利用Python进行数据分析

class DataFrame(pandas.core.generic.NDFrame)
|  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_csv : from CSV files
|  DataFrame.from_items : from sequence of (key, value) pairs
|  pandas.read_csv, pandas.read_table, pandas.read_clipboard