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| other:python:misc_by_jyp [2023/05/04 13:19] – [Base notions] jypeter | other:python:misc_by_jyp [2024/11/04 15:01] (current) – [Extra tutorials] Added links to ruff and flake8 jypeter |
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| </WRAP> | </WRAP> |
| |
| | ===== Extra tutorials ===== |
| |
| | Only **when you have already read all the content of this page several times**, and you are looking for new ideas |
| | |
| | * [[https://medium.com/pythons-gurus/clean-code-in-python-good-vs-bad-practices-examples-2df344bddacc|Clean Code in Python: Good vs. Bad Practices Examples]] |
| | * [[https://peps.python.org/pep-0008/|PEP 8 – Style Guide for Python Code]] |
| | * [[https://realpython.com/python-pep8/|How to Write Beautiful Python Code With PEP 8]] |
| | * [[https://www.datacamp.com/tutorial/pep8-tutorial-python-code|PEP-8 Tutorial: Code Standards in Python]] |
| | * Some checkers/linters: [[https://docs.astral.sh/ruff/|ruff]], [[https://flake8.pycqa.org/en/stable/|flake8]] |
| | * [[https://medium.com/@yaduvanshineelam09/ultimate-python-cheat-sheet-practical-python-for-everyday-tasks-8a33abc0892f|Ultimate Python Cheat Sheet: Practical Python For Everyday Tasks]] |
| | * [[https://medium.com/pythoneers/16-hacks-that-will-take-your-python-skills-to-the-next-level-12e7a9b97421|16 Hacks That Will Take Your Python Skills to the Next Level]] |
| | * [[https://levelup.gitconnected.com/modular-coding-in-python-finally-solve-your-import-errors-af2fd172fcf7|Modular Coding in Python: Finally Solve your Import Errors]] (understanding and fixing ModuleNotFoundError and ImportError) |
| | * [[https://medium.com/@moraneus/understanding-multithreading-and-multiprocessing-in-python-1ed39bb078d5|Understanding Multithreading and Multiprocessing in Python]] |
| ===== Reading/setting environments variables ===== | ===== Reading/setting environments variables ===== |
| |
| |
| |
| | ===== Using log files (aka logging) ===== |
| | |
| | It is always possible to display information messages using the ''print()'' command, but it is more efficient to use //logging// tools when you want to **display correctly a lot of information about a script progress |
| | ** |
| | * [[https://loguru.readthedocs.io/|Loguru]] is a library which aims to bring enjoyable logging in Python |
| | * See also [[https://betterstack.com/community/guides/logging/loguru/|A Complete Guide to Logging in Python with Loguru]] |
| | * More on [[https://betterstack.com/community/guides/logging/#python|logging with python]] |
| | * The default (but not easy to use) Python ''[[https://docs.python.org/3/library/logging.html|logging]]'' module |
| ===== Stopping a script ===== | ===== Stopping a script ===== |
| |
| ===== Playing with strings ===== | ===== Playing with strings ===== |
| |
| | ==== String formatting ==== |
| |
| | * Knowing how to display/print a string correctly is always useful for information and debugging purpose |
| | * There are lots of different ways to display strings |
| | |
| | === String formatting examples === |
| | |
| | You will find below some examples of //quick printing//, as well as using //old style formatting//, //formatted string literals (f-strings)// and the //String ''format()'' Method//. More details in the next section |
| | |
| | <code python> |
| | >>> # Basic (but quick and efficient) printing |
| | |
| | >>> year = 1984 |
| | >>> print(year) |
| | 1984 |
| | >>> print('[', year, 'is a famous book ]') |
| | [ 1984 is a famous book ] |
| | |
| | >>> # Old style formatting |
| | |
| | >>> print('[ %i is a famous book ]' % (year,)) |
| | [ 1984 is a famous book ] |
| | >>> print('[ %10i is a famous book ]' % (year,)) |
| | [ 1984 is a famous book ] |
| | >>> print('[ %-10i is a famous book ]' % (year,)) |
| | [ 1984 is a famous book ] |
| | >>> print('[ %010i is a famous book ]' % (year,)) |
| | [ 0000001984 is a famous book ] |
| | |
| | >>> # Formatted string literals (f-strings) |
| | |
| | >>> print(f'[ {year} is a famous book ]') |
| | [ 1984 is a famous book ] |
| | >>> print(f'[ {year=} is a famous book ]') |
| | [ year=1984 is a famous book ] |
| | >>> print(f'[ {year:10} is a famous book ]') |
| | [ 1984 is a famous book ] |
| | >>> print(f'[ {year:<10} is a famous book ]') |
| | [ 1984 is a famous book ] |
| | >>> print(f'[ {year:010} is a famous book ]') |
| | [ 0000001984 is a famous book ] |
| | >>> print(f'[ {year:10.2f} is a famous book (yes, {year}!) ]') |
| | [ 1984.00 is a famous book (yes, 1984!) ] |
| | |
| | >>> # The String format() Method |
| | |
| | >>> print('[ {} is a famous book ]'.format(year)) |
| | [ 1984 is a famous book ] |
| | >>> print('[ {:10} is a famous book ]'.format(year)) |
| | [ 1984 is a famous book ] |
| | >>> print('[ {:<10} is a famous book ]'.format(year)) |
| | [ 1984 is a famous book ] |
| | >>> print('[ {:010} is a famous book ]'.format(year)) |
| | [ 0000001984 is a famous book ] |
| | >>> print('[ {:10.2f} is a famous book (yes, {}!) ]'.format(year, year)) |
| | [ 1984.00 is a famous book (yes, 1984!) ] |
| | >>> print('[ {title:10.2f} is a famous book (yes, {title}!) ]'.format(title=year)) |
| | [ 1984.00 is a famous book (yes, 1984!) ] |
| | >>> print('[ {title:10.2e} is a famous book ]'.format(title=year)) |
| | [ 1.98e+03 is a famous book ]</code> |
| | |
| | === String formatting references === |
| | |
| | * [[https://docs.python.org/3/tutorial/inputoutput.html#formatted-string-literals|Formatted String Literals]] (//f-strings//) |
| | * Available in Python >= 3.6 |
| | * [[https://docs.python.org/3/reference/lexical_analysis.html#f-strings|More documentation]] |
| | * [[https://docs.python.org/3/library/string.html#formatspec|Format Specification Mini-Language]] |
| | * See also the [[https://pyformat.info/|PyFormat site]] |
| | |
| | * [[https://docs.python.org/3/tutorial/inputoutput.html#the-string-format-method|The String format() Method]] |
| | * [[https://docs.python.org/3/library/string.html#formatspec|Format Specification Mini-Language]] |
| | * See also the [[https://pyformat.info/|PyFormat site]] |
| | |
| | * [[https://pyformat.info/|PyFormat site]]: string formatting using the //old style// and the //String ''format()'' method// |
| | * <wrap hi>Hint</wrap>: this can also be used as an **easy documentation for //f-strings// format**! |
| | |
| | * [[https://docs.python.org/3/tutorial/inputoutput.html#the-string-format-method|Old string formatting]] |
| ==== Splitting (complex) strings ==== | ==== Splitting (complex) strings ==== |
| |
| ==== Working with paths and filenames ==== | ==== Working with paths and filenames ==== |
| |
| If you are in a hurry, you can just use string functions to work with path and file names. But you will need some specific functions to check if a file exists, and similar operations. All these are available in 2 libraries that have similar functions. Both of these libraries can deal with Unix-type paths on Linux computers, and Windows-type paths on Windows computers | If you are in a hurry, you can just use string functions to work with paths and file names. |
| | |
| | |
| | You will need some specific objects and functions to check if a file exists, and similar operations. Check the libraries listed below, that can automatically deal with Unix-type paths on Linux and MacOS computers, and Windows-type paths on Windows computers |
| |
| * [[https://docs.python.org/3/library/os.path.html|os.path]] //Common pathname manipulations// | * [[https://docs.python.org/3/library/os.path.html|os.path]]: //common pathname manipulations// |
| * Available since... a long time! Use this if you want to avoid backward compatibility problems | * Available since... a long time! Use this if you want to avoid backward compatibility problems |
| * Some functions are directly in [[https://docs.python.org/3/library/os.html|os]] //Miscellaneous operating system interfaces//\\ e.g. [[https://docs.python.org/3/library/os.html#os.remove|os.remove]] and [[https://docs.python.org/3/library/os.html#os.rmdir|os.rmdir]] | * Some functions are directly in [[https://docs.python.org/3/library/os.html|os]] //Miscellaneous operating system interfaces//\\ e.g. [[https://docs.python.org/3/library/os.html#os.remove|os.remove]] and [[https://docs.python.org/3/library/os.html#os.rmdir|os.rmdir]] |
| * [[https://docs.python.org/3/library/pathlib.html|pathlib]] //Object-oriented filesystem paths// | * [[https://docs.python.org/3/library/pathlib.html|pathlib]]: a **more recent** //object-oriented// way to deal with //filesystem paths// |
| * Available since Python version 3.4 | * Available since Python version 3.4 |
| * [[https://docs.python.org/3/library/pathlib.html#correspondence-to-tools-in-the-os-module|Matching pathlib, and os or os.path functions]] | * [[https://docs.python.org/3/library/pathlib.html#correspondence-to-tools-in-the-os-module|Matching pathlib, and os or os.path functions]] |
| * [[https://docs.python.org/3/library/shutil.html|High-level file operations]] | * [[https://docs.python.org/3/library/shutil.html|shutil]]: High-level file operations, e.g copy/move a file or directory tree |
| |
| |
| === Example: getting the full path of the Python used === | === Example: getting the full path of the Python executable used === |
| |
| Note: the actual python may be different from the default python! | Note: the actual python may be different from the default python! |
| /usr/bin/python | /usr/bin/python |
| |
| $ /modfs/modtools/miniconda3//envs/analyse_3.6_test/bin/python | $ /home/share/unix_files/cdat/miniconda3_21-02/envs/cdatm_py3/bin/python |
| >>> import sys, shutil | >>> import sys, shutil |
| >>> shutil.which('python') | >>> shutil.which('python') |
| '/usr/bin/python' | '/usr/bin/python' |
| >>> sys.executable | >>> sys.executable |
| '/modfs/modtools/miniconda3//envs/analyse_3.6_test/bin/python'</code> | '/home/share/unix_files/cdat/miniconda3_21-02/envs/cdatm_py3/bin/python'</code> |
| |
| |
| </code> | </code> |
| |
| | |
| | === Example: system independent paths with pathlib === |
| | |
| | Note: the following example was generated on a Linux server and uses a <wrap em>/</wrap> character as a path separator |
| | |
| | <code>>>> my_home = Path.home() |
| | >>> my_home |
| | PosixPath('/home/users/my_login') |
| | >>> my_conf = my_home / '.config' / 'evince' |
| | >>> my_conf |
| | PosixPath('/home/users/my_login/.config/evince') |
| | >>> my_conf.is_dir() |
| | True |
| | >>> my_conf.is_file() |
| | False |
| | >>> list(my_conf.glob('*')) |
| | [PosixPath('/home/users/my_login/.config/evince/evince_toolbar.xml'), PosixPath(' /home/users/my_login/.config/evince/accels')] |
| | >>> [ ff.name for ff in my_conf.glob('*') ] |
| | ['evince_toolbar.xml', 'accels'] |
| | </code> |
| |
| === Example: getting the size(s) of all the files in a directory === | === Example: getting the size(s) of all the files in a directory === |
| ['c', 'd', 'b', 'a']</code> | ['c', 'd', 'b', 'a']</code> |
| |
| | |
| | ===== Efficient looping with numpy, map, itertools and list comprehension ===== |
| | |
| | <wrap hi>Big, nested, explicit ''for'' loops should be avoided at all cost</wrap>, in order to reduce a script execution time! |
| | |
| | * **''numpy'' arrays** should be used when dealing with //numerical data// |
| | * **Masked arrays** can be used to deal with //special cases// and remove tests from loops |
| | |
| | * The built-in [[https://docs.python.org/3/library/functions.html?highlight=map#map|map]] function (and similar functions like [[https://docs.python.org/3/library/functions.html?highlight=zip#zip|zip]], [[https://docs.python.org/3/library/functions.html?highlight=filter#filter|filter]], ...) can be used to efficiently apply a function (possibly a //simple// [[https://docs.python.org/3/tutorial/controlflow.html#lambda-expressions|lambda]] function) to all the elements of a list |
| | * <code>>>> my_ints = [1, 2, 3] |
| | |
| | >>> map(str, my_ints) |
| | ['1', '2', '3'] |
| | |
| | >>> map(lambda ii: str(10*ii + 5), my_ints) |
| | ['15', '25', '35']</code> |
| | |
| | * The [[https://docs.python.org/3/library/itertools.html|itertools]] module defines many more fancy iterators that can be used for efficient looping |
| | * Example: replacing nested loops with [[https://docs.python.org/3/library/itertools.html#itertools.product|product]] |
| | * <code>>>> it.product('AB', '01') |
| | <itertools.product object at 0x2b35a7b5f100> |
| | |
| | >>> list(it.product('AB', '01')) |
| | [('A', '0'), ('A', '1'), ('B', '0'), ('B', '1')] |
| | |
| | >>> for c1, c2 in it.product('AB', '01'): |
| | ... print(c1 + c2) |
| | ... |
| | A0 |
| | A1 |
| | B0 |
| | B1 |
| | |
| | >>> for c1, c2 in it.product(['A', 'B'], ['0', '1']): |
| | ... print(c1 + c2) |
| | ... |
| | A0 |
| | A1 |
| | B0 |
| | B1 |
| | |
| | >>> for c1, c2, c3 in it.product('AB', '01', '$!'): |
| | ... print(c1 + c2 + c3, end=', ') |
| | ... |
| | A0$, A0!, A1$, A1!, B0$, B0!, B1$, B1!,</code> |
| | |
| | * The [[https://docs.python.org/3/tutorial/datastructures.html?highlight=comprehension#list-comprehensions|list comprehension]] (aka //implicit loops//) can also be used to generate lists from lists |
| | * Example: converting a list of integers to a list of strings\\ Note: in that case, you should rather use the ''map'' function detailed above |
| | * <code>>>> my_ints = [1, 2, 3] |
| | |
| | >>> [ str(ii) for ii in my_ints ] |
| | ['1', '2', '3']</code> |
| ===== numpy related stuff ===== | ===== numpy related stuff ===== |
| |
| array([3. , 4.5, 8. ])</code> | array([3. , 4.5, 8. ])</code> |
| |
| | ==== Exercise your brain with numpy ==== |
| | |
| | Have a look at [[https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises.ipynb|100 numpy exercises]] |
| |
| ===== matplotlib related stuff ===== | ===== matplotlib related stuff ===== |
| A few notes for a future section or page about about //data representation// (bits and bytes) on disk and in memory, vs //data format// | A few notes for a future section or page about about //data representation// (bits and bytes) on disk and in memory, vs //data format// |
| |
| FIXME Add parts (pages 28 to 37) of this [[https://wiki.lsce.ipsl.fr/pmip3/doku.php/other:python:jyp_steps#part_2|old tutorial]] to this section | FIXME Add parts (pages 28 to 37) of this [[http://www.lsce.ipsl.fr/Phocea/file.php?class=page&file=5/pythonCDAT_jyp_2sur2_070306.pdf|old tutorial]] to this section |
| |
| ==== Base notions ==== | ==== Base notions ==== |
| |
| * **Never forget** that all the bits and pieces of information we use are coded in [[https://en.wikipedia.org/wiki/Binary_number#Counting_in_binary|base 2]] (''0''s and ''1''s), grouped in bytes! | * **Never forget** that all the bits and pieces of information we use are coded in [[https://en.wikipedia.org/wiki/Binary_number#Counting_in_binary|base 2]] (''0''s and ''1''s ...), grouped in bytes! |
| * Some things can be stored exactly (integers, characters, ...) | * Some things can be stored exactly (integers, characters, ...) |
| * In other cases (**//real// numbers** that we work with all the time, compressed images/videos/music) we only store **//good enough approximation//** | * In other cases (**//real// numbers** that we work with all the time, compressed images/videos/music) we only store **//good enough approximation//** |
| * ''0000'' <=> ''0'',\\ ''0010'' <=> ''1'', ...,\\ ''1111'' <=> ''F'' | * ''0000'' <=> ''0'',\\ ''0010'' <=> ''1'', ...,\\ ''1111'' <=> ''F'' |
| * ''1101'' <=> ''D'' in hexadecimal <=> ''13'' in decimal (''**1** * 8 + **1** * 4 + **0** * 2 + **1** * 1'') | * ''1101'' <=> ''D'' in hexadecimal <=> ''13'' in decimal (''**1** * 8 + **1** * 4 + **0** * 2 + **1** * 1'') |
| * ''11111101'' <=> ''1111 1101'' <=> ''FD'' in hexadecimal <=> ''253'' in decimal (''15 * 16 + 13'') | * ''11111101'' in //base 2// <=> ''1111 1101'' <=> ''FD'' in //hexadecimal// <=> ''253'' (''15 * 16 + 13'') in //decimal// |
| |
| * Conversion with Python | * Base conversion with Python |
| * <code>>>> hex(13) # Decimal to Hexadecimal conversion | * <code>>>> hex(13) # Decimal to Hexadecimal conversion |
| '0xd' | '0xd' |
| >>> int('0x100', 16) # Hexadecimal to Decimal conversion | >>> int('0x100', 16) # Hexadecimal to Decimal conversion |
| 256 | 256 |
| >>> int('11', 2) | |
| 3 | |
| >>> int('1111', 2) # Binary to Decimal conversion | >>> int('1111', 2) # Binary to Decimal conversion |
| 15 | 15 |
| >>> int('11111101', 2) | >>> int('11111101', 2) # '11111101' <=> '1111 1101' <=> 'FD' <=> 15 * 16 + 13 = 253 |
| 253 | |
| >>> 15 * 16 + 13 | |
| 253 | 253 |
| >>> 013 # DANGER! Python considers an integer to be in OCTAL base if it starts with a 0 | >>> 013 # DANGER! Python considers an integer to be in OCTAL base if it starts with a 0 |
| >>> int('13', 8) # 1*8 + 3 | >>> int('13', 8) # 1*8 + 3 |
| 11</code> | 11</code> |
| | |
| | * More technical topics |
| | * [[https://en.wikipedia.org/wiki/Bit_numbering|Bit numbering]]: the art of ordering bits, everything about MSB (Most Significant Byte) and LSB (Least Significant Byte) |
| | * [[https://en.wikipedia.org/wiki/Endianness|Endianness]]: the art of ordering bytes |
| ==== Numerical values ==== | ==== Numerical values ==== |
| |
| * Binary data representation of some numbers (not everythin is listed here): | * Binary data representation of some numbers (only some common types are listed here): |
| | * Languages and packages **references** used below: |
| | * Python: [[https://numpy.org/doc/stable/reference/arrays.scalars.html#sized-aliases|NumPy Sized aliases]] |
| | * NetCDF: [[https://docs.unidata.ucar.edu/nug/current/md_types.html|Data Types]], [[https://docs.unidata.ucar.edu/netcdf-fortran/current/f90-variables.html#f90-language-types-corresponding-to-netcdf-external-data-types|Fortran related Data Types]], [[https://docs.unidata.ucar.edu/nug/current/_c_d_l.html#cdl_data_types|CDL Data Types]] |
| | * Fortran: Intel Fortran Compiler [[https://www.intel.com/content/www/us/en/docs/fortran-compiler/developer-guide-reference/2023-1/intrinsic-data-types.html|Intrinsic Data Types]] |
| * [[https://en.wikipedia.org/wiki/Integer_(computer_science)|Integers]] | * [[https://en.wikipedia.org/wiki/Integer_(computer_science)|Integers]] |
| * Range: | * Range: |
| * 4-byte integers: −2,147,483,648 to 2,147,483,647 | * 4-byte //signed// integers: ''−2,147,483,648'' to ''2,147,483,647'' |
| * Python: ''numpy.int32'' | * Python: ''numpy.int32'' |
| * [[https://docs.unidata.ucar.edu/nug/current/md_types.html|NetCDF]], [[https://docs.unidata.ucar.edu/netcdf-fortran/current/f90-variables.html#f90-language-types-corresponding-to-netcdf-external-data-types|NetCDF-Fortran]]: ''int'', ''NC_INT64'', ''NF90_INT'' | * NetCDF: ''int'', ''NC_INT'' or ''NC_LONG'', ''NF90_INT'' |
| * Fortran: | * Fortran: ''INTEGER*4'' |
| * 8-byte integers: −9,223,372,036,854,775,808 to 9,223,372,036,854,775,807 | * 8-byte //signed// integers: ''−9,223,372,036,854,775,808'' to ''9,223,372,036,854,775,807'' |
| * Python: ''numpy.int64'' | * Python: ''numpy.int64'' |
| * [[https://docs.unidata.ucar.edu/nug/current/md_types.html|NetCDF]]: ''int64'', ''NC_INT64'' | * NetCDF: ''int64'', ''NC_INT64'' |
| * Fortran: | * Fortran: ''INTEGER*8'' |
| * Tech note: signed integers use [[https://en.wikipedia.org/wiki/Two%27s_complement|two's complement]] for coding negative integers | * Tech note: signed integers use [[https://en.wikipedia.org/wiki/Two%27s_complement|two's complement]] for coding negative integers |
| * [[https://en.wikipedia.org/wiki/IEEE_754|Floating point numbers]] (//IEEE 754// standard aka //IEEE Standard for Binary Floating-Point for Arithmetic//) | * [[https://en.wikipedia.org/wiki/IEEE_754|Floating point numbers]] (//IEEE 754// standard aka //IEEE Standard for Binary Floating-Point for Arithmetic//) |
| * Range: | * Range: |
| * 4-byte float: ~8 significant digits * 10E±38 | * 4-byte float: ''~8 significant digits * 10E±38'' |
| * Python: ''numpy.float32'' | * Python: ''numpy.float32'' |
| * [[https://docs.unidata.ucar.edu/nug/current/md_types.html|NetCDF]], [[https://docs.unidata.ucar.edu/netcdf-fortran/current/f90-variables.html#f90-language-types-corresponding-to-netcdf-external-data-types|NetCDF-Fortran]]: | * NetCDF: ''float'', ''NC-FLOAT'', ''NF90_FLOAT'' |
| * Fortran: | * Fortran:''REAL*4'' |
| * See also [[https://en.wikipedia.org/wiki/Single-precision_floating-point_format|Single-precision floating-point format]] | * See also [[https://en.wikipedia.org/wiki/Single-precision_floating-point_format|Single-precision floating-point format]] |
| * 8-byte float: ~15 significant digits * 10E±308 | * 8-byte float: ''~15 significant digits * 10E±308'' |
| * Python: ''numpy.float64'' | * Python: ''numpy.float64'' |
| * [[https://docs.unidata.ucar.edu/nug/current/md_types.html|NetCDF]], [[https://docs.unidata.ucar.edu/netcdf-fortran/current/f90-variables.html#f90-language-types-corresponding-to-netcdf-external-data-types|NetCDF-Fortran]]: | * NetCDF: ''double'', ''NC_DOUBLE'', ''NF90_DOUBLE'' |
| * Fortran: | * Fortran: ''REAL*8'' |
| * Special values: | * **Special values**: |
| * [[https://en.wikipedia.org/wiki/NaN|NaN]] (''numpy.nan''): //Not a Number// | * [[https://en.wikipedia.org/wiki/NaN|NaN]]: //Not a Number// |
| * Infinity (''-numpy.inf'' and ''numpy.inf'') | * Python: ''numpy.nan'' |
| * Note: it is cleaner to use masks (and [[https://numpy.org/doc/stable/reference/maskedarray.generic.html|Numpy masked arrays]]) than NaNs, when you have to deal with missing values ! | * Infinity |
| * [[https://en.wikipedia.org/wiki/Bit_numbering|Bit numbering]] | * Python: ''-numpy.inf'' and ''numpy.inf'' |
| * [[https://en.wikipedia.org/wiki/Endianness|Endianness]] | * Note: it is cleaner to use masks (and [[https://numpy.org/doc/stable/reference/maskedarray.generic.html|Numpy masked arrays]]) rather than ''NaN''s, when you have to deal with missing values ! |
| | * <wrap hi>The RISKS of working with (the wrong) floats</wrap>: |
| | * [[https://en.wikipedia.org/wiki/Round-off_error|Round-off error]] |
| | * [[https://en.wikipedia.org/wiki/Catastrophic_cancellation|Catastrophic cancellation]] |
| | * [[https://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html|What Every Computer Scientist Should Know About Floating-Point Arithmetic]] |
| * A rather technical example: we //play// with a numpy 4-byte integer scalar | * A rather technical example: we //play// with a numpy 4-byte integer scalar |
| * <code>>>> one_int32 = np.int32(1) | * <code>>>> one_int32 = np.int32(1) |
