I should warn you that any refund will take account of the use you have had of the item prior to it breaking down. We wire the sternum back together and in most cases, the bone knits back together just like any other broken bone.
When slicing, both the start bound AND the stop bound are included , if present in the index. Integers are valid labels, but they refer to the label and not the position. The following are valid inputs:. For getting a cross section using a label equivalent to df.
For getting a value explicitly equivalent to deprecated df. If at least one of the two is absent, but the index is sorted, and can be compared against start and stop labels, then slicing will still work as expected, by selecting labels which rank between the two:. However, if at least one of the two is absent and the index is not sorted, an error will be raised since doing otherwise would be computationally expensive, as well as potentially ambiguous for mixed type indexes.
For instance, in the above example, s. Pandas provides a suite of methods in order to get purely integer based indexing. The semantics follow closely Python and NumPy slicing.
These are 0-based indexing. When slicing, the start bounds is included , while the upper bound is excluded. Trying to use a non-integer, even a valid label will raise an IndexError. For getting a cross section using an integer position equiv to df.
Note that using slices that go out of bounds can result in an empty axis e. A single indexer that is out of bounds will raise an IndexError. A list of indexers where any element is out of bounds will raise an IndexError. The callable must be a function with one argument the calling Series, DataFrame or Panel and that returns valid output for indexing.
You can use callable indexing in Series. This has caused quite a bit of user confusion over the years. Here we will select the appropriate indexes from the index, then use label indexing. This can also be expressed using. For getting multiple indexers, using.
In prior versions, using. This behavior is deprecated and will show a warning message pointing to this section. The recommended alternative is to use. The idiomatic way to achieve selecting potentially not-found elmenents is via.
See also the section on reindexing. Alternatively, if you want to select only valid keys, the following is idiomatic and efficient; it is guaranteed to preserve the dtype of the selection.
Having a duplicated index will raise for a. A random selection of rows or columns from a Series, DataFrame, or Panel with the sample method. By default, sample will return each row at most once, but one can also sample with replacement using the replace option:.
By default, each row has an equal probability of being selected, but if you want rows to have different probabilities, you can pass the sample function sampling weights as weights. These weights can be a list, a NumPy array, or a Series, but they must be of the same length as the object you are sampling.
Missing values will be treated as a weight of zero, and inf values are not allowed. If weights do not sum to 1, they will be re-normalized by dividing all weights by the sum of the weights. When applied to a DataFrame, you can use a column of the DataFrame as sampling weights provided you are sampling rows and not columns by simply passing the name of the column as a string.
In the Series case this is effectively an appending operation. A DataFrame can be enlarged on either axis via. This is like an append operation on the DataFrame. Since indexing with  must handle a lot of cases single-label access, slicing, boolean indexing, etc. If you only want to access a scalar value, the fastest way is to use the at and iat methods, which are implemented on all of the data structures.
Similarly to loc , at provides label based scalar lookups, while, iat provides integer based lookups analogously to iloc. Another common operation is the use of boolean vectors to filter the data. These must be grouped by using parentheses, since by default Python will evaluate an expression such as df. List comprehensions and map method of Series can also be used to produce more complex criteria:. With the choice methods Selection by Label , Selection by Position , and Advanced Indexing you may select along more than one axis using boolean vectors combined with other indexing expressions.
Consider the isin method of Series , which returns a boolean vector that is true wherever the Series elements exist in the passed list. This allows you to select rows where one or more columns have values you want:.
In addition to that, MultiIndex allows selecting a separate level to use in the membership check:. DataFrame also has an isin method. When calling isin , pass a set of values as either an array or dict. If values is an array, isin returns a DataFrame of booleans that is the same shape as the original DataFrame, with True wherever the element is in the sequence of values.
Just make values a dict where the key is the column, and the value is a list of items you want to check for. To select a row where each column meets its own criterion:. Selecting values from a Series with a boolean vector generally returns a subset of the data. To guarantee that selection output has the same shape as the original data, you can use the where method in Series and DataFrame. Selecting values from a DataFrame with a boolean criterion now also preserves input data shape.
The code below is equivalent to df. In addition, where takes an optional other argument for replacement of values where the condition is False, in the returned copy. By default, where returns a modified copy of the data.
There is an optional parameter inplace so that the original data can be modified without creating a copy:. The signature for DataFrame. Furthermore, where aligns the input boolean condition ndarray or DataFrame , such that partial selection with setting is possible.
This is analogous to partial setting via. Where can also accept axis and level parameters to align the input when performing the where.
Where can accept a callable as condition and other arguments. The function must be with one argument the calling Series or DataFrame and that returns valid output as condition and other argument. DataFrame objects have a query method that allows selection using an expression. You can get the value of the frame where column b has values between the values of columns a and c.
Do the same thing but fall back on a named index if there is no column with the name a. If the name of your index overlaps with a column name, the column name is given precedence.
You can also use the levels of a DataFrame with a MultiIndex as if they were columns in the frame:. If the levels of the MultiIndex are unnamed, you can refer to them using special names:. Note that in and not in are evaluated in Python, since numexpr has no equivalent of this operation.
For example, in the expression. In general, any operations that can be evaluated using numexpr will be. You will only see the performance benefits of using the numexpr engine with DataFrame. This plot was created using a DataFrame with 3 columns each containing floating point values generated using numpy.
If you want to identify and remove duplicate rows in a DataFrame, there are two methods that will help: Each takes as an argument the columns to use to identify duplicated rows. By default, the first observed row of a duplicate set is considered unique, but each method has a keep parameter to specify targets to be kept.
To drop duplicates by index value, use Index. The same set of options are available for the keep parameter. Each of Series, DataFrame, and Panel have a get method which can return a default value. Sometimes you want to extract a set of values given a sequence of row labels and column labels, and the lookup method allows for this and returns a NumPy array. The pandas Index class and its subclasses can be viewed as implementing an ordered multiset.
However, if you try to convert an Index object with duplicate entries into a set , an exception will be raised. Index also provides the infrastructure necessary for lookups, data alignment, and reindexing. The easiest way to create an Index directly is to pass a list or other sequence to Index:. You can also pass a name to be stored in the index:. See Advanced Indexing for usage of MultiIndexes. These can be directly called as instance methods or used via overloaded operators.
Difference is provided via the. This is equivalent to the Index created by idx1. Even though Index can hold missing values NaN , it should be avoided if you do not want any unexpected results. For example, some operations exclude missing values implicitly. There are a couple of different ways. To create a new, re-indexed DataFrame:. The append keyword option allow you to keep the existing index and append the given columns to a MultiIndex:.
The output is more similar to a SQL table or a record array. The names for the columns derived from the index are the ones stored in the names attribute. You can use the level keyword to remove only a portion of the index:. If you create an index yourself, you can just assign it to the index field:. When setting values in a pandas object, care must be taken to avoid what is called chained indexing.
Here is an example. These both yield the same results, so which should you use? It is instructive to understand the order of operations on these and why method 2. Contrast this to df. This allows pandas to deal with this as a single entity.
Furthermore this order of operations can be significantly faster, and allows one to index both axes if so desired. The problem in the previous section is just a performance issue.
But it turns out that assigning to the product of chained indexing has inherently unpredictable results. To see this, think about how the Python interpreter executes this code:. You may be wondering whether we should be concerned about the loc property in the first example.
If we decide that a member is misusing returns on eBay, or the eBay Money Back Guarantee, they may be subject to a range of actions, including limits on buying and selling privileges and account suspension. Examples of misuse include:. Why was my refund less than the amount I paid? Depending on the condition you sent the item back in, the seller may reduce the refund they issue you.
For more details, please see our Condition of returned items policy. What should I do if I sent back the item, but the seller didn't receive it? When returning an item you should always use tracked shipping.
If you didn't, and think the seller should have gotten the item back by now, you can ask us to step in and help. If I don't receive a return shipping label, how should I return the item? When returning an item you should always use tracked shipping to maintain proof you returned the item to the seller which ensures your refund. Learn more about return shipping for buyers. Can I return an item if my account is suspended?
Can I return more than one item from my order? Depending on the listing, you can return one item or multiple items.
Here are your options:. If you don't agree with our decision after we've stepped in to help resolve an issue between you and a seller, you can appeal by providing new information within 30 days of the case being closed. If you didn't get your item, you're covered under the eBay Money Back Guarantee.
We'll make sure you either receive the item you ordered, or get your money back. Skip to main content. Start a return After you've started your return, what happens next? To improve your help experience, please sign in to your account. Are you a seller and need help with a return request? You received the wrong item, or it arrived faulty or damaged.
Here's how the seller may respond: When the seller replies, we'll send you an email with details of the next steps. Frequently asked questions about returns Why was my refund less than the amount I paid? Here are your options: Lots, sets, and bundled items — You need to return the entire quantity of the order.
For example, if you bought a set of paintbrushes you can't return just one of them. Multi-quantity listings — You can return any quantity of your purchased items.
For example, you selected a quantity of five t-shirts at checkout. You can return some or all of them, but you only have one opportunity to do so. If you choose to return two t-shirts, you can't return the other three later. Multiple listings from the same seller — You can return each item. For example, you buy three books that the seller listed individually, but you paid for all of them at once at checkout.
You can return each book individually. Top Takeaway If the item you received doesn't match the listing description, or if it arrived faulty or damaged, you can return it even if the seller's returns policy says they don't accept returns. Was this article helpful for you?
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