An Improperly Formatted Option Was Encountered While Reading a Point
SettingwithCopyWarning: How to Set This Warning in Pandas
Published: July five, 2017
SettingWithCopyWarning is one of the most common hurdles people run into when learning pandas. A quick spider web search will reveal scores of Stack Overflow questions, GitHub issues and forum posts from programmers trying to wrap their heads effectually what this warning ways in their detail situation. It's no surprise that many struggle with this; there are so many means to index pandas data structures, each with its own particular nuance, and even pandas itself does not guarantee ane unmarried consequence for two lines of lawmaking that may look identical.
This guide explains why the warning is generated and shows you how to solve information technology. It as well includes under-the-hood details to give you a better understanding of what'southward happening and provides some history on the topic, giving y'all perspective on why information technology all works this manner.
In order to explore SettingWithCopyWarning, we're going to utilize a data gear up of the prices of Xboxes sold in 3-solar day auctions on eBay from the book Modelling Online Auctions. Let's take a expect:
import pandas as pd data = pd.read_csv('xbox-3-day-auctions.csv') data.head() As y'all can see, each row of our data gear up concerns a single bid on a specific eBay Xbox auction. Here is a brief clarification of each column:
-
auctionid— A unique identifier of each sale. -
bid— The value of the bid. -
bidtime— The age of the auction, in days, at the fourth dimension of the bid. -
bidder— eBay username of the applicant. -
bidderrate– The applicant'south eBay user rating. -
openbid— The opening bid prepare by the seller for the auction. -
price— The winning bid at the close of the auction.
What is SettingWithCopyWarning?
The first affair to understand is that SettingWithCopyWarning is a warning, and non an mistake.
While an error indicates that something is broken, such equally invalid syntax or an endeavor to reference an undefined variable, the chore of a warning is to alert the programmer to potential bugs or problems with their code that are however permitted operations within the language. In this example, the alarm very likely indicates a serious but inconspicuous mistake.
SettingWithCopyWarning informs you that your operation might not take worked every bit expected and that you should cheque the result to brand certain you haven't made a error.
It can be tempting to ignore the warning if your lawmaking nevertheless works as expected. This is bad exercise and SettingWithCopyWarning should never be ignored. Accept some time to understand why you are getting the alert before taking action.
To sympathise what SettingWithCopyWarning is almost, it's helpful to sympathise that some actions in pandas tin can return a view of your data, and others will return a copy.
As you can see above, the view df2 on the left is just a subset of the original df1, whereas the copy on the right creates a new, unique object df2.
This potentially causes trouble when we endeavour to brand changes:
Depending on what we're doing we might want to exist modifying the original df1 (left), or nosotros might desire to be modifying simply df2 (right). The warning is letting u.s. know that our code may have done i, when we want it to have done the other.
We'll expect at this in depth later, but for now let'due south get to grips with the two main causes of the warning and how to set up them.
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Common consequence #1: Chained consignment
Pandas generates the warning when it detects something called chained assignment. Let's define a few terms nosotros'll exist using to explicate things:
- Assignment — Operations that ready the value of something, for example
information = pd.read_csv('xbox-3-day-auctions.csv'). Often referred to as a set. - Access — Operations that render the value of something, such as the below examples of indexing and chaining. Frequently referred to as a get.
- Indexing — Any assignment or access method that references a subset of the data; for example
information[one:5]. - Chaining — The use of more than than one indexing operation back-to-back; for example
data[1:5][i:3].
Chained assignment is the combination of chaining and assignment. Let's take a quick await at an example with the data set we loaded before. We will go over this in more detail subsequently on. For the sake of this example, allow'southward say that nosotros accept been told that the user 'parakeet2004''s applicant rating is wrong and we must update information technology. Allow'due south beginning by looking at the current values.
data[data.bidder == 'parakeet2004'] We accept three rows to update the bidderrate field on; let'due south go ahead and do that.
data[information.bidder == 'parakeet2004']['bidderrate'] = 100 /Library/Frameworks/Python.framework/Versions/iii.six/lib/python3.6/ipykernel/__main__.py:1: SettingWithCopyWarning: A value is trying to exist set on a copy of a slice from a DataFrame.Try using .loc[row_indexer,col_indexer] = value insteadSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy if __name__ == '__main__': Oh no! We've mysteriously stumbled upon the SettingWithCopyWarning!
If we take a await, we can run across that in this instance the values were not changed:
data[data.bidder == 'parakeet2004'] The warning was generated because we have chained two indexing operations together. This is made easier to spot because we've used square brackets twice, but the same would be true if we used other admission methods such as .bidderrate, .loc[], .iloc[], .ix[] and and then on. Our chained operations are:
-
data[data.bidder == 'parakeet2004'] -
['bidderrate'] = 100
These two chained operations execute independently, one after some other. The offset is an access method (get operation), that volition return a DataFrame containing all rows where bidder equals 'parakeet2004'. The 2d is an consignment operation (prepare performance), that is called on this new DataFrame. We are not operating on the original DataFrame at all.
The solution is elementary: combine the chained operations into a single operation using loc so that pandas tin can ensure the original DataFrame is set. Pandas will always ensure that unchained prepare operations, like the below, work.
# Setting the new value data.loc[data.bidder == 'parakeet2004', 'bidderrate'] = 100 # Taking a look at the result information[data.bidder == 'parakeet2004']['bidderrate'] 6 100 7 100 8 100 Name: bidderrate, dtype: int64 This is what the warning suggests we do, and it works perfectly in this instance.
Moving on to the second most common mode people encounter SettingWithCopyWarning. Permit's investigate winning bids. We volition create a new dataframe to work with them, taking care to use loc going forward now that we have learned our lesson about chained assignment.
winners = data.loc[data.bid == information.price] winners.head() Nosotros might write several subsequent lines of code working with our winners variable.
mean_win_time = winners.bidtime.mean() ... # 20 lines of code mode_open_bid = winners.openbid.style() By chance, we come up across another fault in our DataFrame. This time the bidder value is missing from the row labelled 304.
winners.loc[304, 'bidder'] nan For the sake of our example, let's say that we know the truthful username of this applicant and update our data.
winners.loc[304, 'bidder'] = 'therealname' /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/pandas/core/indexing.py:517: SettingWithCopyWarning: A value is trying to exist assault a re-create of a piece from a DataFrame.Endeavor using .loc[row_indexer,col_indexer] = value insteadSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-re-create self.obj[item] = due south Another SettingWithCopyWarning! Simply nosotros used loc, how has this happened once again? To investigate, let's take a look at the result of our code:
print(winners.loc[304, 'bidder']) therealname Information technology worked this time, so why did nosotros become the alert?
Chained indexing tin can occur across 2 lines also equally within one. Because winners was created as the output of a get operation (information.loc[information.bid == information.cost]), it might be a copy of the original DataFrame or information technology might not be, just until we checked there was no way to know! When we indexed winners, nosotros were actually using chained indexing.
This ways that we may accept also modified data as well when we were trying to modify winners.
In a real codebase, these lines could occur very far autonomously and so tracking down the source of the problem might be more difficult, but the state of affairs is the same.
To prevent the warning in this case, the solution is to explicitly tell pandas to brand a copy when we create the new dataframe:
winners = data.loc[information.bid == data.price].copy() winners.loc[304, 'bidder'] = 'therealname' impress(winners.loc[304, 'applicant']) print(data.loc[304, 'bidder']) therealname nan And that's information technology! It's that simple.
The trick is to larn to identify chained indexing and avoid it at all costs. If you want to change the original, use a single assignment operation. If yous want a copy, make sure y'all strength pandas to do just that. This volition save time and make your code h2o-tight.
Likewise note that fifty-fifty though the SettingWithCopyWarning volition only occur when you are setting, it's best to avoid chained indexing for gets likewise. Chained operations are slower and will cause issues if you lot make up one's mind to add consignment operations after.
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Tips and tricks for dealing with SettingWithCopyWarning
Before we do a much more than in-depth assay down below, let's pull out the microscope and accept a look at some of the effectively points and nitty-gritty details of the SettingWithCopyWarning.
Turning off the alarm
First off, this article would never be complete without discussing how to explicitly control the SettingWithCopy settings. The pandas mode.chained_assignment option tin accept one of the values:
-
'enhance'— to heighten an exception instead of a warning. -
'warn'— to generate a warning (default). -
None— to switch off the alert entirely.
For example, permit's switch off the warning:
pd.set_option('mode.chained_assignment', None) data[information.applicant == 'parakeet2004']['bidderrate'] = 100 Because this gives us no warning whatsoever, information technology'due south not recommended unless you lot have a total grasp of what you are doing. If you feel fifty-fifty the tiniest clue of doubt, this isn't advised. Some developers take SettingWithCopy very seriously and cull to elevate it to an exception instead, similar so:
pd.set_option('mode.chained_assignment', 'raise') data[information.applicant == 'parakeet2004']['bidderrate'] = 100 ---------------------------------------------------------------------------SettingWithCopyError Traceback (about recent call last)<ipython-input-13-80e3669cab86> in <module>() 1 pd.set_option('mode.chained_assignment', 'raise')----> ii information[data.bidder == 'parakeet2004']['bidderrate'] = 100/Library/Frameworks/Python.framework/Versions/3.half-dozen/lib/python3.six/pandas/core/frame.py in __setitem__(self, key, value) 2427 else: 2428 # set column-> 2429 self._set_item(key, value) 2430 2431 def _setitem_slice(cocky, key, value):/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/pandas/cadre/frame.py in _set_item(self, key, value) 2500 # value exception to occur beginning 2501 if len(cocky):-> 2502 self._check_setitem_copy() 2503 2504 def insert(self, loc, column, value, allow_duplicates=False):/Library/Frameworks/Python.framework/Versions/3.six/lib/python3.6/pandas/core/generic.py in _check_setitem_copy(self, stacklevel, t, force) 1758 1759 if value == 'raise':-> 1760 enhance SettingWithCopyError(t) 1761 elif value == 'warn': 1762 warnings.warn(t, SettingWithCopyWarning, stacklevel=stacklevel)SettingWithCopyError: A value is trying to be assault a re-create of a slice from a DataFrame.Endeavor using .loc[row_indexer,col_indexer] = value insteadSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy This can be especially useful if you're working on a projection with inexperienced pandas developers on your team, or a project that requires a high level of rigor or certainty in its integrity.
A more precise way to use this setting is by using a context manager.
# resets the option we fix in the previous lawmaking segment pd.reset_option('style.chained_assignment') with pd.option_context('way.chained_assignment', None): information[data.bidder == 'parakeet2004']['bidderrate'] = 100 Equally you lot can see, this approach enables fine-grained warning suppression, rather than indiscriminately affecting the unabridged surroundings.
The is_copy property
Another play a joke on that can be used to avoid the alarm, is to alter one of the tools pandas uses to interpret a SettingWithCopy scenario. Each DataFrame has an is_copy property that is None by default only uses a weakref to reference the source DataFrame if it's a copy. By setting is_copy to None, you can avoid generating a warning.
winners = information.loc[data.bid == information.price] winners.is_copy = None winners.loc[304, 'bidder'] = 'therealname' However, note this will not miraculously solve the problem, but it does make bug detection potentially very difficult.
Unmarried vs multi-dtyped objects
A further point that is worth stressing is the stardom between single-dtyped and multi-dtyped objects. A DataFrame is single-dtyped if all its columns are of the same dtype; for example:
import numpy as np single_dtype_df = pd.DataFrame(np.random.rand(5,two), columns=list('AB')) print(single_dtype_df.dtypes) single_dtype_df A float64 B float64dtype: object Whereas a DataFrame is multi-dtyped if its columns do not all take the aforementioned dtype, such as:
multiple_dtype_df = pd.DataFrame({'A': np.random.rand(5),'B': list('abcde')}) print(multiple_dtype_df.dtypes) multiple_dtype_df A float64 B object dtype: object For reasons explained in the History section below, an indexer-get performance on a multi-dtyped object will always return a re-create. Nonetheless, mainly for efficiency, an indexer get performance on a unmarried-dtyped object near always returns a view; the caveat here being that this depends on the memory layout of the object and is not guaranteed.
False positives
Simulated positives, or situations where chained assignment is inadvertently reported, used to exist more mutual in earlier versions of pandas simply have since been mostly ironed out. For abyss, it's useful to include some examples here of stock-still simulated positives. If you experience whatever of the situations below with earlier versions of pandas, then the warning can safely be ignored or suppressed (or avoided birthday by upgrading!)
Adding a new column to a DataFrame using a electric current cavalcade's values used to generate a warning, just this has been stock-still.
data['bidtime_hours'] = data.bidtime.map(lambda x: x * 24) data.caput(ii) Until recently, a false positive as well occurred when setting using the apply method on a slice of a DataFrame, although this too has been fixed.
data.loc[:, 'bidtime_hours'] = information.bidtime.apply(lambda x: x * 24) data.head(two) And finally, until version 0.17.0, there was a problems in the DataFrame.sample method that caused spurious SettingWithCopy warnings. The sample method now returns a copy every time.
sample = data.sample(2) sample.loc[:, 'price'] = 120 sample.head() Chained consignment in Depth
Let's reuse our earlier example where we were trying to update the bidderrate column for each row in data with a bidder value of 'parakeet2004'.
data[data.bidder == 'parakeet2004']['bidderrate'] = 100 /Library/Frameworks/Python.framework/Versions/three.vi/lib/python3.6/ipykernel/__main__.py:1: SettingWithCopyWarning: A value is trying to exist set on a copy of a piece from a DataFrame.Effort using .loc[row_indexer,col_indexer] = value insteadSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy if __name__ == '__main__': What pandas is actually telling usa with this SettingWithCopyWarning is that the behavior of our lawmaking is ambiguous, but to empathize why this is and the diction of the alert, it will be helpful to go over a few concepts.
We talked briefly about views and copies earlier. There are two possible means to access a subset of a DataFrame: either one could create a reference to the original data in retention (a view) or copy the subset into a new, smaller DataFrame (a copy). A view is a fashion of looking at a particular portion the original data, whereas a copy is a clone of that data to a new location in memory. As our diagram earlier showed, modifying a view will modify the original variable merely modifying a copy will not.
For reasons that nosotros volition go into later on, the output of 'become' operations in pandas is not guaranteed. Either a view or a copy could exist returned when you index a pandas data structure, which means go operations on a DataFrame return a new DataFrame that tin incorporate either:
- A copy of data from the original object.
- A reference to the original object'southward information without making a copy.
Because we don't know what will happen and each possibility has very dissimilar beliefs, ignoring the warning is playing with fire.
To illustrate views, copies and this ambiguity more clearly, let's create a uncomplicated DataFrame and index it:
df1 = pd.DataFrame(np.arange(6).reshape((iii,ii)), columns=list('AB')) df1 And let'southward assign a subset of df1 to df2:
df2 = df1.loc[:one] df2 Given what we have learned, we know that df2 could be a view on df1 or a copy of a subset of df1.
Earlier we can get to grips with our problem, we also need to take some other await at chained indexing. Expanding on our case with 'parakeet2004', nosotros have chained together two indexing operations:
information[information.bidder == 'parakeet2004'] __intermediate__['bidderrate'] = 100 Where __intermediate__ represents the output of the first call and is completely subconscious from u.s.a.. Remember that we would become the same problematic outcome if we had used aspect access:
data[information.bidder == 'parakeet2004'].bidderrate = 100 The same applies to any other form of chained call because nosotros are generating this intermediate object.
Nether the hood, chained indexing means making more than one call to __getitem__ or __setitem__ to accomplish a unmarried operation. These are special Python methods that are invoked past the utilize of square brackets on an instance of a class that implements them, an case of what is called syntactic sugar. Permit'due south look at what the Python interpreter will execute in our example.
# Our code data[data.applicant == 'parakeet2004']['bidderrate'] = 100 # Lawmaking executed data.__getitem__(data.__getitem__('bidder') == 'parakeet2004').__setitem__('bidderrate', 100) Equally you may take realized already, SettingWithCopyWarning is generated equally a result of this chained __setitem__ call. You lot tin can attempt this for yourself – the lines above function identically. For clarity, note that the second __getitem__ call (for the applicant column) is nested and not at all function of the chaining trouble here.
In full general, every bit discussed, pandas does not guarantee whether a get operation will return a view or a re-create of the information. If a view is returned in our example, the second expression in our chained assignment will be a telephone call to __setitem__ on the original object. But, if a copy is returned, it's the copy that will be modified instead – the original object does non go modified.
This is what the warning means by "a value is trying to be assault a copy of a slice from a DataFrame". As there are no references to this copy, it will ultimately be garbage nerveless. The SettingWithCopyWarning is letting us know that pandas cannot determine whether a view or a copy was returned by the commencement __getitem__ call, and so it's unclear whether the assignment changed the original object or not. Another style to think most why pandas gives us this warning is because the answer to the question "are we modifying the original?" is unknown.
We practise want to alter the original, and the solution that the alarm suggests is to convert these two dissever, chained operations into a unmarried consignment functioning using loc. This volition remove chained indexing from our lawmaking and nosotros volition no longer receive the warning. Our fixed code and its expanded version will look like this:
# Our code data.loc[data.bidder == 'parakeet2004', 'bidderrate'] = 100 # Code executeddata.loc.__setitem__((data.__getitem__('bidder') == 'parakeet2004', 'bidderrate'), 100) Our DataFrame's loc holding is guaranteed to be the original DataFrame itself but with expanded indexing capabilities.
False negatives
Using loc doesn't end our issues because go operations with loc tin withal render either a view or a re-create. Let'south quickly examine a somewhat convoluted example.
data.loc[data.applicant == 'parakeet2004', ('bidderrate', 'bid')] We've pulled two columns out this time rather than just the one. Permit's try to set all the bid values.
data.loc[data.applicant == 'parakeet2004', ('bidderrate', 'bid')]['bid'] = 5.0 data.loc[data.bidder == 'parakeet2004', ('bidderrate', 'bid')] No effect and no warning! Nosotros have fix a value on a copy of a slice but it was not detected by pandas – this is a simulated negative. Just considering we accept used loc doesn't mean nosotros can start using chained assignment again. At that place is an quondam, unresolved event on GitHub for this detail bug.
The correct way to practise this is equally follows:
information.loc[data.bidder == 'parakeet2004', 'bid'] = 5.0 data.loc[data.bidder == 'parakeet2004', ('bidderrate', 'bid')] Yous might wonder how someone could peradventure end upward with such a problem in practice, merely it'south easier than yous might await when assigning the results of DataFrame queries to variables as we practise in the adjacent section.
Subconscious chaining
Let's look again at our hidden chaining example from earlier, where nosotros were trying to set the bidder value from the row labelled 304 in our winners variable.
winners = data.loc[data.bid == data.price] winners.loc[304, 'applicant'] = 'therealname' /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/pandas/core/indexing.py:517: SettingWithCopyWarning: A value is trying to be assail a copy of a slice from a DataFrame.Endeavour using .loc[row_indexer,col_indexer] = value insteadSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-re-create self.obj[item] = s We get another SettingWithCopyWarning even though nosotros used loc. This problem can be incredibly confusing as the warning message appears to be suggesting that nosotros do what we accept already done.
Just think about the winners variable. What actually is it? Given that we instantiated it via data.loc[data.bid == data.cost], we cannot know whether it's a view or a copy of our original information DataFrame (because become operations return either a view or a copy). Combining the instantiation with the line that generated the warning makes articulate our mistake.
data.loc[information.bid == information.cost].loc[304, 'applicant'] = 'therealname' /Library/Frameworks/Python.framework/Versions/iii.6/lib/python3.vi/pandas/cadre/indexing.py:517: SettingWithCopyWarning: A value is trying to be set on a re-create of a slice from a DataFrame.Try using .loc[row_indexer,col_indexer] = value insteadSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy self.obj[item] = s We used chained consignment once again, but this time it was broken beyond two lines. Some other way to think about this is to ask the question "does this modify one or ii things?" In our example, the answer is unknown: if winners is a copy and then merely winners is affected just if it's a view both winners and data will show updated values. This situation can occur betwixt lines that are very far autonomously within a script or codebase, making the source of the problem potentially very difficult to runway down.
The intention of the alert here to prevent us from thinking our lawmaking will alter the original DataFrame when information technology won't, or that we're modifying a re-create rather than the original. Delving into old issues on pandas' GitHub repo, you can read the devs explaining this themselves.
How we resolve this trouble depends very much on our own intentions. If we are happy to work with a copy of our original information, the solution is merely to forcefulness pandas to make a copy.
winners = data.loc[information.bid == data.price].copy() winners.loc[304, 'applicant'] = 'therealname' print(data.loc[304, 'bidder']) # Original impress(winners.loc[304, 'bidder']) # Copy nan therealname If, on the other manus, you require that the original DataFrame is updated then you should work with the original DataFrame instead of instantiating other variables with unknown behavior. Our prior code would become:
# Finding the winners winner_mask = data.bid == data.price # Taking a peek information.loc[winner_mask].caput() # Doing analysis mean_win_time = data.loc[winner_mask, 'bidtime'].mean() ... # 20 lines of code mode_open_bid = data.loc[winner_mask, 'openbid'].mode() # Updating the username data.loc[304, 'applicant'] = 'therealname' In more circuitous circumstances, such as modifying a subset of a subset of a DataFrame, instead of using chained indexing 1 can change the slices ane is making via loc on the original DataFrame. For example, you could change our new winner_mask variable above or create a new variable that selected a subset of winners, similar so:
high_winner_mask = winner_mask & (data.price > 150) information.loc[high_winner_mask].head() This technique is more robust to future codebase maintenance and scaling.
History
You might exist wondering why the whole SettingWithCopy trouble can't only be avoided entirely by explicitly specifying indexing methods that return either a view or a re-create rather than creating the confusing situation we find ourselves in. To empathize this, we must expect into pandas' past.
The logic pandas uses to determine whether it returns a view or a copy stems from its use of the NumPy library, which underlies pandas' operation. Views actually entered the pandas lexicon via NumPy. Indeed, views are useful in NumPy considering they are returned predictably. Because NumPy arrays are unmarried-typed, pandas attempts to minimize space and processing requirements by using the most advisable dtype. As a result, slices of a DataFrame that comprise a single dtype can be returned equally a view on a single NumPy array, which is a highly efficient way to handle the performance. However, multi-dtype slices tin't exist stored in the same manner in NumPy then efficiently. Pandas juggles versatile indexing functionality with the ability to apply its NumPy cadre most effectively.
Ultimately, indexing in pandas was designed to be useful and versatile in a way that doesn't exactly marry the functionality of the underlying NumPy arrays at its core. The interaction between these elements of blueprint and office over time has led to a complex ready of rules that determine whether or non a view or a copy tin can be returned. Experienced pandas developers are generally happy with pandas' behaviors because they are comfortable l navigating its indexing behaviors.
Unfortunately for newcomers to the library, chained indexing is almost unavoidable despite not being the intended approach simply because get operations return indexable pandas objects. Furthermore, in the words of Jeff Reback, 1 of the core developers of pandas for several years, "It's simply not possible from a language perspective to observe chain indexing directly; it has to be inferred".
Consequently, the warning was introduced in version 0.xiii.0 near the stop of 2013 as a solution to the silent failure of chained consignment encountered by many developers.
Prior to version 0.12, the 9 indexer was the about popular (in the pandas classification, "indexers" such equally ix, loc and iloc are just constructs that permit objects to be indexed with square brackets only similar arrays, but with special beliefs). But information technology was around this time, in mid-2013, that the pandas project was beginning to gain momentum and catering to novice users was of rise importance. Since this release the loc and iloc indexers accept consequently been preferred for their more explicit nature and easier to interpret usages.
Google Trends: pandas
The SettingWithCopyWarning has connected to evolve after its introduction, was hotly discussed in many GitHub issues for several years, and is fifty-fifty still being updated, but it's here to stay and understanding it remains crucial to becoming a pandas adept.
Wrapping upwardly
The complexity underlying the SettingWithCopyWarning is one of the few rough edges in the pandas library. Its roots are very securely embedded in the library and should not be ignored. In Jeff Reback's own words there "are no cases that I am aware [of] that y'all should actually ignore this warning. … If you lot exercise certain types of indexing it will never work, others it volition work. Yous are really playing with fire."
Fortunately, addressing the warning only requires you to identify chained assignment and fix it. If in that location's but 1 affair to take away from all this, it's that.
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Source: https://www.dataquest.io/blog/settingwithcopywarning/
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