dddddooc

docs/methods_and_properties.md

@@ -7,30 +7,41 @@ Methods
 Brief
  - `copy(deep=True)` : Return a copy. See also [pandas.DataFrame.copy](
     https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.copy.html) 
- - `copy_empty()` : Return a new DictOfSeries object, with same properties than the original. 
+ - [copy_empty()](#diosdictofseriescopy_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](
     https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.all.html)
  - `any(axis=0)` : Return whether any element is True, potentially over an axis. See also [pandas.DataFrame.any](
     https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.any.html)
- - `squeeze(axis=None)` : Squeeze a 1-dimensional axis objects into scalars. Eg. a 1-column Dios is squeezes to the 
-    underling Series. If `axis=None` it is also tried, to squeeze the possibly returned Series, from the (outer) 
-    Dios-squeeze.
+ - `squeeze(axis=None)` : Squeeze a 1-dimensional axis objects into scalars. 
     See also [pandas.DataFrame.squeeze](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.squeeze.html)
- - `to_df()` : Transform the Dios to a pandas.DataFrame
+ - [to_df()](#diosdictofseriesto_df) : Transform the Dios to a pandas.DataFrame
  - `to_string(kwargs)` : Return a string representation of the Dios.
- - `apply(func, args=(), **kwds)` : apply the given function to every column in the dios eg. 
+ - [apply()](#diosdictofseriesapply) : 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(method='union)`: Return a single(!) Index that is constructed from all the indexes of the columns. 
- - `in`
- - `is`
+ - [index_of()](#diosdictofseriesindex_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.
 
- `copy_empty(columns=True)`
- --------------------------
+   
+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.  
  
@@ -60,8 +71,11 @@ Columns: []
 ```
 
 
-`to_df()`
----------
+dios.DictOfSeries.to_df
+----------------------
+
+`DictOfSeries.to_df()`
+
 Transform the Dios to a pandas.DataFrame. Missing common indices are filled with NaN's.
 
 **Examples**
@@ -90,9 +104,11 @@ columns     a    b     c    d
 10        NaN  NaN   NaN  4.0
 ```
    
+   
 dios.DictOfSeries.apply
--------------------------------------------------
-`apply(func, axis=0, raw=False, args=(), **kwds)` 
+-----------------------
+
+`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. 
@@ -187,21 +203,51 @@ dtype: object
    
 dios.DictOfSeries.index_of
 ---------------------------
-`index_of(method='union)`
+`DictOfSeries.index_of(method='union)`
 
-: return a single(!) Index that is constructed from all the indexes of the columns. 
-   
-   
-   
-   
+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**
    
-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.
+**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')
+```