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| K-means clustering and PCA for failure analysis |
Introduction |
| ML model complexity versus dataset size |
Introduction |
| Clusters (Kubernetes, Apache Mesos, Spark Standalone, Apache Hadoop YARN) in Apache Spark |
Introduction |
| Leveraging precision, speed, and automation: Integrating Mask R-CNN and YOLOv8 |
Introduction |
| Feature selection: removing unnecessary (constant & quasi constant) features |
Introduction |
| Comparison between Clouds (Amazon, IBM, Google ...) |
Introduction |
| Feature Selection: Chi Square to select dependent and independent variables |
Introduction |
| Comparison between CNN, CNN with Attention and Autoencoder |
Introduction |
| Mask R-CNN (Mask Region-based Convolutional Neural Network) |
Introduction |
| Configuring Spark |
Introduction |
| Deploy modes for driver process in Apache Spark: client mode and cluster mode |
Introduction |
| Apache Spark applications to a Kubernetes cluster |
Introduction |
| Apache Spark on IBM Cloud |
Introduction |
| Catalyst in Spark |
Introduction |
| Coarsening data |
Introduction |
| Principles of ethical and responsible ML (selection bias, confirmation bias, automation bias, model fairness) |
Introduction |
| Image classification with ML |
Introduction |
| Google Cloud Shell |
Introduction |
| Drawbacks of Coursera classes |
Introduction |
| Spark Core of Apache Spark |
Introduction |
| Parallel computing and distributed computing |
Introduction |
| Calculation of Principal Component Analysis (PCA) |
Introduction |
| Squared Pearson correlation coefficient |
Introduction |
| Covariance |
Introduction |
| Covariance versus Covariance Matrix |
Introduction |
| Principal Component Analysis (PCA) versus Uniform Manifold Approximation and Projection (UMAP) |
Introduction |
| Impact of corpus narrowness on language model training |
Introduction |
| Context-free grammar (CFG) |
Introduction |
| CaptionBot |
Introduction |
| tf.keras.datasets (e.g. MNIST, CIFAR-10, CIFAR-100, Fashion MNIST) |
Introduction |
| Image convolution |
Introduction |
| Code blocks |
Introduction |
| Python Conference |
Introduction |
| Computer hardware architecture |
Introduction |
| Labor cost of data analysis with and without automation and ML techniques |
Introduction |
| Computer vision |
Introduction |
| Clustering in computer vision |
Introduction |
| Trade-off between minimizing loss and minimizing complexity |
Introduction |
| Nearest-neighbor (NN) classification |
Introduction |
| Replaces a symbol/character/letter in a string |
Introduction |
| Analyzing the impact of fabrication conditions on semiconductor wafer fail rates |
Introduction |
| Plot with letters/words/character as x-/y-axis |
Introduction |
| Fit and Smooth Plotted Curves |
Introduction |
| Maintaining arc-consistency |
Introduction |
| Constraint Satisfaction Problems (CSPs) as Search Problems |
Introduction |
| Arc consistency |
Introduction |
| Binary constraint |
Introduction |
| Node consistency |
Introduction |
| Unary constraint |
Introduction |
| Soft constraints and hard constraints |
Introduction |
| Constraint satisfaction problem |
Introduction |
| Hill Climbing |
Introduction |
| Evaluation (Precision and Recall) in Text classification with Naive Bayes |
Introduction |
| Text classification with Naive Bayes |
Introduction |
| Markov chain |
Introduction |
| Unconditional probability |
Introduction |
| Path and Path Cost in ML |
Introduction |
| Cost/loss function versus reward function |
Introduction |
| Cost (expense) and speed (fastest and slowest) of computation in ML |
Introduction |
| Credit assignment problem in reinforcement learning |
Introduction |
| Electroencephalogram cap (EEG cap) for brain |
Introduction |
| Nonlinear extensions of Independent Component Analysis (ICA) |
Introduction |
| Cumulative Distribution Function (CDF) |
Introduction |
| Formats of datasets for classification |
Introduction |
| Finding a correct loss (risk, objective) function for a specific problem |
Introduction |
| Cache |
Introduction |
| Logistic regression as a one-neuron/single-layer neural network (connection between linear & activation parts) |
Introduction |
| Softmax regression (multinomial logistic regression)/softmax multi-class network/softmax classifier |
Introduction |
| Extract/confirm any substrings with any pattern (e.g. dot (.)) |
Introduction |
| Dependently Identically Distributed/Correlated Identically Distributed |
Introduction |
| kfp.dsl package versus pipelines and components |
Introduction |
| Probably Approximately Correct (PAC) learning |
Introduction |
| Sample Complexity |
Introduction |
| Convergence and Optimization |
Introduction |
| Model Complexity |
Introduction |
| Leave-One-Out Cross-Validation (LOOCV) |
Introduction |
| Plot pixel intensity (histogram) along a line (row/column/x-axis/y-axis) of an image |
Introduction |
| K-Fold Cross-Validation |
Introduction |
| CIFAR (Canadian Institute for Advanced Research) (CIFAR-10 and CIFAR-100) |
Introduction |
Linear correlation between two variables with Pearson Correlation Coefficient, Spearman Rank Correlation Coefficient, Kendall's Tau, Linear Regression, Coefficient of Determination and Correlation Ratio
|
Introduction |
| Choice of parameters for training models |
Introduction |
| Training error versus model complexity |
Introduction |
| Cross-Validation in ML |
Introduction |
| Comparison among classifier, hyperplane and decision boundary |
Introduction |
| Optimal margin classifier/maximum margin separator |
Introduction |
| Conditional probability |
Introduction |
| Custom AI/ML chips/ICs |
Introduction |
| Energy consumption in computation of machine learning |
Introduction |
| Convolutional Layers (CONV) in Deep Learning |
Introduction |
| Fully Connected Layers (FC) in Deep Learning |
Introduction |
| Laplace smoothing/Laplace correction/add-one smoothing |
Introduction |
| Categorical distribution |
Introduction |
| Canonical response function |
Introduction |
| Conferences on machine learning |
Introduction |
| Linear regression versus classification |
Introduction |
Comparison between mean squared error (MSE), absolute error (L1 Loss) and fourth-power loss
|
Introduction |
| Comparison between L1 Regularization and L1 Loss (absolute loss or mean absolute error (MAE)) |
Introduction |
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| Close() and close opened file |
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Close file after reading a file: avoid file locking |
Introduction |
| ◆ |
cv2.waitKey(1): Will display a frame for 1 ms, after which display will be automatically closed. Since the OS has a minimum time between switching threads, the function will not wait exactly 1 ms, it will wait at least 1 ms, depending on what else is running on your computer at that time. |
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close(): It closes the file or webpage, and frees the memory space acquired by that file. |
(code) |
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win32clipboard.CloseClipboard() |
code. |
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Open and close any type of files with default programs/apps (e.g. word, excel, dm3, dm4, Digital Micrograph, powerpoint, internet explorer, chrome, and so on) in windows |
Introduction |
| ◆ |
Minimize/maximize/restore/activate/resize/move/close Window objects |
Introduction |
| ◆ |
Open and close specific files |
Introduction |
| ◆ |
Fill in closed curves |
Introduction |
| ◆ |
Methods to open google chrome (problems: Google chrome closes immediately after being launched with selenium) (with close-browser and quit-driver function) |
(Introduction) |
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| Convolution and convolutional layers |
Introduction |
| Comparison of regression classes |
Introduction |
| Independent Component Analysis (ICA) |
Introduction |
| Convex optimization, convexity of loss functions, convex functions and convex sets |
Introduction |
| Cocktail party problem |
Introduction |
| Lipschitzness/Lipschitz continuity |
Introduction |
| Point-Biserial Correlation |
Introduction |
| Kendall Tau Rank Correlation Coefficient |
Introduction |
| Pearson Correlation Coefficient/Pearson's r/Correlation Coefficient |
Introduction |
| Spearman Rank Correlation/Spearman's rho/Spearman correlation |
Introduction |
| Epsilon cover/ε-cover/epsilon-net |
Introduction |
| Infinite Hypothesis Class |
Introduction |
| Finite Hypothesis Class/finite Hypothesis Analysis |
Introduction |
| Complementary inequality |
Introduction |
| Core Steps/Procedure/Designing of Machine Learning |
Introduction |
| Finite Hypothesis Class versus Infinite Hypothesis Class |
Introduction |
| Various names or terms that describe similar concepts or techniques in ML |
Introduction |
| Hypothesis class/hypothesis family/predictor class/model class/hypothesis family/predictor family/model family (h) |
Introduction |
| Concentration inequality |
Introduction |
| Central Limit Theorem (CLT) |
Introduction |
| Covariance matrix |
Introduction |
| Modify/replace the line in a text file if a line contains specific string |
Introduction |
| Well-specified case |
Introduction |
| Uniform convergence |
Introduction |
| Consistency in Statistics |
Introduction |
| Defect Detection and Classification by using Machine Learning |
Introduction |
| Cross entropy (log loss/logistic loss) |
Introduction |
| Trick: generic code/script templates for complex automation |
Introduction |
| Loss (risk, cost, objective) function |
Introduction |
| Confusion matrix heatmap |
Introduction |
| Common Words for Classification of Groups of Texts |
Introduction |
| Good research topics in the field of semiconductor manufacturing and computer vision |
Introduction |
| Autonomous vehicles/cars and machine learning |
Introduction |
| Classification tree/decision tree for classification |
Introduction |
| Fréchet Inception Distance (FID) coefficient |
Introduction |
| Misclassification rate (classification error rate or error rate) in machine learning |
Introduction |
| Corpus |
Introduction |
| Class Activation Mapping (CAM) |
Introduction |
| Similarity-based clustering method (SCM) |
Introduction |
| Comparison between supervised and unsupervised learning |
Introduction |
| Convert a list to a matrix |
Introduction |
| Execute a command on Command Prompt of Windows |
Introduction |
Continue script execution no matter whether some try fails or not (finally)
|
Introduction |
| Create a temporary file or directory/folder |
Introduction |
| Remove unwanted/unnecessary parts from strings in a column of dataframe |
Introduction |
| Convert dataframe row/column into a comma separated string |
Introduction |
Compare string entries/cells/elements of columns in different dataframes
|
Introduction |
| Candidate keywords |
Introduction |
| Built-ins/Builtins Commands in Python |
Introduction |
| Build own/customized keyword candidates |
Introduction |
| Electrical characteristics of MOS capacitor |
Introduction |
| Count how many empty strings in a list |
Introduction |
| Semantic clustering |
Introduction |
| Clustering versus Classification of texts and documents |
Introduction |
| Clustering of texts |
Introduction |
| Classification of texts |
Introduction |
| Classification of groups of texts |
Introduction |
| (Text and image) contrastive learning |
Introduction |
| String template class for formating strings (F-strings (for calculation) (f"{}"), format() method ({}), %s, %d, Template ($)) |
Introduction |
| webdriver.Chrome() |
(Instruction) |
| ODBC (Open Database Connectivity) |
Introduction |
| Access and use SQL Database on SSMS (Microsoft SQL Server Management Studio Express) with pyodbc: localhost, insert rows, update, count updated, delete rows, comparision between extract data by Python and SQL itself |
Introduction |
| Python drivers for SQL server (pyodbc, pymssql, PyMySQL, cx_Oracle) |
Introduction |
| Call and run another script in a different/any (parent or children) directory/path/subfolder from a script |
Introduction |
| Get the current directory/folder path |
Introduction |
| Change the current directory to any directory/path (e.g. with os.chdir) |
Introduction |
| Count number of lines in a text file |
Introduction |
| Find common/different elements/items between two lists/sets |
Introduction |
| Plot confidence bands |
Introduction |
| Convert between numpy array and string |
Introduction |
| igraph for clustering and network |
Introduction |
| Call/run/execute JMP from Python |
Introduction |
| Count the number of the pages in a single multi-page/frame image |
Introduction |
| "@echo off" and "pause" in Command Prompt Window |
Introduction |
| Create a log (log.log) file to monitor script execution |
Introduction |
| Combine multiple images into a single multi-page image or vice versa |
Introduction |
| Convert/change the case of all letters/word into uppercase (capital) or lowercase in a list of strings |
Introduction |
| Compare dates (x days after or before a date), and difference between two dates in days |
Introduction |
| Convert set into a list and vice versa |
Introduction |
| Merge dictionaries (update(), **, chain(), ChainMap(), |, |=) |
Introduction |
| Calculating the area fraction of each circle overlapped (filled with color) by a square grid and build wafer map with integration |
Introduction |
| Convert strings to number (integers/float) |
Introduction |
| Trick: Get coordinate difference between mouse positions |
Introduction |
| Select/skip columns by index in DataFrame without changing the DataFrame itself, and change the order of of the selected columns |
Introduction |
| Convert all elements of specific column or in entire dataframe into strings |
Introduction |
| Find repeating patterns in columns, group them as cycles, and column correlations |
Introduction |
| Find the same elements in columns in two separate dataframes and then merge them |
Introduction |
| Remove the substring after the first or last character "::" in a given string, or extract the substring between the first and last "::" |
Introduction |
| Codes: Automation of Mouse Movements and Clicks, and keyboard control (comparison among pyautogui, pygetwindow, pydirectinput, autoit, Quartz, platform, ctypes, uiautomation and Sikuli) |
Introduction |
| Principle and troubleshooting: Automation of Mouse Movements and Clicks (comparison among pyautogui, pygetwindow, pydirectinput, autoit, Quartz, platform, ctypes, uiautomation and Sikuli) |
Introduction |
| Create table on pptx with certain rows and columns of strings |
Introduction |
| Create table on pptx with certain rows and columns in DataFrame |
Introduction |
| Click a menus of an application |
Introduction |
| exception KeyboardInterrupt, Raised when the user hits the interrupt key (normally Control-C/ctrl-c or Delete). code. |
Introduction |
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| Check |
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Compare (pattern/ratio of) two different columns, check whether column values match in DataFrame |
Introduction |
| ✔ |
Check whether one column contains number only and another column contains letters only or mixture of numbers and letters in DataFrame |
Introduction |
| ✔ |
Check the difference between two columns in DataFrame |
Introduction |
| ◆ |
checkpoint_path |
Introduction |
| ◆ |
Check if two circles intersect or overlap |
Introduction |
| ◆ |
Check if rectangles overlap |
Introduction |
| ◆ |
Check if two lists are same/identical |
Introduction |
| ◆ |
Check all the imported/current modules/libraries |
Introduction |
| ◆ |
Model checking |
Introduction |
| ◆ |
Model Checking Algorithms and Modus Ponens Algorithms |
Introduction |
| ◆ |
save_checkpoints_steps |
Introduction |
| ◆ |
RegEx (Regular Expression) (characters to check if a string contains a specified search pattern, remove double spaces, and clean texts) |
Introduction |
| ◆ |
Check ... empty |
|
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✔ |
Check if an item/element is in a list or not |
Introduction |
| |
✔ |
Check if a list is empty or not |
Introduction |
| |
✔ |
Check file existence with partial filename |
Introduction |
| |
✔ |
Check if a file/folder exists or not (Cannot find a specific file/folder? a specific folder in the path? select specific folders to form a string, split a dos path into its components, and then print the list, or check files with extension) |
Introduction |
| |
✔ |
Check if Windows/PC screen is locked |
Introduction |
| |
✔ |
Check if two lists have the same elements |
Introduction |
| |
✔ |
Check whether a file is empty or not |
Introduction |
| |
✔ |
Check if a variable does exist/is assigned/defined |
Introduction |
| |
✔ |
Check if a string is empty or space only |
Introduction |
| |
✔ |
Check if a key exists in a dictionary |
Introduction |
| |
✔ |
'xyz'.isalpha(): Check if string is alphabet (letter, or one type of character) |
|
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✔ |
Check all the imported/current modules/libraries |
Introduction |
| |
✔ |
|
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| ◆ |
Check if one list is subset of another (partially (part of)) |
Introduction |
| ◆ |
Check if a variable is a number or string |
Introduction |
| ◆ |
Check if an element in a sublist of a list |
Introduction |
| ◆ |
Check if a file exists again (double check) |
Introduction |
| ◆ |
Clean clipboard and/or check if clipboard is empty, text or image |
Introduction |
| ◆ |
Check updated new files in a folder |
Introduction |
| ◆ |
Check if all the (and how many, length of a string) characters in the text are digits/numbers |
Introduction |
| ◆ |
Check to see if or get a window with a name containing specific titles or texts |
Introduction |
| ◆ |
Check the letters and symbols starting and ending with |
code |
| ◆ |
Keyword search function/check whether or not a string is within another string (a space is included as a string character) |
Introduction |
| ◆ |
Check if a popup dialog is a window or not for Selenium app |
Introduction |
| ◆ |
checkbox |
Introduction |
| ◆ |
Check/find/get a file name or the last folder name (e.g. from a path/directory) |
Introduction |
| ◆ |
Check if both files are the same file, e.g. symbolic link, shortcut |
Introduction |
| ◆ |
Check if a letter is in a string |
Introduction |
| ◆ |
Check if a string can be converted to float |
Introduction |
| ◆ |
Check if a letter/character is in a string |
Introduction |
| ◆ |
Extract text/check specific text from multiple powerpoint files |
Introduction |
| ◆ |
Add a new slide into an existing ppt, or work on existing slides, check the existence of a pptx file, if does not exist then create it |
Introduction |
| ◆ |
Compare/check if two text files have the same contents |
Introduction |
| ◆ |
Check existence of phrase on text file line-by-line |
Introduction |
| ◆ |
from keyboard import is_pressed (Esc, check pressed key) |
Introduction |
| ◆ |
Check and drop negative from dataframe pandas |
Introduction |
| ◆ |
Check (difference) whether or not a cell value in a column of a CSV file Matchs a value in a column of another CSV file, then do something: e.g. add a value to another column of a csv file |
Introduction |
| ◆ |
File name, folder name. {}{}....format. Manipulation of file and folder names (rename file name and folder name): i) Check and create a new folder and then copy all files from a folder to the new folder and rename the file, and then open the file. If the folder exists, then no file will be copied, but the file will still be opened. ii) Print and export the folder names and file names (with or without extensions) from a folder into a text file. iii) csv2image filename. |
Introduction |
| ◆ |
Check if CSV cell value is NaN |
Introduction |
| ◆ |
Check if a CSV file contains all of the specified strings |
Introduction |
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| CSV |
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Convert between rows and columns from csv: Convert resulting row from CSV search into a column. |
Introduction, CSV |
| ◆ |
Convert CSV to images, row-by-row, with pixel values: each row is an image |
code. |
| ◆ |
Heatmap with input from a csv/pkl file |
Introduction |
| ◆ |
Print all values cell by cell in order of row and column in the csv file |
Introduction |
| ◆ |
Replace/change to new headers in a csv file |
Introduction |
| ◆ |
Remove duplicate cell values from a csv file/dataframe (e.g. keeping the first/top one; drop_duplicates(); .duplicated(); .get_option(); .set_option(); keep='first'/keep='last'; display.max_rows; display.max_columns) |
Introduction |
| ◆ |
Skip rows and/or columns in csv |
Introduction. |
| ◆ |
Data cleaning examples in csv files |
Introduction |
| ◆ |
Skip/remove empty rows (row-by-row) in DataFrame/csv |
Introduction |
| ◆ |
Convert a CSV file to a JSON file |
Introduction |
| ◆ |
csv workflow: Read into dataframe, Select a specific column from DataFrame, Select several specific columns to plot |
| ◆ |
Convert a CSV file to an image with one column and another column as x-axis and y-axis, respectively. |
code. |
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Change/convert a colored image to a grey image(, and then show pixel values). |
cv2, cv2, cv2/skimage. cv2/skimage. PIL. matplotlib. |
| ◆ |
Convert a CSV file to a TXT file |
Introduction |
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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 |
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Change/rename a column name/header in a CSV file |
Introduction |
| ◆ |
Delete the column/row in a CSV file if they are empty or less than a number (or header/index only) |
Introduction |
| ◆ |
Read specific cells (cell by cell) in csv file |
Introduction |
| ◆ |
Create CSV files (e.g. with headers only) |
Introduction |
| ◆ |
CSV column transposer (rows/columns) |
Introduction |
| ◆ |
Input a sentence and then output a sentence based on a dictionary obtained from csv |
Introduction |
| ◆ |
Creates a dictionary from a csv file |
Introduction |
| ◆ |
Skip/replace empty cells/NaN value from DataFrame/CSV file |
Introduction |
| ◆ |
Trick: Output a portion of rows and columns from a csv file cell by cell into another csv file |
Introduction |
| ◆ |
Nearest/most similar lyrics of a sentence/text to a CSV file |
Introduction |
| ◆ |
Write special/certain rows (row-by-row) of one csv file to another csv file |
Introduction |
| ◆ |
Convert csv/dataframe column to a list or vice versa |
Introduction |
| ◆ |
Plot graph/figure/image from CSV file/DataFrame by removing/hiding blank/empty cells with axis range (plt.xlim()) |
Introduction |
| ◆ |
Plot graph/figure/image from CSV file |
Introduction |
| ◆ |
Creates a dictionary from a csv file |
Introduction |
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Correlations/similarity/dissimilarity/pair/match of two columns in csv data |
Introduction |
| ◆ |
Put the keywords in a grouped string into the first available cells in the corresponding columns in a csv file |
Introduction |
| ◆ |
Separately plot data into the same graph/figure/image from different csv files for each category (import multiple CSV files and concatenate into one DataFrame): append row-by-row or column-by-column |
Introduction |
| ◆ |
Convert a csv column to a string seperated by comma |
Introduction |
| ◆ |
Summary and cheatsheet of command for csv file |
Introduction |
| ◆ |
Collect the file list in a folder into a csv file |
Introduction |
| ◆ |
pandas for CSV |
Introduction |
| ◆ |
Write cvs cells |
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✔ |
.to_csv()/.writerow() |
Instruction |
| |
✔ |
pandas.DataFrame.to_csv(): DataFrame.to_csv(path_or_buf=None, sep=',', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, mode='w', encoding=None, compression='infer', quoting=None, quotechar='"', line_terminator=None, chunksize=None, date_format=None, doublequote=True, escapechar=None, decimal='.', errors='strict', storage_options=None). Write the contents of the Frame into a CSV file |
code |
| |
✔ |
writerow()/.writerows():: Save data into a CSV file. code. delete all items |
Introduction |
| |
✔ |
Write cell by cell with .loc |
Instruction |
| |
✔ |
cv2.imwrite:: Save images |
code. code |
| ◆ |
Search/print/output the rows: Print the rows if their cell values are greater than a specific value, in the csv file with numbers only; output the rows if the cell value is in a specific range. code. |
Introduction |
| ◆ |
Sort a csv file with column: used "key = operator.itemgetter()" |
Instruction |
| ◆ |
Calculation and data extraction in csv under conditions: Compares with the ones which cannot be used for math calculation, find the maximum in a column |
Introduction |
| ◆ |
Replace/change to new headers in a csv file |
Introduction |
| ◆ |
Skip rows and/or columns in csv |
Introduction |
| ◆ |
Merge/combine two csv files |
Introduction |
| ◆ |
Split columns and merge in csv: Split columns and then merge the splits in a csv file. |
Introduction |
| ◆ |
Count the number of lines (rows) and columns in a txt (and a csv) file, count different numbers in each region in a column, count missing or not available values. code |
Introduction |
| ◆ |
Sort a csv file with column: used "key = operator.itemgetter()" |
Instruction |
| ◆ |
Calculation in csv: Compares with the ones which cannot be used for math calculation, find the maximum in a column |
Introduction |
| ◆ |
Replace/change to new headers in a csv file |
Introduction |
| ◆ |
Skip rows and/or columns in csv |
Introduction |
| ◆ |
Filters the rows based on the condition of being within n days of today's date |
Introduction |
| ◆ |
.axes[0] and .axes [1]. for csv. |
(code) |
| ◆ |
.sum(): sum and percentage for csv |
Introduction |
| ◆ |
Count duplicates/occurrence and show unique values in csv files |
Introduction |
| ◆ |
Search in csv file: code. code |
Instruction |
| ◆ |
Split columns and merge in csv: Split columns and then merge the splits in a csv file |
Introduction |
| ◆ |
Count the number of lines (rows) and columns in a txt (and a csv) file, count different numbers in each region in a column, count missing or not available values. code. |
Introduction |
| ◆ |
Search/print/output the rows: Print the rows if their cell values are greater than a specific value, in the csv file with numbers only; output the rows if the cell value is in a specific range. code. |
Introduction |
| ◆ |
for ... in rang() in csv |
Introduction |
| ◆ |
Guess and check algorithm with a combination of a for loop and an if statement |
Introduction |
| ◆ |
Sort a csv file with column: used "key = operator.itemgetter()" |
Instruction |
| ◆ |
Calculation in csv: Compares with the ones which cannot be used for math calculation, find the maximum in a column |
Introduction |
| ◆ |
datatable.Frame.to_csv(): Write the contents of the Frame into a CSV file. If no path is given, then the Frame will be serialized into a string, and that string will be returned |
|
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csv.Sniffer(): The Sniffer class is used to deduce the format of a CSV file. It expects a sample string, not a file |
code |
| ◆ |
next(): Skip headers in CSV |
code |
| ◆ |
info(): Print information in CSV |
(code) |
| ◆ |
Series.to_csv(): Is a 1-D ndarray with axis labels and writes the given series object to a comma-separated values (csv) file/format |
code |
| ◆ |
line: Print line by line from a CSV file |
code |
| ◆ |
names: Names in headers of csv files |
code. code |
| ◆ |
DataFrame.equals(): Confirm if the two csv files are the same or not |
code |
| ◆ |
.groupby(): sort/group columns. |
(code). CSV: (code) |
| ◆ |
quoting: Set quoting rules as in csv module (default csv.QUOTE_MINIMAL) |
|
| ◆ |
dialect: string or csv.Dialect instance to expose more ways to specify the file format. To expose more ways to specify the file format |
code |
| ◆ |
Create a file (e.g. csv, pptx files): See open(). |
|
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csv.writer(). Return a writer object responsible for converting the user’s data into delimited strings on the given file-like object. csvfile can be any object with a write() method.. code. delete all items. |
Instruction |
| ◆ |
Count duplicates/occurrence and show unique values in csv files |
Introduction |
| ◆ |
Split columns and merge in csv: Split columns and then merge the splits in a csv file |
Introduction |
| ◆ |
Count the number of lines (rows) and columns in a txt (and a csv) file, count different numbers in each region in a column, count missing or not available values |
Introduction. code. |
| ◆ |
Split columns and merge in csv: Split columns and then merge the splits in a csv file. |
Introduction |
| ◆ |
Skip rows and/or columns in csv |
Introduction. |
| ◆ |
Print/obtain a specific digit in a number, e.g. in a cell value in csv |
Introduction |
| ◆ |
Write to a specific cell in a csv file |
Introduction |
| ◆ |
Mean (average, .mean())/.sum()/maximum(.max())/minimum(.min())/number of non-null values(.count())/.median()/variance(.var())/standard deviation(.std()/pstdev()) |
Introduction |
| ◆ |
Get the csv/pandas cell value with certain condition |
Introduction |
| ◆ |
Add/insert a column into an existing csv file |
Introduction |
| ◆ |
Change csv cell value under conditions (e.g. if a cell value is equal to another cell value, then compare the third cell value; if both cell value is the same, then change its value to a value) |
Introduction |
| |
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| Read |
|
| ◆ |
csv.reader(): Return a reader object which will iterate over lines in the given csvfile |
Introduction |
| ◆ |
tupleize_cols: If False (default), write as a list of tuples, otherwise write in an expanded line format
suitable for read_csv |
|
| ◆ |
DictReader() |
Introduction |
| ◆ |
pandas.read_table(): code |
Introduction |
| ◆ |
pandas.read_csv() |
Introduction |
| ◆ |
Iterate over rows in a DataFrame/read and print row-by-row (number of columns and rows, df.shape[0]/df.shape[1]) |
Introduction |
| ◆ |
Read columns with numeric values/numbers only in dataframe |
Introduction |
| ◆ |
cv2.imread(/path/to/image, flag): A method loads an image from the specified file. If the image cannot be read (because of missing file, improper permissions, unsupported or invalid format) then this method returns an empty matrix. imread() decodes the image into a matrix with the color channels stored in the order of Blue, Green, Red and A (Transparency) respectively: (:, :, 0) represents Blue channel; (:, :, 1) represents Green channel; (:, :, 2) represents Red channel; (:, :, 3) represents Transparency channel. The flag is optional. |
code. code. code. code. |
| ◆ |
cv2.IMREAD_GRAYSCALE: Reads the image as grey image. If the source image is color image, grey value of each pixel is calculated by taking the average of color channels. = 0, |
code. |
| |
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| DataFrame workflow: Drop/delete rows with empty cells in a column, Sort DataFrame by time/date order |
| |
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| Electrical circuit simulations |
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NgSpice/PySpice |
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Switches simulations |
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Diodes simulations |
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Electrical characteristics of the MOS capacitor |
Introduction |
| ◆ |
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| Change date/month/year format |
Introduction |
| Get header/column names from DataFrame |
Introduction |
| Get the frequency of occurrence of a string in a column DataFrame |
Introduction |
Difference/comparison between real mouse click and click from script/program, e.g. Pyautogui
|
Introduction |
| Plot images with certain image size and in color |
Introduction |
| Plot confidence bands |
Introduction |
| .bat (batch) files for Command Prompt Windows |
Introduction |
| Trick: pd.concat() for merging/adding (two) columns |
Introduction |
| CycleGAN (Cycle-Consistent Adversarial Networks) |
Introduction |
| Copy a file or all files (with os.mkdir) to save to somewhere (create a directory first if it does not exist) |
Introduction |
| Remove decimal part in a string with comma |
Introduction |
| Form a list of strings from an old string with all the 6 digits by removing all special characters or spaces |
Introduction |
| Class |
Introduction |
| self and __init__ method in Class |
Introduction |
| Find the computer name |
Introduction |
| __str__ method for a class |
Introduction |
| __add__, __call__, __contains__, __delitem__, __delattr__, __eq__, __enter__, __ge__, __getattribute__, __getnewargs__, __getattr__, __getitem__, __gt__, __hash__, __reduce__, __iadd__, __imul__, __init_subclass__, __index__, __int__, __invert__, __new__, __neg__, __reduce_ex__, __reversed__, __rmul__, __radd__, __rand__, __rdivmod__, __rfloordiv__, __rlshift__, __rmod__, __ror__, __round__,__rpow__, __rrshift__, __rshift__, __rsub__, __rtruediv__, __rxor__, __dir__, __doc__, __divmod__, __iter__, __le__, __lt__, __len__, __ne__, __repr__, __setattr__, __setitem__, __sizeof__, __lshift__, __sub__, __subclasshook__, __str__ |
Introduction |
| Safely use credentials (username and password) in Python project |
Introduction |
| Convert a sentence/text to a list |
Introduction |
| Merge columns which contain specific strings |
Introduction |
| Merge rows/columns of a csv file into an old csv file if the rows/columns are not in the old csv file |
Introduction |
| Select specific columns from a DataFrame to form a new DataFrame |
Introduction |
| Output the row into dataframe if the value of the cell in a column contains a specific substring in a csv file (with headers) |
Introduction |
| Add letter/commas/numbers/characters to the end/beginning of strings in a list |
Introduction |
| Word cloud visualization |
Introduction |
| Extract the last column as subdataframe |
Introduction |
| Convert a floating-point number to exponential format |
Introduction |
| Apply a formatting function to all cells in a DataFrame |
Introduction |
| Color the Tables in pptx (PowerPoint) |
Introduction |
| Color and rotate/vertical text in pptx |
Introduction |
| Selecting only numeric/number columns, and then select two specific columns for plot |
Introduction |
| Merge columns with character/symbol Separation |
Introduction |
| Plot workflow: Create new empty column in DataFrame, Move the cells in a column to another column under certain condition, Select specific columns for scatter plot |
| Hide x-axis tick labels (only show some labels) where x values are under certain conditions |
Introduction |
Create table with merged cells on pptx |
Introduction |
| Change the width of the cell in ppt |
Introduction |
| Highlight the plotted dots uncer certain condition |
Introduction |
| Only use the first 4 characters in the headers of the table for pptx/dataframe |
Introduction |
| Embed/hide codes or markers into HTML files |
Introduction |
| Cheatsheet about headers (column names) in DataFrame |
Introduction |
| Plot a heatmap with three columns of data |
Introduction |
| Aggregate duplicates in columns of data |
Introduction |
| Comparative overview of multivariate statistical methods (Correlation Analysis, Regression Analysis, Factor Analysis, Cluster Analysis, Principal Component Analysis (PCA), Canonical Correlation Analysis, Discriminant Analysis, Path Analysis, Structural Equation Modeling (SEM), Multivariate Analysis of Variance (MANOVA), Analysis of Covariance (ANCOVA) ): purposes, variables, and outputs |
Introduction |
| Overcoming automation challenges and forward-looking suggestions |
Introduction |
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| .create_sheet('') |
(code) |
| from typing import List, Dict |
code. |
| inplace=True |
Change the items permanently. (code). (code). |
| .str.split() |
(code). |
| expand=True |
(code). |
| rename() |
(code). |
| |
|
| columns= |
(code). (code). |
| regex=True |
(code). |
| |
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| |
|
| delim_whitespace |
Parse whitespace-delimited (spaces or tabs) file (much faster than using a regular
expression) |
| compression |
decompress ’gzip’ and ’bz2’ formats on the fly. Set to ’infer’ (the default) to guess a
format based on the file extension. |
| |
|
| dtype |
A data type name or a dict of column name to data type. If not specified, data types will be inferred.
(Unsupported with engine=’python’) |
| header= |
Whether to write out the column names (default True). Introduction. (code). Row number(s) to use as the column names, and the start of the data. Defaults to 0 if no names
passed, otherwise None. Explicitly pass header=0 to be able to replace existing names. The header can be a
list of integers that specify row locations for a multi-index on the columns E.g. [0,1,3]. Intervening rows that are
not specified will be skipped (e.g. 2 in this example are skipped). Note that this parameter ignores commented
lines and empty lines if skip_blank_lines=True (the default), so header=0 denotes the first line of data
rather than the first line of the file. |
| skip_blank_lines |
whether to skip over blank lines rather than interpreting them as NaN values |
| skiprows |
A collection of numbers for rows in the file to skip. Can also be an integer to skip the first n rows |
| index_col |
column number, column name, or list of column numbers/names, to use as the index (row
labels) of the resulting DataFrame. By default, it will number the rows without using any column, unless there
is one more data column than there are headers, in which case the first column is taken as the index. |
| names |
List of column names to use as column names. To replace header existing in file, explicitly pass
header=0. |
| |
|
| true_values |
list of strings to recognize as True |
| false_values |
list of strings to recognize as False |
| keep_default_na |
whether to include the default set of missing values in addition to the ones specified in
na_values |
| parse_dates |
if True then index will be parsed as dates (False by default). You can specify more complicated
options to parse a subset of columns or a combination of columns into a single date column (list of ints or names,
list of lists, or dict) [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column [[1, 3]] -> combine
columns 1 and 3 and parse as a single date column {‘foo’ : [1, 3]} -> parse columns 1, 3 as date and call result
‘foo’ |
| keep_date_col |
if True, then date component columns passed into parse_dates will be retained in the
output (False by default). |
| date_parser |
function to use to parse strings into datetime objects. If parse_dates is True, it defaults
to the very robust dateutil.parser. Specifying this implicitly sets parse_dates as True. You can also
use functions from community supported date converters from date_converters.py |
| .to_datetime() |
Change data format. (code) |
| dayfirst |
if True then uses the DD/MM international/European date format (This is False by default) |
| thousands |
specifies the thousands separator. If not None, this character will be stripped from numeric
dtypes. However, if it is the first character in a field, that column will be imported as a string. In the PythonParser,
if not None, then parser will try to look for it in the output and parse relevant data to numeric dtypes. Because it
has to essentially scan through the data again, this causes a significant performance hit so only use if necessary. |
| lineterminator |
string (length 1), default None, Character to break file into lines. Only valid with C
parser |
| quotechar |
string, The character to used to denote the start and end of a quoted item. Quoted items can
include the delimiter and it will be ignored. |
| |
|
| skipinitialspace |
boolean, default False, Skip spaces after delimiter |
| escapechar |
string, to specify how to escape quoted data |
| comment |
Indicates remainder of line should not be parsed. If found at the beginning of a line, the line will be ignored altogether. This parameter must be a single character. Like empty lines, fully commented lines are ig-
nored by the parameter header but not by skiprows. For example, if comment=’#’, parsing ‘#emptyn1,2,3na,b,c’ with header=0 will result in ‘1,2,3’ being treated as the header. |
| .value_counts() |
.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True). (code) (code) |
| skip_footer |
number of lines to skip at bottom of file (default 0) (Unsupported with engine=’c’) |
| confirm() |
confirm(text='', title='', buttons=['OK', 'Cancel']) (code) |
| converters |
a dictionary of functions for converting values in certain columns, where keys are either integers
or column labels |
| encoding |
a string representing the encoding to use for decoding unicode data, e.g. ’utf-8‘ or
’latin-1’. Full list of Python standard encodings |
| verbose |
show number of NA values inserted in non-numeric columns |
| squeeze |
if True then output with only one column is turned into Series |
| error_bad_lines |
if False then any lines causing an error will be skipped bad lines |
| usecols |
skip column. Only store the columns which are need so that a subset of columns is returned, resulting in much faster parsing time and lower memory usage. code. code. code. code. |
| mangle_dupe_cols |
boolean, default True, then duplicate columns will be specified as ‘X.0’...’X.N’, rather
than ‘X’...’X’ |
| tupleize_cols |
boolean, default False, if False, convert a list of tuples to a multi-index of columns, otherwise, leave the column index as a list of tuples |
| float_precision |
string, default None. Specifies which converter the C engine should use for floating-point values. The options are None for the ordinary converter, ‘high’ for the high-precision converter, and ‘round_trip’ for the round-trip converter. |
| |
|
| path_or_buf |
A string path to the file to write or a StringIO |
| sep |
Field delimiter for the output file (default ”,”) |
| na_rep |
A string representation of a missing value (default ‘’) |
| float_format |
Format string for floating point numbers |
| cols |
Columns to write (default None) |
| index |
whether to write row (index) names (default True) |
| index_label |
Column label(s) for index column(s) if desired. If None (default), and header and index are
True, then the index names are used. (A sequence should be given if the DataFrame uses MultiIndex). |
| mode |
Python write mode, default ‘w’ |
| encoding |
a string representing the encoding to use if the contents are non-ASCII, for python versions prior
to 3 |
| line_terminator |
Character sequence denoting line end (default ‘\n’) |
| |
|
| quotechar |
Character used to quote fields (default ‘”’) |
| doublequote |
Control quoting of quotechar in fields (default True) |
| escapechar |
Character used to escape sep and quotechar when appropriate (default None) |
| chunksize |
Number of rows to write at a time |
| |
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| date_format |
Format string for datetime objects |
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| lineterminator |
code. |
| |
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| describe() |
Print statistic summary of the data. (code) |
| import pandas as pd |
code. |
| skiprows |
code. code. code. |
| |
|
| skipfooter |
skip rows from bottom. code. |
| header |
skip header. code. code. code. code. |
| engine |
code. |
| |
|
| skiprows = lambda x: |
code. code. code. |
| index_col |
Skip column index. code. code. code. |
| |
|
| .fillna() |
Replace empty cells with anything. (code) |
| |
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| |
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| sort_values(by=...) |
Introduction |
| drop() |
(code)(code) |
| head() |
(code) |
| dropna() |
Allows the user to analyze and drop Rows/Columns with Null values in different ways. code. |
| reset_index() |
code. |
| |
|
.groupby('...')['...']
|
(code) |
| unique() |
(code) |
| nunique() |
(code) |
| left_index=, right_index= |
(code) |
| classification_report |
(code) |
| confusion_matrix |
(code). (code) |
| click() |
.click() function is just a convenient wrapper around these two .mouseDown() and .mouseUp() function calls. click() without parameters gives a single, left-button mouse click at the mouse’s current position; click(x, y) calls moveTo() before the click. 'left', 'middle', and 'right' specify a different mouse button.
(code) |
.clear() |
Delete all the dictionary's key-value pairs. General. |
| .config() |
(code) |
| continue |
Introduction |
| from pptx.dml.color import RGBColor |
(code) |
| .color. |
(code) |
| __class__ |
|
| __class_getitem__ |
|
| clear |
|
| copy |
|
| __ceil__ |
|
| __class__ |
|
| count |
|
| conjugate |
|
| math.ceil() |
The ceiling of a given number is the nearest integer greater than or
equal to that number. For example, the ceiling of 4.568 is 5. Code |
| math.ceil() |
|
| math.cos() |
|
| math.copysign(x, y) |
Copy sign: The sign of the second argument is returned along with the result
on the execution of this function. x: Integer value to be converted,
y: Integer whose sign is required. Example code |
| math.cosh() |
|
| case_sensitive=True |
(code) |
Integer/fractions/round /decimal/digits/floating
/ceil/floor |
It does not have any fractional part. Introduction. int: Example code. |
| Complex |
It can store real and imaginary parts |
Conversions |
Cast, or convert a variable from one type to another. Input() always stores a string, even if the value inputted is a number.
Casting = temporarily converting a value to another type.
There could be loss of precision: i.e. int(1.5) turns it into 1. code. |
| int() |
Converts a float number or a string to an integer, cast the number. code1 |
| float() |
Returns a floating point number constructed from a number or string |
| str() |
Introduction. Returns a string which is fairly human readable. code. |
| "str()" and "," difference |
(code) |
| chr() |
Convert an integer to a string of one character whose ASCII code is same as the integer. Introduction. |
| complex() |
Print a complex number with the value real + imag*j or convert
a string or number to a complex number |
| ord() |
Returns an integer representing Unicode code point for the given
Unicode character. code. code |
| hex() |
Convert an integer number (of any size) to a lowercase hexadecimal string prefixed with
“0x” |
| oct() |
Convert an integer number (of any size) to an octal string prefixed with “0o” |
| .convert() |
Image.convert(mode=None, matrix=None, dither=None, palette=0, colors=256). Dither: Dithering method, used when converting from mode “RGB” to “P” or from “RGB” or “L” to “1”. Available methods are NONE or FLOYDSTEINBERG (default). code. code. |
| |
| capitalize() |
(Code) Returns a copy of the original string and converts the first character of the string to a capital (uppercase) letter while making all other characters in the string lowercase letters. code. code. |
| os.chdir |
code |
| ctypes |
Code. Code. |
| ctypes.windll.user32.MessageBoxW |
Code. Code. |
| shutil.copyfileobj |
code. |
| shutil.copy() |
(code) |
| .correlate2d |
code. code. |
| Correlate1D() |
Performs a 1D correlation using
Fourier transforms. |
| Correlate2D |
Performs a 2D correlation using
Fourier transforms and uses ft.fft2d and ft.ifft2d. |
| Correlate2DF |
Can be is used in cases where the input and
filter are already in Fourier space, and can also be used to finish the correlation computation between the inputs and
filter. Code. |
| conjugate( ) |
Code. |
| ConfigParser |
Manipulate data and manage user-editable configuration files for an application. The configuration files are organized into sections, and each section can contain name-value pairs for configuration dat |
| scipy.linalg.circulant |
Create a circulant matrix. |
| scipy.linalg.companion |
Create a companion matrix. |
| scipy.linalg.convolution_matrix |
Create a convolution matrix. |
| linalg.cholesky(a) |
Cholesky decomposition. |
| linalg.cond(x[, p]) |
Compute the condition number of a matrix. |
| command |
Introduction. code. code. |
| cmap=plt.cm.gray |
code. |
| __call__/method-wrapper |
name/type: implementation of the () operator; a.k.a. the callable object protocol |
| __closure__/tuple |
name/type: the function closure, i.e. bindings for free variables (often is None) |
| __code__/code |
name/type: function metadata and function body compiled into bytecode |
| skimage.measure.compare_mse(im1, im2) |
Compute the mean-squared error between two images. |
| skimage.measure.compare_nrmse(im_true, im_test) |
Compute the normalized root mean-squared error (NRMSE) between two images. |
| skimage.measure.compare_psnr(im_true, im_test) |
Compute the peak signal to noise ratio (PSNR) for an image. |
| skimage.measure.correct_mesh_orientation(...) |
Correct orientations of mesh faces. |
| skimage.measure.CircleModel() |
Total least squares estimator for 2D circles. |
compare_ssim(X, Y[, ...]), or skimage.measure.compare_ssim(X, Y[, ...])
|
Is a function from scikit-image, a score (structural similarity index between the two input images. Compute the mean structural similarity index between two images. This value can fall into the range [-1, 1] with a value of one being a “perfect match”) and difference image can be calculated. code. |
| import clipboard |
(code) |
| Chainer |
Is a competitor to Hebel. It aims at increasing the flexibility of deep learning models. The three key focus areas of chainer include :
a. Transportation system: The makers of Chainer have consistently shown an inclination towards automatic driving cars and they have been in talks with Toyota Motors about the same.
b. Manufacturing industry: From object recognition to optimization, Chainer has been used effectively for robotics and several machine learning tools.
c. Bio-health care: To deal with the severity of cancer, the makers of Chainer have invested in research of various medical images for early diagnosis of cancer cells. |
| v2.rectangle(image, start_point, end_point, color of border line, border thickness) |
border. Compute the bounding box of the contour and then draw the bounding box on an image to represent where the ROI is. code. code. code. |
| cv2.circle() |
cv2.circle(image, center_coordinates, radius, color, thickness). (code). code. code. |
| cv2.EVENT_FLAG_LBUTTON |
Mouse callback function with a single left click. code. |
| cv2.EVENT_LBUTTONDOWN |
Mouse callback function with a single left click. code. (code) |
| cv2.EVENT_FLAG_MBUTTON |
Mouse callback function with a single left click. code. |
| cv2.EVENT_LBUTTONUP |
Mouse callback function with a single left click. code. |
| cv2.EVENT_LBUTTONDBLCLK |
Mouse callback function with double left clicks. code. |
| cv2.EVENT_RBUTTONDOWN |
Mouse callback function with a single right click. code. |
| cv2.EVENT_RBUTTONUP |
Mouse callback function with a single right click. code. |
| cv2.EVENT_FLAG_RBUTTON |
Mouse callback function with a single right click. code. |
| cv2.EVENT_FLAG_CTRLKEY |
Mouse callback function with double right clicks. code. |
| cv2.EVENT_MBUTTONDOWN |
Mouse callback function with the single middle mouse click. code. |
| cv2.EVENT_MBUTTONUP |
Mouse callback function with the single middle mouse click. code. |
| cv2.EVENT_MOUSEMOVE |
Mouse move. code. |
| cv2.matchTemplate |
Introduction. Returns a correlation map,
essentially a grayscale image. Other than contour filtering, matching keypoints, contour detection and processing (with thresholding, edge detection, etc. to generate a binary image), template matching is arguably one of the most simple forms of object detection (only 2-3 lines of code), which can detect multiple instances of the same/similar object in an input image. This method quickly fails when there are unknown changes of rotation, scale, viewing angle, etc. In those cases, you should use dedicated object detectors including HOG + Linear SVM, Faster R-CNN, SSDs, YOLO, etc. code. code. code. code. code.
Limitations: The matching can fail (if there is no special treatments in the script) if the size of the template is substantially smaller than the feature in the image being searched. |
| cv2.Canny()/Canny filter |
Introduction. Canny Edge Detection is a popular edge detection algorithm. This detection is susceptible to noise in the image, so that first step is to remove the noise in the image with a 5x5 Gaussian filter. Code. code. code. |
| cv2.TM_SQDIFF() |
 |
| cv2.TM_SQDIFF_NORMED |
 |
| cv2.TM_CCORR |
 |
| cv2.TM_CCORR_NORMED |
 |
| cv2.TM_CCOEFF_NORMED() |
The third parameter here is the method used for matching. code, code. code. |
| cv2.TM_CCOEFF |
 |
| cv2.add |
code. |
| |
|
| cv2.resize |
Resizing does only change the width and height of the image. code. |
| cv2.waitKey(0) |
Will display the window infinitely until any keypress (it is suitable for image display). Therefore, if you use waitKey(0) you see a still image until you actually press something. code. code. code. code. |
| cv2.setMouseCallback |
Mouse clicks: introduction. code. code. |
| cv2.destroyAllWindows() |
Simply destroys all the windows we created. code. code. |
| cv2.destroyWindow() |
To destroy any specific window with the exact window name. |
| cv2.imshow |
A method is used to display an image in a window. The window automatically fits to the image size. First argument is a window name which is a string. Second argument is
our image. code. code. Code. |
| cv2.cvtColor() |
Is used to convert an image from one color space to another. code. |
| cv2.COLOR_BGR2GRAY |
code. |
| cv2.THRESH_BINARY |
If pixel intensity is greater than the set threshold, value set to 255, else set to 0 (black). code. code |
| cv2.THRESH_BINARY_INV |
Inverted or Opposite case of cv2.THRESH_BINARY. code. code |
| cv2.line() |
Draw a line in a image. code |
| cv2.arrowedLine() |
cv2.arrowedLine(image, start_point, end_point, color[, thickness[, line_type[, shift[, tipLength]]]]) is used to draw arrow segment pointing from the start point to the end point. The parameters of the cv2.arrowedLine function are the same as those for cv2.line. code. |
| cv2.putText() |
cv2.putText(image, text, org, font, fontScale, color[, thickness[, lineType[, bottomLeftOrigin]]]) is used to draw a text string on any image. The font types are FONT_HERSHEY_SIMPLEX = 0, FONT_HERSHEY_PLAIN = 1, FONT_HERSHEY_DUPLEX = 2, FONT_HERSHEY_COMPLEX = 3, FONT_HERSHEY_TRIPLEX = 4, FONT_HERSHEY_COMPLEX_SMALL = 5, FONT_HERSHEY_SCRIPT_SIMPLEX = 6, FONT_HERSHEY_SCRIPT_COMPLEX = 7, and FONT_ITALIC = 16. The thickness of the line is in pixel. lineType: This is an optional parameter.It gives the type of the line to be used. bottomLeftOrigin: This is an optional parameter. When it is true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner. code. (code) |
| cv2.namedWindow |
code. |
| cv2.moveWindow |
Set the position (coordinates) of the opened window. code. |
| cv.THRESH_TRUNC |
If pixel intensity value is greater than threshold, it is truncated to the threshold. The pixel values are set to be the same as the threshold. All other values remain the same. code. code |
| cv.THRESH_TOZERO |
Pixel intensity is set to 0, for all the pixels intensity, less than the threshold value. code. code |
| cv.THRESH_TOZERO_INV |
Inverted or Opposite case of cv2.THRESH_TOZERO. code. code |
| v2.rectangle(image, start_point, end_point, color of border line, border thickness) |
border. Compute the bounding box of the contour and then draw the bounding box on an image to represent where the ROI is. code. code. code. |
Get dimensions (sizes) of image:
dimensions = img.shape
Get height, width, number of channels in image
height = img.shape[0]
width = img.shape[1]
channels = img.shape[2] |
General, code. code. code. code. code. code. |
| Split a list into columns |
Introduction |
| Colors |
cmaps['Perceptually Uniform Sequential'] = ['viridis', 'plasma', 'inferno', 'magma', 'cividis']
cmaps['Sequential'] = ['Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',
'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn']
cmaps['Sequential (2)'] = ['binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink',
'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia',
'hot', 'afmhot', 'gist_heat', 'copper']
cmaps['Diverging'] = [
'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu',
'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic']
cmaps['Cyclic'] = ['twilight', 'twilight_shifted', 'hsv']
cmaps['Qualitative'] = ['Pastel1', 'Pastel2', 'Paired', 'Accent',
'Dark2', 'Set1', 'Set2', 'Set3',
'tab10', 'tab20', 'tab20b', 'tab20c']
cmaps['Miscellaneous'] = ['flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern',
'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg',
'gist_rainbow', 'rainbow', 'jet', 'turbo', 'nipy_spectral', 'gist_ncar']. code |
| matplotlib.cbook |
(code) (code) |
| va='center' |
code. |
| |
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| Caffe2 |
Is a Lightweight, Modular, and Scalable Deep Learning Framework. It aims to provide an easy and straightforward way for you to experiment with deep learning. |
| random.choice() |
Randomly select from options. (code) |
| 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) |
| GetKeyState(VK_CAPITAL) |
Caps Lock, .press("capslock"). (code). |
| from pynput.mouse import Controller |
(code) |
| .click(Button.left, x) |
x clicks of mouse. (code) |
| .locateCenterOnScreen() |
x, y = MySearch_img to get the x- and y-coordinates of centers of the feature. (code) |
| ctrlleft |
Introduction |
| ctrlright |
Introduction |
| ctrl |
Introduction |
| from selenium.webdriver.common.keys import Keys |
(code) |
| from selenium.webdriver.common.by import By |
(code) |
| .center() |
(code). |
| hotkey('c') |
Introduction |
| Type capital letters |
Introduction |
| .getAllTitles() |
Get all the Python program windows, *IDLE Shell window, e.g. *IDLE Shell 3.9.5, the most front window on Dreamweaver, the most front webpage on each Chrome window, the name of each opened applications, e.g. DigitalMicrograph. Introduction |
| confidence= |
(code), (code), (code). |
| .configure() |
(code) |
| |
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| .copy() |
Copy text. (code) |
| CountVectorizer() |
Introduction. It is the bag of words technique, which
means counting how many times each word appears and puts them into a vector. |
| cross_val_score |
(code). |
| from collections import defaultdict |
(code). |
| CBW model (for training) |
(code) |
| driver.current_window_handle |
<Instruction> |
| Cm |
Inches, Emu, Cm, Mm, Pt, and Px are base class for length classes, providing properties for converting length values to convenient units. |
| .pixelMatchesColor() |
Introduction |
| from pptx.chart.data import ChartData: ChartData() |
(code) |
| .categories |
(code) |
| .chart |
(code) |
| bokeh.plotting.figure.circle() |
The circle() function in plotting module of bokeh library is used to Configure and add Circle glyphs to a figure.
Syntax: circle(x, y, *, angle=0.0, angle_units=’rad’, fill_alpha=1.0, fill_color=’gray’, line_alpha=1.0, line_cap=’butt’, line_color=’black’, line_dash=[], line_dash_offset=0, line_join=’bevel’, line_width=1, name=None, radius=None, radius_dimension=’x’, radius_units=’data’, size=4, tags=[], **kwargs)
Parameters: This method accept the following parameters that are described below:
x: This parameter is the x-coordinates for the center of the markers.
y: This parameter is the y-coordinates for the center of the markers.
angle: This parameter is the angles to rotate the markers.
fill_alpha: This parameter is the fill alpha values for the markers.
fill_color: This parameter is the fill color values for the markers.
line_alpha: This parameter is the line alpha values for the markers with default value of 1.0 .
line_cap: This parameter is the line cap values for the markers with default value of butt.
line_color: This parameter is the line color values for the markers with default value of black.
line_dash: This parameter is the line dash values for the markers with default value of [].
line_dash_offset: This parameter is the line dash offset values for the markers with default value of 0.
line_join: This parameter is the line join values for the markers with default value of bevel.
line_width: This parameter is the line width values for the markers with default value of 1.
mode: This parameter can be one of three values : [“before”, “after”, “center”].
name: This parameter is the user-supplied name for this model.
tags: This parameter is the user-supplied values for this model.
radius: This parameter is the radius values for circle markers .
radius_dimension: This parameter is the dimension to measure circle radii along.
size: This parameter is the size (diameter) values for the markers in screen space units.
alpha: This parameter is used to set all alpha keyword arguments at once.
color: This parameter is used to to set all color keyword arguments at once.
legend_field: This parameter is the name of a column in the data source that should be used or the grouping.
legend_group: This parameter is the name of a column in the data source that should be used or the grouping.
legend_label: This parameter is the legend entry is labeled with exactly the text supplied here.
muted: This parameter contains the bool value.
name: This parameter is the optional user-supplied name to attach to the renderer.
source: This parameter is the user-supplied data source.
view: This parameter is the view for filtering the data source.
visible: This parameter contains the bool value.
x_range_name: This parameter is the name of an extra range to use for mapping x-coordinates.
y_range_name: This parameter is the name of an extra range to use for mapping y-coordinates.
level: This parameter specify the render level order for this glyph.
Introduction |
| |
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| plt.colorbar() |
(code) |
| sklearn.cluster.KMeans() |
Introduction |
| .DataFrame() |
Introduction. .drop(),
index,
columns,
axes,
dtypes,
size,
shape,
ndim,
empty,
T (swap between column and row),
values |
| |
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| Image overlap. |
Code. copy method |
| CNTK |
Introduction |
| Crowd’s error |
Introduction |
| cosine similarity/distance |
Introduction |
| Add padding/black/colored edge to images |
Introduction |
| Save the text in clipboard to a txt file |
Introduction |
| Common values in two pandas series |
Introduction |
| |
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| Save contents in the webpages obtained by Google search into a text file |
Introduction |
| Color in table obtained by matplotlib.pyplot/change background color of cells in table |
Introduction |
| Locate/find the center/coordinates of a bright (maximum/highest intensity) spot in an image |
Introduction |
| Median blurring and cv2.medianBlur() |
Introduction |
| |
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| Three dimensional (3D) shapes/structures (e.g. cylinder) |
Introduction |
| Graphlab Create |
Introduction |
| Categorical variables |
Introduction |
| |
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| Save the image in clipboard to an image file |
Introduction |
| Compile model |
Introduction |
| Colab |
Introduction |
| Categorical features preprocessing layers |
Introduction |
| GPUs/CPUs |
Introduction |
| googlecoursera/console.cloud.google |
Introduction |
| Categorical bins |
Introduction |
| tf.constant() |
Introduction |
| find_element(CSS_SELECTOR, " ") |
Introduction |
| tf.feature_column.categorical_column_with_identity |
Introduction |
| |
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| Convert |
|
| ◆ |
Convert a number type to another |
Introduction |
Comparisons |
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Typical training setup in AI and comparisons of different training libraries |
Introduction |
| ◆ |
Comparison between Python and C/C++ |
Introduction |
| ◆ |
Data structures (Data science, and comparison between list, tuple, set, dictionary) |
Introduction |
| ◆ |
Comparison between steps and epochs in TensorFlow |
Introduction |
| ◆ |
Comparison between iteration algorithm and recursive algorithm: a function repeat itself |
Introduction |
| ◆ |
Comparison of qualifications and skills between data science manager, engineering and scientist |
Introduction |
| ◆ |
Comparison between machine learning and human beings |
Introduction |
| ◆ |
Comparison between strings |
code |
| ◆ |
Comparison with classical wafer map inspection algorithms |
Introduction |
| ◆ |
Speed comparison with and without numpy |
Introduction |
| ◆ |
Comparison between =, ==, .copy() and copy.copy() for "list": changes of "list" |
Introduction |
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| Convert capital alphabet letters/characters to number |
Introduction |
| |
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| Extract three blue, green, red images from a color image or grey image, or convert RGB (color) image into three blue, green, red images |
code |
| Convert jpg format to tif format |
code |
| Print and set file path as a variable (e.g. convert all characters in the pathname to lowercase) |
Introduction |
| Convert a text file to a string |
Introduction |
| Find and convert the file time/date and compare with the current time |
Introduction |
| Libraries used to convert incident documents into numerical vectors |
Introduction |
| Convert PDF file to text file |
Introduction |
| |
|
| Put most code into a function or class |
code |
| Use __name__ to control execution of the code |
code |
Match on images to find and to highlight unsimilar (threshold=0) to identical (threshold=1) regions of an image that match a template with a cross-correlation method |
code |
| Holidays/Festivals/Vocations (Thanksgiving/Christmas) |
Introduction |
| Call and then run your own functions and modules in different/other Python files |
Introduction |
| Automatically review, scroll, click webpage and its link |
Introduction |
| Positions and colors of mouse/cursor and features |
Introduction |
| while True, e.g. with the "while True" loop, e,g. it constantly/continuously refreshes actions. |
Introduction |
| Coordinate at center of features, e.g. images |
Introduction |
| Skip, remove, extract, use specific columns |
Introduction |
| Automation of mouse movements and clicks |
Introduction |
| Create a new presentation |
Introduction |
| Move the cursor/mouse to the found, similar spots one-by-one |
Introduction |
| Table of PC/computer/Windows shortcut hotkeys |
Introduction |
| Table of Chrome shortcut hotkeys |
Introduction |
| Selection between choices or options |
Introduction |
| |
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| Copy and apply formatting in Word and PowerPoint |
Introduction |
| Work (read, write, insert and delete rows and columns, and merge and unmerge cells, shift/move cell values) in Excel sheets |
Introduction |
| Calculation in an Excel Sheet, Style, Bold, and Color |
Introduction |
| Move the mouse/cursor to the left or right |
Introduction |
| Get the name of the current/most front window |
Introduction |
| Bind/link/combined multiple commands to buttons |
Introduction |
| |
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| stopwatch and timing/countering
a process |
Introduction |
| Copy and then store it into memory and it can be pasted for use later (multiple clipboard) |
Introduction |
| Copy text to Clipboard |
Introduction |
| Courses/classes for Computer Science |
Introduction |
| Text classification/sort/prediction, train/test e.g. Youtube spam |
Introduction |
| Print colored text in Python IDLE's terminal |
Introduction |
| Copy text into clipboard and then use it immediately (one time clipboard) |
Introduction |
| matplotlib.pyplot axis/text color (xticks, rotation, xlabel, ylabel, title, fontsize, grid(), legend(), show()) |
Introduction |
| Template matching: a technique for finding areas of an image that are similar to a patch (template).
Patch are small images with a certain feature. The goal of template matching is to find and/or highlight the patch/template in an image. |
| Image matching with cross correlation and overlap of template edge. In this matching process, Normalized cross-correlation with those edge images is performed. |
code. |
| Cross correlation between two images |
code |
| Cross correlation between two images in any sizes. Multiscaling is used to avoid the issue caused by the different sizes of the template and original image, in order to find match in a original image, namely, the size of template is larger than the original image. |
code |
| watchdog to look for filesystem changes |
Introduction |
| Skip, remove, extract, use specific columns |
Introduction |
| Copy methods:
b = [*a]; c = a * 1; d = a[:]; "e = []; e.extend(a)"; f = a[0:len(a)]; *g, = a;
h = list(a); i = [z for z in a]; "j = [] for item in a: j.append(item)"; a.copy(); "import copy
l = copy.copy(a)". |
code. code. |
| |
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| Calibrate and put a scale bar, and draw a line segment on an image |
Introduction |
| Matrix conversion to image |
image. |
| Calculator of length accuracy in 3D structure |
Introduction |
| Option/selection/choice methods ("pop-up windows of Yes and No ") |
Introduction |
| Copy text into clipboard and then you can paste it anywhere |
Introduction |
| Calculate/pass the arbitrary (any) number of variables or input arguments |
Introduction |
| Merge/combine two text files into a new text file, add a new line to the beginning of a text file |
Introduction |
| Create an executable (.exe) file from a Python script |
Introduction |
| Mixing of using numbers and strings by conversions |
Introduction |
| Calculators |
Introduction |
| Numpy: Access the element at the second row, the third entry, access a specific row or a column, access some elements (submatrix), or replace/modify an element in the array, print a transfer of an array, access array under conditions or filtering |
Introduction |
| Calculations in DataFrame:
Add a column, calculate for a new column, delete a column, all the rows with values greater than 30 in "Score A" column |
Introduction |
| Calculations in DataFrame:
Add a column, calculate for a new column, delete a column, all the rows with values greater than 30 in "Score A" column |
Introduction |
| Pint summary of the statistic data, change data format, sort/group columns |
Introduction |
Handle NaN value in DataFrame, replace empty cells with ...
|
Introduction |
| Copy text into clipboard and then you can paste it a webpage, text/txt, word or powerpoint file automatically |
(code) |
Count the numbers of uppercase letters, lowercase letters and spaces in a string and then swap the cases of the letters.
|
code |
| Count the times of repeated excutions |
code |
| Markers (e.g. color cross, scatter, and circles) at specific coordinates with x- and y-axis |
Matplotlib |
| |
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| Find a similar feature and then click it |
Introduction |
| Computing equations and formulas with Python |
Examples. Numerical integration at code. |
| Prevent other applications to modify the content until other Python script runs |
code. |
| Choose a file with simple dialog. |
Code. code. code. With a default folder: code1 and code2. . |
| Open webpages in internet browsers (Chrome, Microsoft edge, IE browser) |
Introduction |
| Open an image to file from URL (webpage), then it can be saved in PC (computer) |
code, code. open with color changed. |
| Copy images to a different folder/Save an image in a new folder |
code, code. code. code. |
| Set the output image to zero everywhere except my mask (color filter), and display red, green, and blue (RGB) channels of an image. |
code, code. |
| Remove letters or characters on either side (both left and right sides) and stops when neither such letters no characters on either side |
code |
| Creates an image from an colored images after remove the grey components (image conversion from color to gray involved) |
code |
| Calculate the coordinates of a point in a given rectangle and the distance of a given point to a line |
code |
| Compute the difference between two images by using Structural Similarity Index
with "pip install --upgrade imutils" |
code. code |
Create images with global, adaptive mean, adaptive Gaussian, binary, trunc, Tozero, and tozero thresholds. |
code |
| Load/launch images and ColorMixing in DigitalMicrograph |
Introduction |
| Automation of mouse movements and clicks, and keyboard control |
Introduction |
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| dropdown box/option/selection/choice |
Introduction |
| Add a new slide into an existing ppt or a created ppt file |
Introduction |
| Draw lines, elbow connectors and arrows in a ppt |
Introduction |
| Find the color of a pixel on the screen |
Introduction |
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| Plot curve/chart in pptx |
Introduction |
| Count and delete slides from ppt |
Introduction |
| Infinite loops (e.g. stop infinite cycling of opening the same images) |
Introduction |
| Get maximum and minimum value of column and its index |
Introduction |
| Find a specific word in a webpage and count occurrences |
Introduction |
| Web Scraping (save contents from a webpage) |
Introduction |
| Copy a file to save to somewhere |
Introduction |
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| Get/list immediate subdirectories/subfolders; get only the last part of a path/folder/drive; split a dos path into its components, and then print the list |
Introduction |
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| Read its nth character in a text file |
Introduction |
| Circular dependencies in Python execution |
Introduction |
| Launch script from another script using subprocess.run/subprocess.call |
Introduction |
| Monitor multiple changed of folder and files |
Introduction |
| Monitor the current folder |
Introduction |
| Find files with a specific file extension/type or with file names ending with specific characters |
Introduction |
| Move/copy all files from original folder in a directory to a new directory |
Introduction |
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| top and left for pptx (e.g. align the top-left corner of the image to the center of the slide no matter how the size of the images changes) |
Introduction |
| Invert the contrasts of black and white images |
(Code) |
| watchdog with conditioning break |
Introduction |
| Count how many (number) files and folders in a directory |
Introduction |
| |
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| Find contours in an image and their areas and coordinates |
Introduction |
| Take a screenshot using a mouse click and drag method |
Introduction |
Global access to a local variable inside a function/class from outside of the function/class externally
|
Introduction |
| |
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Calculation with combinations of variables from lists
|
Introduction |
| Get mouse position/coordinates on click |
Introduction |
| Measure length/distance on an image w/o calibrated bar |
Introduction |
| Get pixel location/coordinates on an image using mouse click/events |
Introduction |
| Crop/snip (without opening the image) part of a image with definition by a pixel line (with transparent added portion) |
Introduction |
| Change/swap values in a list |
Introduction |
| Modify file path/directory by changing folder names by merging a list |
Introduction |
| Merge/combine two pptx files into one |
Introduction |
| Plot distance between points calculated by coordinates |
Introduction |
| Break/exit/skip a function/code line after a certain time |
Introduction |
| Classification and algorithms for classification |
Introduction |
|
Introduction |
| Clustering |
Introduction |
| Count occurrence/nubmer of words/phrase in a text file |
Introduction |
| Remove/repace (part) character(s) from string |
Introduction |
| Change/capitalize the case of the first letter of a string |
Introduction |
| Write/save content to a text file |
Introduction |
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| Generate text file with the bank of collecting all words, characters and strings from news |
Introduction |
| Critical thinking in data science |
Introduction |
| Support-vector clustering (SVC) |
Introduction |
| Model-based clustering |
Introduction |
| Nearest-neighbor (NN) cluster removal |
Introduction |
| K-Means clustering for images |
Introduction |
| Image segmentation ("clustering") in color |
Introduction |
| Clustering of Laplacian |
Introduction |
| Mask an image with a threshold or with a color as a threshold |
Introduction |
| Convolutional neural networks (CNN) |
Introduction |
| Wafer map failure pattern recognition (WMFPR)/wafer failure pattern detection (WFPD)/defect classification |
Introduction |
| Convert images between Cartesian and Polar forms |
Introduction |
| Draw circles/lines on images |
Introduction |
Detection and classification of defective dies/chips in wafer map
|
Introduction |
| Convolutional Autoencoder (CAE) |
Introduction |
| Plot a list of x, y coordinates to an image |
Introduction |
| Store images in pandas dataframe column |
Introduction |
| Write contents of DataFrame/memory into text file |
Introduction |
| Convert DataFrame to a HTML Table and save as a HTML webpage |
Introduction |
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