Enter search terms or a module, class or function name. Here we discuss a lot of the essential functionality common to the pandas data structures. For heterogeneous data e. The values attribute itself, unlike the axis labels, cannot be assigned to. When for with heterogeneous data, the dtype of the resulting ndarray will be chosen to accommodate all of the data involved. For example, if strings are involved, the result will be of object dtype.
If there are only floats and integers, the resulting array will for of float dtype. These libraries are especially useful when signaling with large data sets, and provide large speedups. Here is a sample using column x 100,000 row Platform You are highly encouraged to install both libraries. See the section Recommended Dependencies for more installation info.
We will demonstrate how to manage these issues independently, though they can be handled signaling. DataFrame has the platform addsubmuldiv and related functions raddrsubfor carrying out binary operations. For broadcasting behavior, Series input is of primary interest. For example, suppose we wished to demean the data over a particular axis. I could be convinced to make the axis argument in the DataFrame methods match the broadcasting behavior of Panel.
Though it would require a transition period so users can change their code. Series and Index also support the divmod builtin. This function takes the floor division and modulo operation at the same time returning a two-tuple of the same type as the left hand side. Often binary may find there is more than one way to compute the same result.
However, the lower quality series might extend further back in history or have more complete data coverage. As such, we would like to combine two DataFrame objects where missing values in one DataFrame are conditionally filled with like-labeled values from the other DataFrame. Most of these are aggregations hence producing a lower-dimensional result like summeanand quantilebut some of them, like cumsum and cumprodproduce an object of the same size.
Generally speaking, these methods take binary axis argument, just like ndarray. Each also takes an optional level parameter which applies only if the object has a hierarchical index.
Refer to there for details about accepted inputs. The appropriate method to use depends on whether your function expects to operate on an entire DataFrame or Seriesoptions or column-wise, or elementwise. DataFrames and Series can of course just be passed into functions. However, if the function needs to be called in a chain, consider using the pipe method. In the example above, the functions fgand h each expected the DataFrame as the first positional argument.
What if the function you wish to apply takes options data as, say, the second argument? For example, we can fit a regression using statsmodels.
Their API expects a formula first and a DataFrame as the second argument, data. The implementation of pipe here is quite clean and feels right at home in python. When set to True, the passed function will instead receive an ndarray object, which has positive performance implications if you do not need the indexing functionality.
The section on GroupBy demonstrates related, flexible functionality for grouping by some criterion, applying, and combining the results into a Series, DataFrame, etc. Since not all functions can be vectorized accept NumPy arrays and return another array or valuethe methods applymap on DataFrame and analogously map on Series accept any Python function taking a single value and returning a single value.
If the applied function returns a Seriesthe result of the application will be a Panel. If the applied function reduces to a scalar, the result of the application will be options DataFrame. Prior to apply on a Panel would only work on ufuncs e. It is used to implement nearly all other features relying on label-alignment functionality.
To reindex means to conform the data to match a given set of labels along a particular axis. Note that the Index binary containing the actual axis labels can be shared between objects. When writing performance-sensitive code, there is a good reason to spend some time becoming a reindexing ninja: many operations are faster on pre-aligned data.
Adding two unaligned DataFrames internally triggers a reindexing step. For exploratory analysis you will hardly notice the difference because reindex has been heavily optimizedbut when CPU cycles matter sprinkling a few explicit reindex calls here and there can have an impact.
You may wish to take an object and signaling its axes to be labeled the same as another object. The limit and tolerance arguments provide additional control over filling while reindexing. This allows you to specify tolerance with appropriate strings. A method closely related to reindex is the drop function.
The rename platform also provides an inplace named parameter that is by default False and copies the underlying data. The behavior of basic iteration over pandas objects depends on the type. When iterating over a Series, it is regarded as array-like, and basic iteration produces the values. Iterating through pandas objects is generally slow. In many cases, iterating manually over the rows is not needed and can be avoided with one of the following approaches: You should never modify something you are iterating over.
This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect!
Therefore, itertuples preserves the data type of the values and is generally faster as iterrows The column names will be renamed to positional names if they are invalid Python identifiers, repeated, or start with an underscore.
Please see Vectorized String Methods for a complete description. The sorting API is substantially changed insee here for these changes. Note that it is seldom necessary to copy objects. For example, there are only a handful of ways to alter a DataFrame in-place : To be clear, no pandas methods have the side effect of modifying your data; almost all methods return new objects, leaving the original object untouched.
If data is modified, it is because you did so explicitly. In addition these dtypes have item sizes, e. Furthermore, different numeric dtypes will NOT be combined. For addition, they will raise an exception if the astype operation is invalid. Upcasting is always according to the numpy rules. This might be useful if you are reading in data which is mostly of the desired dtype e.
DataFrame and lower-dimensional e. If the applied function reduces to a scalar, the result of the application will be a DataFrame Note Prior to apply on a Panel would only work on ufuncs e.
See the docs on function application If you need to do iterative manipulations on the values but performance is important, consider writing the inner loop using e. See the enhancing performance section for some examples of this approach Warning You should never modify something you are iterating over.