├── .coveragerc ├── .github └── workflows │ ├── deploy-on-pypi.yml │ └── test-jalali-pandas.yml ├── .gitignore ├── .pre-commit-config.yaml ├── .pylintrc ├── LICENSE ├── README.md ├── docs ├── _config.yml ├── assets │ └── github-jalali-pandas.png └── index.md ├── examples └── basic_usage.ipynb ├── jalali_pandas ├── __init__.py ├── df_handler.py └── serie_handler.py ├── poetry.lock ├── pyproject.toml ├── requirements.txt └── tests ├── __init__.py ├── df_test.py └── series_test.py /.coveragerc : -------------------------------------------------------------------------------- 1 | [run] 2 | source=jalali_pandas 3 | -------------------------------------------------------------------------------- /.github/workflows/deploy-on-pypi.yml: -------------------------------------------------------------------------------- 1 | name: Python package 2 | on: 3 | push: 4 | tags: 5 | - "v*.*.*" 6 | jobs: 7 | build: 8 | runs-on: ubuntu-latest 9 | steps: 10 | - uses: actions/checkout@v2 11 | - name: Build and publish to pypi 12 | uses: JRubics/poetry-publish@v1.10 13 | with: 14 | pypi_token: ${{ secrets.PYPI_TOKEN }} 15 | -------------------------------------------------------------------------------- /.github/workflows/test-jalali-pandas.yml: -------------------------------------------------------------------------------- 1 | name: code-coverage 2 | 3 | on: [push] 4 | jobs: 5 | run: 6 | runs-on: ${{ matrix.os }} 7 | strategy: 8 | matrix: 9 | os: [ubuntu-latest] 10 | env: 11 | OS: ${{ matrix.os }} 12 | PYTHON: '3.8' 13 | steps: 14 | - uses: actions/checkout@master 15 | - name: Setup Python 16 | uses: actions/setup-python@master 17 | with: 18 | python-version: 3.8 19 | - name: Generate coverage report 20 | run: | 21 | pip install pytest 22 | pip install pytest-cov 23 | if [ -f requirements.txt ]; then pip install -r requirements.txt; fi 24 | 25 | pytest --cov=jalali_pandas --cov-report=xml 26 | - name: Upload coverage to Codecov 27 | uses: codecov/codecov-action@v2 28 | with: 29 | token: ${{ secrets.CODECOV_TOKEN }} 30 | env_vars: OS,PYTHON 31 | fail_ci_if_error: false 32 | name: codecov-umbrella 33 | verbose: true 34 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | 131 | 132 | .vscode/ 133 | -------------------------------------------------------------------------------- /.pre-commit-config.yaml: -------------------------------------------------------------------------------- 1 | repos: 2 | - repo: https://github.com/pre-commit/pre-commit-hooks 3 | rev: v4.0.1 4 | hooks: 5 | - id: check-yaml 6 | - id: end-of-file-fixer 7 | - id: trailing-whitespace 8 | - repo: https://github.com/psf/black 9 | rev: 21.10b0 10 | hooks: 11 | - id: black 12 | 13 | - repo: local 14 | hooks: 15 | - id: pylint 16 | name: pylint 17 | entry: pylint 18 | language: python 19 | types: [python] 20 | args: [--rcfile=.pylintrc] 21 | -------------------------------------------------------------------------------- /.pylintrc: -------------------------------------------------------------------------------- 1 | [MASTER] 2 | # A comma-separated list of package or module names from where C extensions may 3 | # be loaded. Extensions are loading into the active Python interpreter and may 4 | # run arbitrary code. 5 | extension-pkg-allow-list= 6 | 7 | # A comma-separated list of package or module names from where C extensions may 8 | # be loaded. Extensions are loading into the active Python interpreter and may 9 | # run arbitrary code. (This is an alternative name to extension-pkg-allow-list 10 | # for backward compatibility.) 11 | extension-pkg-whitelist= 12 | 13 | # Return non-zero exit code if any of these messages/categories are detected, 14 | # even if score is above --fail-under value. Syntax same as enable. Messages 15 | # specified are enabled, while categories only check already-enabled messages. 16 | fail-on= 17 | 18 | # Specify a score threshold to be exceeded before program exits with error. 19 | fail-under=10.0 20 | 21 | # Files or directories to be skipped. They should be base names, not paths. 22 | ignore=CVS 23 | 24 | # Add files or directories matching the regex patterns to the ignore-list. The 25 | # regex matches against paths. 26 | ignore-paths= 27 | 28 | # Files or directories matching the regex patterns are skipped. The regex 29 | # matches against base names, not paths. 30 | ignore-patterns= 31 | 32 | # Python code to execute, usually for sys.path manipulation such as 33 | # pygtk.require(). 34 | #init-hook= 35 | 36 | # Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the 37 | # number of processors available to use. 38 | jobs=1 39 | 40 | # Control the amount of potential inferred values when inferring a single 41 | # object. This can help the performance when dealing with large functions or 42 | # complex, nested conditions. 43 | limit-inference-results=100 44 | 45 | # List of plugins (as comma separated values of python module names) to load, 46 | # usually to register additional checkers. 47 | load-plugins= 48 | 49 | # Pickle collected data for later comparisons. 50 | persistent=yes 51 | 52 | # Min Python version to use for version dependend checks. Will default to the 53 | # version used to run pylint. 54 | py-version=3.8 55 | 56 | # When enabled, pylint would attempt to guess common misconfiguration and emit 57 | # user-friendly hints instead of false-positive error messages. 58 | suggestion-mode=yes 59 | 60 | # Allow loading of arbitrary C extensions. Extensions are imported into the 61 | # active Python interpreter and may run arbitrary code. 62 | unsafe-load-any-extension=no 63 | 64 | 65 | [MESSAGES CONTROL] 66 | 67 | # Only show warnings with the listed confidence levels. Leave empty to show 68 | # all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED. 69 | confidence= 70 | 71 | # Disable the message, report, category or checker with the given id(s). You 72 | # can either give multiple identifiers separated by comma (,) or put this 73 | # option multiple times (only on the command line, not in the configuration 74 | # file where it should appear only once). You can also use "--disable=all" to 75 | # disable everything first and then reenable specific checks. For example, if 76 | # you want to run only the similarities checker, you can use "--disable=all 77 | # --enable=similarities". If you want to run only the classes checker, but have 78 | # no Warning level messages displayed, use "--disable=all --enable=classes 79 | # --disable=W". 80 | disable=raw-checker-failed, 81 | bad-inline-option, 82 | locally-disabled, 83 | file-ignored, 84 | suppressed-message, 85 | useless-suppression, 86 | deprecated-pragma, 87 | use-symbolic-message-instead 88 | 89 | # Enable the message, report, category or checker with the given id(s). You can 90 | # either give multiple identifier separated by comma (,) or put this option 91 | # multiple time (only on the command line, not in the configuration file where 92 | # it should appear only once). See also the "--disable" option for examples. 93 | enable=c-extension-no-member 94 | 95 | 96 | [REPORTS] 97 | 98 | # Python expression which should return a score less than or equal to 10. You 99 | # have access to the variables 'error', 'warning', 'refactor', and 'convention' 100 | # which contain the number of messages in each category, as well as 'statement' 101 | # which is the total number of statements analyzed. This score is used by the 102 | # global evaluation report (RP0004). 103 | evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10) 104 | 105 | # Template used to display messages. This is a python new-style format string 106 | # used to format the message information. See doc for all details. 107 | #msg-template= 108 | 109 | # Set the output format. Available formats are text, parseable, colorized, json 110 | # and msvs (visual studio). You can also give a reporter class, e.g. 111 | # mypackage.mymodule.MyReporterClass. 112 | output-format=text 113 | 114 | # Tells whether to display a full report or only the messages. 115 | reports=no 116 | 117 | # Activate the evaluation score. 118 | score=yes 119 | 120 | 121 | [REFACTORING] 122 | 123 | # Maximum number of nested blocks for function / method body 124 | max-nested-blocks=5 125 | 126 | # Complete name of functions that never returns. When checking for 127 | # inconsistent-return-statements if a never returning function is called then 128 | # it will be considered as an explicit return statement and no message will be 129 | # printed. 130 | never-returning-functions=sys.exit,argparse.parse_error 131 | 132 | 133 | [LOGGING] 134 | 135 | # The type of string formatting that logging methods do. `old` means using % 136 | # formatting, `new` is for `{}` formatting. 137 | logging-format-style=old 138 | 139 | # Logging modules to check that the string format arguments are in logging 140 | # function parameter format. 141 | logging-modules=logging 142 | 143 | 144 | [SPELLING] 145 | 146 | # Limits count of emitted suggestions for spelling mistakes. 147 | max-spelling-suggestions=4 148 | 149 | # Spelling dictionary name. Available dictionaries: none. To make it work, 150 | # install the 'python-enchant' package. 151 | spelling-dict= 152 | 153 | # List of comma separated words that should be considered directives if they 154 | # appear and the beginning of a comment and should not be checked. 155 | spelling-ignore-comment-directives=fmt: on,fmt: off,noqa:,noqa,nosec,isort:skip,mypy: 156 | 157 | # List of comma separated words that should not be checked. 158 | spelling-ignore-words= 159 | 160 | # A path to a file that contains the private dictionary; one word per line. 161 | spelling-private-dict-file= 162 | 163 | # Tells whether to store unknown words to the private dictionary (see the 164 | # --spelling-private-dict-file option) instead of raising a message. 165 | spelling-store-unknown-words=no 166 | 167 | 168 | [MISCELLANEOUS] 169 | 170 | # List of note tags to take in consideration, separated by a comma. 171 | notes=FIXME, 172 | XXX, 173 | TODO 174 | 175 | # Regular expression of note tags to take in consideration. 176 | #notes-rgx= 177 | 178 | 179 | [TYPECHECK] 180 | 181 | # List of decorators that produce context managers, such as 182 | # contextlib.contextmanager. Add to this list to register other decorators that 183 | # produce valid context managers. 184 | contextmanager-decorators=contextlib.contextmanager 185 | 186 | # List of members which are set dynamically and missed by pylint inference 187 | # system, and so shouldn't trigger E1101 when accessed. Python regular 188 | # expressions are accepted. 189 | generated-members= 190 | 191 | # Tells whether missing members accessed in mixin class should be ignored. A 192 | # mixin class is detected if its name ends with "mixin" (case insensitive). 193 | ignore-mixin-members=yes 194 | 195 | # Tells whether to warn about missing members when the owner of the attribute 196 | # is inferred to be None. 197 | ignore-none=yes 198 | 199 | # This flag controls whether pylint should warn about no-member and similar 200 | # checks whenever an opaque object is returned when inferring. The inference 201 | # can return multiple potential results while evaluating a Python object, but 202 | # some branches might not be evaluated, which results in partial inference. In 203 | # that case, it might be useful to still emit no-member and other checks for 204 | # the rest of the inferred objects. 205 | ignore-on-opaque-inference=yes 206 | 207 | # List of class names for which member attributes should not be checked (useful 208 | # for classes with dynamically set attributes). This supports the use of 209 | # qualified names. 210 | ignored-classes=optparse.Values,thread._local,_thread._local 211 | 212 | # List of module names for which member attributes should not be checked 213 | # (useful for modules/projects where namespaces are manipulated during runtime 214 | # and thus existing member attributes cannot be deduced by static analysis). It 215 | # supports qualified module names, as well as Unix pattern matching. 216 | ignored-modules= 217 | 218 | # Show a hint with possible names when a member name was not found. The aspect 219 | # of finding the hint is based on edit distance. 220 | missing-member-hint=yes 221 | 222 | # The minimum edit distance a name should have in order to be considered a 223 | # similar match for a missing member name. 224 | missing-member-hint-distance=1 225 | 226 | # The total number of similar names that should be taken in consideration when 227 | # showing a hint for a missing member. 228 | missing-member-max-choices=1 229 | 230 | # List of decorators that change the signature of a decorated function. 231 | signature-mutators= 232 | 233 | 234 | [VARIABLES] 235 | 236 | # List of additional names supposed to be defined in builtins. Remember that 237 | # you should avoid defining new builtins when possible. 238 | additional-builtins= 239 | 240 | # Tells whether unused global variables should be treated as a violation. 241 | allow-global-unused-variables=yes 242 | 243 | # List of names allowed to shadow builtins 244 | allowed-redefined-builtins= 245 | 246 | # List of strings which can identify a callback function by name. A callback 247 | # name must start or end with one of those strings. 248 | callbacks=cb_, 249 | _cb 250 | 251 | # A regular expression matching the name of dummy variables (i.e. expected to 252 | # not be used). 253 | dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_ 254 | 255 | # Argument names that match this expression will be ignored. Default to name 256 | # with leading underscore. 257 | ignored-argument-names=_.*|^ignored_|^unused_ 258 | 259 | # Tells whether we should check for unused import in __init__ files. 260 | init-import=no 261 | 262 | # List of qualified module names which can have objects that can redefine 263 | # builtins. 264 | redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io 265 | 266 | 267 | [FORMAT] 268 | 269 | # Expected format of line ending, e.g. empty (any line ending), LF or CRLF. 270 | expected-line-ending-format= 271 | 272 | # Regexp for a line that is allowed to be longer than the limit. 273 | ignore-long-lines=^\s*(# )??$ 274 | 275 | # Number of spaces of indent required inside a hanging or continued line. 276 | indent-after-paren=4 277 | 278 | # String used as indentation unit. This is usually " " (4 spaces) or "\t" (1 279 | # tab). 280 | indent-string=' ' 281 | 282 | # Maximum number of characters on a single line. 283 | max-line-length=100 284 | 285 | # Maximum number of lines in a module. 286 | max-module-lines=1000 287 | 288 | # Allow the body of a class to be on the same line as the declaration if body 289 | # contains single statement. 290 | single-line-class-stmt=no 291 | 292 | # Allow the body of an if to be on the same line as the test if there is no 293 | # else. 294 | single-line-if-stmt=no 295 | 296 | 297 | [SIMILARITIES] 298 | 299 | # Comments are removed from the similarity computation 300 | ignore-comments=yes 301 | 302 | # Docstrings are removed from the similarity computation 303 | ignore-docstrings=yes 304 | 305 | # Imports are removed from the similarity computation 306 | ignore-imports=no 307 | 308 | # Signatures are removed from the similarity computation 309 | ignore-signatures=no 310 | 311 | # Minimum lines number of a similarity. 312 | min-similarity-lines=4 313 | 314 | 315 | [BASIC] 316 | 317 | # Naming style matching correct argument names. 318 | argument-naming-style=snake_case 319 | 320 | # Regular expression matching correct argument names. Overrides argument- 321 | # naming-style. 322 | #argument-rgx= 323 | 324 | # Naming style matching correct attribute names. 325 | attr-naming-style=snake_case 326 | 327 | # Regular expression matching correct attribute names. Overrides attr-naming- 328 | # style. 329 | #attr-rgx= 330 | 331 | # Bad variable names which should always be refused, separated by a comma. 332 | bad-names=foo, 333 | bar, 334 | baz, 335 | toto, 336 | tutu, 337 | tata 338 | 339 | # Bad variable names regexes, separated by a comma. If names match any regex, 340 | # they will always be refused 341 | bad-names-rgxs= 342 | 343 | # Naming style matching correct class attribute names. 344 | class-attribute-naming-style=any 345 | 346 | # Regular expression matching correct class attribute names. Overrides class- 347 | # attribute-naming-style. 348 | #class-attribute-rgx= 349 | 350 | # Naming style matching correct class constant names. 351 | class-const-naming-style=UPPER_CASE 352 | 353 | # Regular expression matching correct class constant names. Overrides class- 354 | # const-naming-style. 355 | #class-const-rgx= 356 | 357 | # Naming style matching correct class names. 358 | class-naming-style=PascalCase 359 | 360 | # Regular expression matching correct class names. Overrides class-naming- 361 | # style. 362 | #class-rgx= 363 | 364 | # Naming style matching correct constant names. 365 | const-naming-style=UPPER_CASE 366 | 367 | # Regular expression matching correct constant names. Overrides const-naming- 368 | # style. 369 | #const-rgx= 370 | 371 | # Minimum line length for functions/classes that require docstrings, shorter 372 | # ones are exempt. 373 | docstring-min-length=-1 374 | 375 | # Naming style matching correct function names. 376 | function-naming-style=snake_case 377 | 378 | # Regular expression matching correct function names. Overrides function- 379 | # naming-style. 380 | #function-rgx= 381 | 382 | # Good variable names which should always be accepted, separated by a comma. 383 | good-names=i, 384 | j, 385 | k, 386 | ex, 387 | Run, 388 | _, 389 | df 390 | 391 | # Good variable names regexes, separated by a comma. If names match any regex, 392 | # they will always be accepted 393 | good-names-rgxs= 394 | 395 | # Include a hint for the correct naming format with invalid-name. 396 | include-naming-hint=no 397 | 398 | # Naming style matching correct inline iteration names. 399 | inlinevar-naming-style=any 400 | 401 | # Regular expression matching correct inline iteration names. Overrides 402 | # inlinevar-naming-style. 403 | #inlinevar-rgx= 404 | 405 | # Naming style matching correct method names. 406 | method-naming-style=snake_case 407 | 408 | # Regular expression matching correct method names. Overrides method-naming- 409 | # style. 410 | #method-rgx= 411 | 412 | # Naming style matching correct module names. 413 | module-naming-style=snake_case 414 | 415 | # Regular expression matching correct module names. Overrides module-naming- 416 | # style. 417 | #module-rgx= 418 | 419 | # Colon-delimited sets of names that determine each other's naming style when 420 | # the name regexes allow several styles. 421 | name-group= 422 | 423 | # Regular expression which should only match function or class names that do 424 | # not require a docstring. 425 | no-docstring-rgx=^_ 426 | 427 | # List of decorators that produce properties, such as abc.abstractproperty. Add 428 | # to this list to register other decorators that produce valid properties. 429 | # These decorators are taken in consideration only for invalid-name. 430 | property-classes=abc.abstractproperty 431 | 432 | # Naming style matching correct variable names. 433 | variable-naming-style=snake_case 434 | 435 | # Regular expression matching correct variable names. Overrides variable- 436 | # naming-style. 437 | #variable-rgx= 438 | 439 | 440 | [STRING] 441 | 442 | # This flag controls whether inconsistent-quotes generates a warning when the 443 | # character used as a quote delimiter is used inconsistently within a module. 444 | check-quote-consistency=no 445 | 446 | # This flag controls whether the implicit-str-concat should generate a warning 447 | # on implicit string concatenation in sequences defined over several lines. 448 | check-str-concat-over-line-jumps=no 449 | 450 | 451 | [IMPORTS] 452 | 453 | # List of modules that can be imported at any level, not just the top level 454 | # one. 455 | allow-any-import-level= 456 | 457 | # Allow wildcard imports from modules that define __all__. 458 | allow-wildcard-with-all=no 459 | 460 | # Analyse import fallback blocks. This can be used to support both Python 2 and 461 | # 3 compatible code, which means that the block might have code that exists 462 | # only in one or another interpreter, leading to false positives when analysed. 463 | analyse-fallback-blocks=no 464 | 465 | # Deprecated modules which should not be used, separated by a comma. 466 | deprecated-modules= 467 | 468 | # Output a graph (.gv or any supported image format) of external dependencies 469 | # to the given file (report RP0402 must not be disabled). 470 | ext-import-graph= 471 | 472 | # Output a graph (.gv or any supported image format) of all (i.e. internal and 473 | # external) dependencies to the given file (report RP0402 must not be 474 | # disabled). 475 | import-graph= 476 | 477 | # Output a graph (.gv or any supported image format) of internal dependencies 478 | # to the given file (report RP0402 must not be disabled). 479 | int-import-graph= 480 | 481 | # Force import order to recognize a module as part of the standard 482 | # compatibility libraries. 483 | known-standard-library= 484 | 485 | # Force import order to recognize a module as part of a third party library. 486 | known-third-party=enchant 487 | 488 | # Couples of modules and preferred modules, separated by a comma. 489 | preferred-modules= 490 | 491 | 492 | [CLASSES] 493 | 494 | # Warn about protected attribute access inside special methods 495 | check-protected-access-in-special-methods=no 496 | 497 | # List of method names used to declare (i.e. assign) instance attributes. 498 | defining-attr-methods=__init__, 499 | __new__, 500 | setUp, 501 | __post_init__ 502 | 503 | # List of member names, which should be excluded from the protected access 504 | # warning. 505 | exclude-protected=_asdict, 506 | _fields, 507 | _replace, 508 | _source, 509 | _make 510 | 511 | # List of valid names for the first argument in a class method. 512 | valid-classmethod-first-arg=cls 513 | 514 | # List of valid names for the first argument in a metaclass class method. 515 | valid-metaclass-classmethod-first-arg=cls 516 | 517 | 518 | [DESIGN] 519 | 520 | # List of qualified class names to ignore when counting class parents (see 521 | # R0901) 522 | ignored-parents= 523 | 524 | # Maximum number of arguments for function / method. 525 | max-args=5 526 | 527 | # Maximum number of attributes for a class (see R0902). 528 | max-attributes=7 529 | 530 | # Maximum number of boolean expressions in an if statement (see R0916). 531 | max-bool-expr=5 532 | 533 | # Maximum number of branch for function / method body. 534 | max-branches=12 535 | 536 | # Maximum number of locals for function / method body. 537 | max-locals=15 538 | 539 | # Maximum number of parents for a class (see R0901). 540 | max-parents=7 541 | 542 | # Maximum number of public methods for a class (see R0904). 543 | max-public-methods=20 544 | 545 | # Maximum number of return / yield for function / method body. 546 | max-returns=6 547 | 548 | # Maximum number of statements in function / method body. 549 | max-statements=50 550 | 551 | # Minimum number of public methods for a class (see R0903). 552 | min-public-methods=2 553 | 554 | 555 | [EXCEPTIONS] 556 | 557 | # Exceptions that will emit a warning when being caught. 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There are many ways you could offer source, and different 655 | solutions will be better for different programs; see section 13 for the 656 | specific requirements. 657 | 658 | You should also get your employer (if you work as a programmer) or school, 659 | if any, to sign a "copyright disclaimer" for the program, if necessary. 660 | For more information on this, and how to apply and follow the GNU AGPL, see 661 | . 662 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![HitCount](http://hits.dwyl.com/ghodsizadeh/jalali-pandas.svg)](http://hits.dwyl.com/ghodsizadeh/jalali-pandas) 2 | ![PyPI - Downloads](https://img.shields.io/pypi/dw/tehran_stocks.svg?color=blue) 3 | [![PyPI version](https://badge.fury.io/py/jalali-pandas.svg)](https://badge.fury.io/py/jalali-pandas) 4 | [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black) 5 | [![codecov](https://codecov.io/gh/ghodsizadeh/jalali-pandas/branch/main/graph/badge.svg?token=LWQ85TN0NU)](https://codecov.io/gh/ghodsizadeh/jalali-pandas) 6 | [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) 7 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ghodsizadeh/jalali-pandas/blob/main/examples/basic_usage.ipynb) 8 | ![GitHub Repo stars](https://img.shields.io/github/stars/ghodsizadeh/jalali-pandas?logoColor=blue&style=social) 9 | 10 | # Jalali Pandas Extentsion 11 | 12 | > A pandas extension that solves all problems of Jalai/Iraninan/Shamsi dates 13 | 14 | ![Jalali Pandas python package](docs/assets/github-jalali-pandas.png) 15 | 16 | ## Features 17 | 18 | #### Series Extenstion 19 | 20 | - Convert _string_ to _Jalali_ date `1388/03/25` --> `jdatetime(1388,3,25,0,0)` 21 | - Convert _gregorian_ date to _Jalali_ date `datetime(2019,11,17,0,0)` --> `jdatetime(1398,8,26,0,0)` 22 | - Convert _Jalali_ date to _gregorian_ date `jdatetime(1398,10,18,0,0)` --> `datetim(2020,1,8,6,19)` 23 | 24 | #### DataFrame extenstion 25 | 26 | - Support grouping by _Jalali_ date 27 | - Group by year, month, days, ... 28 | - Shortcuts for groups: `ymd` for `['year','month','day']` and more 29 | - Resampling: Convenience method for frequency conversion and resampling of time series but in _Jalali_ dateformat. (comming soon) 30 | 31 | ## Installation 32 | 33 | pip install -U jalali-pandas 34 | 35 | ## Usage 36 | 37 | Just import jalali-pandas and use pandas just use `.jalali` as a method for series and dataframes. Nothin outside pandas. 38 | 39 | > `jalali-pandas` is an extentsion for pandas, that add a mehtod for series/columns and dataframes. 40 | 41 | ### Series 42 | 43 | ```python 44 | import pandas as pd 45 | import jalali_pandas 46 | 47 | # create dataframe 48 | df = pd.DataFrame({"date": pd.date_range("2019-01-01", periods=10, freq="D")}) 49 | 50 | # convert to jalali 51 | df["jdate"] = df["date"].jalali.to_jalali() 52 | 53 | # convert to gregorian 54 | df["gdate"] = df["jdate"].jalali.to_gregorian() 55 | 56 | # parse string to jalali 57 | df1 = pd.DataFrame({"date": ["1399/08/02", "1399/08/03", "1399/08/04"]}) 58 | df1["jdate"] = df1["date"].jalali.parse_jalali("%Y/%m/%d") 59 | 60 | 61 | # get access to jalali year,quarter ,month, day and weekday 62 | df['year'] = df["jdate"].jalali.year 63 | df['month'] = df["jdate"].jalali.month 64 | df['quarter'] = df["jdate"].jalali.quarter 65 | df['day'] = df["jdate"].jalali.day 66 | df['weekday'] = df["jdate"].jalali.weekday 67 | 68 | ``` 69 | 70 | ### DataFrame 71 | 72 | ```python 73 | 74 | import pandas as pd 75 | import jalali_pandas 76 | 77 | df = pd.DataFrame( 78 | { 79 | "date": pd.date_range("2019-01-01", periods=10, freq="M"), 80 | "value": range(10), 81 | } 82 | ) 83 | # make sure to create a column with jalali datetime format. (you can use any name) 84 | df["jdate"] = df["date"].jalali.to_jalali() 85 | 86 | 87 | # group by jalali year 88 | gp = df.jalali.groupby("year") 89 | gp.sum() 90 | 91 | #group by month 92 | mean = df.jalali.groupby('mean') 93 | 94 | #groupby year and month and day 95 | mean = df.jalali.groupby('ymd') 96 | # or 97 | mean = df.jalali.groupby(['year','month','day']) 98 | 99 | 100 | #groupby year and quarter 101 | mean = df.jalali.groupby('yq') 102 | # or 103 | mean = df.jalali.groupby(['year','quarter']) 104 | ``` 105 | 106 | --- 107 | 108 | # راهنمای فارسی 109 | 110 | برای مطالعه راهنمای فارسی استفاده از کتابخانه به این آدرس مراجعه کنید. 111 | 112 | [معرفی بسته pandas-jalali | آموزش کار با تاریخ شمسی در pandas](https://learnwithmehdi.ir/posts/jalali-pandas/) 113 | [معرفی بسته pandas-jalali | آموزش کار با تاریخ شمسی در pandas](https://learnwithmehdi.ir/posts/jalali-pandas/) 114 | 115 | ## راهنمای ویدیویی 116 | 117 | [![IMAGE ALT TEXT HERE](https://img.youtube.com/vi/PYS4Hxmzbyg/0.jpg)](https://www.youtube.com/watch?v=PYS4Hxmzbyg) 118 | 119 | ## ToDos: 120 | 121 | - [x] add gregorian to Jalali Conversion 122 | - [x] add Jalali to gregorian Conversion 123 | - [ ] add support for sampling 124 | - [x] add date parser from other columns 125 | - [x] add date parser from string 126 | -------------------------------------------------------------------------------- /docs/_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-midnight 2 | -------------------------------------------------------------------------------- /docs/assets/github-jalali-pandas.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ghodsizadeh/jalali-pandas/ce480829a3c50a53cb2a17c94e5a49648d18ca42/docs/assets/github-jalali-pandas.png -------------------------------------------------------------------------------- /docs/index.md: -------------------------------------------------------------------------------- 1 | [![HitCount](http://hits.dwyl.com/ghodsizadeh/jalali-pandas.svg)](http://hits.dwyl.com/ghodsizadeh/jalali-pandas) 2 | ![PyPI - Downloads](https://img.shields.io/pypi/dw/tehran_stocks.svg?color=blue) 3 | [![PyPI version](https://badge.fury.io/py/jalali-pandas.svg)](https://badge.fury.io/py/jalali-pandas) 4 | [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black) 5 | [![codecov](https://codecov.io/gh/ghodsizadeh/jalali-pandas/branch/main/graph/badge.svg?token=LWQ85TN0NU)](https://codecov.io/gh/ghodsizadeh/jalali-pandas) 6 | [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) 7 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ghodsizadeh/jalali-pandas/blob/main/examples/basic_usage.ipynb) 8 | ![GitHub Repo stars](https://img.shields.io/github/stars/ghodsizadeh/jalali-pandas?logoColor=blue&style=social) 9 | 10 | # Jalali Pandas Extentsion 11 | 12 | > A pandas extension that solves all problems of Jalai/Iraninan/Shamsi dates 13 | 14 | ![Jalali Pandas python package](assets/github-jalali-pandas.png) 15 | 16 | ## Features 17 | 18 | #### Series Extenstion 19 | 20 | - Convert _string_ to _Jalali_ date `1388/03/25` --> `jdatetime(1388,3,25,0,0)` 21 | - Convert _gregorian_ date to _Jalali_ date `datetime(2019,11,17,0,0)` --> `jdatetime(1398,8,26,0,0)` 22 | - Convert _Jalali_ date to _gregorian_ date `jdatetime(1398,10,18,0,0)` --> `datetim(2020,1,8,6,19)` 23 | 24 | #### DataFrame extenstion 25 | 26 | - Support grouping by _Jalali_ date 27 | - Group by year, month, days, ... 28 | - Shortcuts for groups: `ymd` for `['year','month','day']` and more 29 | - Resampling: Convenience method for frequency conversion and resampling of time series but in _Jalali_ dateformat. (comming soon) 30 | 31 | ## Installation 32 | 33 | pip install -U jalali-pandas 34 | 35 | ## Usage 36 | 37 | Just import jalali-pandas and use pandas just use `.jalali` as a method for series and dataframes. Nothin outside pandas. 38 | 39 | > `jalali-pandas` is an extentsion for pandas, that add a mehtod for series/columns and dataframes. 40 | 41 | ### Series 42 | 43 | ```python 44 | import pandas as pd 45 | import jalali_pandas 46 | 47 | # create dataframe 48 | df = pd.DataFrame({"date": pd.date_range("2019-01-01", periods=10, freq="D")}) 49 | 50 | # convert to jalali 51 | df["jdate"] = df["date"].jalali.to_jalali() 52 | 53 | # convert to gregorian 54 | df["gdate"] = df["jdate"].jalali.to_gregorian() 55 | 56 | # parse string to jalali 57 | df1 = pd.DataFrame({"date": ["1399/08/02", "1399/08/03", "1399/08/04"]}) 58 | df1["jdate"] = df1["date"].jalali.parse_jalali("%Y/%m/%d") 59 | 60 | 61 | # get access to jalali year,quarter ,month, day and weekday 62 | df['year'] = df["jdate"].jalali.year 63 | df['month'] = df["jdate"].jalali.month 64 | df['quarter'] = df["jdate"].jalali.quarter 65 | df['day'] = df["jdate"].jalali.day 66 | df['weekday'] = df["jdate"].jalali.weekday 67 | 68 | ``` 69 | 70 | ### DataFrame 71 | 72 | ```python 73 | 74 | import pandas as pd 75 | import jalali_pandas 76 | 77 | df = pd.DataFrame( 78 | { 79 | "date": pd.date_range("2019-01-01", periods=10, freq="M"), 80 | "value": range(10), 81 | } 82 | ) 83 | # make sure to create a column with jalali datetime format. (you can use any name) 84 | df["jdate"] = df["date"].jalali.to_jalali() 85 | 86 | 87 | # group by jalali year 88 | gp = df.jalali.groupby("year") 89 | gp.sum() 90 | 91 | #group by month 92 | mean = df.jalali.groupby('mean') 93 | 94 | #groupby year and month and day 95 | mean = df.jalali.groupby('ymd') 96 | # or 97 | mean = df.jalali.groupby(['year','month','day']) 98 | 99 | 100 | #groupby year and quarter 101 | mean = df.jalali.groupby('yq') 102 | # or 103 | mean = df.jalali.groupby(['year','quarter']) 104 | ``` 105 | 106 | --- 107 | 108 | # راهنمای فارسی 109 | 110 | برای مطالعه راهنمای فارسی استفاده از کتابخانه به این آدرس مراجعه کنید. 111 | 112 | [معرفی بسته pandas-jalali | آموزش کار با تاریخ شمسی در pandas](https://learnwithmehdi.ir/posts/jalali-pandas/) 113 | 114 | ## راهنمای ویدیویی 115 | 116 | [![IMAGE ALT TEXT HERE](https://img.youtube.com/vi/PYS4Hxmzbyg/0.jpg)](https://www.youtube.com/watch?v=PYS4Hxmzbyg) 117 | -------------------------------------------------------------------------------- /examples/basic_usage.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ghodsizadeh/jalali-pandas/blob/main/examples/basic_usage.ipynb)\n", 8 | "![GitHub Repo stars](https://img.shields.io/github/stars/ghodsizadeh/jalali-pandas?logoColor=blue&style=social)" 9 | ] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 1, 14 | "metadata": { 15 | "colab": { 16 | "base_uri": "https://localhost:8080/" 17 | }, 18 | "id": "2Q0mm14pq6Vl", 19 | "outputId": "c141b374-74e2-4b43-cac3-12e730283e28" 20 | }, 21 | "outputs": [ 22 | { 23 | "name": "stdout", 24 | "output_type": "stream", 25 | "text": [ 26 | "Requirement already satisfied: jalali-pandas in /usr/local/lib/python3.7/dist-packages (0.1.1)\n", 27 | "Requirement already satisfied: jdatetime<4.0.0,>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from jalali-pandas) (3.6.4)\n" 28 | ] 29 | } 30 | ], 31 | "source": [ 32 | "%pip install jalali-pandas" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": 9, 38 | "metadata": { 39 | "id": "4SdQuawtwDav" 40 | }, 41 | "outputs": [], 42 | "source": [ 43 | "import pandas as pd\n", 44 | "import jalali_pandas" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 10, 50 | "metadata": { 51 | "id": "iAa-llPyzFqe" 52 | }, 53 | "outputs": [], 54 | "source": [ 55 | "df = pd.DataFrame({\"date\": pd.date_range(\"2019-01-01\", periods=10, freq=\"D\")})" 56 | ] 57 | }, 58 | { 59 | "cell_type": "markdown", 60 | "metadata": { 61 | "id": "bLP3LfwRz6Yf" 62 | }, 63 | "source": [ 64 | "# Working with Series" 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": 11, 70 | "metadata": { 71 | "colab": { 72 | "base_uri": "https://localhost:8080/", 73 | "height": 111 74 | }, 75 | "id": "bYB97cjm0AHN", 76 | "outputId": "15738f62-344f-424e-b143-d232dad944b3" 77 | }, 78 | "outputs": [ 79 | { 80 | "data": { 81 | "text/html": [ 82 | "
\n", 83 | "\n", 96 | "\n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | "
datejdate
02019-01-011397-10-11 00:00:00
12019-01-021397-10-12 00:00:00
\n", 117 | "
" 118 | ], 119 | "text/plain": [ 120 | " date jdate\n", 121 | "0 2019-01-01 1397-10-11 00:00:00\n", 122 | "1 2019-01-02 1397-10-12 00:00:00" 123 | ] 124 | }, 125 | "execution_count": 11, 126 | "metadata": {}, 127 | "output_type": "execute_result" 128 | } 129 | ], 130 | "source": [ 131 | "# convert to jalali\n", 132 | "df['jdate'] = df.date.jalali.to_jalali()\n", 133 | "df.head(2)" 134 | ] 135 | }, 136 | { 137 | "cell_type": "code", 138 | "execution_count": 12, 139 | "metadata": { 140 | "id": "1DZxvM6P0Me9" 141 | }, 142 | "outputs": [], 143 | "source": [ 144 | "# convert jalali to gregorian\n", 145 | "df['gdate'] = df['jdate'].jalali.to_gregorian()" 146 | ] 147 | }, 148 | { 149 | "cell_type": "code", 150 | "execution_count": 13, 151 | "metadata": { 152 | "id": "xgo7DmQF0VDl" 153 | }, 154 | "outputs": [], 155 | "source": [ 156 | "df1 = pd.DataFrame({\"date\": [\"1399/08/02\", \"1399/08/03\", \"1399/08/04\"]})\n", 157 | "df1[\"jdate\"] = df1[\"date\"].jalali.parse_jalali(\"%Y/%m/%d\")" 158 | ] 159 | }, 160 | { 161 | "cell_type": "code", 162 | "execution_count": 14, 163 | "metadata": { 164 | "id": "H0pUA87hAaJ-" 165 | }, 166 | "outputs": [], 167 | "source": [ 168 | "# get access to jalali year,quarter ,month, day and weekday\n", 169 | "df['year'] = df[\"jdate\"].jalali.year\n", 170 | "df['month'] = df[\"jdate\"].jalali.month\n", 171 | "df['quarter'] = df[\"jdate\"].jalali.quarter\n", 172 | "df['day'] = df[\"jdate\"].jalali.day\n", 173 | "df['weekday'] = df[\"jdate\"].jalali.weekday" 174 | ] 175 | }, 176 | { 177 | "cell_type": "markdown", 178 | "metadata": { 179 | "id": "v0BvJe70AeD7" 180 | }, 181 | "source": [ 182 | "# Working With DataFrames" 183 | ] 184 | }, 185 | { 186 | "cell_type": "code", 187 | "execution_count": 15, 188 | "metadata": { 189 | "id": "5bEkMiCBAkfN" 190 | }, 191 | "outputs": [], 192 | "source": [ 193 | "df = pd.DataFrame(\n", 194 | " {\n", 195 | " \"date\": pd.date_range(\"2019-01-01\", periods=10, freq=\"M\"),\n", 196 | " \"value\": range(10),\n", 197 | " }\n", 198 | ")\n", 199 | "# make sure to create a column with jalali datetime format. (you can use any name)\n", 200 | "df[\"jdate\"] = df[\"date\"].jalali.to_jalali()" 201 | ] 202 | }, 203 | { 204 | "cell_type": "code", 205 | "execution_count": 16, 206 | "metadata": { 207 | "colab": { 208 | "base_uri": "https://localhost:8080/", 209 | "height": 159 210 | }, 211 | "id": "R6efLrJYArh-", 212 | "outputId": "d04460b7-cad2-454f-9dc6-25fd942c026d" 213 | }, 214 | "outputs": [ 215 | { 216 | "name": "stdout", 217 | "output_type": "stream", 218 | "text": [ 219 | "Column \"jdate\" will be the refrence.\n" 220 | ] 221 | }, 222 | { 223 | "data": { 224 | "text/html": [ 225 | "
\n", 226 | "\n", 239 | "\n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | "
value
__year
13971
139844
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" 262 | ], 263 | "text/plain": [ 264 | " value\n", 265 | "__year \n", 266 | "1397 1\n", 267 | "1398 44" 268 | ] 269 | }, 270 | "execution_count": 16, 271 | "metadata": {}, 272 | "output_type": "execute_result" 273 | } 274 | ], 275 | "source": [ 276 | "# group by jalali year\n", 277 | "gp = df.jalali.groupby(\"year\")\n", 278 | "gp.sum()\n" 279 | ] 280 | }, 281 | { 282 | "cell_type": "code", 283 | "execution_count": 20, 284 | "metadata": { 285 | "id": "_4D09DRxAssq" 286 | }, 287 | "outputs": [], 288 | "source": [ 289 | "mean = df.jalali.groupby('month').mean()" 290 | ] 291 | }, 292 | { 293 | "cell_type": "code", 294 | "execution_count": 22, 295 | "metadata": { 296 | "colab": { 297 | "base_uri": "https://localhost:8080/", 298 | "height": 390 299 | }, 300 | "id": "2wi2vCEDAu72", 301 | "outputId": "2b26a46a-1910-465b-ed0b-fa03d592145a" 302 | }, 303 | "outputs": [ 304 | { 305 | "data": { 306 | "text/html": [ 307 | "
\n", 308 | "\n", 321 | "\n", 322 | " \n", 323 | " \n", 324 | " \n", 325 | " \n", 326 | " \n", 327 | " \n", 328 | " \n", 329 | " \n", 330 | " \n", 331 | " \n", 332 | " \n", 333 | " \n", 334 | " \n", 335 | " \n", 336 | " \n", 337 | " \n", 338 | " \n", 339 | " \n", 340 | " \n", 341 | " \n", 342 | " \n", 343 | " \n", 344 | " \n", 345 | " \n", 346 | " \n", 347 | " \n", 348 | " \n", 349 | " \n", 350 | " \n", 351 | " \n", 352 | " \n", 353 | " \n", 354 | " \n", 355 | " \n", 356 | " \n", 357 | " \n", 358 | " \n", 359 | " \n", 360 | " \n", 361 | " \n", 362 | " \n", 363 | " \n", 364 | " \n", 365 | " \n", 366 | " \n", 367 | " \n", 368 | " \n", 369 | " \n", 370 | " \n", 371 | " \n", 372 | " \n", 373 | " \n", 374 | " \n", 375 | " \n", 376 | " \n", 377 | " \n", 378 | " \n", 379 | " \n", 380 | " \n", 381 | " \n", 382 | " \n", 383 | " \n", 384 | " \n", 385 | " \n", 386 | " \n", 387 | " \n", 388 | " \n", 389 | " \n", 390 | "
value
__year__month__day
139711110
1291
13981112
2103
3104
495
596
697
788
899
\n", 391 | "
" 392 | ], 393 | "text/plain": [ 394 | " value\n", 395 | "__year __month __day \n", 396 | "1397 11 11 0\n", 397 | " 12 9 1\n", 398 | "1398 1 11 2\n", 399 | " 2 10 3\n", 400 | " 3 10 4\n", 401 | " 4 9 5\n", 402 | " 5 9 6\n", 403 | " 6 9 7\n", 404 | " 7 8 8\n", 405 | " 8 9 9" 406 | ] 407 | }, 408 | "execution_count": 22, 409 | "metadata": {}, 410 | "output_type": "execute_result" 411 | } 412 | ], 413 | "source": [ 414 | "mean = df.jalali.groupby('ymd')\n", 415 | "mean.mean()" 416 | ] 417 | }, 418 | { 419 | "cell_type": "code", 420 | "execution_count": 24, 421 | "metadata": { 422 | "colab": { 423 | "base_uri": "https://localhost:8080/", 424 | "height": 204 425 | }, 426 | "id": "F2d3yGmqA6Jx", 427 | "outputId": "6bcbeb43-d154-4474-a9e6-9a6445e21007" 428 | }, 429 | "outputs": [ 430 | { 431 | "data": { 432 | "text/html": [ 433 | "
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value
__year__quarter
139740.5
139813.0
26.0
38.5
\n", 481 | "
" 482 | ], 483 | "text/plain": [ 484 | " value\n", 485 | "__year __quarter \n", 486 | "1397 4 0.5\n", 487 | "1398 1 3.0\n", 488 | " 2 6.0\n", 489 | " 3 8.5" 490 | ] 491 | }, 492 | "execution_count": 24, 493 | "metadata": {}, 494 | "output_type": "execute_result" 495 | } 496 | ], 497 | "source": [ 498 | "mean = df.jalali.groupby('yq')\n", 499 | "mean.mean()" 500 | ] 501 | } 502 | ], 503 | "metadata": { 504 | "colab": { 505 | "name": "JalaliPandas.ipynb", 506 | "provenance": [] 507 | }, 508 | "kernelspec": { 509 | "display_name": "Python 3", 510 | "name": "python3" 511 | }, 512 | "language_info": { 513 | "name": "python" 514 | } 515 | }, 516 | "nbformat": 4, 517 | "nbformat_minor": 0 518 | } 519 | -------------------------------------------------------------------------------- /jalali_pandas/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | Jalali Pandas 3 | """ 4 | from .df_handler import JalaliDataframeAccessor 5 | from .serie_handler import JalaliSerieAccessor 6 | -------------------------------------------------------------------------------- /jalali_pandas/df_handler.py: -------------------------------------------------------------------------------- 1 | """ 2 | handle jalaali dates in pandas dataframes 3 | """ 4 | from typing import List, Union 5 | import pandas as pd 6 | import jdatetime 7 | 8 | # pylint: disable=unused-import 9 | # from .serie_handler import JalaliSerieAccessor 10 | LSTR = List[str] 11 | 12 | 13 | @pd.api.extensions.register_dataframe_accessor("jalali") 14 | class JalaliDataframeAccessor: 15 | """ 16 | Accessor methods on pandas series to handle jalali dates 17 | 18 | """ 19 | 20 | TEMP_COLUMNS = ["__year", "__month", "__quarter", "__weekday", "__day"] 21 | 22 | def __init__(self, pandas_obj: pd.DataFrame): 23 | """[summary] 24 | 25 | Args: 26 | pandas_obj (pd.Dataframe): [description] 27 | """ 28 | self._obj = pandas_obj # type: pd.DataFrame 29 | self.columns = self._obj.columns # type: pd.Index 30 | self.jdate = "jdate" 31 | self.__validate() 32 | 33 | def __validate(self): 34 | """ 35 | check if the pandas object is a dataframe with a jdatetime on it 36 | 37 | Args: 38 | pandas_obj (pd.DataFrame): [description] 39 | """ 40 | for col in self.columns: 41 | if isinstance(self._obj[col].iloc[0], jdatetime.date): 42 | print(f'Column "{col}" will be the refrence.') 43 | self.jdate = col 44 | return 45 | raise ValueError("No jdatetime column found in the dataframe.") 46 | 47 | @property 48 | def __df(self) -> pd.DataFrame: 49 | """Genreate temp data frame for the groupby 50 | 51 | Returns: 52 | pd.DataFrame: a dataframe with year, month, day, week, dayofweek, dayofmonth 53 | """ 54 | df = self._obj.copy() 55 | df["__year"] = df[self.jdate].jalali.year 56 | df["__month"] = df[self.jdate].jalali.month 57 | df["__day"] = df[self.jdate].jalali.day 58 | df["__quarter"] = df[self.jdate].jalali.quarter 59 | df["__weekday"] = df[self.jdate].jalali.weekday 60 | 61 | return df 62 | 63 | # a function that get str or list of str 64 | 65 | def __clean_groupby(self, group: pd.Grouper) -> pd.Grouper: 66 | """ 67 | clean the groupby object 68 | 69 | Args: 70 | group (pd.Grouper): [description] 71 | 72 | Returns: 73 | pd.Grouper: [description] 74 | """ 75 | remaining_columns = list(set(self.columns).difference(self.TEMP_COLUMNS)) 76 | # breakpoint 77 | return group[remaining_columns] 78 | 79 | def groupby(self, grouper: Union[str, LSTR] = "md") -> pd.Grouper: 80 | """ 81 | groupby jalali date 82 | 83 | Args: 84 | kind (str, optional): [description]. Defaults to 'md'. 85 | 86 | Returns: 87 | pd.Grouper: [description] 88 | """ 89 | possible_keys = [ 90 | "year", 91 | "month", 92 | "day", 93 | "week", 94 | "dayofweek", 95 | "dayofmonth", 96 | "ym", 97 | "yq", 98 | "ymd", 99 | "md", 100 | ] 101 | df = self.__df 102 | if grouper not in possible_keys: 103 | raise ValueError( 104 | f"{grouper} is not a valid groupby type. Choose from {possible_keys}" 105 | ) 106 | 107 | keys = { 108 | "md": ["month", "day"], 109 | "ym": ["year", "month"], 110 | "yq": ["year", "quarter"], 111 | "ymd": ["year", "month", "day"], 112 | } 113 | if grouper in keys: 114 | grouper = keys[grouper] 115 | grouper = [f"__{g}" for g in grouper] 116 | else: 117 | grouper = [f"__{grouper}"] 118 | 119 | group = df.groupby(grouper) 120 | group = self.__clean_groupby(group) 121 | return group 122 | 123 | def resample(self, resample_type: str) -> pd.DataFrame: 124 | """[summary] 125 | 126 | Raises: 127 | NotImplementedError: [description] 128 | """ 129 | raise NotImplementedError 130 | -------------------------------------------------------------------------------- /jalali_pandas/serie_handler.py: -------------------------------------------------------------------------------- 1 | """ 2 | handle jalaali dates in pandas series 3 | """ 4 | import jdatetime 5 | import pandas as pd 6 | 7 | 8 | @pd.api.extensions.register_series_accessor("jalali") 9 | class JalaliSerieAccessor: 10 | """ 11 | Accessor methods on pandas series to handle jalali dates 12 | 13 | """ 14 | 15 | def __init__(self, pandas_obj: pd.Series): 16 | """[summary] 17 | 18 | Args: 19 | pandas_obj (pd.Series): [description] 20 | """ 21 | # self._validate(pandas_obj) 22 | self._obj = pandas_obj 23 | 24 | def __validate(self): 25 | """validate pandas series is datetime or not. 26 | 27 | Raises: 28 | TypeError: [description] 29 | """ 30 | 31 | if not all(isinstance(x, (str, jdatetime.date)) for x in self._obj): 32 | raise TypeError("pandas series must be jdatetime or string of jdate") 33 | 34 | def to_jalali(self) -> pd.Series: 35 | """convert python datetime to jalali datetime. 36 | 37 | Returns: 38 | pd.Series: pd.Series of jalali datetime. 39 | """ 40 | return self._obj.apply(lambda x: jdatetime.datetime.fromgregorian(date=x)) 41 | 42 | def to_gregorian(self) -> pd.Series: 43 | """convert jalali datetime to python default datetime. 44 | 45 | Returns: 46 | pd.Series: pd.Series of python datetime. 47 | """ 48 | 49 | return self._obj.apply(jdatetime.datetime.togregorian) 50 | 51 | # pylint: disable=redefined-builtin 52 | def parse_jalali(self, format: str = "%Y-%m-%d") -> pd.Series: 53 | """[summary] 54 | 55 | Args: 56 | format (str, optional): like gregorian datetime format. Defaults to "%Y-%m-%d". 57 | 58 | Returns: 59 | pd.Series: pd.Series of jalali datetime. 60 | """ 61 | return self._obj.apply(lambda x: jdatetime.datetime.strptime(x, format)) 62 | 63 | @property 64 | def year(self) -> pd.Series: 65 | """get Jalali year 66 | 67 | Returns: 68 | pd.Series: Jalali year 69 | """ 70 | self.__validate() 71 | return self._obj.apply(lambda x: x.year) 72 | 73 | @property 74 | def month(self) -> pd.Series: 75 | """get Jalali 76 | 77 | 78 | Returns: 79 | pd.Series: Jalali month 80 | """ 81 | self.__validate() 82 | return self._obj.apply(lambda x: x.month) 83 | 84 | @property 85 | def day(self) -> pd.Series: 86 | """get Jalali day 87 | 88 | Returns: 89 | pd.Series: Jalali day 90 | """ 91 | self.__validate() 92 | return self._obj.apply(lambda x: x.day) 93 | 94 | @property 95 | def hour(self) -> pd.Series: 96 | """get Jalali hour 97 | 98 | Returns: 99 | pd.Series: Jalali hour 100 | """ 101 | self.__validate() 102 | return self._obj.apply(lambda x: x.hour) 103 | 104 | @property 105 | def minute(self) -> pd.Series: 106 | """get Jalali minute 107 | 108 | Returns: 109 | pd.Series: Jalali minute 110 | """ 111 | self.__validate() 112 | return self._obj.apply(lambda x: x.minute) 113 | 114 | @property 115 | def second(self) -> pd.Series: 116 | """get Jalali second 117 | 118 | Returns: 119 | pd.Series: Jalali second 120 | """ 121 | self.__validate() 122 | return self._obj.apply(lambda x: x.second) 123 | 124 | @property 125 | def weekday(self) -> pd.Series: 126 | """get Jalali weekday 127 | 128 | Returns: 129 | pd.Series: Jalali weekday 130 | """ 131 | self.__validate() 132 | return self._obj.apply(lambda x: x.weekday()) 133 | 134 | @property 135 | def weeknumber(self) -> pd.Series: 136 | """get Jalali day of year 137 | 138 | Returns: 139 | pd.Series: Jalali day of year 140 | """ 141 | self.__validate() 142 | return self._obj.apply(lambda x: x.weeknumber()) 143 | 144 | @property 145 | def quarter(self): 146 | """get Jalali quarter 147 | 148 | Returns: 149 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hash = "sha256:1fea9cd438686e6682271d36f3481a9f3636195578bab9ca3382e2f5f01fc185"}, 604 | ] 605 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.poetry] 2 | name = "jalali_pandas" 3 | version = "0.2.2" 4 | description = "A Pandas extension to make work with Jalali Date easier." 5 | authors = ["Mehdi Ghodsizadeh "] 6 | license = "GPL-3.0-only" 7 | readme = "README.md" 8 | homepage = "https://ghodsizadeh.github.io/jalali-pandas/" 9 | repository = "https://github.com/ghodsizadeh/jalali-pandas" 10 | 11 | [tool.poetry.dependencies] 12 | python = "^3.6" 13 | jdatetime = "^3.6.4" 14 | 15 | [tool.poetry.urls] 16 | "Bug Tracker" = "https://github.com/ghodsizadeh/jalali-pandas/issues" 17 | 18 | [build-system] 19 | requires = ["poetry-core>=1.0.0"] 20 | build-backend = "poetry.core.masonry.api" 21 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | astroid==2.8.4 2 | backports.entry-points-selectable==1.1.1 3 | black==21.10b0 4 | cfgv==3.3.1 5 | click==8.0.3 6 | distlib==0.3.3 7 | filelock==3.3.2 8 | identify==2.3.5 9 | isort==5.10.1 10 | jdatetime 11 | lazy-object-proxy==1.6.0 12 | mccabe==0.6.1 13 | mypy-extensions==0.4.3 14 | nodeenv==1.6.0 15 | pathspec==0.9.0 16 | platformdirs==2.4.0 17 | pre-commit==2.15.0 18 | pylint==2.11.1 19 | pandas 20 | PyYAML==6.0 21 | regex==2021.11.10 22 | six==1.16.0 23 | toml==0.10.2 24 | tomli==1.2.2 25 | typing-extensions==3.10.0.2 26 | virtualenv==20.10.0 27 | wrapt==1.13.3 28 | -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ghodsizadeh/jalali-pandas/ce480829a3c50a53cb2a17c94e5a49648d18ca42/tests/__init__.py -------------------------------------------------------------------------------- /tests/df_test.py: -------------------------------------------------------------------------------- 1 | """Test Series class 2 | """ 3 | import pandas as pd 4 | import pytest 5 | from jalali_pandas import ( # pylint: disable=W0611 6 | JalaliSerieAccessor, 7 | JalaliDataframeAccessor, 8 | ) 9 | 10 | 11 | class TestJalaliDataFrame: 12 | """Test Cases for JalaliSerieAccessor""" 13 | 14 | @property 15 | def df(self) -> pd.DataFrame: 16 | """get test dataframe 17 | 18 | Returns: 19 | pd.DataFrame: test dataframe 20 | """ 21 | df = pd.DataFrame( 22 | { 23 | "date": pd.date_range("2019-01-01", periods=10, freq="M"), 24 | "value": range(10), 25 | } 26 | ) 27 | df["jdate"] = df["date"].jalali.to_jalali() 28 | return df 29 | 30 | def test_jalali_groupby(self): 31 | """Test jalali property like year, month, weeknumber""" 32 | df = self.df 33 | mean = df.jalali.groupby("year").mean() 34 | assert (mean.index == [1397, 1398]).all(), "Year grouping is wrong" 35 | mean = df.jalali.groupby("month").mean() 36 | assert not set(mean.index).difference( 37 | {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12} 38 | ), "computaion is wrong" 39 | 40 | def test_jalali_groupby_shorts(self): 41 | """Test jalali property like ymd, ym, yq, md""" 42 | df = self.df 43 | mean = df.jalali.groupby("ymd").mean() 44 | assert mean.index.names == [ 45 | "__year", 46 | "__month", 47 | "__day", 48 | ], "ymd grouping is wrong" 49 | mean = df.jalali.groupby("ym").mean() 50 | assert mean.index.names == ["__year", "__month"], "ym grouping is wrong" 51 | mean = df.jalali.groupby("yq").mean() 52 | assert mean.index.names == ["__year", "__quarter"], "yq grouping is wrong" 53 | mean = df.jalali.groupby("md").mean() 54 | assert mean.index.names == ["__month", "__day"], "md grouping is wrong" 55 | 56 | def test_check_wrong_groupby(self): 57 | """Test check_df""" 58 | df = self.df 59 | # check it raise Value Error 60 | with pytest.raises(ValueError): 61 | df.jalali.groupby("wrong") 62 | 63 | def test_not_implemeneted_resampling(self): 64 | """Test implemented resampling""" 65 | df = self.df 66 | with pytest.raises(NotImplementedError): 67 | df.jalali.resample("D").mean() 68 | 69 | def test_validation(self): 70 | """Test validation""" 71 | df = self.df.copy() 72 | del df["jdate"] 73 | with pytest.raises(ValueError): 74 | df.jalali # pylint: disable=W0104 75 | -------------------------------------------------------------------------------- /tests/series_test.py: -------------------------------------------------------------------------------- 1 | """Test Series class 2 | """ 3 | import jdatetime 4 | import pandas as pd 5 | import pytest 6 | from jalali_pandas import ( # pylint: disable=W0611 7 | JalaliDataframeAccessor, 8 | JalaliSerieAccessor, 9 | ) 10 | 11 | 12 | class TestJalaliSerie: 13 | """Test Cases for JalaliSerieAccessor""" 14 | 15 | @property 16 | def df(self) -> pd.DataFrame: 17 | """get test dataframe 18 | 19 | Returns: 20 | pd.DataFrame: test dataframe 21 | """ 22 | df = pd.DataFrame({"date": pd.date_range("2019-01-01", periods=10, freq="D")}) 23 | df["jdate"] = df["date"].jalali.to_jalali() 24 | return df 25 | 26 | def test_jalali_convertor(self): 27 | """Test jalali convertor from gregorian to jalali""" 28 | df = self.df 29 | assert df["jdate"].iloc[0] == jdatetime.datetime(year=1397, month=10, day=11) 30 | assert df["date"].iloc[0] == pd.Timestamp("2019-01-01") 31 | 32 | def test_gregorian_convertor(self): 33 | """Test jalali convertor from jalali to gregorian""" 34 | 35 | df = self.df 36 | df["gdate"] = df["jdate"].jalali.to_gregorian() 37 | date = df["gdate"].iloc[0] 38 | assert date.year == 2019, "year is not 2019" 39 | assert date.month == 1, "month is not 1" 40 | assert date.day == 1, "day is not 1" 41 | 42 | def test_on_not_jdatetime(self): 43 | """Test jalali raise error on wrong columns""" 44 | df = self.df 45 | with pytest.raises(TypeError): 46 | df["date"].jalali.year # pylint: disable=W0104 47 | 48 | def test_jalali_property(self): 49 | """Test jalali property like year, month, weeknumber""" 50 | df = self.df 51 | assert df["jdate"].jalali.year[0] == 1397, "year is not 1397" 52 | assert df["jdate"].jalali.month[0] == 10, "month is not 10" 53 | assert df["jdate"].jalali.day[0] == 11, "day is not 11" 54 | assert df["jdate"].jalali.hour[0] == 0, "hour is not 0" 55 | assert df["jdate"].jalali.minute[0] == 0, "minute is not 0" 56 | assert df["jdate"].jalali.second[0] == 0, "second is not 0" 57 | assert df["jdate"].jalali.weekday[0] == 3, "weekday is not 4" 58 | assert df["jdate"].jalali.weeknumber[0] == 42, "weeknumber is not 42" 59 | assert df["jdate"].jalali.quarter[0] == 4, "quarter is not 4" 60 | 61 | 62 | def test_jalali_strptime(): 63 | """Test jalali convertor from str to jalali""" 64 | df = pd.DataFrame({"date": ["1399/08/02", "1399/08/03", "1399/08/04"]}) 65 | df["jdate"] = df["date"].jalali.parse_jalali("%Y/%m/%d") 66 | date = df["jdate"].iloc[0] 67 | assert date.year == 1399, "year is not 1399" 68 | assert date.month == 8, "month is not 8" 69 | assert date.day == 2, "day is not 2" 70 | --------------------------------------------------------------------------------