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| LASSO (Least Absolute Shrinkage and Selection Operator) |
Introduction |
| Software/interface/API (Application Programming Interface) used in data science and machine learning |
Introduction |
| Comparison between CNN, CNN with Attention and Autoencoder |
Introduction |
| Attention-Guided Neural Network (AGNN) |
Introduction |
| Memory resources in Apache Spark applications |
Introduction |
| Clusters (Kubernetes, Apache Mesos, Spark Standalone, Apache Hadoop YARN) in Apache Spark |
Introduction |
| Comparison between Clouds (Amazon, IBM, Google ...) |
Introduction |
| Deploy modes for driver process in Apache Spark: client mode and cluster mode |
Introduction |
| Apache Spark applications to a Kubernetes cluster |
Introduction |
| Run an Apache Spark application |
Introduction |
| Apache Spark architecture |
Introduction |
| Aggregating data in SparkSQL |
Introduction |
| Apache Data Ingestion Frameworks (ADIF) for CSV to DataFrame Conversion |
Introduction |
| Generate automated reports using Python |
Introduction |
| Apache Flink |
Introduction |
| Apache Kudu |
Introduction |
| Apache Impala |
Introduction |
| Personalizing applications with ML |
Introduction |
| Principles of ethical and responsible ML (selection bias, confirmation bias, automation bias, model fairness) |
Introduction |
| Data labeling and annotation in supervised ML |
Introduction |
| Example of Vertex AutoML Vision |
Introduction |
| Parametric and non-parametric learning algorithms |
Introduction |
| Machine learning algorithms |
Introduction |
| Directed Acyclic Graph (DAG) |
Introduction |
| Spark Core of Apache Spark |
Introduction |
| Comparisons among SparkML, MLlib, and AutoML |
Introduction |
| Apache HBase |
Introduction |
| Analyzing Data in Hadoop (HDFS, YARN, Apache Hive, Pig, HBase, Spark) |
Introduction |
| Hadoop MapReduce used by Google, Netflix, Amazon and Machine Learning |
Introduction |
| Apache Spark |
Introduction |
| Apache hadoop and hadoop ecosystem |
Introduction |
| Mathematical algorithms of artificial intelligence for semiconductor industry |
Introduction |
| Analytics and Technology Automation (ATA) |
Introduction |
| Calculation of Principal Component Analysis (PCA) |
Introduction |
| Principal Component Analysis (PCA) versus Uniform Manifold Approximation and Projection (UMAP) |
Introduction |
| Uniform Manifold Approximation and Projection (UMAP) |
Introduction |
| QuAM (Question-Answering Machine) |
Introduction |
| Attention in ML |
Introduction |
| AutoML (Automated Machine Learning) versus Generative AI |
Introduction |
| Virtual reality (VR), augmented reality (AR), and mixed reality (MR) |
Introduction |
| Exploratory data analysis (EDA) |
Introduction |
| Default mutable argument |
Introduction |
| Abstraction in Python programming |
Introduction |
| Computer hardware architecture |
Introduction |
| Labor cost of data analysis with and without automation and ML techniques |
Introduction |
| Function approximation |
Introduction |
| L1 Loss (Absolute Loss or Mean Absolute Error (MAE)) |
Introduction |
| Self-attention in ML |
Introduction |
| Plot with letters/words as x-/y-axis |
Introduction |
| Maintaining arc-consistency |
Introduction |
| Arc consistency |
Introduction |
| Simulated Annealing |
Introduction |
| Markov assumption |
Introduction |
| Sampling Methods for Approximate Inference |
Introduction |
| Approximate inference |
Introduction |
| Knowledge-based agents |
Introduction |
| A* (A-star) Search |
Introduction |
| Action in ML |
Introduction |
| Agent in ML |
Introduction |
| State-action rewards in Markov Decision Process (MDP) |
Introduction |
| Q-Learning with Function Approximation (Deep Q-Network - DQN) |
Introduction |
| Algorithm sensitivity to zero values |
Introduction |
| Credit assignment problem in reinforcement learning |
Introduction |
| Nonlinear extensions of Independent Component Analysis (ICA) |
Introduction |
| Mixture of Gaussians (MoG) versus Factor Analysis (FA) |
Introduction |
| Factor Analysis Model |
Introduction |
| Plot pixel intensity (histogram) along a line (row/column/x-axis/y-axis) of an image |
Introduction |
| Isolation forest algorithm |
Introduction |
| Anomaly detection |
Introduction |
| Learning algorithm (ensemble learning) and pipeline |
Introduction |
| Example of building robot (self-driving) systems with automated ML: helicopter |
Introduction |
| Weighted accuracy in ML |
Introduction |
| Close file after reading a file: avoid file locking |
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 |
| Model = architecture + parameters |
Introduction |
| Neuron (= linear + activation) introduction |
Introduction |
| AdaBoost (Adaptive Boosting) Model |
Introduction |
| Experiences of developing machine learning algorithms |
Introduction |
| Bagging (Bootstrap Aggregating) |
Introduction |
| Additive structure/additive model in ML |
Introduction |
| Comparisons among Manual Search, Vertex Vizier, AutoML and Early stopping on google cloud |
Introduction |
| Difference between estimation and approximation errors |
Introduction |
| Example of ML debugging: Anti-Spam |
Introduction |
| Approximation error |
Introduction |
| Assumptions related to distribution of data in ML |
Introduction |
| Probably Approximately Correct (PAC) learning |
Introduction |
| CIFAR (Canadian Institute for Advanced Research) (CIFAR-10 and CIFAR-100) |
Introduction |
| Minimum A Priori (MAP) |
Introduction |
| Mean Average Precision (MAP) |
Introduction |
| Maximum A Posteriori (MAP) |
Introduction |
| Knuth-Morris-Pratt (KMP) algorithm (a string-searching algorithm) |
Introduction |
| Apple Neural Engine (ANE) |
Introduction |
| Custom AI/ML chips/ICs |
Introduction |
| Laplace smoothing/Laplace correction/add-one smoothing |
Introduction |
| Comparisons among artificial intelligence (AI), machine learning (ML) and quantum machine learning (QML) |
Introduction |
| Discriminative algorithms versus generative models |
Introduction |
| Artificial Neural Networks (ANNs) |
Introduction |
| Discriminative algorithms/discriminative models |
Introduction |
Comparison between mean squared error (MSE), absolute error (L1 Loss) and fourth-power loss
|
Introduction |
| Perceptron algorithm |
Introduction |
| Perceptron algorithm and logistic regression |
Introduction |
| Classification and algorithms for classification |
Introduction |
| Comparison between L1 Regularization and L1 Loss (absolute loss or mean absolute error (MAE)) |
Introduction |
| Autoencoders |
Introduction |
| Parametric learning algorithm |
Introduction |
| Non-parametric learning algorithm |
Introduction |
| Iterative algorithms |
Introduction |
| Algorithms for directly finding the global optimum |
Introduction |
| Learning Algorithm (estimator) |
Introduction |
| Linear regression and its algorithm |
Introduction |
| Gradient descent algorithm (for updating θ) |
Introduction |
| Actual Probability of Deviation |
Introduction |
| Send a variable from one script (back) to another script with a function |
Introduction |
| Rapid Automatic Keyword Extraction (RAKE) |
Introduction |
| Question answering retrieval |
Introduction |
| Leetcode for Google/Amazon |
Introduction |
| Check/find/get a file name/all file names or the last folder name (e.g. from a path/directory) |
Introduction |
| Compare dates (x days after or before a date), and difference between two dates in days |
Introduction |
| Multinomial Naive Bayes algorithm |
Introduction |
Feature analysis/feature importance analysis
|
Introduction |
| Scalability in automation and machine learning projects |
Introduction |
| Autonomous vehicles/cars and machine learning |
Introduction |
| Train/Test versus Model Accuracy |
Introduction |
| RSquare (R^2) versus RASE (Root Average Squared Error) |
Introduction |
| Adjusted R-squared values of two or more regression models |
Introduction |
| |
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| Nonasymptotic versus asymptotic analysis |
Introduction |
| |
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| any() |
Introduction |
| |
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| Add an item to a dictionary |
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 |
| CycleGAN (Cycle-Consistent Adversarial Networks) |
Introduction |
| Class Activation Mapping (CAM) |
Introduction |
| AI/machine learning algorism for text analysis |
Introduction |
| Access and use SQL Database on SSMS (Microsoft SQL Server Management Studio Express) with pyodbc |
Introduction |
| Check if all the (and how many, length of a string) characters in the text are digits/numbers |
Introduction |
| Calculating the area fraction of each circle overlapped by a square grid and build wafer map |
Introduction |
| Extract any substrings with any pattern (e.g. dot (.)) |
Introduction |
| Convert all elements of specific column or in entire dataframe into strings |
Introduction |
| Trick: pd.concat() for merging/adding (two) columns |
Introduction |
| Automatically run a file in an application |
Introduction |
| Click a menus of an application |
Introduction |
Different behavior of automation execution (e.g. pyautogui) locally or remotely through internet
|
Introduction |
| Trick: generic code/script templates for complex automation |
Introduction |
| Generative Adversarial Network (GAN) technologies |
Introduction |
| |
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| Asymptotic analysis |
Introduction |
| ◆ |
Well-specified case of "asymptotic approach" |
Introduction |
| |
|
| Compare (pattern/ratio of) two different columns, check whether column values match in DataFrame |
Introduction |
| ◆ |
Check whether one column contains number only and another column contains letters only or mixture of numbers and letters in DataFrame |
Introduction |
| ◆ |
Check the difference between two columns in DataFrame |
Introduction |
| ◆ |
|
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| Check if a file exists again (double check) |
Introduction |
| ArithmeticError (arithmeticException) |
Introduction |
| Avoid duplicates when creating text file |
Introduction |
| Automatically restart script execution after it breaks/fails/error |
Introduction |
| Call and run another script in a different/any (parent or children) directory/path/subfolder from a script |
Introduction |
| Modify HTML webpage (e.g. with graph network by adding/inserting text/hyperlink in) |
Introduction |
| Convert/change the case of all letters/word into uppercase (capital) or lowercase in a list of strings |
Introduction |
| Check if a variable does exist/is assigned/defined |
Introduction |
| Check all the imported/current modules/libraries |
Introduction |
| Plot multiple datasets on the same scatter graph with different x- and y-axis values |
Introduction |
| Write special/certain rows (row-by-row) of one csv file to another csv file |
Introduction |
| y axis values are not ordered (disordered) |
Introduction |
| matplotlib.pyplot to plot/generate images (with axis/colored text or annotation) |
Introduction |
| Avoid two or multiple plots being wrongly/incorrectly/unnecessarily mixed/overlap |
Introduction |
| Check if one list is subset of another |
Introduction |
| Remove/reload/unload (all) imported module/function/script |
Introduction |
| Plot graph/figure/image from CSV file/DataFrame by removing/hiding blank/empty cells with axis range (plt.xlim()) |
Introduction |
| Copy a file or all files (with os.mkdir) to save to somewhere (create a directory first if it does not exist) |
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 |
| sort_values(by=... ascending/descending order) |
Introduction |
| Plot multiple images on the same figure by hiding x- and y-labels on axis |
Introduction |
| Convert between numpy array and string |
Introduction |
| Continue script execution no matter whether some try fails or not (finally, always executes) or else |
Introduction |
| Plot a figure with a colored arrow between text lines/steps |
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 |
| Get the date and time (a past date) of N days ago |
Introduction |
| Check if all/any values are true or false in a range of data |
Introduction |
| Merge/append rows/columns of a csv file into an old csv file if the rows/columns are not in the old csv file |
Introduction |
| Add letter/commas/numbers/characters to the end/beginning of strings in a list |
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 |
| Analytics and Technology Automation (ATA) |
Introduction |
| Aggregate duplicates in columns of data |
Introduction |
| .apply(pd.Series) |
Introduction |
| .apply(tuple, axis=1) |
Introduction |
| Output data if any or same element in a string are in two lists |
Introduction |
| Overcoming automation challenges and forward-looking suggestions |
Introduction |
| Aggregation functions in data manipulation and database queries |
Introduction |
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| accuracy_score() |
(code) |
| Accuracy/precision test |
Introduction |
| .axes[0] and .axes [1] |
CSV: for csv. (code) |
| alert() |
alert(text='', tilte='', button='Ok'). (code) |
| 3 types of logical operators/booleans |
Represent one of two values: True or False. code. code. code. |
| and / & |
The output is 'true' or 'false', when both the
conditions are 'true' or 'false'. Both/all conditions must be true for the statement to be true. "and" is binary, but not is unary. Introduction. Example code. |
| import ... as ... |
|
| .append() |
Introduction. Appends an element to the end of the list. copy method. Add a list/item at the end of the list. |
| A = A[ : :-1] |
Reverse an array |
| sys.argv |
Introduction |
| as |
|
| from ... import ... as ... |
|
| import ... as ... |
E.g. code for opening an image. , code2 |
| from tkinter.filedialog import askopenfilename |
code. code. |
| with ... as ... |
code. code. |
| .shapes.add_textbox |
(code) |
| slides.add_slide() |
(code) |
| .shapes.add_picture() |
(code) |
| .add_run() |
(code) |
| assert() |
code. |
| __abs__ |
|
| watchdog.observers.api: Immutables |
|
| class watchdog.observers.api.ObservedWatch(path, recursive) |
Parameters: path – Path string. recursive – True if watch is recursive; False otherwise. |
| class watchdog.observers.api.EventQueue(maxsize=0) |
| class watchdog.observers.api.EventEmitter(event_queue, watch, timeout=1) |
| class watchdog.observers.api.EventDispatcher(timeout=1) |
| class watchdog.observers.api.BaseObserver(emitter_class, timeout=1) |
| watchdog.observers.api.EventDispatcher |
Base observer. |
| add_handler_for_watch(event_handler, watch) |
Adds 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 add a handler for. |
| watchdog.observers.api.BaseObserver |
Platform-independent observer that polls a directory to detect file system changes. |
| watchdog.observers.api.BaseObserver |
File system independent observer that polls a directory to detect changes. |
| math.acos() |
Returns the arc cosine of x in radians. |
| math.atan() |
Returns the arc tangent of x, in radians. |
| math.acosh() |
|
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| math.atan2() |
Returns atan(y / x), in radians. |
| math.asinh() |
|
| math.asin() |
|
| math.atanh() |
|
| as_integer_ratio |
|
| with mss.mss() as sct |
(code) |
| Multiple assignment |
E.g. a, b = 4, 3 |
| *args |
"*" is the unpacking operator, which is not a list but a tuple. *args allows you to pass multiple, varying arguments or keyword arguments to a function. Note that args is just a name. You’re not required to use the name args. You can choose any name that you prefer. Introduction. |
| asctime() |
code |
| auto-py-to-exe |
(code) |
| .axis() |
Introduction |
| __annotations__/dict |
name/type: parameter and return annotations |
| skimage.measure.approximate_polygon(coords, ...) |
Approximate a polygonal chain with the specified tolerance. |
| asyncio |
Comparison between multithreading, multiprocessing and asyncio at page4797. |
| cv2.add |
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. |
| ax |
matplotlib subplot object to plot on; if nothing passed, uses active matplotlib subplot |
| alpha |
The plot fill opacity (from 0 to 1) |
| arrowprops= |
code. |
| .annotate() |
code. |
| arrowstyle |
code. |
| .argwhere() |
(code) |
| .array |
General, number array . for image. code. in csv. |
| .arange() |
Introduction. Can change with an increment. General, code. code. |
| hotkey('a') |
Introduction |
| alt |
Introduction |
| .active |
(code). (code). |
| .activate() |
Activate a window: with the active cursor in the window and the window is brought to the most front on the monitor. (code) |
| 'xyz'.isalpha() |
Check if string is alphabet (letter, or one type of character) |
| auto_size_text=True |
(code). |
| .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). |
| add_connector() |
shapes.add_connector(MSO_CONNECTOR.STRAIGHT, Begin_x, Begin_y, End_x, End_y)
(code). |
| .add_series() |
(code) |
| .add_chart() |
(code) |
| Comparison between *, .extend((), append(), =, ==, .copy() and copy.copy() for "list": changes of "list" |
Introduction |
| 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 |
| .at[] |
Introduction |
| os.path.abspath() |
(code). |
| Variable length arguments (*args and **kwargs) |
Introduction |
| numpy.all/np.all |
Introduction |
| .DataFrame() |
Introduction. .drop(),
index,
columns,
axes,
dtypes,
size,
shape,
ndim,
empty,
T,
values |
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| Assert and assertion |
Introduction |
| Vertex AI Feature Store |
Introduction |
| Data Augmentation |
Introduction |
| Keyword arguments |
Introduction |
Automation with programming
| Introduction |
| ◆ |
Reasons of automation and how to start |
Introduction |
| ◆ |
Generic (or generalized) robot programming (GRP) |
Introduction |
| ◆ |
Graphical user interface (GUI) |
Introduction |
| ◆ |
API (Application Programming Interface), e.g. weather, temperature |
Introduction |
| ◆ |
Keyboard and mouse automation |
|
| |
✔ |
Functions of mouse |
Introduction |
| |
✔ |
Codes: Automation of Mouse Movements and Clicks (comparison among pyautogui, pygetwindow, pydirectinput, autoit, Quartz, platform, pynput, 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 |
| |
✔ |
Automatically review, scroll, click webpage and its link |
Introduction |
| |
✔ |
Copy text into clipboard and then you can paste it a webpage, text/txt, word or powerpoint file automatically |
(code) |
| ◆ |
Automatically fill contexts into or select options on webpage |
Introduction |
| ◆ |
AutoML |
Introduction |
| ◆ |
Automation of EELS data extraction |
Introduction |
| ◆ |
Ranking/most popular automation testing tools |
Introduction |
| ◆ |
Ranking/most popular IT automation software tools |
Introduction |
| ◆ |
Comparison between Python, Blue Prism, UiPath, Automation Anywhere |
Introduction |
| ◆ |
Automated defect scanning in wafer map |
Introduction |
| ◆ |
Robots and Robotic Process Automation (RPA) |
Introduction |
| |
✔ |
Python |
Introduction |
| |
|
X |
Reasons of using Python for automation |
Introduction |
| |
✔ |
UiPath |
|
| |
|
X |
Examples of UiPath applications |
Introduction |
| |
|
X |
Installation of UiPath and its packages and creation of new projects |
Introduction |
| |
|
X |
Comparison between Python, Blue Prism, UiPath, Automation Anywhere |
Introduction |
| API |
|
| ◆ |
3 ways to create a Keras model with TensorFlow |
|
| |
✔ |
Sequential API to create a Keras model with TensorFlow |
Introduction |
| |
✔ |
Functional API to create a Keras model with TensorFlow |
Introduction |
| |
✔ |
Model Subclassing to create a Keras model with TensorFlow |
Introduction |
| ◆ |
tf.data API |
Introduction |
| ◆ |
tf.keras API |
Introduction |
| ◆ |
|
|
| Add padding/black/colored edge to images |
Introduction |
| except ... as -- Exception |
Introduction |
| Evaluation of accuracy in machine learning process |
Introduction |
| Hide/turn on/off axes/axis on matplotlib |
Introduction |
| Adam optimization algorithm |
Introduction |
| Activation functions in machine learning |
Introduction |
| Analysis process in ML |
Introduction |
| Linear algebra |
Introduction |
| Vertex AI |
Introduction |
| .astype() |
(Code) |
| after_run function |
Introduction |
| except AttributeError |
Introduction |
| TensorFlow APIs |
Introduction |
| Axis/dimension of tensor |
Introduction |
| adapt() |
Introduction |
| .as_default() |
Introduction |
| |
|
| Table of applications of Python and its libraries |
Introduction |
| Open an application window through search at start |
Introduction |
| Add a new slide into an existing ppt or a created ppt file |
Introduction |
| Launch the existing opened application if there is or start a new one if there is not |
Introduction |
| 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 |
| Copy and apply formatting in Word and PowerPoint |
Introduction |
| Move the active window to make space for other apps |
Introduction |
| Go to the pointed tab on an app |
Introduction |
| Convert capital alphabet letters/characters to number |
Introduction |
| Minimize/maximize/restore/activate/resize/move/close Window objects |
Introduction |
| Bring/activate an application to most front/foreground |
Introduction |
| Typical training setup in Artificial intelligence (AI) |
Introduction |
| matplotlib.pyplot axis/text color |
Introduction |
| Convert a CSV file to an image with one column and another column as x-axis and y-axis, respectively. |
code. |
| Calculator of length accuracy in 3D structure |
Introduction |
| Swap the order of arguments in a function |
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 |
| Access variable inside and outside a function |
Introduction |
| Access elements in a dictionary and subdictionary |
Introduction |
| Access elements in a list and sublist |
Introduction |
| Numpy: Access the element at the second row, the third entry, access a specific row or a column, access some elements (submatrix), or replace/modify an element in the array, print a transfer of an array, access array under conditions or filtering |
Introduction |
| .T (Transfer of array in Python) |
Introduction |
| 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 |
| Markers (e.g. color cross, scatter, and circles) at specific coordinates with x- and y-axis |
Matplotlib |
| Keyword search function/check whether or not a string is within another string (a space is included as a string character) |
Introduction |
| Draw an arrow segment pointing from the start point to the end point in an image. |
code. |
| Draw lines manually and then label them with arrows |
code |
| Prevent other applications to modify the content until other Python script runs |
code. |
| Show/open images in any image viewer |
code, 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. |
| Sum two images after resizing them |
code |
Create images with global, adaptive mean, adaptive Gaussian, binary, trunc, Tozero, and tozero thresholds. |
code |
| Draw lines, elbow connectors and arrows in a ppt |
Introduction |
| Remove an/all/duplicate
item(s)/elements from a list |
Introduction |
| Comparison between iteration algorithm and recursive algorithm: a function repeat itself |
Introduction |
| Add a new slide into an existing ppt, or work on existing slides |
Introduction |
| Work with (e.g. open) all/every files and subfolders/subdirectory in a folder (does not include any files or include all files in subfolders); get a list of all files and directories in the same folder where the Python script file is. |
Introduction |
| Insert all the images into a ppt file (one image per slide) |
(Introduction) |
| Mean (average, .mean())/.sum()/maximum(.max())/minimum(.min())/number of non-null values(.count())/.median()/variance(.var())/standard deviation(.std()) |
Introduction |
| Add markers on a map |
Introduction |
| Add/insert a column into an existing csv file |
Introduction |
Call and then run your own functions and modules in different/other Python files; Python run another Python script
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Introduction |
| Run multiple Python files/scripts one after another |
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; or observer runs in the background and pass the value of obtained events to a/another function call) |
Introduction |
| Watchdog for monitoring specific file or files with specific extension, and then run another file from watchdog |
Introduction |
| Launch script from another script using subprocess.run/subprocess.call |
Introduction |
| Simple ways to execute another python file when a new file has been uploaded |
Introduction |
| Move(remove) all files from original folder in a directory to a new directory |
Introduction |
| List all files and directories which has specific files or files with specific extensions |
Introduction |
| top and left for pptx (e.g. align the top-left corner of the image to the center of the slide no matter how the size of the images changes) |
Introduction |
| Subtract/minus one image from another image |
Introduction |
| Find contours in an image and their areas and coordinates |
Introduction |
Global access to a local variable inside a function/class from outside of the function/class externally
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Introduction |
| if not hasattr/if hasattr (attribute) |
Introduction |
| Limit event/action numbers in the event List, then stop |
Introduction |
| Rotate (alignment) an image by line along the x- or y-axis |
Introduction |
| Shift/translate image along x-axis/y-axis |
Introduction |
| Modify a list (e.g. add/insert an item between items) |
Introduction |
| Artificial intelligence (AI) |
Introduction |
| Applications of artificial intelligence/machine learning in industry |
Introduction |
| Manual analysis of data |
Introduction |
| Ranking/most popular programming languages for data analysts |
Introduction |
| Write/save content to a text file/append a string into a text file. |
Introduction |
| Generate text file with the bank of collecting all words, characters and strings from news |
Introduction |
| Add/insert text to an image |
Introduction |
| Pass variables between functions/from one to another |
Introduction |
| Convolutional Autoencoder (CAE) |
Introduction |
| Apply a formatting function to all cells in a DataFrame |
Introduction |
| Hide x-axis tick labels (only show some labels) where x values are under certain conditions |
Introduction |
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