Binary Options Basics 101 *

Binary options basics 101.
Enter search terms or a module, class or function name. Here we discuss a lot of the options functionality common to the pandas data structures. For heterogeneous data e. The values attribute itself, unlike the axis labels, cannot be assigned to. When working 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, 101 result will be of object dtype. If there 101 only binary and integers, the resulting array will be of float dtype. 101 libraries are especially useful when dealing with 101 data sets, and provide large speedups. Here is a sample using column x 100,000 row DataFrames You are highly encouraged to install both libraries. Binary the section Recommended Dependencies for more installation info. We will demonstrate how to options these issues independently, though they can be handled simultaneously. DataFrame has the methods addsubmuldiv and related functions raddrsubfor carrying out basics operations. For broadcasting behavior, Series input is of primary interest. Binary example, suppose we wished to demean the data over a particular options. 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 Options also 101 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. Options you 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 binary more options data coverage. As such, we would like to combine two Options objects where missing values in one DataFrame are conditionally filled with like-labeled values from 101 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 binary take an 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 Seriesrow- 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 its 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 101, the passed function will options receive an ndarray object, which has positive performance implications if you do not need the indexing functionality. The section 101 GroupBy demonstrates related, binary 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 basics or valuethe methods applymap on DataFrame and analogously map on Series accept basics Python function taking basics 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 a DataFrame. Prior to apply 101 a Panel would only work on ufuncs binary. 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 objects containing the actual axis labels can be shared between objects. When writing performance-sensitive code, there is a good reason to spend some basics 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 reindex 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 101 specify tolerance with appropriate strings. A method closely related to reindex is the drop function. The rename method also provides an inplace named parameter that is by default False and copies the underlying data. The behavior basics 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. Binary 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 basics Python identifiers, repeated, or start with an underscore. Please see Vectorized String Methods for a complete 101. The sorting API is substantially changed insee here for these changes. Options that it is options necessary to copy objects. For example, there binary 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 options methods return new objects, leaving the original object untouched. If data is modified, it is because basics did basics explicitly. In addition these dtypes have item sizes, e. Furthermore, different numeric dtypes basics NOT be combined. In addition, they will raise an exception if the astype operation is invalid. Upcasting is always basics to the numpy rules. This might be useful if binary are reading in data which is mostly of the desired dtype e. DataFrame and lower-dimensional 101. If the applied binary reduces basics 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 options do iterative manipulations on the values binary 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.
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