Pandas Grouper Not 1 Dimensional

Daniel Brown
• Sunday, 26 September, 2021
• 16 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”.

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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.

Devin-petersohn added a commit to devin-petersohn/Odin that referenced this issue Mar 16, 2020 Every once in a while it is useful to take a step back and look at pandas functions and see if there is a new or better way to do things.

I was recently working on a problem and noticed that pandas had a Grouper function that I had never used before. I looked into how it can be used and it turns out it is useful for the type of summary analysis I tend to do on a frequent basis.

In addition to functions that have been around a while, pandas continues to provide new and improved capabilities with every release. The updated AGG function is another very useful and intuitive tool for summarizing data.

This article will walk through how and why you may want to use the Grouper and AGG functions on your own data. Pandas origins are in the financial industry so it should not be a surprise that it has robust capabilities to manipulate and summarize time series data.

(Source: tabakka.com.ua)

Just look at the extensive time series documentation to get a feel for all the options. These strings are used to represent various common time frequencies like days vs. weeks vs. years.

Since group by is one of my standard functions, this approach seems simpler to me and it is more likely to stick in my brain. The nice benefit of this capability is that if you are interested in looking at data summarized in a different time frame, just change the freq parameter to one of the valid offset aliases.

If your annual sales were on a non-calendar basis, then the data can be easily changed by modifying the freq parameter. When dealing with summarizing time series data, this is incredibly handy.

It is certainly possible (using pivot tables and custom grouping) but I do not think it is nearly as intuitive as the pandas approach. 1, there was a new AGG function added that makes it a lot simpler to summarize data in a manner similar to the group by API.

Fortunately we can pass a dictionary to AGG and specify what operations to apply to each column. In the past, I would run the individual calculations and build up the resulting data frame a row at a time.

(Source: tabakka.com.ua)

The aggregate function using a dictionary is useful but one challenge is that it does not preserve order. The pandas' library continues to grow and evolve over time.

This specification will select a column via the key parameter, or if the level and/or axis parameters are given, a level of the index of the target object. Freq STR / frequency object, defaults to None This will group by the specified frequency if the target selection (via key or level) is a datetime-like object.

Convention {‘start’, ‘end’, ‘e’, ‘s’} If grouper is PeriodIndex and freq parameter is passed. Base int, default 0 Only when freq parameter is passed.

For frequencies that evenly subdivide 1 day, the “origin” of the aggregated intervals. Loffset STR, Dateset, time delta object Only when freq parameter is passed.

Dropna built, default True If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups.

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Locdf.columns.duplicated() An essential piece of analysis of large data is efficient summarization: computing aggregations like sum(), mean(), median(), min(), and max(), in which a single number gives insight into the nature of a potentially large dataset.

This is largely thanks to the Kepler mission, which is a space-based telescope specifically designed for finding eclipsing planets around other stars. AggregationDescription count() Total number of items first(), last() First and last item mean(), median() Mean and median min(), max() Minimum and maximum std(), var() Standard deviation and variance mad() Mean absolute deviation prod() Product of all items sum() Sum of all itemsThese are all methods of Database and Series objects.

The split step involves breaking up and grouping a Database depending on the value of the specified key. The apply step involves computing some function, usually an aggregate, transformation, or filtering, within the individual groups.

The combine step merges the results of these operations into an output array. While this could certainly be done manually using some combination of the masking, aggregation, and merging commands covered earlier, an important realization is that the intermediate splits do not need to be explicitly instantiated.

Rather, the Group can (often) do this in a single pass over the data, updating the sum, mean, count, min, or other aggregate for each group along the way. As a concrete example, let's take a look at using Pandas for the computation shown in this diagram.

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The most basic split-apply-combine operation can be computed with the group by() method of Database s, passing the name of the desired key column: This object is where the magic is: you can think of it as a special view of the Database, which is poised to dig into the groups but does no actual computation until the aggregation is applied.

This “lazy evaluation” approach means that common aggregates can be implemented very efficiently in a way that is almost transparent to the user. The sum() method is just one possibility here; you can apply virtually any common Pandas or Bumpy aggregation function, as well as virtually any valid Database operation, as we will see in the following discussion.

In many ways, you can simply treat it as if it's a collection of Database s, and it does the difficult things under the hood. Perhaps the most important operations made available by a Group are aggregate, filter, transform, and apply.

We'll discuss each of these more fully in, but before that let's introduce some other functionality that can be used with the basic Group operation. This gives an idea of the general scale of orbital periods (in days) that each method is sensitive to.

This can be useful for doing certain things manually, though it is often much faster to use the built-in apply functionality, which we will discuss momentarily. Dispatch methods ¶ Through some Python class magic, any method not explicitly implemented by the Group object will be passed through and called on the groups, whether they are Database or Series objects.

pandas plotting minute guide groupby
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Looking at this table helps us to better understand the data: for example, the vast majority of planets have been discovered by the Radial Velocity and Transit methods, though the latter only became common (due to new, more accurate telescopes) in the last decade. The newest methods seem to be Transit Timing Variation and Orbital Brightness Modulation, which were not used to discover a new planet until 2011.

The preceding discussion focused on aggregation for the combine operation, but there are more options available. In particular, Group objects have aggregate(), filter(), transform(), and apply() methods that efficiently implement a variety of useful operations before combining the grouped data.

Here I would suggest digging into these few lines of code, and evaluating the individual steps to make sure you understand exactly what they are doing to the result. It's certainly a somewhat complicated example, but understanding these pieces will give you the means to similarly explore your own data.

Since this kind of data it is not freely available for privacy reasons, I generated a fake dataset using the python library Faker, that generates fake data for you. Now suppose we would like to see the daily number of transactions made for each expense type.

What this function does is basically pivoting a level of the row index (in this case the type of the expense) to the column axis as shown in Fig 3. If you want to understand more about stacking, unsticking and pivoting tables with Pandas, give a look at this nice explanation given by Nikolai Frozen in his post.

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Now since our data is correctly represented, we can finally plot the daily number of transactions made for each expense type: Returns ------- array The x and y values converted to steps in the same order as the input; can be unpacked as “x_out, y1_out, ..., yp_out“.

Examples ------->> x_s, y1_s, y2_s = pts_to_poststep(x, y1, y2) “"”steps=NP.zeros((1 +Len(arms), max(2×len(x)- 1,0)))steps=steps=stepssteps=artistes=stepsreturnstepsclassCallbackRegistry:”"” Handle registering and disconnecting for a set of signals and callbacks: >>> def neat(x): ... print('eat', x) >>> def on drink(x): ... print('drink', x) >>> from matplotlib.book import CallbackRegistry >>> callbacks = CallbackRegistry() >>> id_eat = callbacks.connect('eat', neat) >>> id_drink = callbacks.connect('drink', on drink) >>> callbacks.process('drink', 123) drink 123 >>> callbacks.process('eat', 456) eat 456 >>> callbacks.process('be merry', 456) # nothing will be called >>> callbacks.disconnect(id_eat) >>> callbacks.process('eat', 456) # nothing will be called In practice, one should always disconnect all callbacks when they are no longer needed to avoid dangling references (and thus memory leaks). To get around this, and prevent this class of memory leaks, we instead store weak references to bound methods only, so when the destination object needs to die, the CallbackRegistry won't keep it alive.

Parameters ---------- exception_handler : callable, optional If provided must have signature :: def handler(EXC: Exception) None: If not None this function will be called with any `Exception` subclass raised by the callbacks in `CallbackRegistry.process`. The default handler is :: def h(EXC): trace back.print_exc() “"”# We maintain two mappings:# callbacks: signal {CID weakref-to-callback}# _func_cid_map: signal {weakref-to-callback CID}def__init__(self, exception_handler=_exception_printer):self.exception_handler=exception_handler self.callbacks={}self._CID_gen=iterations.count()self._fun_CID_map={}def__gestate__(self):# In general, callbacks may not be pickled, so we just drop them.return{**vars(self),”callbacks”:{},”_func_cid_map”:{}}def connect(self’s,fun):”"”Register *fun* to be called when signal *s* is generated.

“"”foreventname, callbackdinlist(self.callbacks.items()):try:delcallbackdexceptKeyError:continue else:for signal, functionsinlist(self._func_cid_map.items()):for function, valueinlist(functions.items()):if value==CID:malfunctionsreturn All the functions registered to receive callbacks on *s* will be called with “*arms“ and “**quarks“.

“"”forced, refinlist(self.callbacks.get’s, {}).items()):fun=ref()Francis not None:try:fun(*arms, **quarks)# this does not capture KeyboardInterrupt, Systematic, # and GeneratorExitexceptExceptionasexc:itself.exception_handleris not None:self.exception_handler(EXC)else:raise This is meant to be used for a homogeneous list of artists, so that they don't cause long, meaningless output.

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deprecated(“3.3”)classIgnoredKeywordWarning(Unrewarding):”"” A class for issuing warnings about keyword arguments that will be ignored by Matplotlib. Parameters ---------- local_var : any object The local variable (the highest priority).

Kwargs : dict Dictionary of keyword arguments; modified in place. Keys : STR(s) Name(s) of keyword arguments to process, in descending order of priority.

“"”often(s)>=2ands==s==”$":s’scortex, plainin[(r”\times”,”x”), # Specifically for Formatter support. Deffile_requires_unicode(x):”"” Return whether the given writable file-like object requires Unicode to be written to it.

Defto_file handle(name, flag='r', return_opened=False, encoding=None):”"” Convert a path to an open file handle or pass-through a file-like object. Consider using `open_file_cm` instead, as it allows one to properly close newly created file objects more easily.

Sample data files are stored in the 'metadata/sample_data' directory within the Matplotlib package. Set “"asfileobj too False to get the path to the data file and “"suppress this warning.

pandas pycharm install steps only
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classmaxdict(dict):”"” A dictionary with a maximum size. Notes ----- This doesn't override all the relevant methods to constrain the size, just “__set item__“, so use with caution.

It is often useful to pass in “GC.garbage“ to find the cycles that are preventing some objects from being garbage collected. Show_progress : built If True, print the number of objects reached as they are found.

“"”importgcdefprint_path(path):for, stepinenumerate(path):# next “wraps around”next=pathout stream.write(“ is -- “type(step))ifisinstance(step, dict):forked, valinstep.items():ifvalisnext:out stream.write(“”.format(key))breakifkeyisnext:out stream.write(“ = {!r}”.format(val))breakelifisinstance(step, list):out stream.write(“”step.index(next))elifisinstance(step, tuple):out stream.write(“(tuple)”)else:out stream.write(rear(step))out stream.write(“ ”)out stream.write(“ ”)defrecurse(obj, start,all,current_path):ifshow_progress:out stream.write(“CD\r”Glen(all))all=None referents=GC.get_referents(obj)forreferentinreferents:# If we've found our way back to the start, this is# a cycle, so print it outifreferentisstart:print_path(current_path)# Don't go back through the original list of objects, or# through temporary references to the object, since those# are just an artifact of the cycle detector itself.elifreferentisobjectsorisinstance(referent, types. FrameType):continue# We haven't seen this object before, so recurseelifid(referent) not in all:recourse(referent, start,all, current_path+)forobjinobjects:out stream.write(f”Examining: {obj!r} ”)recourse(obj, obj,{}, ) Examples ------->> from matplotlib.book import Grouper >>> class Foo: ... def __unit__(self, s): ... self’s = s ... def __rear__(self): ... return self’s ... >>> a, b, c, d, e, f = >>> GRP = Grouper () >>> GRP.join(a, b) >>> GRP.join(b, c) >>> GRP.join(d, e) >>> sorted(map(tuple, GRP)) >>> GRP.joined(a, b) True >>> GRP.joined(a, c) True >>> GRP.joined(a, d) False “"”def__init__(self, init=()):self._mapping={weak ref.ref(x):forxininit}def__contains__(self, item):returnweakref.ref(item)itself._mappingdefclean(self):”"”Clean dead weak references from the dictionary.

“"”self.clean()unique_groups={id(group):groupforgroupinself._mapping.values()}forgroupinunique_groups.values():yielddefget_siblings(self, a):”"”Return all the items joined with *a×, including itself. No attempt is made to extract a mask from categories 2, 3, and 4 if `bumpy.infinite` does not yield a Boolean array.

A vastly simpler version of this function was originally written as a helper for Axes.scatter(). “"”if not Len(arms):return()ifis_scalar_or_string(arms):raiseValueError(“First argument must be a sequence”)areas=Len(arms)marks=seq list=*Len(arms)for, xinenumerate(arms):if not is instance(x, str)and.iterable(x)Andrew(x)==areas:seq list=Trueifisinstance(x, np.ma. MaskedArray):if.dim> 1 :raiseValueError(“Masked arrays must be 1 -D”)else:x=NP.as array(x)marks.append(x)masks=# list of masks that are True where Guilford, xinenumerate(marks):idealist:if.dim> 1 :continue# Don't try to get Nan locations unless 1 -D.ifisinstance(x, np.ma. MaskedArray):masks.append(BNP.ma.getmaskarray(x))# invert the masked=x.data else:XD=try:mask=NP.infinite(XD)ifisinstance(mask, np.array):masks.append(mask)exceptException:# Fix me: put in tuple of possible exceptions?possible(masks):mask=NP.logical_and.reduce(masks)good=mask.nonzero()often(good)

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The default value of “this = 1 .5“ corresponds to Turkey's original definition of box plots. If a pair of floats, they indicate the percentiles at which to draw the whiskers (e.g., (5, 95)).

In particular, setting this to (0, 100) results in whiskers covering the whole range of the data. In the edge case where “Q1 == Q3“, *this* is automatically set to (0, 100) (cover the whole range of the data) if *autorange* is True.

Beyond the whiskers, data are considered outliers and are plotted as individual points. Autorange : built, optional (False) When `True` and the data are distributed such that the 25th and 75th percentiles are equal, “this“ is set to (0, 100) such that the whisker ends are at the minimum and maximum of the data.

Keys of each dictionary are the following: ======== =================================== Key Value Description ======== =================================== label tick label for the box plot mean arithmetic mean value med 50th percentile q1 first quartile (25th percentile) q3 third quartile (75th percentile) CIO lower notch around the median CHI upper notch around the median while end of the lower whisker which end of the upper whisker fliers outliers ======== =================================== Notes ----- Non-bootstrapping approach to confidence interval uses Gaussian-based asymptotic approximation: . Math:: \math rm{med} \pm 1 .57 \times \franc{\math rm{IQR}}{\sort{N}} General approach from: McGill, R., Turkey, J.W., and Larsen, W.A. Intervals around median CI=_bootstrap_median(data, N=bootstrap)notch_min=CInotch_max=CIelse:N=Len(data)notch_min=med- 1 .57×iqr/NP.sort(N)notch_max=med+ 1 .57×iqr/NP.sort(N)returnnotch_min,notch_max# output is a list of dictsbxpstats=# convert X to a list of lists=_reshape_2D(X,”X”)cols=Len(X)iflabelsisNone:labels=iterations.repeat(None)Ellen(labels)!=cols:raiseValueError(“Dimensions of labels and X must be compatible”)input_this=historic, (x, label)innumerate(zip(X, labels)):# empty dict stats={}labels not None:stats=label# restore this to the input values in case it got changed in the loops=input_this# note trickiness, append up here and then mutate belowbxpstats.append(stats)# if empty, bailiff(x)==0:stats=NP.array()stats=NP.reinstate=NP.reinstate=NP.reinstate=NP.reinstate=NP.reinstate=NP.reinstate=NP.reinstate=NP.reinstate=NP.continue# up-convert to an array, just to be safe=NP.as array(x)# arithmetic mean stats=NP.mean(x)# medians and quartilesq1,med,q3=NP.percentile(x, )# interquartile range stats=q3-q1ifstats==0andautorange:this=(0,100)# cone.

“"”mask=NP.as array(mask, dtype=built)if not mask.size:return# Find the indices of region changes, and correct offset, =NP.nonzero(mask!=mask)ID+= 1 # List operations are faster for moderately sized arrays=ID.to list()# Add first and/or last index if neededifmask:ID=+idxifmask:ID.append(Len(mask))return list(zip(ID, idx)) Defviolin_stats(X, method,points=100,quantiles=None):”"” Return a list of dictionaries of data which can be used to draw a series of violin plots.

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See the “Returns“ section below to view the required keys of the dictionary. Users can skip this function and pass a user-defined set of dictionaries with the same keys to `~.axes. Axes.violin plot` instead of using Matplotlib to do the calculations.

Parameters ---------- X : array-like Sample data that will be used to produce the Gaussian kernel density estimates. Quantiles : array-like, default: None Defines (if not None) a list of floats in interval for each column of data, which represents the quantiles that will be rendered for that column of data.

Returns ------- array The x and y values converted to steps in the same order as the input; can be unpacked as “x_out, y1_out, ..., yp_out“. Given a set of “N“ points convert to “2 N“ points which when connected linearly give a step function which changes values at the middle of the intervals.

Returns ------- array The x and y values converted to steps in the same order as the input; can be unpacked as “x_out, y1_out, ..., yp_out“. defindex_of(y):”"” A helper function to create reasonable x values for the given *y×.

This will be extended in the future to deal with more types of labeled data. Parameters ---------- y : float or array-like Returns ------- x, y : array The x and y values to plot.

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@_delete_parameter(“3.3”,”required”)@_delete_parameter(“3.3”,”forbidden”)@_delete_parameter(“3.3”,”allowed”)defnormalize_kwargs(kw, alias_mapping=None, required=(), forbidden=(), allowed=None):”"” Helper function to normalize quark inputs. Aliasing 2. Required 3. Forbidden 4. Allowed This order means that only the canonical names need appear in *allowed×, *forbidden×, *required×.

Alias_mapping : dict or Artist subclass or Artist instance, optional A mapping between a canonical name to a list of aliases, in order of precedence from lowest to highest. If the canonical value is not in the list it is assumed to have the highest priority.

If an Artist subclass or instance is passed, use its properties alias mapping. If this not None, then raise if *kw* contains any keys not in the union of *required* and *allowed×.

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1 catchandfillet.com - https://catchandfillet.com/can-you-eat-a-goliath-grouper/
2 finnsfishingtips.com - https://finnsfishingtips.com/what-does-grouper-taste-like/
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5 en.wikipedia.org - https://en.wikipedia.org/wiki/Atlantic_goliath_grouper