doc_methods_and_properties.markdown

Dios Methods and Properies

Methods

Brief

• copy(deep=True) : Return a copy. See also pandas.DataFrame.copy
• copy_empty() : Return a new DictOfSeries object, with same properties than the original.
• all(axis=0) : Return whether all elements are True, potentially over an axis. See also pandas.DataFrame.all
• any(axis=0) : Return whether any element is True, potentially over an axis. See also pandas.DataFrame.any
• squeeze(axis=None) : Squeeze a 1-dimensional axis objects into scalars. See also pandas.DataFrame.squeeze
• to_df() : Transform the Dios to a pandas.DataFrame
• to_string(kwargs) : Return a string representation of the Dios.
• apply() : apply the given function to every column in the dios eg.
• astype() : Cast the data to the given data type.
• isin() : return a boolean dios, that indicates if the corresponding value is in the given array-like
• isna() : Return a bolean array that is True if the value is a Nan-value
• notna() : inverse of isnan()
• dropna() : drop all Nan-values
• index_of(): Return a single(!) Index that is constructed from all the indexes of the columns.
• len(Dios) : return the number of columns the dios has.

Properties

• columns : Column index
• indexes : Series of indexes of columns
• lengths : Series of lengths of columns
• values : A array of length of the columns, with arrays of values, as sub-arrays
• dtypes : Series of dtypes of columns
• itype : The index type the Dios accept
• empty : True if the dios holds no data. Nevertheless the dios can have empty columns.

dios.DictOfSeries.copy_empty

DictOfSeries.copy_empty(columns=True)

Return a new DictOfSeries object, with same properties than the original. If columns=True, the copy will have the same, but empty columns like the original.

Parameter:

• columns : bool, default True

  Function to apply to each column or row.

Examples

>>> d
    a |    b |     c |     d | 
===== | ==== | ===== | ===== | 
0   0 | 2  5 | 4   7 | 6   0 | 
1   7 | 3  6 | 5  17 | 7   1 | 
2  14 | 4  7 | 6  27 | 8   2 | 
3  21 | 5  8 | 7  37 | 9   3 | 
4  28 | 6  9 | 8  47 | 10  4 | 

>>> d.copy_empty()
Empty DictOfSeries
Columns: ['a', 'b', 'c', 'd']

>>> d.copy_empty(columns=False)
Empty DictOfSeries
Columns: []

dios.DictOfSeries.to_df

DictOfSeries.to_df()

Transform the Dios to a pandas.DataFrame. Missing common indices are filled with NaN's.

Examples

>>> d
    a |    b |     c |     d | 
===== | ==== | ===== | ===== | 
0   0 | 2  5 | 4   7 | 6   0 | 
1   7 | 3  6 | 5  17 | 7   1 | 
2  14 | 4  7 | 6  27 | 8   2 | 
3  21 | 5  8 | 7  37 | 9   3 | 
4  28 | 6  9 | 8  47 | 10  4 | 

>>> d.to_df()
columns     a    b     c    d
0         0.0  NaN   NaN  NaN
1         7.0  NaN   NaN  NaN
2        14.0  5.0   NaN  NaN
3        21.0  6.0   NaN  NaN
4        28.0  7.0   7.0  NaN
5         NaN  8.0  17.0  NaN
6         NaN  9.0  27.0  0.0
7         NaN  NaN  37.0  1.0
8         NaN  NaN  47.0  2.0
9         NaN  NaN   NaN  3.0
10        NaN  NaN   NaN  4.0

dios.DictOfSeries.apply

DictOfSeries.apply(func, axis=0, raw=False, args=(), **kwds)

Apply the given function to every column in the dios. This is a very mighty tool to apply functions that are defined on pandas.Series to multiple columns.

Parameters:

• func : function

  Function to apply to each column or row.

• axis : {0 or ‘index’, 1 or ‘columns’}, default 0

  Axis along which the function is applied:

  • 0 or ‘index’: apply function to each column.
  • 1 or ‘columns’: apply function to each row. not implemented

• raw : bool, default False

  Determines if row or column is passed as a Series or ndarray object:

  • False : passes each row or column as a Series to the function.
  • True : the passed function will receive ndarray objects instead. If you are just applying a NumPy reduction function this will achieve much better performance.

• args : tuple

  Positional arguments to pass to func in addition to the array/series.

• **kwds

  Additional keyword arguments to pass as keywords arguments to func.

Returns: Series or DataFrame

• Result of applying func along the given axis of the DataFrame.

See also:

pandas.DataFrame.apply

Examples

>>> d
    a |    b |     c |     d | 
===== | ==== | ===== | ===== | 
0   0 | 2  5 | 4   7 | 6   0 | 
1   7 | 3  6 | 5  17 | 7   1 | 
2  14 | 4  7 | 6  27 | 8   2 | 
3  21 | 5  8 | 7  37 | 9   3 | 
4  28 | 6  9 | 8  47 | 10  4 | 

>>> d.apply(max)
columns
a    28
b     9
c    47
d     4
dtype: int64

>>> d.apply(pd.Series.count)
columns
a    5
b    5
c    5
d    5
dtype: int64

>>> d.apply(pd.Series.value_counts, normalize=True)
      a |      b |       c |      d | 
======= | ====== | ======= | ====== | 
7   0.2 | 7  0.2 | 7   0.2 | 4  0.2 | 
14  0.2 | 6  0.2 | 37  0.2 | 3  0.2 | 
21  0.2 | 5  0.2 | 47  0.2 | 2  0.2 | 
28  0.2 | 9  0.2 | 27  0.2 | 1  0.2 | 
0   0.2 | 8  0.2 | 17  0.2 | 0  0.2 | 

>>> d.apply(lambda s : 'high' if max(s) > 10 else 'low')
columns
a    high
b     low
c    high
d     low
dtype: object

>>> d.apply(lambda s : ('high', max(s)) if min(s) > (max(s)//2) else ('low',max(s)))
     a |       b |      c |      d | 
====== | ======= | ====== | ====== | 
0  low | 0  high | 0  low | 0  low | 
1   28 | 1     9 | 1   47 | 1    4 | 

dios.DictOfSeries.index_of

DictOfSeries.index_of(method='union)

Aggregate indexes of all columns to one index by a given method.

Parameters:

• method : str, default "union"

  Aggregation method

  • 'all' : get all indices from all columns
  • 'shared' : get indices that are present in every columns
  • 'uniques' : get indices that are only present in a single column
  • 'union' : alias for 'all'
  • 'intersection' : alias for 'shared'
  • 'non-uniques' : get indices that are present in more than one column

• axis : {0 or ‘index’, 1 or ‘columns’}, default 0

Returns: pandas.Index

The aggregated Index

Examples

>>> di
    a |      b |      c |     d | 
===== | ====== | ====== | ===== | 
0   0 | 2    5 | 4    7 | 6   0 | 
1   7 | 3    6 | 5   17 | 7   1 | 
2  14 | 4    7 | 6   27 | 8   2 | 
3  21 | 5    8 | 7   37 | 9   3 | 
4  28 | 6    9 | 8   47 | 10  4 | 
5  35 | 7   10 | 9   57 | 11  5 | 
6  42 | 8   11 | 10  67 | 12  6 | 
7  49 | 9   12 | 11  77 | 13  7 | 
8  56 | 10  13 | 12  87 | 14  8 | 
9  63 | 11  14 | 13  97 | 15  9 | 

>>> di.index_of()
RangeIndex(start=0, stop=16, step=1)

>>> di.index_of("shared")
Int64Index([6, 7, 8, 9], dtype='int64')

>>> di.index_of("uniques")
Int64Index([0, 1, 14, 15], dtype='int64')