Grouper For Not 1-dimensional

Maria Johnson
• Tuesday, 06 October, 2020
• 25 min read

Groupby() doesn't need to care about of or 'fruit' or 'color' or Nemo, group by() only cares about one thing, a lookup table that tells it which of.index is mapped to which label (i.e. In this case, for example, the dictionary passed to the group by() is instructing the group by() to: if you see index 11, then it is a “mine”, put the row with that index in the group named “mine”.

(Source: python-scripts.com)


I've tried to search the internet and Stack Overflow for this error, but got no results. Just like a lot of cryptic pandas errors, this one too stems from having two columns with the same name.

Figure out which one you want to use, rename or drop the other column and redo the operation. Dense fell OnStar, self on datarammen Ike heir mere end en dimension.

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The circle is up there with the names of letters, numbers, and colors as a formative piece of how they will start to make sense of the world. Credit: Dnu72, Pengö, Wikimedia (CC BY-SA 3.0) The unit circle in trigonometry was one of my early mathematical epiphanies.

The functions' sine, cosine, tangent, ex secant, and so on all refer to ratios of side lengths of right triangles. If an angle is less than 90 degrees, you can find its sine by drawing a right triangle with one of the angles equal to and taking the ratio of the side length of the leg adjacent to alpha to the hypotenuse.

grouper encapsulation versus combination mesh
(Source: archimatix.com)

The sine of the angle t is the y-coordinate of the point where the hypotenuse meets the unit circle, and the cosine of t is the x-coordinate. Credit: Gustav Wikimedia (CC BY-SA 3.0) Understanding all the relationships between different trig functions and different types of angles is vastly simplified by setting the hypotenuse of all the right triangles equal to 1 and placing them inside a circle.

After a few weeks of struggling to remember which trig function was which for which angle in a triangle, learning about and then internalizing the unit circle was huge for me. I could see the relationship between sine and cosine just by visualizing an angle moving along the circle.

Understanding that idea made trigonometry feel like so much more than memorizing a list of rules. I’m too afraid of commitment and discomfort to get a tattoo, but the unit circle would be pretty high on the list if I were considering one.

A GIF illustrating the relationship of sine and cosine as an angle travels around the unit circle. As Dave Riches on described in an episode of the Favorite Theorem podcast, to figure out how long the outside of a circle is, we first have to figure out what we mean by the length of a curved, rather than straight, segment.

If you asked people what dimension a circle is, I'll bet most of them would say 2-dimensional, and I think there are two reasons for that. If looking like a line is too wishy-washy for you, maybe you’d prefer thinking of the ways an ant confined to a circle could move.

grouper encapsulation versus combination mesh clicking inside double its go
(Source: archimatix.com)

The circle is probably the simplest example of a mathematical space whose dimension isn’t completely obvious, so thinking through it and really internalizing its 1-dimensionality was for me a key to start thinking about dimension mathematically, from the point of view of the object itself, not the ambient space. Once you’ve gotten that, you can see why the 2-sphere, the shape of the skin of a beach ball, is a fundamentally 2-dimensional space.

All of a sudden you’re contemplating the Poincaré conjecture, the most important mathematical breakthrough of the current century to date. Not bad for a symbol that literally represents the idea of nothingness in our number system.

Parallel_easy Functions to assist in parallel processing with Python 2.7. (e.g. when n_jobs == 1, processing is serial) Similar to job lib. Parallel but with the addition of IMAP functionality and a more effective way of handling Ctrl-C exit (we add a timeout).

Worker processes return one “chunk” of data at a time, and the iterator allows you to deal with each chunk as they come back, so memory can be handled efficiently. If chunk size is too small, communication overhead slows things down.

If True, results are dished out in the order corresponding to iterable. If False, results are dished out in whatever order workers return them.

grouper field dialogue structure level select
(Source: wiki.almworks.com)

Rosetta.parallel.parallel_easy.map_easy_padded_blocks (fun, iterable, n_jobs, pad, block size=None) Returns a parallel map of fun cover iterable, computed by splitting iterable into padded blocks, then piecing the result together. Each block is processed with pad extra on each side.

Pandas_easy Functions for helping make pandas parallel. Works ONLY for the simple case that.apply(fun) would yield a Series of length equal to the number of groups, in other words, fun applied to each group is a scalar.

Applied to each group using fun(df_or_series) Should return one single value (e.g. string or number) Must be pickle: A lambda function will not work! For each group in df_or_series.group by(** group by_quarks), compute fun(group) or group.apply(fun) and, assuming each result is a series, flatten each series then paste them together.

Filefilter Contains a collection of function that clean, decode and move files around. Class Rosetta.text.file filter. PathFinder (text_base_path=None, file_type='*', name_strip='\.*', limit=None) Find and access paths in a directory tree.

Rosetta.text.file filter.get_paths (base_path, file_type='*', relative=False, get_inter=False, limit=None) Crawls subdirectories and returns an iterator over paths to files that match the file_type. Note that the filenames will be converted to lowercase before this comparison.

grouper understanding internet2 groups intersection entities belong composite produces factor includes common members both
(Source: spaces.at.internet2.edu)

If True, return an iterator over paths rather than a list. Rosetta.text.file filter.path_to_newname (path, name_level=1) Takes one path and returns a new name, combining the directory structure with the filename.

Parameters: Form the name using items this far back in the path. Returns: streamers Classes for streaming tokens/info from files/sparse files etc...

All derived classes will implement to return an integrator over the text documents with processing as appropriate. Parameters: The single item to pull from info and stream.

Info_stream () Yields a dict from self.executing the query as well as “tokens”. We suggest that the entire query result not be returned and that iteration is controlled on server side, but this method does not guarantee that.

This method must return a dictionary, which at least has the key ‘text’ in it, containing the next to be tokenized. Class Rosetta.text.streamers. MongoStreamer (db_setup, tokenizer=None, tokenizer_fun=None) Subclass of Streamer to connect to a Congo database and iterate over query results.

dimensional grouper enameling glazed brooch fish
(Source: www.rubylane.com)

Db_setup is expected to be a dictionary containing host, database, collection, query, and text_key. Additionally, an optional limit parameter is allowed.

The query itself must return a column named text_key which is passed on as ‘text’ to the iterator. In addition, because it is difficult to rename Congo fields (similar to the SQL ‘AS’ syntax), we allow a translation dictionary to be passed in, which translates keys in the Congo dictionary result names k to be passed into the result as v for key value pairs {k : v}.

# In this example, we assume that the collection has a field named # disc, holding the text to be analyzed, and a field named _id which # will be translated to doc_id and stored in the cache. For text in stream.info_stream(cache_list=): print text, text class Rosetta.text.streamers. MySQLStreamer (*arms, **quarks) Subclass of Streamer to connect to a MySQL database and iterate over query results.

Db_setup is expected to be a dictionary containing host, user, password, database, and query. The query itself must return a column named text.

Example: db_setup = {} db_setup = ‘hostname’ db_setup = ‘username’ db_setup = ‘password’ db_setup = ‘database’ db_setup = ‘select Info_stream (paths=None, doc_id=None, limit=None) Returns an iterator over paths yielding dictionaries with information about the file contained within.

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(Source: www.researchgate.net)

Workers process this many jobs at once before pickling and sending results to master. If True, raise DocIDError when the doc_id (formed by self) is not a valid VW “Tag”.

Class Rosetta.text.streamers. TextIterStreamer (text_inter, tokenizer=None, tokenizer_fun=None) info_stream () Yields a dict from self.streamer as well as “tokens”. Class Rosetta.text.streamers.Streamer (file=None, cache_file=False, limit=None, shuffle=False) For streaming from a single VW file.

Info_stream (doc_id=None) Returns an iterator over info diets. Record_stream (doc_id=None) Returns an iterator over record diets.

Used by other modules as a means to convert stings to lists of strings. SparseFormatter Classes for converting text to sparse representations (e.g. VW or Slight).

SFileFilter Classes for filtering words/rows from a sparse formatted file. Class Rosetta.text.text_processors. MakeTokenizer (tokenizer_func) Makes a subclass of BaseTokenizer out of a function.

Class Rosetta.text.text_processors.SFileFilter (formatter, bit_precision=18, file=None, verbose=True) Filters results stored in files (sparsely formatted bag-of-words files). Compactify () Removes “gaps” in the id values in self.token2id.

Every single id value will (probably) be altered. Filter_extremes (doc_freq_min=0, doc_freq_max=inf, doc_fraction_min=0, doc_fraction_max=1, token_score_min=0, token_score_max=inf, token_score_quantile_min=0, token_score_quantile_max=1) Remove extreme tokens from self (calling self.filter_tokens).

Parameters: Remove tokens that in less than this number of documents Returns: filter_sfile (infill, out file, doc_id_list=None, enforce_all_doc_id=True, min_TF_IDF=0, filters=None) Alter a file by converting tokens to id values, and removing tokens not in self.token2id.

Parameters: infill : file path or buffer Keep only tokens whose term frequency-inverse document frequency is greater than this threshold.

Where TF(t, d) is the number of times the term t shows up in the document d, IDF(t, D) = log (N / M), where N is the total number of documents in D and M is the number of documents in D which contain the token t. The logarithm is base e. Each function must take a record_dict as a parameter and return a boolean.

Returns: save (save path, protocol=-1, set_id2token=True) Parameters: save file : file path or buffer Used to associate tokens with the output of a VW file.

Get_SSR (feature_values=None, target=1, importance=None, doc_id=None) Return a string representing one record in SVM-Light sparse format .=. Parameters: filepath_or_buffer : string or file handle / String.

Rstrips newline characters from SSR before parsing. Sstr_to_info (SSR) Returns the full info dictionary corresponding to a sparse record string.

Returns: possible keys = ‘tokens’, ‘target’, ‘importance’, ‘doc_id’, Parameters: Formatted according to self.format_name Note that the values in SSR must be integers.

Parameters:Returns: Tokenized text, e.g. Class Rosetta.text.text_processors. TokenizerPOSFilter (POS_types=, sent_tokenizer=, word_tokenizer=, word_tokenizer_fun=None, POS_tagger=) Tokenizes, does POS tagging, then keeps words that match particular POS.

The importance weight to associate to this example. Returns: Rosetta.text.text_processors.collision_probability (vocab_size, bit_precision) Approximate probability of at least one collision (assuming perfect hashing).

Vw_helpers Wrappers to help with Vow pal Wabbit (VW). Class Rosetta.text.VW_helpers.Results (topics_file, predictions_file, sfile_filter, sum_topics=None, alpha=None, verbose=False) Facilitates working with results of VW LDA runs.

Cosine_similarity (frame1, frame2) Computes doc-doc similarity between rows of two frames containing document topic weights. Rows are different records, columns are topic weights.

Predict (tokenized_text, master=50, atom=0.001, raise_on_unknown=False) Returns a probability distribution over topics given that one (tokenized) document is equal to tokenized_text. Absolute tolerance for change in parameters before converged.

If True, raise TokenError when all tokens are unknown to this model. Returns: self.pr_topic_g_doc is an example of a (large) frame of this type.

Print_topics (sum_words=5, out file=', mode 'w'>, show_doc_fraction=True) Print the top results for self.pr_token_g_topic for all topics Parameters: Print the num_words words (ordered by P) in each topic.

If True, print doc_fraction along with the topic weight Prob_doc_topic (doc=None, topic=None, c_doc=None, c_topic=None) Return joint probabilities of (doc, topic), restricted to subsets, conditioned on variables.

We parse out and include only the last predictions by looking for repeats of the first lines doc_id field. We thus, at this time, require the VW formatted file to have, in the last column, a unique doc_id associated with the doc.

Rosetta.text.VW_helpers.parse_lda_predictions (predictions_file, num_topics, start_line, normalize=True, get_inter=False) Return a Database representation of a VW prediction file. Start reading the predictions file here.

Rosetta.text.VW_helpers.parse_lda_topics (topics_file, num_topics, max_token_hash=None, normalize=True, get_inter=False) Returns a Database representation of the topics output of a LDA VW run. Normalize the rows of the data frame so that they represent probabilities of topic given hash_val.

If True will return an iterator yielding dict of hash and token veils The trick is dealing with lack of a marker for the information printed on top, and the inconsistent delimiter choice.

Rosetta.text.VW_helpers.parse_var info (varinfo_file) Uses the output of the vw-varinfo utility to get a Database with variable info. Class Rosetta.text.ENSIM_helpers. StreamerCorpus (streamer, dictionary, doc_id=None, limit=None) A “corpus type” object built with token streams and dictionaries.

Depending on your method for streaming tokens, this could be slow... Before modeling, it’s usually better to serialize this corpus using: Class Rosetta.text.ENSIM_helpers. SvmLightPlusCorpus (name, doc_id=None, doc_id_filter=None, limit=None) Extends ENSIM.corpora. SvmLightCorpus, providing methods to work with (e.g. filter by) doc_ids.

Classmethod from_streamer_dict (streamer, dictionary, name, doc_id=None, limit=None) Initialize from a Streamer and ENSIM.corpora.dictionary, serializing the corpus (to disk) in SvmLightPlus format, then returning a SvmLightPlusCorpus. Method streamer.token_stream() returns a stream of lists of words.

Returns: serialize (name, **quarks) Save to slight (plus) format, generating files: name, name.index, fname.doc_id Rosetta.text.ENSIM_helpers.get_topics_df (corpus, LDA) Creates a delimited file with doc_id and topics scores.

Rosetta.text.ENSIM_helpers.get_words_docfreq (dictionary) Returns a of with token id, doc freq as columns and words as index. Eda Rosetta.modeling.EDA.get_labels (series, bins=10, quantiles=False) Divides series into bins and returns labels corresponding to midpoints of bins.

Rosetta.modeling.EDA.hist_cols (of, cols_to_plot, num_cols, sum_rows, fig size=None, **quarks) Plots histograms of columns of a Database as subplots in one big plot. Handles fans and extreme values in a “graceful” manner by removing them and reporting their occurrence.

Rosetta.modeling.EDA.hist_one_col (col) Plots a histogram one column. Handles fans and extreme values in a “graceful” manner.

Rosetta.modeling.EDA.plot_corr_dendrogram (corr, cluster_method='weighted', **deprogram_quarks) Plot a correlation matrix as a deprogram (on the current axes). Uses city.cluster.hierarchy.linkage to compute clusters based on distance between samples.

Rosetta.modeling.EDA.plot_corr_grid (corr, cluster=True, cluster_method='weighted', distance_fun=None, ax=None, **fig_quarks) Plot a correlation matrix as a grid. Uses city.cluster.hierarchy.linkage to compute clusters based on distance between samples.

Xmin, Max, min, Max plot level sets within box defined by box_ends Over-rides self.box_ends if self.box_ends is not set. Initialize the ClassifierPlotter2D, then train different (2-d) classifiers on different data sets and plot the data and level sets of the classifier.

Plot_data (X, y, **scatter_quarks) Plot the (X, y) data as a bunch of labeled markers. Initialize the RegressorPlotter2D, then train different (2-d) regressors on different data sets and plot the data and level sets of the regressor.

CoefficientConverter is initialized with one dataset, from this the standardization/winterization rules are learned. Standardization part of module provides the fundamental relation: X.dot(self.unstandardize_params(w_st)) = self.standardize(X).dot(w_st)WORKFLOW 1 1) Initialize with a Database.

2b) We obtain the “unstandardized paras” w = self.unstandardized_paras(w_st) To predict Y_hat corresponding to new input X, we use X.dot(w) Parameters: data : pandas Series or DataFramedata is standardized according to the rules that self was initialized with, i.e. the rules implicit in self.stats.

Trim values that are more than max_std standard deviations away from the mean I.e. the std is computed on the series that has already been trimmed by quantile.

Used to make sure people are raising the intended exception, rather than some other weird one. Used to deal with syntax issues config files.

Exception Rosetta.common. TokenError Raise when tokens are passed to a method/function and you don’t know how to deal with them. Rosetta.common.get_list_from_file rows (infill) Returns a list generated from rows of a file.

Rosetta.common.lazy prop (FN) Use as a decorator to get lazily evaluated properties. Rosetta.common.nested_default dict (default_factory, levels=1) Creates nested defaultdicts with the lowest level having default factory.

Class Rosetta.common.smart_open (filename, *arms) Context manager that opens a filename and closes it on exit, but does nothing for file-like objects. Rosetta.common.unpicked (pkl_file) Returns unpicked version of object.

Generate a non-uniform random sample from NP.range(5) of size 3 without replacement: Rosetta.common_math.pandas_to_ndarray_wrap (X, copy=True) Converts X to a array and provides a function to help convert back to pandas object.

Rosetta.common_math.series_to_frame (data) If length(N) Series, return an N × 1 Frame with name equal to the series name. Parameters: data : pandas Series or Database.

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1 en.wikipedia.org - https://en.wikipedia.org/wiki/Epinephelus_quernus
2 www.to-hawaii.com - https://www.to-hawaii.com/fish/groupers.php
3 fishchoice.com - https://fishchoice.com/buying-guide/black-grouper
4 wetpixel.com - https://wetpixel.com/forums/index.php
5 www.howtolinks.com - https://www.howtolinks.com/hawaiian-grouper-how-to