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This file demonstrates various NumPy functionalities including array creation, reshaping, and properties.
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Functional AssessmentVerdict: ✅ Completed
🧠 User Story ID: TDRS-001-A — File Management System Update📝 Feature CompletenessThe Requirement was.. The system must successfully incorporate and validate changes across nine specific files within the architecture to ensure code updates are recognized and integrated. This is what is built... Implemented and verified updates across nine files containing NumPy implementation logic, including array creation, manipulation, and mathematical operations. 📊 Implementation Status
✅ Completed Components
🎯 Conclusion & Final AssessmentImportant 🟢 Completed Features: Key completed features include the successful implementation and integration of nine specific Python files (P01 through P08 and num01) covering NumPy array initialization, data types, attributes, manipulation, string functions, and arithmetic operations. |
⚙️ DevOps and Release Automation🟢 Status: Passed🌟 Excellent work! Your code passed the DevOps review. |
🔍 Technical Quality Assessment📋 SummaryWe have added a collection of educational scripts designed to help our technical team master a powerful data-handling tool called NumPy. These files serve as a 'training manual' in code form, helping developers learn how to process large amounts of information and perform complex calculations more efficiently. Since these are practice files, they do not change how our customers use our products or affect our live website. 💼 Business Impact
🎯 Purpose & Scope
📊 Change AnalysisFiles by Category:
Impact Distribution:
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| File | Status | Description | Impact | Issues Detected |
|---|---|---|---|---|
Test/P01_Introduction.py |
Added ( +83/ -0) | Introduces basic NumPy concepts including array creation with arange, zeros, ones, and random, as well as array reshaping and attribute inspection. | Low – This is an introductory script for educational purposes and does not affect core application logic. | 0 |
Test/P02_NumpyDataTypes.py |
Added ( +34/ -0) | Added a script demonstrating various NumPy data types and how to explicitly cast arrays using the dtype parameter. | Low – This is an educational or test script demonstrating NumPy functionality. It does not affect core application logic. | 0 |
Test/P03_NumpyAttributes.py |
Added ( +24/ -0) | Added a script demonstrating various NumPy array attributes such as size, shape, ndim, and itemsize. | Low – This is an educational or test script with no impact on core application logic. | 0 |
Test/P04_ArrayFromNumericalRanges.py |
Added ( +43/ -0) | This script demonstrates various ways to create NumPy arrays using numerical ranges, including arange, linspace, and logspace functions. | Low – The changes are educational and illustrative, showing how to use NumPy's range-based array creation functions. It has minimal impact on existing systems as it is a new standalone script. | 2 |
Test/P05_NumpyArrayManipulation.py |
Added ( +83/ -0) | Added a script demonstrating various NumPy array manipulation techniques including reshaping, flattening, transposing, and appending. | Low – This is an educational or test script demonstrating NumPy features; it does not impact core application logic. | 1 |
Test/P06_NumpyStringFunctions.py |
Added ( +43/ -0) | Added a script demonstrating various NumPy string operations using the np.char module. | Low – This is an educational or test script demonstrating NumPy functionality; it does not affect core application logic. | 1 |
Test/P07_NumpyMathematicalFunctions.py |
Added ( +30/ -0) | Added a script demonstrating NumPy mathematical functions including trigonometric operations, inverse functions, and rounding methods. | Low – This is a standalone educational or test script demonstrating NumPy functions. It does not affect core application logic. | 1 |
Test/P08_NumpyArithmeticOperations.py |
Added ( +25/ -0) | Added a script demonstrating basic NumPy arithmetic operations including addition, subtraction, multiplication, division, and power. | Low – This is a standalone educational script and does not affect existing application logic. | 1 |
Test/num01 |
Added ( +83/ -0) | Added a Python script demonstrating basic NumPy array operations including creation, reshaping, and random initialization. | Low – This is an educational or utility script with no direct impact on core application logic. | 1 |
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| myArray = np.linspace(10, 20, 5, endpoint = False) | ||
| print(myArray) # [ 10. 12. 14. 16. 18.] | ||
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| # ndarray.lopspace returns an ndarray object that contains the numbers that are evenly spaced |
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Typo in Function Name in Comment
I noticed a small typo in the comment describing the logspace function. It's currently written as lopspace, which might be confusing for someone trying to learn the API. Let's correct it to logspace to match the actual NumPy function name.
| # ndarray.lopspace returns an ndarray object that contains the numbers that are evenly spaced | |
| # ndarray.logspace returns an ndarray object that contains the numbers that are evenly spaced |
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| # ndarray.lopspace returns an ndarray object that contains the numbers that are evenly spaced | ||
| # on a log scale. | ||
| # ndarray.logscale(start, stop, num, endpoint, base, dtype) |
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Incorrect Function Name in Comment
In the comment on line 31, the function is referred to as ndarray.logscale. However, the actual NumPy function is np.logspace. Using the wrong name in the documentation part of the code can lead to confusion. Let's update it to logspace.
| # ndarray.logscale(start, stop, num, endpoint, base, dtype) | |
| # ndarray.logspace(start, stop, num, endpoint, base, dtype) |
| # [[1 2 3] | ||
| # [4 5 6]] | ||
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| print(np.append(myArray, [7, 8, 9])) |
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Potential In-Place Modification Confusion
I noticed we're using np.append() here. It's important to remember that np.append() does not modify the original array in place but returns a new one. Since the result isn't assigned back to myArray, the original array remains unchanged for any subsequent operations. If the intent was to update the array, we should assign the result back to the variable.
| print(np.append(myArray, [7, 8, 9])) | |
| myArray = np.append(myArray, [7, 8, 9]) |
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| # numpy.char.join(): This method returns a string in which the individual characters are joined by | ||
| # separator character specified. | ||
| print(np.char.join(':','dmy')) # d:m:y |
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Potential TypeError in String Joining
I noticed that we're using np.char.join with a single string 'dmy'. While this works for individual characters, if the intent was to join a list of strings (like 'day', 'month', 'year'), passing a single string will join its characters instead. If 'dmy' was meant to be a sequence of strings, it should be passed as a list to avoid unexpected character-level joining.
| print(np.char.join(':','dmy')) # d:m:y | |
| print(np.char.join(':', ['d', 'm', 'y'])) |
Reasons & Gaps
Reasons
- np.char.join treats a single string as a sequence of characters
- Joining 'dmy' results in 'd:m:y', which might not be the intended data structure
- Using a list makes the sequence of elements to be joined explicit and clear
Gaps
- The developer's intent for 'dmy' is not explicitly clear from the context
- The current code is syntactically valid but potentially logically misleading
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| # for computing inverse of trigonometric functions | ||
| sine = np.sin(angles * np.pi/180) | ||
| sineinv = np.arcsin(sine) |
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Potential Domain Error in Inverse Sine
I noticed we're calculating arcsin on the entire sine array. While this works for most values, np.sin can occasionally produce values slightly outside the [-1, 1] range due to floating-point precision errors (e.g., 1.0000000000000002). This would cause np.arcsin to return NaN for those elements. It's safer to clip the input values to the valid domain before computing the inverse.
| sineinv = np.arcsin(sine) | |
| sineinv = np.arcsin(np.clip(sine, -1, 1)) |
| firstArray = np.arange(12).reshape(3, 4) | ||
| print(firstArray) | ||
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| secondArray = np.arange(4) |
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Potential Division by Zero
I noticed that secondArray is initialized using np.arange(4), which creates the array [0, 1, 2, 3]. When we perform np.divide(firstArray, secondArray), the first element of secondArray is zero, which will result in a 'division by zero' warning and produce inf or nan values in the output. It's usually safer to ensure the divisor doesn't contain zeros or handle the resulting infinity values explicitly.
| secondArray = np.arange(4) | |
| secondArray = np.arange(1, 5) |
| myArray = np.arange(20) | ||
| print(myArray) # [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19] | ||
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| # an array from 10 to 20 |
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Incorrect Comment Description for arange()
The comment on line 17 states that the array goes from 10 to 20, but np.arange(10, 20) excludes the stop value. The resulting array is [10, 11, 12, 13, 14, 15, 16, 17, 18, 19], which contains 10 elements, not 11. Updating the comment to '10 to 19' or '10 up to 20' would be more accurate for someone learning the library.
| # an array from 10 to 20 | |
| # an array from 10 to 19 |
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| Coding Standard | Violations | Citation |
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Variable naming convention |
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🗂️ Test/P02_NumpyDataTypes.py
| Coding Standard | Violations | Citation |
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Variable naming convention |
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🗂️ Test/P03_NumpyAttributes.py
| Coding Standard | Violations | Citation |
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Variable naming convention |
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🗂️ Test/P04_ArrayFromNumericalRanges.py
| Coding Standard | Violations | Citation |
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Variable naming convention |
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🗂️ Test/P05_NumpyArrayManipulation.py
| Coding Standard | Violations | Citation |
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Variable naming convention |
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🗂️ Test/P06_NumpyStringFunctions.py
| Coding Standard | Violations | Citation |
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Variable naming convention |
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🗂️ Test/P07_NumpyMathematicalFunctions.py
| Coding Standard | Violations | Citation |
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Variable naming convention |
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🗂️ Test/P08_NumpyArithmeticOperations.py
| Coding Standard | Violations | Citation |
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Variable naming convention |
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🗂️ Test/num01
| Coding Standard | Violations | Citation |
|---|---|---|
Variable naming convention |
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| import numpy as np | ||
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| # we have a function arange() which makes an array of the specified dimension. Example: | ||
| myArray = np.arange(20) |
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Non-Standard Variable Naming (camelCase in Python)
In Python, variable names should follow the snake_case convention. Additionally, 'myArray' is a generic name; consider using a more descriptive name like numbers_array or sequence_array to improve clarity.
| myArray = np.arange(20) | |
| numbers_array = np.arange(20) |
Reasons & Gaps
Reasons
- Python naming conventions (PEP 8) strictly recommend snake_case for variables
- The prefix 'my' is redundant and adds no semantic value to the variable purpose
- Descriptive names like 'numbers_array' better communicate the data structure content
Gaps
- The variable name is used in an educational/tutorial context where generic names are common
- camelCase is technically functional in Python but violates PEP 8 style standards
| import numpy as np | ||
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| # while creating a numpy array, any data type from above can be explicitly specified. | ||
| myArray = np.arange(10) |
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Variable Naming Improvement
The variable name myArray uses camelCase, which is inconsistent with Python's snake_case convention. Additionally, myArray is partially generic; a more descriptive name like sequence_array or numbers_array would improve clarity.
| myArray = np.arange(10) | |
| numbers_array = np.arange(10) |
Reasons & Gaps
Reasons
- Python naming conventions (PEP 8) recommend snake_case for variable names
- Descriptive names like 'numbers_array' convey the purpose better than 'myArray'
- Consistency in naming style reduces cognitive load during code reviews
Gaps
- The term 'myArray' is common in educational snippets or quick scripts
- Small script scope makes the generic nature of the name less impactful
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| import numpy as np | ||
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| myArray = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) |
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Variable Naming Convention Violation
The variable name 'myArray' uses camelCase, which violates the Python snake_case naming convention. Additionally, 'my' is a generic prefix that adds no descriptive value to the variable's purpose.
| myArray = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) | |
| matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) |
Reasons & Gaps
Reasons
- Python naming conventions (PEP 8) require snake_case for variable names.
- The prefix 'my' is considered a forbidden generic/redundant naming pattern.
- Using descriptive names like 'matrix' or 'data_grid' improves code maintainability.
Gaps
- The term 'array' is a technical term, but the 'my' prefix is discouraged in most enterprise standards.
- Project-specific conventions might allow camelCase, though it deviates from PEP 8.
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| # ndarray.arange(start, stop, step, dtype) | ||
| # Creates a numpy array from 1 to 20 | ||
| myArray = np.arange(1, 21) |
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Non-Standard Abbreviation in Variable Name
The variable name 'myArray' uses the 'my' prefix and 'Array' suffix which are considered generic. A more descriptive name like 'numerical_range' or 'sequence_array' would better reflect the data's purpose.
| myArray = np.arange(1, 21) | |
| numerical_range = np.arange(1, 21) |
Reasons & Gaps
Reasons
- The 'my' prefix is a generic filler that adds no semantic value to the variable name
- Using 'Array' as a suffix is redundant when the type is obvious from the assignment
- Descriptive names like 'numerical_range' improve code maintainability and intent clarity
Gaps
- 'myArray' is a very common naming pattern in educational or tutorial-style code snippets
- The variable is functional and its type is clear from the context of the NumPy call
| import numpy as np | ||
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| # numpy.reshape(array_to_reshape, tuple_of_new_shape) gives new shape (dimension) to our array | ||
| myArray = np.arange(0, 30, 2) |
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Non-Standard Variable Naming (camelCase in Python)
In Python, variable names should follow the snake_case convention. Additionally, 'myArray' is a generic name; consider using a more descriptive name that reflects the content or purpose of the array.
| myArray = np.arange(0, 30, 2) | |
| even_numbers_array = np.arange(0, 30, 2) |
Reasons & Gaps
Reasons
- Python naming conventions (PEP 8) require snake_case for variable names
- 'myArray' is a generic name that lacks descriptive context about the data
- Consistent naming improves maintainability and aligns with the Python ecosystem
Gaps
- The variable name is functional and common in educational snippets
- camelCase is sometimes used in specific teams despite PEP 8 guidelines
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| # for computing inverse of trigonometric functions | ||
| sine = np.sin(angles * np.pi/180) | ||
| sineinv = np.arcsin(sine) |
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Cryptic Abbreviation in Variable Name
The variable name sineinv uses a cryptic abbreviation by concatenating 'sine' and 'inv'. Following Python naming conventions, it is better to use underscores for readability or a more descriptive name like sine_inverse or angles_in_radians.
| sineinv = np.arcsin(sine) | |
| sine_inverse = np.arcsin(sine) |
Reasons & Gaps
Reasons
- Concatenated abbreviations like 'sineinv' increase cognitive load compared to snake_case
- Standard Python naming conventions (PEP 8) recommend using underscores to separate words
- Using 'sine_inverse' explicitly communicates the mathematical relationship of the data
Gaps
- 'inv' is a common mathematical abbreviation for inverse, which might be acceptable in scientific computing contexts
- The variable name is technically understandable within the local trigonometric context of the script
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| import numpy as np | ||
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| firstArray = np.arange(12).reshape(3, 4) |
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Variable Naming Convention Violation
The variable name 'firstArray' uses camelCase, which violates the Python snake_case naming convention. Additionally, 'firstArray' is partially generic; a name reflecting the data or shape would be more descriptive.
| firstArray = np.arange(12).reshape(3, 4) | |
| first_array = np.arange(12).reshape(3, 4) |
Reasons & Gaps
Reasons
- Python naming conventions (PEP 8) require snake_case for variable names
- Using camelCase in Python scripts reduces consistency with the standard library
- Descriptive names improve code maintainability and reduce cognitive load
Gaps
- The term 'array' is a technical term often accepted in mathematical or tutorial contexts
- Project-specific style guides might occasionally permit camelCase for consistency with other languages
| firstArray = np.arange(12).reshape(3, 4) | ||
| print(firstArray) | ||
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| secondArray = np.arange(4) |
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Variable Naming Convention Violation
The variable name 'secondArray' uses camelCase instead of the standard Python snake_case. It is recommended to use descriptive names that follow the language's style guidelines.
| secondArray = np.arange(4) | |
| second_array = np.arange(4) |
Reasons & Gaps
Reasons
- Violates PEP 8 recommendation for lowercase with underscores for variable names
- Mixed naming styles (camelCase in Python) can confuse developers from other backgrounds
- Consistent naming improves searchability and readability across the codebase
Gaps
- In simple arithmetic examples, ordinal naming (first, second) is common but not ideal
- Small script context might make strict naming enforcement feel pedantic
| print(np.divide(firstArray, secondArray)) | ||
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| # numpy.power(): returns array element raised to the specified value result | ||
| array = np.array([1, 2, 3]) |
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Forbidden Generic Variable Name
The variable name 'array' is a forbidden generic name under the enterprise coding standard. It is too vague and matches a type name, which can lead to shadowing or confusion.
| array = np.array([1, 2, 3]) | |
| input_array = np.array([1, 2, 3]) |
Reasons & Gaps
Reasons
- Generic names like 'array' fail to communicate the specific purpose of the data
- Using type-like names for variables can lead to naming collisions in larger scopes
- Specific names like 'input_array' or 'base_values' provide better semantic context
Gaps
- 'array' is a very common name in NumPy tutorials and short code snippets
- The context of a power operation makes the generic name somewhat understandable
| import numpy as np | ||
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| # we have a function arange() which makes an array of the specified dimension. Example: | ||
| myArray = np.arange(20) |
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Variable Naming Convention Violation
The variable name 'myArray' uses camelCase, which violates the Python snake_case naming convention. Additionally, 'myArray' is a generic name that includes the type 'Array' (Hungarian notation), which should be avoided.
| myArray = np.arange(20) | |
| numbers = np.arange(20) |
Reasons & Gaps
Reasons
- Python naming conventions (PEP 8) require variable names to be in snake_case
- Including the data type in the name (Hungarian notation) is redundant and discouraged
- Using 'my' prefix is a generic naming pattern that doesn't describe the data's purpose
Gaps
- The variable name is used in an educational/tutorial context where 'myArray' is a common teaching pattern
- The script is a standalone demonstration where strict domain naming might be less critical
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