diff --git a/docs/methods_and_properties.md b/docs/methods_and_properties.md
index 0be26b467058b4c6ae87b3397ac02a1b7ac0c719..f8231b169742769f602cee94bda785b3d7f927d3 100644
--- a/docs/methods_and_properties.md
+++ b/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')
+```