uproot.interpretation.library.Pandas — Uproot documentation

# uproot.interpretation.library.Pandas

Defined in uproot.interpretation.library on line 813.

Inheritance order:
uproot.interpretation.library.Library

class uproot.interpretation.library.Pandas

  A uproot.interpretation.library.Library that presents `TBranch` data as Pandas Series and DataFrames. The standard name for this library is `"pd"`.

  The single-`TBranch` (with a single `TLeaf`) form for this library is `pandas.Series`, and the “group” form is `pandas.DataFrame`.

  The “group” behavior for this library is:

  - `how=None` or a string: passed to `pandas.merge` as its `how` parameter, which would be relevant if jagged arrays with different multiplicity are requested.

  - `how=dict`: a dict of str → array, mapping the names to `pandas.Series`.

  - `how=tuple`: a tuple of `pandas.Series`, in the order requested. (Names are assigned to the `pandas.Series`.)

  - `how=list`: a list of `pandas.Series`, in the order requested. (Names are assigned to the `pandas.Series`.)

  Pandas Series and DataFrames are indexed, so `global_index` adjusts them.

## imported

Inherited from uproot.interpretation.library.Library.

Pandas.imported

## empty

Inherited from uproot.interpretation.library.Library.

Pandas.empty(shape, dtype)
  Parameters:

    - shape (tuple of int) – NumPy array `shape`. (The first item must be zero.)

    - dtype (`numpy.dtype` or its constructor argument) – NumPy array `dtype`.

  Returns an empty NumPy-like array.

## zeros

Inherited from uproot.interpretation.library.Library.

Pandas.zeros(shape, dtype)
  Parameters:

    - shape (tuple of int) – NumPy array `shape`. (The first item must be zero.)

    - dtype (`numpy.dtype` or its constructor argument) – NumPy array `dtype`.

  Returns a NumPy-like array of zeros.

## finalize

Inherited from uproot.interpretation.library.Library.

Pandas.finalize(array, branch, interpretation, entry_start, entry_stop)
  Parameters:

    - array (array) – Internal, temporary, trimmed array. If this is a NumPy array, it may be identical to the output array.

    - branch (uproot.TBranch) – The `TBranch` that is represented by this array.

    - interpretation (uproot.interpretation.Interpretation) – The interpretation that produced the `array`.

    - entry_start (int) – First entry that is included in the output.

    - entry_stop (int) – FIrst entry that is excluded (one greater than the last entry that is included) in the output.

  Create a library-appropriate output array for this temporary `array`.

  This array would represent one `TBranch` (i.e. not a “group”).

## group

Inherited from uproot.interpretation.library.Library.

Pandas.group(arrays, expression_context, how)
  Parameters:

    - arrays (dict of str → array) – Mapping from names to finalized array objets to combine into a group.

    - expression_context (list of (str, dict) tuples) – Expression strings and a dict of metadata about each.

    - how (None, str, or container type) – Library-dependent instructions for grouping. The only recognized container types are `tuple`, `list`, and `dict`. Note that the container type itself must be passed as `how`, not an instance of that type (i.e. `how=tuple`, not `how=()`).

  Combine the finalized `arrays` into a library-appropriate group type.

## global_index

Inherited from uproot.interpretation.library.Library.

Pandas.global_index(arrays, global_offset)
  Parameters:

    - array (array) – The library-appropriate array whose global index needs adjustment.

    - global_offset (int) – A number to add to the global index of `array` to correct it.

  Apply in-place corrections to the global index of `array` by adding `global_offset`.

  Even though the operation is performed in-place, this method returns the `array`.

## concatenate

Inherited from uproot.interpretation.library.Library.

Pandas.concatenate(all_arrays)
  Parameters:

    all_arrays (list of arrays) – A list of library-appropriate arrays that need to be concatenated.

  Returns a concatenated version of `all_arrays`.