module mlhelper.table_formula

Short summary

module pyensae.mlhelper.table_formula

Adds functionalities to a dataframe.

source on GitHub

Classes

class	truncated documentation
TableFormula	Extends class :epkg:`pandas:DataFrame` or proposes extensions to existing functions using lambda functions. See …

Properties

property	truncated documentation
_AXIS_NAMES
_AXIS_NUMBERS
_can_fast_transpose	Can we transpose this DataFrame without creating any new array objects.
_constructor
_constructor_expanddim
_data
_info_axis
_is_cached	Return boolean indicating if self is cached or not.
_is_homogeneous_type	Whether all the columns in a DataFrame have the same type. Returns ——- bool See Also …
_is_mixed_type
_is_view	Return boolean indicating if self is view of another array
_obj_with_exclusions	internal compat with SelectionMixin
_selected_obj	internal compat with SelectionMixin
_selection_list
_selection_name	Return a name for myself; This would ideally be called the ‘name’ property, but we cannot conflict with …
_series
_stat_axis
_values	internal implementation
at	Access a single value for a row/column label pair. Similar to loc, in that both provide label-based lookups. …
attrs	Dictionary of global attributes of this dataset.
axes	Return a list representing the axes of the DataFrame. It has the row axis labels and column axis labels as the …
dtypes	Return the dtypes in the DataFrame. This returns a Series with the data type of each column. The result’s …
empty	Indicator whether DataFrame is empty. True if DataFrame is entirely empty (no items), meaning any of the …
flags	Get the properties associated with this pandas object. The available flags are
iat	Access a single value for a row/column pair by integer position. Similar to iloc, in that both provide integer-based …
iloc	Purely integer-location based indexing for selection by position. .iloc[] is primarily integer position based …
loc	Access a group of rows and columns by label(s) or a boolean array. .loc[] is primarily label based, but may …
ndim	Return an int representing the number of axes / array dimensions. Return 1 if Series. Otherwise return 2 if DataFrame. …
shape	Return a tuple representing the dimensionality of the DataFrame. See Also ——– ndarray.shape …
size	Return an int representing the number of elements in this object. Return the number of rows if Series. Otherwise …
style	Returns a Styler object. Contains methods for building a styled HTML representation of the DataFrame. …
T
values	Return a Numpy representation of the DataFrame.

Methods

method	truncated documentation
add_column_index	Changes the index.
add_column_vector	Adds a column knowing its name and a vector of values.
addc	Adds a column knowing its name and a lambda function.
fgroupby	Groups information based on columns defined by lambda functions.
graph_XY
sort	Sorts rows based on the values returned by function_sort.

Documentation

Adds functionalities to a dataframe.

source on GitHub

class pyensae.mlhelper.table_formula.TableFormula(data=None, index: Optional[Axes] = None, columns: Optional[Axes] = None, dtype: Optional[Dtype] = None, copy: bool = False)

Bases: pandas.core.frame.DataFrame

Extends class :epkg:`pandas:DataFrame` or proposes extensions to existing functions using lambda functions. See Extending Pandas.

source on GitHub

property _constructor

Used when a manipulation result has the same dimensions as the original.

add_column_index(index, name=None)

Changes the index.

Parameters

• index – new_index

• name – name of the index

The changes happen inplace.

source on GitHub

add_column_vector(name, values)

Adds a column knowing its name and a vector of values.

Parameters

• name – name of the column

• values – values

The changes happen inplace.

source on GitHub

addc(name, function_value)

Adds a column knowing its name and a lambda function.

Parameters

• name – name of the column

• function_value – function

The changes happen inplace.

source on GitHub

fgroupby(function_key, function_values, columns=None, function_agg=None, function_weight=None)

Groups information based on columns defined by lambda functions.

Parameters

• function_key – defines the key

• function_values – defines the values

• columns – name of the columns, if None, new ones will be created

• function_agg – how to aggregate the data, if None, the default is :epkg:`pandas:DataFrame:sum`.

• function_weight – defines weights, can be None

The function uses columns __key__, __weight__. You should not use these names. Others columns are created __value_{0}__ and __weight_{0}__. All of them are created and removed before returning the result.

Example:

group = table.groupby(lambda v: v["name"],
          [lambda v: v["d_a"]],
          ["sum_d_a"],
          [lambda vec, w: sum(vec) / w],
          lambda v: v["d_b"])

source on GitHub

graph_XY(curves, xlabel=None, ylabel=None, marker=True, link_point=False, title=None, format_date='%Y-%m-%d', legend_loc=0, figsize=None, ax=None)

Parameters

• curves – list of 3-uples (generator for X, generator for Y, label) for some layout, it can also be: (generator for X, generator for Y, generator for labels, label)

• xlabel – label for X axis

• ylabel – label for Y axis

• marker – add a marker for each point

• link_point – link points between them

• title – graph title

• format_date – if X axis is a datetime object, the function will use this format to print dates

• legend_loc – location of the legend

• figsize – size of the figure

• ax – :epkg:`matplotlib:Axis` or None to create a new one

Returns

:epkg:`matplotlib:Axis`

For the legend position, see matplotlib.

Example:

table.graph_XY ( [ [ lambda v: v["sum_a"], lambda v: v["sum_b"], "xy label 1"],
                   [ lambda v: v["sum_b"], lambda v: v["sum_c"], "xy label 2"],
                    ])

source on GitHub

sort(function_sort, reverse=False)

Sorts rows based on the values returned by function_sort.

Parameters

• function_sort – lambda function

• reverse – reverse order

The function creates a column __key__ and removes it later. The changes happen inplace.

source on GitHub