""" Two-dimensional size-mutable, potentially heterogeneous tabular data
structure with labeled axes (rows and columns). Arithmetic operations
align on both row and column labels. Can be thought of as a dict-like
container for Series objects. The primary pandas data structure
Parameters
----------
data : numpy ndarray (structured or homogeneous), dict, or DataFrame
    Dict can contain Series, arrays, constants, or list-like objects
index : Index or array-like
    Index to use for resulting frame. Will default to np.arange(n) if
    no indexing information part of input data and no index provided
columns : Index or array-like
    Column labels to use for resulting frame. Will default to
    np.arange(n) if no column labels are provided
dtype : dtype, default None
    Data type to force, otherwise infer
copy : boolean, default False
    Copy data from inputs. Only affects DataFrame / 2d ndarray input
Examples
--------
>>> d = {'col1': ts1, 'col2': ts2}
>>> df = DataFrame(data=d, index=index)
>>> df2 = DataFrame(np.random.randn(10, 5))
>>> df3 = DataFrame(np.random.randn(10, 5),
...                 columns=['a', 'b', 'c', 'd', 'e'])
See also
--------
DataFrame.from_records : constructor from tuples, also record arrays
DataFrame.from_dict : from dicts of Series, arrays, or dicts
DataFrame.from_items : from sequence of (key, value) pairs
pandas.read_csv, pandas.read_table, pandas.read_clipboard
"""
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