diff --git a/docs/methods_and_properties.md b/docs/methods_and_properties.md
index 9a4b662d29a0dcabc68d4b745c55a047f5a6415b..0be26b467058b4c6ae87b3397ac02a1b7ac0c719 100644
--- a/docs/methods_and_properties.md
+++ b/docs/methods_and_properties.md
@@ -4,37 +4,204 @@ Dios Methods and Properies
Methods
--------
-- `copy()`
-- `copy_empty()`
-- `all()`
-- `any()`
-- `squeeze()`
-- `to_df()`
-- `to_string()`
-- `apply()`
-- `astype()`
-- `isin()`
-- `isna()`
-- `notna()`
-- `dropna()`
-- `memory_usage()`
-- `index_of()`
-- `in`
-- `is`
-- `len(Dios)`
-
-TODO: make brief description table
+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.
+ - `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.
+ 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_string(kwargs)` : Return a string representation of the Dios.
+ - `apply(func, args=(), **kwds)` : 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`
+ - `len(Dios)` : return the number of columns the dios has.
+ `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: []
+```
+
+
+`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
+-------------------------------------------------
+`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](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html)
+
+
+**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
+---------------------------
+`index_of(method='union)`
+
+: return a single(!) Index that is constructed from all the indexes of the columns.
+
+
+
+
+
Properties
----------
-- columns
-- indexes (series of indexes of all series's)
-- lengths (series of lengths of all series's)
-- values (not fully pd-like - np.array of series's values)
-- dtypes
-- itype (see section Itype)
-- empty
-- size
-
-TODO: make brief description table
+- `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.
+