Datasets:

CoIR-Retrieval

/

CodeSearchNet-ccr-python-queries-corpus

like 0

q100

Step.invoke_step

train

def invoke_step(self, context): """Invoke 'run_step' in the dynamically loaded step module. Don't invoke this from outside the Step class. Use pypyr.dsl.Step.run_step instead. invoke_step just does the bare module step invocation, it does not evaluate any of the decorator logic surrounding the step. So

python

q101

Step.run_conditional_decorators

train

def run_conditional_decorators(self, context): """Evaluate the step decorators to decide whether to run step or not. Use pypyr.dsl.Step.run_step if you intend on executing the step the same way pypyr does. Args: context: (pypyr.context.Context) The pypyr context. This arg will mutate. """ logger.debug("starting") # The decorator attributes might contain formatting expressions that # change whether they evaluate True or False, thus apply formatting at # last possible instant. run_me = context.get_formatted_as_type(self.run_me, out_type=bool) skip_me = context.get_formatted_as_type(self.skip_me, out_type=bool) swallow_me = context.get_formatted_as_type(self.swallow_me, out_type=bool) if run_me: if not skip_me: try: if self.retry_decorator: self.retry_decorator.retry_loop(context, self.invoke_step) else: self.invoke_step(context=context) except Exception as ex_info:

python

q102

Step.run_foreach_or_conditional

train

def run_foreach_or_conditional(self, context): """Run the foreach sequence or the conditional evaluation. Args: context: (pypyr.context.Context) The pypyr context. This arg will mutate. """ logger.debug("starting") # friendly reminder [] list obj (i.e empty) evals False

python

q103

Step.run_step

train

def run_step(self, context): """Run a single pipeline step. Args: context: (pypyr.context.Context) The pypyr context. This arg will mutate. """ logger.debug("starting") # the in params should be added to context before step execution. self.set_step_input_context(context) if self.while_decorator:

python

q104

Step.set_step_input_context

train

def set_step_input_context(self, context): """Append step's 'in' parameters to context, if they exist. Append the[in] dictionary to the context. This will overwrite existing values if the same keys are already in there. I.e if in_parameters has {'eggs': 'boiled'} and key 'eggs' already exists in context, context['eggs'] hereafter will be 'boiled'. Args: context: (pypyr.context.Context) The pypyr context. This arg will mutate - after method execution will contain the new

python

q105

RetryDecorator.exec_iteration

train

def exec_iteration(self, counter, context, step_method): """Run a single retry iteration. This method abides by the signature invoked by poll.while_until_true, which is to say (counter, *args, **kwargs). In a normal execution chain, this method's args passed by self.retry_loop where context and step_method set. while_until_true injects counter as a 1st arg. Args: counter. int. loop counter, which number of iteration is this. context: (pypyr.context.Context) The pypyr context. This arg will mutate - after method execution will contain the new updated context. step_method: (method/function) This is the method/function that will execute on every loop iteration. Signature is: function(context) Returns: bool. True if step execution completed without error. False if error occured during step execution. """ logger.debug("starting") context['retryCounter'] = counter logger.info(f"retry: running step with counter {counter}") try: step_method(context) result = True except Exception as ex_info: if self.max: if counter == self.max: logger.debug(f"retry: max {counter} retries exhausted. " "raising error.") # arguably shouldn't be using errs for control of flow. # but would lose the err info if not, so lesser of 2 evils. raise if self.stop_on or self.retry_on: error_name = get_error_name(ex_info) if self.stop_on: formatted_stop_list = context.get_formatted_iterable(

python

q106

RetryDecorator.retry_loop

train

def retry_loop(self, context, step_method): """Run step inside a retry loop. Args: context: (pypyr.context.Context) The pypyr context. This arg will mutate - after method execution will contain the new updated context. step_method: (method/function) This is the method/function that will execute on every loop iteration. Signature is: function(context) """ logger.debug("starting") context['retryCounter'] = 0 sleep = context.get_formatted_as_type(self.sleep, out_type=float) if self.max: max = context.get_formatted_as_type(self.max, out_type=int) logger.info(f"retry decorator will try {max} times at {sleep}s " "intervals.") else: max = None logger.info(f"retry decorator will try indefinitely at {sleep}s " "intervals.") # this will never be false. because on counter

python

q107

WhileDecorator.exec_iteration

train

def exec_iteration(self, counter, context, step_method): """Run a single loop iteration. This method abides by the signature invoked by poll.while_until_true, which is to say (counter, *args, **kwargs). In a normal execution chain, this method's args passed by self.while_loop where context and step_method set. while_until_true injects counter as a 1st arg. Args: counter. int. loop counter, which number of iteration is this. context: (pypyr.context.Context) The pypyr context. This arg will mutate - after method execution will contain the new updated context. step_method: (method/function) This is the method/function that will execute on every loop iteration. Signature is: function(context) Returns: bool. True if self.stop evaluates to True after step execution,

python

q108

WhileDecorator.while_loop

train

def while_loop(self, context, step_method): """Run step inside a while loop. Args: context: (pypyr.context.Context) The pypyr context. This arg will mutate - after method execution will contain the new updated context. step_method: (method/function) This is the method/function that will execute on every loop iteration. Signature is: function(context) """ logger.debug("starting") context['whileCounter'] = 0 if self.stop is None and self.max is None: # the ctor already does this check, but guess theoretically # consumer could have messed with the props since ctor logger.error(f"while decorator missing both max and stop.") raise PipelineDefinitionError("the while decorator must have " "either max or stop, or both. " "But not neither.") error_on_max = context.get_formatted_as_type( self.error_on_max, out_type=bool) sleep = context.get_formatted_as_type(self.sleep, out_type=float) if self.max is None: max = None logger.info(f"while decorator will loop until {self.stop} " f"evaluates to True at {sleep}s intervals.") else: max = context.get_formatted_as_type(self.max, out_type=int) if max < 1: logger.info( f"max {self.max} is {max}. while only runs when max > 0.") logger.debug("done") return if self.stop is None: logger.info(f"while decorator will loop {max} times at " f"{sleep}s intervals.") else: logger.info(f"while decorator will loop {max} times, or "

python

q109

run_step

train

def run_step(context): """Load a yaml file into the pypyr context. Yaml parsed from the file will be merged into the pypyr context. This will overwrite existing values if the same keys are already in there. I.e if file yaml has {'eggs' : 'boiled'} and context {'eggs': 'fried'} already exists, returned context['eggs'] will be 'boiled'. Args: context: pypyr.context.Context. Mandatory. The following context key must exist - fetchYaml - path. path-like. Path to file on disk. - key. string. If exists, write yaml to this context key. Else yaml writes to context root. All inputs support formatting expressions. Also supports a passing path as string to fetchYaml, but in this case you won't be able to specify a key. Returns: None. updates context arg. Raises: FileNotFoundError: take a guess pypyr.errors.KeyNotInContextError: fetchYamlPath missing in context. pypyr.errors.KeyInContextHasNoValueError: fetchYamlPath exists but is

python

q110

run_step

train

def run_step(context): """pypyr step saves current utc datetime to context. Args: context: pypyr.context.Context. Mandatory. The following context key is optional: - nowUtcIn. str. Datetime formatting expression. For full list of possible expressions, check here: https://docs.python.org/3.7/library/datetime.html#strftime-and-strptime-behavior All inputs support pypyr formatting expressions. This step creates now in context, containing a string representation of the timestamp. If input formatting not specified, defaults to ISO8601. Default is: YYYY-MM-DDTHH:MM:SS.ffffff+00:00, or, if microsecond is 0, YYYY-MM-DDTHH:MM:SS Returns: None. updates context arg. """ logger.debug("started")

python

q111

run_step

train

def run_step(context): """Assert that something is True or equal to something else. Args: context: dictionary-like pypyr.context.Context. context is mandatory. Uses the following context keys in context: - assert - this. mandatory. Any type. If assert['equals'] not specified, evals as boolean. - equals. optional. Any type. If assert['this'] evaluates to False raises error. If assert['equals'] is specified, raises error if assert.this != assert.equals. assert['this'] & assert['equals'] both support string substitutions. Returns: None Raises: ContextError: if assert evaluates to False. """ logger.debug("started") assert context, f"context must have value for {__name__}" deprecated(context) context.assert_key_has_value('assert', __name__) assert_this = context['assert']['this'] is_equals_there = 'equals' in context['assert'] if is_equals_there: assert_equals = context['assert']['equals'] # compare assertThis to assertEquals logger.debug("comparing assert['this'] to assert['equals'].") assert_result = (context.get_formatted_iterable(assert_this) == context.get_formatted_iterable(assert_equals)) else: # nothing to compare means treat assertThis as a bool. logger.debug("evaluating assert['this'] as a boolean.") assert_result = context.get_formatted_as_type(assert_this,

python

q112

tar_archive

train

def tar_archive(context): """Archive specified path to a tar archive. Args: context: dictionary-like. context is mandatory. context['tar']['archive'] must exist. It's a dictionary. keys are the paths to archive. values are the destination output paths. Example: tar: archive: - in: path/to/dir out: path/to/destination.tar.xs - in: another/my.file out: ./my.tar.xs This will archive directory path/to/dir to path/to/destination.tar.xs, and also archive file another/my.file to ./my.tar.xs """ logger.debug("start") mode = get_file_mode_for_writing(context) for item in context['tar']['archive']: # value is the destination tar. Allow

python

q113

tar_extract

train

def tar_extract(context): """Extract all members of tar archive to specified path. Args: context: dictionary-like. context is mandatory. context['tar']['extract'] must exist. It's a dictionary. keys are the path to the tar to extract. values are the destination paths. Example: tar: extract: - in: path/to/my.tar.xs out: /path/extract/here - in: another/tar.xs out: . This will extract path/to/my.tar.xs to /path/extract/here, and also extract another/tar.xs to $PWD. """ logger.debug("start") mode = get_file_mode_for_reading(context) for item in context['tar']['extract']: # in is the path to the tar to

python

q114

run_step

train

def run_step(context): """Run shell command without shell interpolation. Context is a dictionary or dictionary-like. Context must contain the following keys: cmd: <<cmd string>> (command + args to execute.) OR, as a dict cmd: run: str. mandatory. <<cmd string>> command + args to execute. save: bool. defaults False. save output to cmdOut. Will execute command string in the shell as a sub-process. The shell defaults to /bin/sh. The context['cmd'] string must be formatted exactly as it would be when typed at the shell prompt. This includes, for example, quoting or backslash escaping filenames with spaces in them.

python

q115

get_args

train

def get_args(get_item): """Parse env, key, default out of input dict. Args: get_item: dict. contains keys env/key/default Returns: (env, key, has_default, default) tuple, where env: str. env var name. key: str. save env value to this context key. has_default: bool. True if default specified. default: the value of default, if specified. Raises: ContextError: envGet is not a list of dicts. KeyNotInContextError: If env or key not found in get_config. """ if not isinstance(get_item, dict): raise ContextError('envGet must contain a list of dicts.') env = get_item.get('env', None) if not env:

python

q116

run_step

train

def run_step(context): """Executes dynamic python code. Context is a dictionary or dictionary-like. Context must contain key 'pycode' Will exec context['pycode'] as dynamically interpreted python statements. context is mandatory. When you execute the pipeline, it should look something like this: pipeline-runner [name here] 'pycode=print(1+1)'. """ logger.debug("started") context.assert_key_has_value(key='pycode', caller=__name__) logger.debug(f"Executing python string: {context['pycode']}") locals_dictionary = locals()

python

q117

get_parser

train

def get_parser(): """Return ArgumentParser for pypyr cli.""" parser = argparse.ArgumentParser( allow_abbrev=True, description='pypyr pipeline runner') parser.add_argument('pipeline_name', help='Name of pipeline to run. It should exist in the ' './pipelines directory.') parser.add_argument(dest='pipeline_context', nargs='?', help='String for context values. Parsed by the ' 'pipeline\'s context_parser function.') parser.add_argument('--dir', dest='working_dir', default=os.getcwd(), help='Working directory. Use if your pipelines ' 'directory is elsewhere. Defaults to cwd.') parser.add_argument('--log', '--loglevel', dest='log_level', type=int, default=20, help='Integer log level. Defaults to 20 (INFO). '

python

q118

main

train

def main(args=None): """Entry point for pypyr cli. The setup_py entry_point wraps this in sys.exit already so this effectively becomes sys.exit(main()). The __main__ entry point similarly wraps sys.exit(). """ if args is None: args = sys.argv[1:] parsed_args = get_args(args) try: return pypyr.pipelinerunner.main( pipeline_name=parsed_args.pipeline_name, pipeline_context_input=parsed_args.pipeline_context, working_dir=parsed_args.working_dir, log_level=parsed_args.log_level, log_path=parsed_args.log_path) except KeyboardInterrupt: # Shell standard is 128 + signum = 130 (SIGINT = 2)

python

q119

run_step

train

def run_step(context): """Remove specified keys from context. Args: Context is a dictionary or dictionary-like. context['contextClear'] must exist. It's a dictionary. Will iterate context['contextClear'] and remove those keys from context. For example, say input context is: key1: value1 key2: value2 key3: value3 key4: value4 contextClear: - key2 - key4 - contextClear This will result in return context: key1: value1

python

q120

run_step

train

def run_step(context): """Run command, program or executable. Context is a dictionary or dictionary-like. Context must contain the following keys: cmd: <<cmd string>> (command + args to execute.) OR, as a dict cmd: run: str. mandatory. <<cmd string>> command + args to execute. save: bool. defaults False. save output to cmdOut. Will execute the command string in the shell as a sub-process. Escape curly braces: if you want a literal curly brace, double it like {{ or }}. If save is True, will save the output to context as follows: cmdOut: returncode: 0 stdout: 'stdout str here. None if empty.'

python

q121

run_step

train

def run_step(context): """Set hierarchy into context with substitutions if it doesn't exist yet. context is a dictionary or dictionary-like. context['defaults'] must exist. It's a dictionary. Will iterate context['defaults'] and add these as new values where their keys don't already exist. While it's doing so, it will leave all other values in the existing hierarchy untouched. List merging is purely additive, with no checks for uniqueness or already existing list items. E.g context [0,1,2] with contextMerge=[2,3,4] will result in [0,1,2,2,3,4] Keep this in mind especially where complex types like dicts nest inside a list - a merge will always add a new dict list item, not merge it into whatever dicts might exist on the list already. For example, say input context is: key1: value1 key2: value2 key3: k31: value31 k32: value32 defaults: key2: 'aaa_{key1}_zzz' key3:

python

q122

get_pipeline_steps

train

def get_pipeline_steps(pipeline, steps_group): """Get the steps attribute of module pipeline. If there is no steps sequence on the pipeline, return None. Guess you could theoretically want to run a pipeline with nothing in it. """ logger.debug("starting") assert pipeline assert steps_group logger.debug(f"retrieving {steps_group} steps from pipeline") if steps_group in pipeline: steps = pipeline[steps_group] if

python

q123

run_failure_step_group

train

def run_failure_step_group(pipeline, context): """Run the on_failure step group if it exists. This function will swallow all errors, to prevent obfuscating the error condition that got it here to begin with. """ logger.debug("starting") try:

python

q124

run_step_group

train

def run_step_group(pipeline_definition, step_group_name, context): """Get the specified step group from the pipeline and run its steps.""" logger.debug(f"starting {step_group_name}") assert step_group_name steps = get_pipeline_steps(pipeline=pipeline_definition,

python

q125

ensure_dir

train

def ensure_dir(path): """Create all parent directories of path if they don't exist. Args: path. Path-like object. Create parent dirs to this path. Return:

python

q126

get_glob

train

def get_glob(path): """Process the input path, applying globbing and formatting. Do note that this will returns files AND directories that match the glob. No tilde expansion is done, but *, ?, and character ranges expressed with [] will be correctly matched. Escape all special characters ('?', '*' and '['). For a literal match, wrap the meta-characters in brackets. For example, '[?]' matches the character '?'. If passing in an iterable of paths, will expand matches for each path in the iterable. The function will return all the matches for each path glob expression combined into a single list. Args: path: Path-like string, or iterable (list or tuple ) of paths. Returns: Combined list of paths found for input glob. """

python

q127

is_same_file

train

def is_same_file(path1, path2): """Return True if path1 is the same file as path2. The reason for this dance is that samefile throws if either file doesn't exist. Args: path1: str or path-like. path2: str or path-like. Returns:

python

q128

move_file

train

def move_file(src, dest): """Move source file to destination. Overwrites dest. Args: src: str or path-like. source file dest: str or path-like. destination file Returns: None. Raises: FileNotFoundError: out path parent doesn't exist. OSError: if any IO operations go wrong.

python

q129

move_temp_file

train

def move_temp_file(src, dest): """Move src to dest. Delete src if something goes wrong. Overwrites dest. Args: src: str or path-like. source file dest: str or path-like. destination file Returns: None. Raises: FileNotFoundError: out path parent doesn't exist. OSError: if any IO operations go wrong. Does its best to clean up after itself and remove temp files. """ try: move_file(src, dest) except Exception: try:

python

q130

FileRewriter.files_in_to_out

train

def files_in_to_out(self, in_path, out_path=None): """Write in files to out, calling the line_handler on each line. Calls file_in_to_out under the hood to format the in_path payload. The formatting processing is done by the self.formatter instance. Args: in_path: str, path-like, or an iterable (list/tuple) of strings/paths. Each str/path can be a glob, relative or absolute path. out_path: str or path-like. Can refer to a file or a directory. will create directory structure if it doesn't exist. If in-path refers to >1 file (e.g it's a glob or list), out path can only be a directory - it doesn't make sense to write >1 file to the same single file (this is no an appender.) To ensure out_path is read as a directory and not a file, be sure to have the path separator (/) at the end. Top tip: Path-like objects strip the trailing slash. If you want to pass in a dir that does not exist yet as out-path with a trailing /, you should be passing it as a str to preserve the /. If out_path is not specified or None, will in-place edit and overwrite the in-files. Returns: None. """ in_paths = get_glob(in_path) in_count = len(in_paths) if in_count == 0: logger.debug(f'in path found {in_count} paths.') else: logger.debug(f'in path found {in_count} paths:') for path in in_paths: logger.debug(f'{path}') logger.debug( 'herewith ends the paths. will now process each file.') if in_paths: # derive the destination directory, ensure it's ready for writing basedir_out = None is_outfile_name_known = False if out_path: # outpath could be a file, or a dir pathlib_out = Path(out_path) # yep, Path() strips trailing /, hence check original string if isinstance(out_path, str) and out_path.endswith(os.sep): # ensure dir - mimic posix mkdir -p pathlib_out.mkdir(parents=True, exist_ok=True) basedir_out = pathlib_out elif pathlib_out.is_dir(): basedir_out = pathlib_out else: if len(in_paths) > 1: raise Error( f'{in_path} resolves to {len(in_paths)} files, ' 'but you specified only a single file as out ' f'{out_path}. If the outpath is meant to be a ' 'directory, put a / at the end.') # at this point it must be a file (not dir) path # make sure that the parent dir exists basedir_out = pathlib_out.parent

python

q131

ObjectRewriter.in_to_out

train

def in_to_out(self, in_path, out_path=None): """Load file into object, formats, writes object to out. If in_path and out_path point to the same thing it will in-place edit and overwrite the in path. Even easier, if you do want to edit a file in place, don't specify out_path, or set it to None. Args: in_path: str or path-like. Must refer to a single existing file. out_path: str or path-like. Must refer to a single destination file location. will create directory structure if it doesn't exist. If out_path is not specified or None, will in-place edit and overwrite the in-files. Returns: None. """ if is_same_file(in_path, out_path): logger.debug( "in path and out path are the same file. writing to temp " "file and then replacing in path with the temp file.") out_path = None logger.debug(f"opening source file: {in_path}") with open(in_path) as infile: obj = self.object_representer.load(infile)

python

q132

StreamRewriter.in_to_out

train

def in_to_out(self, in_path, out_path=None): """Write a single file in to out, running self.formatter on each line. If in_path and out_path point to the same thing it will in-place edit and overwrite the in path. Even easier, if you do want to edit a file in place, don't specify out_path, or set it to None. Args: in_path: str or path-like. Must refer to a single existing file. out_path: str or path-like. Must refer to a single destination file location. will create directory structure if it doesn't exist. If out_path is not specified or None, will in-place edit and overwrite the in-files. Returns: None. """ is_in_place_edit = False if is_same_file(in_path, out_path): logger.debug( "in path and out path are the same file. writing to temp "

python

q133

JsonRepresenter.dump

train

def dump(self, file, payload): """Dump json oject to open file output. Writes json with 2 spaces indentation. Args:

python

q134

run_step

train

def run_step(context): """Parse input file and replace a search string. This also does string substitutions from context on the fileReplacePairs. It does this before it search & replaces the in file. Be careful of order. If fileReplacePairs is not an ordered collection, replacements could evaluate in any given order. If this is coming in from pipeline yaml it will be an ordered dictionary, so life is good. Args: context: pypyr.context.Context. Mandatory. The following context keys expected: - fileReplace - in. mandatory. str, path-like, or an iterable (list/tuple) of strings/paths. Each str/path can be a glob, relative or absolute path. - out. optional. path-like. Can refer to a file or a directory.

python

q135

set_logging_config

train

def set_logging_config(log_level, handlers): """Set python logging library config. Run this ONCE at the start of your process. It formats the python logging module's output.

python

q136

set_root_logger

train

def set_root_logger(root_log_level, log_path=None): """Set the root logger 'pypyr'. Do this before you do anything else. Run once and only once at initialization. """ handlers = [] console_handler = logging.StreamHandler() handlers.append(console_handler) if log_path: file_handler = logging.FileHandler(log_path) handlers.append(file_handler)

python

q137

get_parsed_context

train

def get_parsed_context(pipeline, context_in_string): """Execute get_parsed_context handler if specified. Dynamically load the module specified by the context_parser key in pipeline dict and execute the get_parsed_context function on that module. Args: pipeline: dict. Pipeline object. context_in_string: string. Argument string used to initialize context. Returns: pypyr.context.Context() instance. Raises: AttributeError: parser specified on pipeline missing get_parsed_context function. """ logger.debug("starting") if 'context_parser' in pipeline: parser_module_name = pipeline['context_parser'] logger.debug(f"context parser found: {parser_module_name}") parser_module = pypyr.moduleloader.get_module(parser_module_name) try: logger.debug(f"running parser {parser_module_name}") result_context = parser_module.get_parsed_context( context_in_string) logger.debug(f"step {parser_module_name} done") # Downstream steps likely to expect context not to be None, hence # empty rather than None. if result_context is None:

python

q138

main

train

def main( pipeline_name, pipeline_context_input, working_dir, log_level, log_path, ): """Entry point for pypyr pipeline runner. Call this once per pypyr run. Call me if you want to run a pypyr pipeline from your own code. This function does some one-off 1st time initialization before running the actual pipeline. pipeline_name.yaml should be in the working_dir/pipelines/ directory. Args: pipeline_name: string. Name of pipeline, sans .yaml at end. pipeline_context_input: string. Initialize the pypyr context with this

python

q139

prepare_context

train

def prepare_context(pipeline, context_in_string, context): """Prepare context for pipeline run. Args: pipeline: dict. Dictionary representing the pipeline. context_in_string: string. Argument string used to initialize context. context: pypyr.context.Context. Merge any new context generated from context_in_string into this context instance. Returns: None. The context instance to use for the pipeline run is contained in the context arg, it's not passed back as

python

q140

load_and_run_pipeline

train

def load_and_run_pipeline(pipeline_name, pipeline_context_input=None, working_dir=None, context=None, parse_input=True, loader=None): """Load and run the specified pypyr pipeline. This function runs the actual pipeline by name. If you are running another pipeline from within a pipeline, call this, not main(). Do call main() instead for your 1st pipeline if there are pipelines calling pipelines. By default pypyr uses file loader. This means that pipeline_name.yaml should be in the working_dir/pipelines/ directory. Args: pipeline_name (str): Name of pipeline, sans .yaml at end. pipeline_context_input (str): Initialize the pypyr context with this string. working_dir (path): Look for pipelines and modules in this directory. If context arg passed, will use context.working_dir and ignore this argument. If context is None, working_dir must be specified. context (pypyr.context.Context): Use if you already have a Context object, such as if you are running a pipeline from within a pipeline and you want to re-use the same context

python

q141

run_pipeline

train

def run_pipeline(pipeline, context, pipeline_context_input=None, parse_input=True): """Run the specified pypyr pipeline. This function runs the actual pipeline. If you are running another pipeline from within a pipeline, call this, not main(). Do call main() instead for your 1st pipeline if there are pipelines calling pipelines. Pipeline and context should be already loaded. Args: pipeline (dict): Dictionary representing the pipeline. context (pypyr.context.Context): Reusable context object. pipeline_context_input (str): Initialize the pypyr context with this string. parse_input (bool): run context_parser in pipeline. Returns: None """ logger.debug("starting") try: if parse_input: logger.debug("executing context_parser") prepare_context(pipeline=pipeline, context_in_string=pipeline_context_input, context=context) else: logger.debug("skipping context_parser") # run main steps pypyr.stepsrunner.run_step_group(

python

q142

run_step

train

def run_step(context): """Write payload out to yaml file. Args: context: pypyr.context.Context. Mandatory. The following context keys expected: - fileWriteYaml - path. mandatory. path-like. Write output file to here. Will create directories in path for you. - payload. optional. Write this to output file. If not specified, output entire context. Returns: None. Raises: pypyr.errors.KeyNotInContextError: fileWriteYaml or fileWriteYaml['path'] missing in context. pypyr.errors.KeyInContextHasNoValueError: fileWriteYaml or fileWriteYaml['path'] exists but is None. """ logger.debug("started") context.assert_child_key_has_value('fileWriteYaml', 'path', __name__) out_path = context.get_formatted_string(context['fileWriteYaml']['path']) # doing it like this to safeguard against accidentally dumping all context # with potentially sensitive values in it to disk if payload exists but is # None.

python

q143

run_step

train

def run_step(context): """Print debug info to console. context is a dictionary or dictionary-like. If you use pypyr.steps.debug as a simple step (i.e you do NOT specify the debug input context), it will just dump the entire context to stdout. Configure the debug step with the following optional context item: debug: keys: str (for single key) or list (of str keys). Only dump the specified keys. format: bool. Defaults False. Applies formatting expressions on dump. """ logger.debug("started") debug = context.get('debug', None) if debug: keys = debug.get('keys', None) format = debug.get('format', False) if keys: logger.debug(f"Writing to output: {keys}") if isinstance(keys, str): payload = {keys: context[keys]}

python

q144

get_error_name

train

def get_error_name(error): """Return canonical error name as string. For builtin errors like ValueError or Exception, will return the bare name, like ValueError or Exception. For all other exceptions, will return modulename.errorname, such as arbpackage.mod.myerror Args: error: Exception object. Returns:

python

q145

get_module

train

def get_module(module_abs_import): """Use importlib to get the module dynamically. Get instance of the module specified by the module_abs_import. This means that module_abs_import must be resolvable from this package. Args: module_abs_import: string. Absolute name of module to import. Raises: PyModuleNotFoundError: if module not found. """ logger.debug("starting") logger.debug(f"loading module {module_abs_import}") try: imported_module = importlib.import_module(module_abs_import) logger.debug("done") return imported_module except ModuleNotFoundError as err: msg = ("The module doesn't exist. Looking for a file like this: " f"{module_abs_import}") extended_msg = (f"{module_abs_import}.py should be in your working " "dir or it should be installed to the python path." "\nIf you have 'package.sub.mod' your current working "

python

q146

set_working_directory

train

def set_working_directory(working_directory): """Add working_directory to sys.paths. This allows dynamic loading of arbitrary python modules in cwd. Args: working_directory: string. path to add to sys.paths """ logger.debug("starting")

python

q147

Context.assert_child_key_has_value

train

def assert_child_key_has_value(self, parent, child, caller): """Assert that context contains key that has child which has a value. Args: parent: parent key child: validate this sub-key of parent exists AND isn't None. caller: string. calling function name - this used to construct error messages Raises: KeyNotInContextError: Key doesn't exist KeyInContextHasNoValueError: context[key] is None AssertionError: if key is None """ assert parent, ("parent parameter must be specified.")

python

q148

Context.assert_key_has_value

train

def assert_key_has_value(self, key, caller): """Assert that context contains key which also has a value. Args: key: validate this key exists in context AND has a value that isn't None. caller: string. calling function name - this used to construct error messages Raises: KeyNotInContextError: Key doesn't exist KeyInContextHasNoValueError: context[key] is None AssertionError: if

python

q149

Context.assert_keys_exist

train

def assert_keys_exist(self, caller, *keys): """Assert that context contains keys. Args: keys: validates that these keys exists in context caller: string. calling function or module name - this used to construct error messages

python

q150

Context.assert_keys_have_values

train

def assert_keys_have_values(self, caller, *keys): """Check that keys list are all in context and all have values. Args: *keys: Will check each of these keys in context caller: string. Calling function name - just

python

q151

Context.get_formatted_iterable

train

def get_formatted_iterable(self, obj, memo=None): """Recursively loop through obj, formatting as it goes. Interpolates strings from the context dictionary. This is not a full on deepcopy, and it's on purpose not a full on deepcopy. It will handle dict, list, set, tuple for iteration, without any especial cuteness for other types or types not derived from these. For lists: if value is a string, format it. For dicts: format key. If value str, format it. For sets/tuples: if type str, format it. This is what formatting or interpolating a string means: So where a string like this 'Piping {key1} the {key2} wild' And context={'key1': 'down', 'key2': 'valleys', 'key3': 'value3'} Then this will return string: "Piping down the valleys wild" Args: obj: iterable. Recurse through and format strings found in dicts, lists, tuples. Does not mutate the input

python

q152

Context.get_formatted_string

train

def get_formatted_string(self, input_string): """Return formatted value for input_string. get_formatted gets a context[key] value. get_formatted_string is for any arbitrary string that is not in the context. Only valid if input_string is a type string. Return a string interpolated from the context dictionary. If input_string='Piping {key1} the {key2} wild' And context={'key1': 'down', 'key2': 'valleys', 'key3': 'value3'} Then this will return string: "Piping down the valleys wild" Args: input_string: string to parse for substitutions. Returns: Formatted string. Raises: KeyNotInContextError: context[key] has {somekey} where somekey does not exist in context dictionary. TypeError: Attempt operation on a non-string type. """ if isinstance(input_string, str): try:

python

q153

Context.get_formatted_as_type

train

def get_formatted_as_type(self, value, default=None, out_type=str): """Return formatted value for input value, returns as out_type. Caveat emptor: if out_type is bool and value a string, return will be True if str is 'True'. It will be False for all other cases. Args: value: the value to format default: if value is None, set to this out_type: cast return as this type Returns: Formatted value of type out_type """ if value is None: value = default if isinstance(value, SpecialTagDirective): result = value.get_value(self) return types.cast_to_type(result, out_type) if isinstance(value, str): result = self.get_formatted_string(value) result_type = type(result) if out_type is result_type: # get_formatted_string result is already a string return result

python

q154

Context.get_processed_string

train

def get_processed_string(self, input_string): """Run token substitution on input_string against context. You probably don't want to call this directly yourself - rather use get_formatted, get_formatted_iterable, or get_formatted_string because these contain more friendly error handling plumbing and context logic. If you do want to call it yourself, go for it, it doesn't touch state. If input_string='Piping {key1} the {key2} wild' And context={'key1': 'down', 'key2': 'valleys', 'key3': 'value3'} An input string with a single formatting expression and nothing else will return the object at that context path: input_string='{key1}'. This means that the return obj will be the same type as the source object. This return object in itself has token substitions run on it iteratively. By comparison, multiple formatting expressions and/or the inclusion of literal text will result in a string return type: input_string='{key1} literal text {key2}' Then this will return string: "Piping down the valleys wild" Args: input_string: string to Parse Returns: any given type: Formatted string with {substitutions} made from context. If it's a !sic string, x from !sic x, with no substitutions made on x. If input_string was a single expression (e.g '{field}'), then returns the object with {substitutions} made for its attributes. Raises: KeyNotInContextError: input_string is not a sic string and has {somekey} where somekey does not exist in context dictionary. """ # arguably, this doesn't really belong here, or at least it makes a # nonsense of the function name. given how py and strings # look and feel pretty much like strings from user's perspective, and # given legacy code back when sic strings were in fact just strings, # keep in here for backwards compatibility. if isinstance(input_string, SpecialTagDirective): return input_string.get_value(self) else: # is this a special one field formatstring? i.e "{field}", with # nothing else?

python

q155

Context.keys_of_type_exist

train

def keys_of_type_exist(self, *keys): """Check if keys exist in context and if types are as expected. Args: *keys: *args for keys to check in context. Each arg is a tuple (str, type) Returns: Tuple of namedtuple ContextItemInfo, same order as *keys. ContextItemInfo(key, key_in_context, expected_type, is_expected_type) Remember if there is only one key in keys,

python

q156

Context.merge

train

def merge(self, add_me): """Merge add_me into context and applies interpolation. Bottom-up merge where add_me merges into context. Applies string interpolation where the type is a string. Where a key exists in context already, add_me's value will overwrite what's in context already. Supports nested hierarchy. add_me can contains dicts/lists/enumerables that contain other enumerables et. It doesn't restrict levels of nesting, so if you really want to go crazy with the levels you can, but you might blow your stack. If something from add_me exists in context already, but add_me's value is of a different type, add_me will overwrite context. Do note this. i.e if you had context['int_key'] == 1 and add_me['int_key'] == 'clearly not a number', the end result would be context['int_key'] == 'clearly not a number' If add_me contains lists/sets/tuples, this merges these additively, meaning it appends values from add_me to the existing sequence. Args: add_me: dict. Merge this dict into context. Returns: None. All operations mutate this instance of context. """ def merge_recurse(current, add_me): """Walk the current context tree in recursive inner function. On 1st iteration, current = self (i.e root of context) On subsequent recursive iterations, current is wherever you're at in the nested context hierarchy. Args: current: dict. Destination of merge. add_me: dict. Merge this to current. """ for k, v in add_me.items(): # key supports interpolation k = self.get_formatted_string(k) # str not mergable, so it doesn't matter if it exists in dest if isinstance(v, str): # just overwrite dest - str adds/edits indiscriminately current[k] = self.get_formatted_string(v) elif isinstance(v, (bytes, bytearray)): # bytes aren't mergable or formattable

python

q157

Context.set_defaults

train

def set_defaults(self, defaults): """Set defaults in context if keys do not exist already. Adds the input dict (defaults) into the context, only where keys in defaults do not already exist in context. Supports nested hierarchies. Example: Given a context like this: key1: value1 key2: key2.1: value2.1 key3: None And defaults input like this: key1: 'updated value here won't overwrite since it already exists' key2: key2.2: value2.2 key3: 'key 3 exists so I won't overwrite Will result in context: key1: value1 key2: key2.1: value2.1 key2.2: value2.2 key3: None Args: defaults: dict. Add this dict into context. Returns: None. All

python

q158

FileInRewriterStep.run_step

train

def run_step(self, rewriter): """Do the file in to out rewrite. Doesn't do anything more crazy than call files_in_to_out on the rewriter. Args: rewriter: pypyr.filesystem.FileRewriter instance. """ assert rewriter, ("FileRewriter instance required

python

q159

ObjectRewriterStep.run_step

train

def run_step(self, representer): """Do the object in-out rewrite. Args: representer: A pypyr.filesystem.ObjectRepresenter instance. """ assert representer, ("ObjectRepresenter instance required to run "

python

q160

StreamRewriterStep.run_step

train

def run_step(self): """Do the file in-out rewrite.""" rewriter

python

q161

StreamReplacePairsRewriterStep.run_step

train

def run_step(self): """Write in to out, replacing strings per the replace_pairs.""" formatted_replacements = self.context.get_formatted_iterable( self.replace_pairs)

python

q162

StreamReplacePairsRewriterStep.iter_replace_strings

train

def iter_replace_strings(replacements): """Create a function that uses replacement pairs to process a string. The returned function takes an iterator and yields on each processed line. Args: replacements: Dict containing 'find_string': 'replace_string' pairs Returns: function with signature: iterator of strings = function(iterable) """ def function_iter_replace_strings(iterable_strings): """Yield a formatted string from iterable_strings using a generator. Args: iterable_strings: Iterable containing strings. E.g a

python

q163

run_step

train

def run_step(context): """Set new context keys from formatting expressions with substitutions. Context is a dictionary or dictionary-like. context['contextSetf'] must exist. It's a dictionary. Will iterate context['contextSetf'] and save the values as new keys to the context. For example, say input context is: key1: value1 key2: value2 key3: value3 contextSetf: key2: 'aaa_{key1}_zzz' key4: 'bbb_{key3}_yyy' This will result in return context: key1: value1 key2: aaa_value1_zzz key3: bbb_value3_yyy

python

q164

cast_to_type

train

def cast_to_type(obj, out_type): """Cast obj to out_type if it's not out_type already. If the obj happens to be out_type already, it just returns obj as is. Args: obj: input object out_type: type. Returns: obj cast to out_type. Usual python conversion / casting rules

python

q165

get_pipeline_yaml

train

def get_pipeline_yaml(file): """Return pipeline yaml from open file object. Use specific custom representers to model the custom pypyr pipeline yaml format, to load in special literal types like py and sic strings. If looking to extend the pypyr pipeline syntax with special types, add these to the tag_representers list. Args: file: open file-like object. Returns:

python

q166

get_yaml_parser_roundtrip

train

def get_yaml_parser_roundtrip(): """Create the yaml parser object with this factory method. The round-trip parser preserves: - comments - block style and key ordering are kept, so you can diff the round-tripped source - flow style sequences ( ‘a: b, c, d’) (based on request and test by Anthony Sottile) - anchor names that are hand-crafted (i.e. not of the form``idNNN``) - merges in dictionaries are preserved

python

q167

get_yaml_parser_roundtrip_for_context

train

def get_yaml_parser_roundtrip_for_context(): """Create a yaml parser that can serialize the pypyr Context. Create yaml parser with get_yaml_parser_roundtrip, adding Context. This allows the yaml parser to serialize

python

q168

run_step

train

def run_step(context): """Load a json file into the pypyr context. json parsed from the file will be merged into the pypyr context. This will overwrite existing values if the same keys are already in there. I.e if file json has {'eggs' : 'boiled'} and context {'eggs': 'fried'} already exists, returned context['eggs'] will be 'boiled'. The json should not be an array [] on the top level, but rather an Object. Args: context: pypyr.context.Context. Mandatory. The following context key must exist - fetchJson - path. path-like. Path to file on disk. - key. string. If exists, write json structure to this context key. Else json writes to context root. Also supports a passing path as string to fetchJson, but in this case you won't be able to specify a key. All inputs support formatting expressions. Returns: None. updates context arg. Raises: FileNotFoundError: take a guess pypyr.errors.KeyNotInContextError: fetchJson.path missing in context. pypyr.errors.KeyInContextHasNoValueError: fetchJson.path exists but is

python

q169

QueryCountMiddleware._ignore_request

train

def _ignore_request(self, path): """Check to see if we should ignore the request.""" return any([

python

q170

QueryCountMiddleware._ignore_sql

train

def _ignore_sql(self, query): """Check to see if we should ignore the sql query.""" return any([

python

q171

QueryCountMiddleware._duplicate_queries

train

def _duplicate_queries(self, output): """Appends the most common duplicate queries to the given output.""" if QC_SETTINGS['DISPLAY_DUPLICATES']: for query, count in self.queries.most_common(QC_SETTINGS['DISPLAY_DUPLICATES']): lines = ['\nRepeated {0} times.'.format(count)]

python

q172

QueryCountMiddleware._calculate_num_queries

train

def _calculate_num_queries(self): """ Calculate the total number of request and response queries. Used for count header

python

q173

_process_settings

train

def _process_settings(**kwargs): """ Apply user supplied settings. """ # If we are in this method due to a signal, only reload for our settings setting_name = kwargs.get('setting', None) if setting_name is not None and setting_name != 'QUERYCOUNT': return # Support the old-style settings if getattr(settings, 'QUERYCOUNT_THRESHOLDS', False): QC_SETTINGS['THRESHOLDS'] = settings.QUERYCOUNT_THRESHOLDS # Apply new-style settings if not getattr(settings, 'QUERYCOUNT', False): return # Duplicate display is a special case, configure it specifically if 'DISPLAY_DUPLICATES' in settings.QUERYCOUNT: duplicate_settings = settings.QUERYCOUNT['DISPLAY_DUPLICATES'] if duplicate_settings is not None: duplicate_settings =

python

q174

NCloudBot._get_webapi_requests

train

def _get_webapi_requests(self): """Update headers of webapi for Requests.""" headers = { 'Accept': '*/*', 'Accept-Language': 'zh-CN,zh;q=0.8,gl;q=0.6,zh-TW;q=0.4', 'Connection': 'keep-alive', 'Content-Type': 'application/x-www-form-urlencoded', 'Referer': 'http://music.163.com', 'Host':

python

q175

NCloudBot._build_response

train

def _build_response(self, resp): """Build internal Response object from given response.""" # rememberLogin # if self.method is 'LOGIN' and resp.json().get('code') == 200: # cookiesJar.save_cookies(resp, NCloudBot.username)

python

q176

NCloudBot.send

train

def send(self): """Sens the request.""" success = False if self.method is None: raise ParamsError() try: if self.method == 'SEARCH': req = self._get_requests() _url = self.__NETEAST_HOST + self._METHODS[self.method] resp = req.post(_url, data=self.data) self._build_response(resp) self.response.ok = True else: if isinstance(self.data, dict): data = encrypted_request(self.data) req = self._get_webapi_requests() _url = self.__NETEAST_HOST + self._METHODS[self.method] if self.method in ('USER_DJ', 'USER_FOLLOWS', 'USER_EVENT'): _url = _url % self.params['uid'] if self.method in ('LYRIC', 'MUSIC_COMMENT'): _url = _url % self.params['id']

python

q177

set_option

train

def set_option(name, value): """ Set plydata option Parameters ---------- name : str Name of the option value : object New value of the option Returns ------- old : object

python

q178

GroupedDataFrame.group_indices

train

def group_indices(self): """ Return group indices """ # No groups if not self.plydata_groups: return np.ones(len(self), dtype=int)

python

q179

_make_verb_helper

train

def _make_verb_helper(verb_func, add_groups=False): """ Create function that prepares verb for the verb function The functions created add expressions to be evaluated to the verb, then call the core verb function Parameters ---------- verb_func : function Core verb function. This is the function called after expressions created and added to the verb. The core function should be one of those that implement verbs that evaluate expressions. add_groups : bool If True, a groups attribute is added to the verb. The groups are the columns created after evaluating the

python

q180

_get_base_dataframe

train

def _get_base_dataframe(df): """ Remove all columns other than those grouped on """ if isinstance(df, GroupedDataFrame): base_df = GroupedDataFrame(

python

q181

_add_group_columns

train

def _add_group_columns(data, gdf): """ Add group columns to data with a value from the grouped dataframe It is assumed that the grouped dataframe contains a single group >>> data = pd.DataFrame({ ... 'x': [5, 6, 7]}) >>> gdf = GroupedDataFrame({ ... 'g': list('aaa'), ... 'x': range(3)}, groups=['g']) >>> _add_group_columns(data, gdf) g x 0 a 5 1 a 6 2 a 7 """ n = len(data) if isinstance(gdf, GroupedDataFrame): for i, col in enumerate(gdf.plydata_groups): if col not in data: group_values = [gdf[col].iloc[0]] * n # Need to be careful and maintain the dtypes # of the group columns if pdtypes.is_categorical_dtype(gdf[col]): col_values = pd.Categorical(

python

q182

_create_column

train

def _create_column(data, col, value): """ Create column in dataframe Helper method meant to deal with problematic column values. e.g When the series index does not match that of the data. Parameters ---------- data : pandas.DataFrame dataframe in which to insert value col : column label Column name value : object Value to assign to column Returns ------- data : pandas.DataFrame Modified original dataframe >>> df = pd.DataFrame({'x': [1, 2, 3]}) >>> y = pd.Series([11, 12, 13], index=[21, 22, 23]) Data index and value index do not match >>> _create_column(df, 'y', y) x y 0 1 11 1 2 12 2 3 13 Non-empty dataframe, scalar value >>> _create_column(df, 'z', 3) x y z 0 1 11 3 1 2 12 3 2 3 13 3 Empty dataframe, scalar value >>> df = pd.DataFrame() >>> _create_column(df, 'w', 3) w 0 3 >>> _create_column(df, 'z', 'abc') w z 0 3 abc """ with suppress(AttributeError):

python

q183

build_expressions

train

def build_expressions(verb): """ Build expressions for helper verbs Parameters ---------- verb : verb A verb with a *functions* attribute. Returns ------- out : tuple (List of Expressions, New columns). The expressions and the new columns in which the results of those expressions will be stored. Even when a result will stored in a column with an existing label, that column is still considered new, i.e An expression ``x='x+1'``, will create a new_column `x` to replace an old column `x`. """ def partial(func, col, *args, **kwargs): """ Make a function that acts on a column in a dataframe Parameters ---------- func : callable Function col : str Column args : tuple Arguments to pass to func kwargs : dict Keyword arguments to func Results ------- new_func : callable Function that takes a dataframe, and calls the original function on a column in the dataframe. """ def new_func(gdf): return func(gdf[col], *args, **kwargs) return new_func def make_statement(func, col): """ A statement of function called on a column in a dataframe Parameters ---------- func : str or callable Function to call on a dataframe column col : str Column """ if isinstance(func, str): expr = '{}({})'.format(func, col) elif callable(func): expr = partial(func, col, *verb.args, **verb.kwargs) else: raise TypeError("{} is not a function".format(func)) return expr def func_name(func): """ Return name of a function. If the function is `np.sin`, we return `sin`. """ if isinstance(func, str): return func try: return func.__name__

python

q184

Evaluator.process

train

def process(self): """ Run the expressions Returns ------- out : pandas.DataFrame Resulting data """ # Short cut if self._all_expressions_evaluated(): if self.drop: # Drop extra columns. They do not correspond to # any expressions. columns = [expr.column for expr in self.expressions] self.data = self.data.loc[:, columns] return

python

q185

Evaluator._all_expressions_evaluated

train

def _all_expressions_evaluated(self): """ Return True all expressions match with the columns Saves some processor cycles """ def present(expr): return expr.stmt ==

python

q186

Evaluator._get_group_dataframes

train

def _get_group_dataframes(self): """ Get group dataframes Returns ------- out : tuple or generator Group dataframes """ if isinstance(self.data, GroupedDataFrame): grouper = self.data.groupby() # groupby on categorical columns uses the categories

python

q187

Evaluator._evaluate_group_dataframe

train

def _evaluate_group_dataframe(self, gdf): """ Evaluate a single group dataframe Parameters ---------- gdf : pandas.DataFrame Input group dataframe Returns ------- out : pandas.DataFrame Result data """ gdf._is_copy

python

q188

Evaluator._concat

train

def _concat(self, egdfs): """ Concatenate evaluated group dataframes Parameters ---------- egdfs : iterable Evaluated dataframes Returns ------- edata : pandas.DataFrame Evaluated data """ egdfs = list(egdfs) edata = pd.concat(egdfs, axis=0, ignore_index=False, copy=False) # groupby can mixup the rows. We try to maintain the original # order, but we can only do that if the result has a one to # one relationship with the original one2one = ( self.keep_index and

python

q189

Selector._resolve_slices

train

def _resolve_slices(data_columns, names): """ Convert any slices into column names Parameters ---------- data_columns : pandas.Index Dataframe columns names : tuple Names (including slices) of columns in the dataframe. Returns ------- out : tuple Names of columns in the dataframe. Has no

python

q190

Selector.select

train

def select(cls, verb): """ Return selected columns for the select verb Parameters ---------- verb : object verb with the column selection attributes: - names - startswith - endswith - contains - matches """ columns = verb.data.columns contains = verb.contains matches = verb.matches groups = _get_groups(verb) names = cls._resolve_slices(columns, verb.names) names_set = set(names) groups_set = set(groups) lst = [[]] if names or groups: # group variable missing from the selection are prepended missing = [g for g in groups if g not in names_set] missing_set = set(missing) c1 = missing + [x for x in names if x not in missing_set]

python

q191

Selector._at

train

def _at(cls, verb): """ A verb with a select text match """ # Named (listed) columns are always included columns = cls.select(verb) final_columns_set = set(cls.select(verb)) groups_set = set(_get_groups(verb)) final_columns_set -= groups_set - set(verb.names) def pred(col):

python

q192

Selector._if

train

def _if(cls, verb): """ A verb with a predicate function """ pred = verb.predicate data = verb.data groups = set(_get_groups(verb)) # force predicate if isinstance(pred, str): if not pred.endswith('_dtype'):

python

q193

get_verb_function

train

def get_verb_function(data, verb): """ Return function that implements the verb for given data type """ try: module = type_lookup[type(data)] except KeyError: # Some guess work for subclasses for type_, mod in type_lookup.items(): if isinstance(data, type_): module = mod break try:

python

q194

Expression

train

def Expression(*args, **kwargs): """ Return an appropriate Expression given the arguments Parameters ---------- args : tuple Positional arguments passed to the Expression class kwargs : dict Keyword arguments passed to the Expression class """ # dispatch

python

q195

EvalEnvironment.with_outer_namespace

train

def with_outer_namespace(self, outer_namespace): """Return a new EvalEnvironment with an extra namespace added. This namespace will be used only for variables that are not found in any

python

q196

EvalEnvironment.subset

train

def subset(self, names): """Creates a new, flat EvalEnvironment that contains only the variables specified.""" vld = VarLookupDict(self._namespaces)

python

q197

Q

train

def Q(name): """ Quote a variable name A way to 'quote' variable names, especially ones that do not otherwise meet Python's variable name rules. Parameters ---------- name : str Name of variable Returns ------- value : object Value of variable Examples -------- >>> import pandas as pd >>> from plydata import define >>> df = pd.DataFrame({'class': [10, 20, 30]}) Since ``class`` is a reserved python keyword it cannot be a variable name, and therefore cannot be used in an expression without quoting it. >>> df >> define(y='class+1') Traceback (most recent call last): File "<string>", line 1 class+1 ^

python

q198

regular_index

train

def regular_index(*dfs): """ Change & restore the indices of dataframes Dataframe with duplicate values can be hard to work with. When split and recombined, you cannot restore the row order. This can be the case even if the index has unique but irregular/unordered. This contextmanager resets the unordered indices of any dataframe passed to it, on exit it restores the original index. A regular index is of the form:: RangeIndex(start=0, stop=n, step=1) Parameters ---------- dfs : tuple Dataframes Yields ------ dfs : tuple Dataframe Examples -------- Create dataframes with different indices >>> df1 = pd.DataFrame([4, 3, 2, 1]) >>> df2 =

python

q199

unique

train

def unique(lst): """ Return unique elements :class:`pandas.unique` and :class:`numpy.unique` cast mixed type lists to the same type. They are faster, but some times we want to maintain the type. Parameters ---------- lst : list-like List of items Returns ------- out : list Unique items in the order that they appear in the input. Examples -------- >>> import pandas as pd >>> import numpy as np >>> lst = ['one', 'two', 123, 'three'] >>> pd.unique(lst) array(['one', 'two', '123', 'three'], dtype=object) >>> np.unique(lst) array(['123', 'one', 'three', 'two'],

python