JMP from Python |
Introduction |
| Extract elements from a list (different way from removing elements to get part of the list) |
Introduction |
| Plot graph/figure/image from CSV file/DataFrame by removing/hiding blank/empty cells with axis range (plt.xlim()) |
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 |
| Write special/certain rows (row-by-row) of one csv file to another csv file |
Introduction |
| Lock a file to prevent deleting, and then release/unlock the file once job is done |
Introduction |
| Trick: Output a portion of rows and columns from a csv file cell by cell into another csv file |
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 |
Difference/comparison between real mouse click and click from script/program, e.g. Pyautogui
|
Introduction |
Different behavior of automation execution (e.g. pyautogui) locally or remotely through internet
|
Introduction |
| Bayes' theorem (Bayes rule or Bayes law) in machine learning |
Introduction |
| Random (Bootstrap) Forests |
Introduction |
| Check if rectangles overlap |
Introduction |
| RegEx (Regular Expression) (characters to check if a string contains a specified search pattern, remove double spaces, and clean texts) |
Introduction |
| BERTScore (Bidirectional Encoder Representations from Transformer) |
Introduction |
| Last n days/weeks/months (.to_datetime(x), .set_index(y), .last(z), .reset_index(), and .max() in pandas) |
Introduction |
| Automatically restart script execution, for xyz times, after it breaks/fails/error |
Introduction |
| Read outlook messages in .msg format |
Introduction |
| IR (Information Retrieval) |
Introduction |
| Remove/reload/unload (all) imported module/function/script |
Introduction |
| Question answering retrieval |
Introduction |
| Access and use SQL Database on SSMS (Microsoft SQL Server Management Studio Express) with pyodbc: localhost, insert rows, update, count updated, delete rows, comparision between extract data by Python and SQL itself |
Introduction |
| Insert data/row into SQL Database on SSMS with pyodbc |
Introduction |
| Remove empty strings from list of strings |
Introduction |
| Remove decimal part in a string with comma |
Introduction |
| YAML reading in Python |
Introduction |
| Call and run another script in a different directory/path from a script |
Introduction |
| Delete the column/row in a CSV file if they are empty or less than a number (or header/index only) |
Introduction |
| pandas.read_csv(): Both read_csv() and read_table() use the same parsing code to intelligently convert tabular data into a DataFrame.
pandas.read_csv(filepath_or_buffer, sep=NoDefault.no_default, delimiter=None, header='infer', names=NoDefault.no_default, index_col=None, usecols=None, squeeze=False, prefix=NoDefault.no_default, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, encoding_errors='strict', dialect=None, error_bad_lines=None, warn_bad_lines=None, on_bad_lines=None, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None, storage_options=None) |
Introduction |
| csv workflow: Read into dataframe, Select a specific column from DataFrame, Select several specific columns to plot |
| 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 |
| CSV column transposer (rows/columns) |
Introduction |
| Skip/remove empty rows (row-by-row) in DataFrame/csv |
Introduction |
| Filters the rows based on the condition of being within n days of today's date |
Introduction |
| Reverse a list |
Introduction |
|
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| Remove/delete the duplicated/same rows in dataframe::>> df_unique = df.drop_duplicates() |
|
| __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 |
| Python IDLE restart during execution |
Introduction |
| range() |
Introduction |
| reversed() and reverse() |
Introduction |
| Check if all/any values are true or false in a range of data |
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 |
| 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 |
| Replace the lines between two lines “xx” and “yy” in a text file with new lines |
Introduction |
| Remove string 0s from the end of back/end of a list until non-zero values |
Introduction |
| Virtual reality (VR), augmented reality (AR), and mixed reality (MR) |
Introduction |
| Duplicate/repeat the same words/elements in a string/list |
Introduction |
| Comparison between decision tree, random forest and XGBoost (extreme gradient boosting) |
Introduction |
| Dimensionality reduction |
Introduction |
| Color and rotate/vertical text in pptx |
Introduction |
| Plot images from different DataFrame in a single row |
Introduction |
| Comparative overview of multivariate statistical methods (Correlation Analysis, Regression Analysis, Factor Analysis, Cluster Analysis, Principal Component Analysis (PCA), Canonical Correlation Analysis, Discriminant Analysis, Path Analysis, Structural Equation Modeling (SEM), Multivariate Analysis of Variance (MANOVA), Analysis of Covariance (ANCOVA) ): purposes, variables, and outputs |
Introduction |
| HTTP(Hypertext Transfer Protocol) webpage/URL (Uniform Resource Locator) |
Introduction |
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| rename() |
CSV: (code). |
| regex=True |
CSV: (code). |
| pandas.read_table() |
CSV: Both read_csv() and read_table() use the same parsing code to intelligently convert tabular data into a DataFrame. Read general delimited file into DataFrame.
pandas.read_table(filepath_or_buffer, sep=NoDefault.no_default, delimiter=None, header='infer', names=NoDefault.no_default, index_col=None, usecols=None, squeeze=False, prefix=NoDefault.no_default, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, dialect=None, error_bad_lines=None, warn_bad_lines=None, on_bad_lines=None, encoding_errors='strict', delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None)[source]. code. |
| pandas.read_csv() |
CSV: Both read_csv() and read_table() use the same parsing code to intelligently convert tabular data into a DataFrame.
pandas.read_csv(filepath_or_buffer, sep=NoDefault.no_default, delimiter=None, header='infer', names=NoDefault.no_default, index_col=None, usecols=None, squeeze=False, prefix=NoDefault.no_default, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, encoding_errors='strict', dialect=None, error_bad_lines=None, warn_bad_lines=None, on_bad_lines=None, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None, storage_options=None). Introduction. code. |
| csv.reader() |
CSV: Return a reader object which will iterate over lines in the given csvfile. code. |
| left_index=, right_index= |
CSV: (code) |
| reset_index() |
CSV: code. |
| .rightClick() |
.rightClick(x=moveToX, y=moveToY) (Code) |
| remove() |
(code) |
| write() |
(Code) |
| .resizable() |
(code) |
| relx |
Relative placement x. (code) |
| rely |
Relative placement y. (code) |
| relwidth |
The relative width of the background. (code) |
| from functools import reduce |
Reduce the list sign to "|". Introduction |
| from pptx.dml.color import RGBColor |
(code) |
| return() |
Is only used inside a function; only 1 return statement from a function; code in function after a return statement will not execute; value associated with return is given to function caller for use later. Introduction. Return 0. Return None. code. return to multiple variables. |
| raise() |
|
| regexes |
(Read-only) Regexes to allow matching event paths. |
| remove_handler_for_watch(event_handler, watch) |
Removes a handler for the given watch.
Parameters: event_handler (watchdog.events.FileSystemEventHandler or a subclass) – An event handler instance that has appropriate event handling methods which will be called by the observer in response to file system events.
watch (An instance of ObservedWatch or a subclass of ObservedWatch) – The watch to remove a handler for. |
run() |
Method representing the thread’s activity.
You may override this method in a subclass. The standard run() method invokes the callable object passed to the object’s constructor as the target argument, if any, with sequential and keyword arguments taken from the args and kwargs arguments, respectively. |
| .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. |
| rstrip() |
(code) |
| Remove lines |
Remove all the lines before the starting text. Introduction. |
| math.radians(x) |
Converts angle x from degrees to radians. |
| os.rename(source, dest) |
(code) |
| real |
|
| repr() |
Returns a printable representation of the given object.
(code) |
Integer/fractions/round /decimal/digits/floating
/ceil/floor |
It does not have any fractional part. Introduction. int: Example code. |
| Rational |
Those having a numerator and a denominator |
| .rfind(sub[start, [end]]) |
find from the end of the string. code. |
| readline([n]) |
Reads a line of the file and returns in form of a string. For specified n, reads at most n bytes. However, does not reads more than one line, even if n exceeds the length of the line. |
| readlines() |
Reads all the lines and return them as each line a string element in a list. |
| read line-by-line |
With double-spaced output, code; by getting rid of that effect of double-spaced output code. |
| urllib.request |
Defines functions and classes that help to open the URL (for opening and reading URLs/www webpages). Introduction. code. |
| Requests Library |
Introduction |
| requests.get |
Introduction. code. code. |
| response.raw |
code. |
| .rgb2gray() |
code. |
| Raw I/O |
Also called unbuffered I/O. f = open("myfile.jpg", "rb", buffering=0). |
| skimage.measure.ransac(data, model_class, ...) |
Fit a model to data with the RANSAC (random sample consensus) algorithm. |
| skimage.measure.regionprops(label_image[, ...]) |
Measure properties of labeled image regions. |
| 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.resize |
Resizing does only change the width and height of the image. (code)
cv2.resize(src, dsize[, dst[, fx[, fy[, interpolation]]]]) |
|
Desired size for the output image |
|
Scale factors along the horizontal and vertical axes |
|
Flag that takes one of the following methods:
INTER_NEAREST – a nearest-neighbor interpolation
INTER_LINEAR – a bilinear interpolation (used by default)
INTER_AREA – resampling using pixel area relation. It may be a preferred method for image decimation. when the image is zoomed, it is similar to the
INTER_NEAREST method.
INTER_CUBIC – a bicubic interpolation over 4×4 pixel neighborhood
INTER_LANCZOS4 – a Lanczos interpolation over 8×8 pixel neighborhood |
| .rgb2gray() |
code. |
| .rcParams |
All of the rc settings are stored in a dictionary-like variable called matplotlib.rcParams. (code) |
| rot |
Rotation of tick labels (0 through 360) |
| multialignment='right' |
code. |
| .random.permutation() |
code. |
| .randint() |
Random integer. code. |
| reshape() |
numpy.reshape(a, newshape, order='C', 'F', or 'A' - optional)[source]. Introduction. in csv. |
| random.rand() |
General. |
| random.random() |
Introduction. |
| random.randn(d0, d1, ..., dn) |
Return a sample (or samples) from the “standard normal” distribution. Introduction. |
| random.default_rng |
Random generator: General. code. |
| Search/print/output the rows |
CSV: Print the rows if their cell values are greater than a specific value, in the csv file with numbers only; output the rows if the cell value is in a specific range. Introduction. code. |
| Convert between rows and columns from csv |
CSV: Convert resulting row from CSV search into a column. Introduction |
| Skip rows and/or columns in csv |
CSV: Introduction. |
| Ramp |
Is used for rapid prototyping of machine learning models. Ramp provides a simple, declarative syntax for exploring features, algorithms, and transformations. It is a lightweight pandas-based machine learning framework and can be used seamlessly with existing python machine learning and statistics tools. |
| .top/.bottom/.left/.right |
Introduction |
| random.choice() |
Randomly select from options. (code) |
| randint(start, end) |
randint() is an inbuilt function of the random module in Python3. (code) |
| .release(Button.left) |
(code) |
| .press(Button.right) |
(code) |
| .release(Button.right) |
(code) |
| hotkey('r') |
Introduction |
| hotkey('right') |
Introduction |
| .restore() |
(code) |
| .resize() |
(code) |
| .resizeTo() |
(code) |
| .bottomright |
(code) |
| FLAT, RAISED, SUNKEN, GROOVE and RIDGE in Tkinter button relief styles |
Introduction |
| '<Return>' |
(code) |
| RandomForestClassifier |
(code). |
| import re |
(code). |
| driver.navigate().refresh() |
Refresh page |
| Screen’s resolutions |
Introduction. |
| .rjust() |
Right-justify/align them so
that they take up the same amount of space, whether the coordinate has one, two, three, or four letters or digits. (code). |
| Image rotation in pptx (.rotation=) |
Introduction. E.g. Rotate an image around the top-left corner as an axis in a pptx file with the same lateral dimension |
| random.seed() |
(code). |
| random.uniform() |
(code). |
| from functools import reduce |
reduce(): Introduction. |
| .DataFrame() |
Introduction. .drop(),
index,
columns,
axes,
dtypes,
size,
shape,
ndim,
empty,
T (swap between column and row),
values, .sample() (randomly print some rows). |
| |
|
