Metadata-Version: 2.1
Name: portable-spreadsheet
Version: 2.1.5
Summary: A simple spreadsheet that keeps tracks of each operation of each cell in defined languages. Logic allows exporting sheets to Excel files (and see how each cell is computed), to the JSON strings with a description of computation of each cell (e. g. in the native language). Other formats, like HTML, CSV and Markdown (MD), are also implemented (user can define own format). It also allows reconstructing behaviours in native Python with NumPy.
Home-page: https://github.com/david-salac/Portable-spreadsheet-generator
Author: David Salac
Author-email: info@davidsalac.eu
License: UNKNOWN
Description: # Simple Portable Python Spreadsheet Generator
        Author: David Salac <https://github.com/david-salac>
        
        Project website: Portable Spreadsheet <https://portable-spreadsheet.com/>
        
        A simple spreadsheet that keeps tracks of each operation of each cell
        in defined languages. Logic allows exporting sheets to Excel files (and
        see how each cell is computed), to the JSON strings with a description
        of computation of each cell (e. g. in the native language). Other
        formats, like HTML, CSV and Markdown (MD), are also implemented (user
        can define own format). It also allows reconstructing behaviours in
        native Python with NumPy. The sheets can be easily created and handled
        in a way similar to Pandas DataFrame.
        
        ## Key components of the library
        There are five main objects in the library:
        
        1. **_Grammar_**: the set of rule that defines language.
        2. **_Cell_**: single cell inside the spreadsheet.
        3. **_Word_**: the word describing how the cell is created in each language.
        4. **_Sheet_**: a set of cells with a defined shape.
        5. **_Cell slice_**: a subset of the spreadsheet. 
        6. **_Work book_**: a set of multiple sheets.
        
        ### Grammar
        The grammar defines a context-free language (by Chomsky hierarchy). It is
        used for describing each operation that is done with the cell. The typical
        world is constructed using prefix, suffix and actual value by creating a
        string like "PrefixValueSuffix". Each supported operation is defined in
        grammar (that tells how the word is created when the operation is called).
        
        There are two system languages (grammars): Python and Excel. There is also
        one language "native" that describes operations in a native tongue logic.
        
        #### Adding the new grammar
        Operations with grammars are encapsulated in the class `GrammarUtils`.
        
        Grammar has to be defined as is described in the file `grammars.py` in the
        global variable `GRAMMAR_PATTERN`. It is basically the dictionary matching
        the description.
        
        To validate the grammar (in the variable `grammar`) use: 
        ```python
        is_valid: bool = GrammarUtils.validate_grammar(grammar)
        ```
        To add the grammar describing some language (in the variable `language`)
        to the system (in the variable `grammar`) use: 
        ```python
        GrammarUtils.add_grammar(grammar, language)
        ```
        
        User can also check what languages are currently available using the
        static method `get_languages`:
        ```python
        languages_in_the_system: Set[str] = GrammarUtils.get_languages()
        ```
        ### Cells
        It represents the smallest element in the spreadsheet. Cell encapsulates basic
        arithmetic and logical operations that are needed. A cell is represented by
        the class of the same name `Cell`. It is highly recommended not to use
        this class directly but only through the spreadsheet instance.
        
        Currently, the supported operations are described in the subsections
        _Computations_ bellow in this document (as all that unary, binary and other
        functions).
        
        The main purpose of the cell is to keep the value (the numerical result of
        the computation) and the word (how is an operation or constant represented
        in all languages).
        
        ### Words
        Word represents the current computation or value of the cell using in given
        languages.
        
        A typical example of the word can be (in language excel):
        ```python
        B2*(C1+C2)
        ```
        The equivalent word in the language Python:
        ```python
        values[1,1]*(values[0,2]+values[1,2])
        ```
        Words are constructed using prefixes and suffixes defined by the grammar.
        Each language also has some special features that are also described in
        the grammar (like whether the first index represents column or a row).
        
        Words are important later when the output is exported to some file in given
        format or to JSON.
        Operations with words (and word as a data structure) are located in the
        class `WordConstructor`. It should not be used directly.
        
        ### Sheet class
        The Sheet is the most important class of the whole package. It is
        located in the file `spreadsheet.py`. It encapsulates the functionality
        related to accessing cells and modifying them as well as the functionality
        for exporting of the computed results to various formats.
        
        Class is strongly motivated by the API of the Pandas DataFrame. 
        
        The functionality of Sheet class is documented in a special section below.
        
        ### Cell slice
        Represents the special object that is created when some part slice of the
        spreadsheet is created. Basically, it encapsulates the set of cells and
        aggregating operations (sum, product, minimum, maximum, average). For example:
        ```python
        some_slice = spreadsheet_instance.iloc[1,:]
        average_of_slice = some_slice.mean()
        ```
        selected the second row in the spreadsheet and compute the average (mean)
        of values in the slice.
        
        Cell slice is represented in the class `CellSlice` in the
        file `cell_slice.py`.
        
        If you want to assign some value to a `CellSlice` object, you can use
        overloaded operator `<<=`
        ```python
        some_slice = spreadsheet_instance.iloc[1,:]
        average_of_slice <<= 55.6  # Some assigned value
        ```
        However, it is strongly recommended to use standard assigning through
        the Sheet object described below.
        
        #### Functionality of the CellSlice class
        Cell slice is mainly related to the aggregating functions described in
        the subsection _Aggregate functions_ bellow.
        
        There is also a functionality related to setting the values to some
        constant or reference to another cell. This functionality should not
        be used directly.
        
        Cell slices can be exported in the same way as a whole spreadsheet (methods
        are discussed below).
        
        ## Sheet functionality
        All following examples expect that user has already imported package.
        ```python
        import portable_spreadsheet as ps
        ```
        The default (or system) languages are Excel and Python. There is also
        a language called 'native' ready to be used.
        
        ### How to create a spreadsheet
        The easiest function is to use the built-in static method `create_new_sheet`:
        
        ```python
        sheet = ps.Sheet.create_new_sheet(
            number_of_rows, number_of_columns, [rows_columns]
        )
        ```
        if you wish to include some user-defined languages or the language
        called 'native' (which is already in the system), you also need to
        pass the argument `rows_columns` (that is a dictionary with keys as
        languages and values as lists with column names in a given non-system
        language).
        
        For example, if you choose to add _'native'_ language (already available in
        grammars), you can use a shorter version:
        
        ```python
        sheet = ps.Sheet.create_new_sheet(
            number_of_rows, number_of_columns,
            {
                "native": cell_indices_generators['native'](number_of_rows,
                                                            number_of_columns),
            },
            name='Sheet Name'
        )
        ```
        
        Other (keywords) arguments:
        1. `name (str)`: Name of the sheet.
        2. `rows_labels (List[Union[str, SkippedLabel]])`: _(optional)_ List of masks
        (aliases) for row names.
        3. `columns_labels (List[Union[str, SkippedLabel]])`: _(optional)_ List of
        masks (aliases) for column names. If the instance of SkippedLabel is
        used, the export skips this label.
        4. `rows_help_text (List[str])`: _(optional)_ List of help texts for each row.
        5. `columns_help_text (List[str])`: _(optional)_ List of help texts for each
        column. If the instance of SkippedLabel is used, the export skips this label.
        6. `excel_append_row_labels (bool)`: _(optional)_ If True, one column is added
        on the beginning of the sheet as a offset for labels.
        7. `excel_append_column_labels (bool)`: _(optional)_ If True, one row is
        added on the beginning of the sheet as a offset for labels.
        8. `warning_logger (Callable[[str], None]])`: Function that logs the warnings
        (or `None` if logging should be skipped).
        
        First two are the most important because they define labels for the columns
        and rows indices. The warnings mention above occurs when the slices are
        exported (which can lead to data losses).
        
        #### How to change the size of the spreadsheet
        You can only expand the size of the spreadsheet (it's because of the
        built-in behaviour of language construction). We, however, strongly recommend
        not to do so. Simplified logic looks like:
        ```python
        # Append 7 rows and 8 columns to existing sheet:
        sheet.expand(
            7, 8,  
            {
                "native": ([...], [...])  # Fill 8 new values for rows, columns here
            }
        )
        ```
        Parameters of the `Sheet.expand` method are of the same
        logic and order as the parameters of `Sheet.create_new_sheet`.
        
        ### Column and row labels
        Labels are set once when a sheet is created (or expanded in size). If you
        want to read them as a tuple of labels, you can use the following properties:
        
        * `columns`: property that returns labels of columns as a tuple of strings.
        It can be called on both slices or directly on `Sheet` class instances.
        * `index`: property that returns the labels of rows as a tuple of strings.
        It can be called on both slices or directly on `Sheet` class instances.
        
        Example:
        ```python
        column_labels: Tuple[str] = sheet.columns  # Get the column labels
        row_labels: Tuple[str] = sheet.index  # Get the row labels
        ```
        
        ### Shape of the Sheet object
        If you want to know what is the actual size of the spreadsheet, you can
        use the property `shape` that behaves as in Pandas. It returns you the
        tuple with a number of rows and number of columns (on the second position).
        
        ### Accessing/setting the cells in the spreadsheet
        You to access the value in the position you can use either the integer
        position (indexed from 0) or the label of the row/column.
        ```python
        # Returns the value at second row and third column:
        value = sheet.iloc[1,2]
        # Returns the value by the name of the row, column
        value = sheet.loc['super the label of row', 'even better label of column']
        ```
        As you can see, there are build-in properties `loc` and `iloc` for accessing
        the values (the `loc` access based on the label, and `iloc` access the cell
        based on the integer position).
        
        The same logic can be used for setting-up the values:
        ```python
        # Set the value at second row and third column:
        sheet.iloc[1,2] = value
        # Set the value by the name of the row, column
        sheet.loc['super the label of row', 'even better label of column'] = value
        ```
        where the variable `value` can be either some constant (string, float or
        created by the `fn` method described below) or the result of some
        operations with cells:
        ```python
        sheet.iloc[1,2] = sheet.iloc[1,3] + sheet.iloc[1,4]
        ```
        In the case that you want to assign the result of some operation (or just
        reference to another cell), make sure that it does not contains any reference
        to itself (coordinates where you are assigning). It would not work
        correctly otherwise.
        
        ### Variables
        Variable represents an imaginary entity that can be used for computation if 
        you want to refer to something that is common for the whole spreadsheet. 
        Technically it is similar to variables in programming languages.
        
        Variables are encapsulated in the property `var` of the class `Sheet`. 
        
        It provides the following functionality:
        
        1. **Setting the variable**, method `set_variable` with parameters: `name` 
        (a lowercase alphanumeric string with underscores), `value` 
        (number or string), and `description` (optional) that serves as a help text.
        2. **Get the variable dictionary**, property `variables_dict`, returns 
        a dictionary with variable names as keys and variable values and descriptions
        as values → following the logic: `{'VARIABLE_NAME': {'description':
        'String value or None', 'value': 'VALUE'}}`.
        3. **Check if the variable exists in a system**, method `variable_exist` with
        a parameter `name` representing the name of the variable. 
        Return true if the variable exists, false otherwise.
        4. **Get the variable as a Cell object**, method `get_variable`, with
        parameter `name` (required as positional only) that returns the variable as a
        Cell object (for computations in a sheet).
        5. **Check if there is any variable in the system**: using the property `empty`
        that returns true if there is no variable in the system, false otherwise. 
        
        To get (and set similarly) the variable as a cell object, you can also use
        the following approach with square brackets:
        ```python
        sheet.iloc[i, j] = sheet.var['VARIABLE_NAME']
        ```
        Same approach can be used for setting the value of variable:
        ```python
        sheet.var['VARIABLE_NAME'] = some_value
        ```
        Getting/setting the variables values should be done preferably by this logic.
        
        For defining Excel format/style of the variable value, use the attribute
        `excel_format` of the `var` property in the following logic:
        ```python
        sheet.var['VARIABLE_NAME'].excel_format = {'num_format': '#,##0'} 
        ```
        
        #### Example
        Following example multiply some cell with value of
        PI constant stored as a variable `pi`:
        ```python
        sheet.set_variable(pi, 3.14159265359)
        sheet.iloc[i,j] = sheet.var['pi'] * sheet.iloc[x,y]
        ```
        
        ### Working with slices
        Similarly, like in NumPy or Pandas DataFrame, there is a possibility
        how to work with slices (e. g. if you want to select a whole row, column
        or set of rows and columns). Following code, select the third column:
        ```python
        sheet.iloc[:,2]
        ```
        On the other hand
        ```python
        sheet.loc[:,'Handy column']
        ``` 
        selects all the rows in the columns with the label _'Handy column'_. 
        
        You can again set the values in the slice to some constant, or the array
        of constants, or to another cell, or to the result of some computation.
        ```python
        sheet.iloc[:,2] = constant  # Constant (float, string)
        sheet.iloc[:,2] = sheet.iloc[1,3] + sheet.iloc[1,4]  # Computation result
        sheet.iloc[:,2] = sheet.iloc[1,3]  # Just a reference to a cell
        ```
        Technically the slice is the instance of `CellSlice` class.
        
        There are two ways how to slice, either using `.loc` or `.iloc` attribute.
        Where `iloc` uses integer position and `loc` uses label of the position
        (as a string).
        
        By default the right-most value is excluded when defining slices. If you want
        to use right-most value indexing, use one of the methods described below.
        
        #### Slicing using method (with the right-most value included option)
        Sometimes, it is quite helpful to use a slice that includes the right-most
        value. There are two functions for this purpose:
        1. `sheet.iloc.get_slice(ROW_INDEX, COLUMN_INDEX, include_right=[True/False])`:
        This way is equivalent to the one presented above with square brackets `[]`.
        The difference is the key-value attribute `include_right` that enables the
        possibility of including the right-most value of the slice (default value is
        False). If you want to use slice as your index, you need to pass some `slice`
        object to one (or both) of the indices. For example: 
        `sheet.iloc.get_slice(slice(0, 7), 3, include_right=True])` selects first nine
        rows (because 8th row - right-most one - is included) from the fourth column
        of the sheet _(remember, all is indexed from zero)_.
        
        2. `sheet.iloc.set_slice(ROW_INDEX, COLUMN_INDEX, VALUE, 
        include_right=[True/False])`: this command set slice to _VALUE_ in the similar
        logic as when you call `get_slice` method (see the first point).
        
        There are again two possibilities, either to use `iloc` with integer position
        or to use `loc` with labels.
        
        #### Aggregate functions
        The slice itself can be used for computations using aggregate functions.
        
        1. **Sum**: return the sum of the cells in the slice. 
            For example: SUM(7, 8, 9) = 25.
            Available as the function `sum` called on the slice object.
            Usage: `sheet.iloc[i,j] = sheet.iloc[p:q,x:y].sum()`
        2. **Product**: return the product of the cells in the slice. 
            For example: PROD(7, 8, 9) = 504.
            Available as the function `product` called on the slice object.
            Usage: `sheet.iloc[i,j] = sheet.iloc[p:q,x:y].product()`
        3. **Minimum**: return the minimum of the cells in the slice. 
            For example: MIN(7, 8, 9) = 7.
            Available as the function `min` called on the slice object.
            Usage: `sheet.iloc[i,j] = sheet.iloc[p:q,x:y].min()`
        4. **Maximum**: return the maximum of the cells in the slice. 
            For example: MAX(7, 8, 9) = 9.
            Available as the function `max` called on the slice object.
            Usage: `sheet.iloc[i,j] = sheet.iloc[p:q,x:y].max()`
        5. **Mean-average**: return the arithmetic mean of the cells in the slice. 
            For example: MEAN(7, 8, 9) = 8.
            Available as the function `mean` and `average` called on the slice object.
            Usage: `sheet.iloc[i,j] = sheet.iloc[p:q,x:y].mean()` or 
            `sheet.iloc[i,j] = sheet.iloc[p:q,x:y].average()` 
        6. **Standard deviation**: return the standard deviation of the cells in the
        slice. 
            For example: STDEV(7, 8, 9) = 1.
            Available as the function `stdev` called on the slice object.
            Usage: `sheet.iloc[i,j] = sheet.iloc[p:q,x:y].stdev()`
        7. **Median**: return the median of the cells in the slice. 
            For example: MEDIAN(7, 8, 9) = 8.
            Available as the function `median` called on the slice object.
            Usage: `sheet.iloc[i,j] = sheet.iloc[p:q,x:y].median()`
        8. **Count**: return the number of the cells in the slice. 
            For example: COUNT(7, 8, 9) = 3.
            Available as the function `count` called on the slice object.
            Usage: `sheet.iloc[i,j] = sheet.iloc[p:q,x:y].count()`
        9. **IRR**: return the Internal Rate of Return (IRR) of the cells in the slice. 
            For example: IRR(-100, 0, 0, 74) = -0.0955.
            Available as the function `irr` called on the slice object.
            Usage: `sheet.iloc[i,j] = sheet.iloc[p:q,x:y].irr()`
        10. **Match negative before positive**: return the position of the last
            negative number in a series of negative numbers in the row or column
            series.
            For example:
            MNBP(-100, -90, -80, 5, -500) = 3 _(equals to position of the number -80)_.
            Available as the function `match_negative_before_positive` called on the
            slice object.
            Usage:
            `sheet.iloc[i,j] = sheet.iloc[p:q,x:y].match_negative_before_positive()`
        
        Aggregate functions always return the cell with the result.
        
        All aggregate functions have parameters:
        
        1. `skip_none_cell (bool)`: If true, skips all the cells with `None` as
        a value (and does not raise an exception), if false an exception is raised
        if the slice contains a cell with `None` value (empty cell).
        
        ### Conditional
        There is a support for the conditional statement (aka if-then-else statement).
        Functionality is implemented in the property `fn` of the `Sheet`
        instance in the method `conditional`. It takes three parameters (positional)
        in precisely this order:
        
        1. **Condition**: the cell with a boolean value that is evaluated (typically
        achieved using operators ==, !=, >, <, etc.).
        2. **Consequent**: the cell that is taken when the condition is evaluated as
        true.
        3. **Alternative**:  the cell that is taken when the condition is evaluated as
        false.
        
        All the parameters are the instance of `Cell` class.
        
        ### Linear interpolation
        There is a support for linear interpolation.
        Functionality is implemented in the property `fn` of the `Sheet`
        instance in the method `linear_interpolation`. 
        It takes five parameters in this order:
        
        1. **x_start**: Where is the x coordinate of the start.
        2. **y_start**: Where is the y OR f(x) coordinate of the start.
        3. **x_end**: Where is the x coordinate of the end.
        4. **y_end**: Where is the y OR f(x) coordinate of the end.
        5. **x**: For what value of x are we interpolating.
        
        All the parameters are the instance of `Cell` class.
        
        #### Example of conditional
        Consider the following example that compares whether two cells are equals,
        if yes, it takes some value in a cell, if not, another value in the
        different cell:
        ```python
        sheet.iloc[i,j] = sheet.fn.conditional(
            # Condition is the first parameter:
            sheet.iloc[1,2] == sheet.iloc[2,2],
            # Consequent (value if condition is true) is the second parameter:
            sheet.iloc[3,1],
            # Alternative (value if condition is false) is the third parameter:
            sheet.iloc[4,1]
        )
        ```
        
        ### Raw statement
        The raw statement represents the extreme way how to set-up value and
        computation string of the cell. It should be used only to circumvent
        issues with missing or defective functionality.
        
        The raw statement is accessible using `fn` property of the Sheet class
        object.
        
        The raw statement should never be used unless you really have to.
        
        #### Example of raw statement
        Consider that you need to compute an arccosine value of some cell:
        ```python
        sheet.iloc[i,j] = sheet.fn.raw(
            # Value that should be used as the result (as a Cell instance):
            sheet.fn.const(numpy.arccos(0.7)),
            # Definition of words in each language:
            {
                'python_numpy': "numpy.arccos(0.7)",
                'excel': "ACOS(0.7)"
                # Potentialy some other languages, like 'native', etc.
            }
        )
        ```
        
        ### Offset function
        The offset function represents the possibility of reading the value
        that is shifted by some number rows left, and some number of columns
        down from some referential cells.
        
        It is accessible from the Sheet instance using `fn`
        property and `offset` method. Parameters are following (only
        positional, in exactly this order):
        * **Reference cell**: Reference cell from that the position is computed.
        * **Cell defining a number of rows to be skipped**: How many rows (down)
        should be skipped.
        * **Cell defining a number of columns to be skipped**: How many columns (left)
        should be skipped.
        
        #### Example:
        Following example assign the value of the cell that is on the third row and 
        second column to the cell that is on the second row and second column.
        ```python
        sheet.iloc[1,1] = sheet.fn.offset(
            sheet.iloc[0,0], sheet.fn.const(2), sheet.fn.const(1)
        )
        ```
        
        ### Computations
        All operations have to be done using the objects of type Cell. 
        
        #### Constants
        If you want to use a constant value, you need to create an un-anchored cell
        with this value. The easiest way of doing so is:
        ```python
        # For creating the Cell for computation with constant value 7
        constant_cell = sheet.fn.const(7)
        ```
        The value OPERAND bellow is always the reference to another cell in the
        sheet or the constant created as just described.
        
        #### Unary operations
        There are the following unary operations (in the following the `OPERAND`
        is the instance of the Cell class): 
        
        1. **Ceiling function**: returns the ceil of the input value.
            For example ceil(4.1) = 5.
            Available in the `fn` property of the `sheet` object.
            Usage: `sheet.iloc[i,j] = sheet.fn.ceil(OPERAND)`
        2. **Floor function**: returns the floor of the input value.
            For example floor(4.1) = 4.
            Available in the `fn` property of the `sheet` object.
            Usage: `sheet.iloc[i,j] = sheet.fn.floor(OPERAND)`
        3. **Round function**: returns the round of the input value.
            For example round(4.5) = 5.
            Available in the `fn` property of the `sheet` object.
            Usage: `sheet.iloc[i,j] = sheet.fn.round(OPERAND)`
        4. **Absolute value function**: returns the absolute value of the input value.
            For example abs(-4.5) = 4.5.
            Available in the `fn` property of the `sheet` object.
            Usage: `sheet.iloc[i,j] = sheet.fn.abs(OPERAND)`
        5. **Square root function**: returns the square root of the input value.
            For example sqrt(16) = 4.
            Available in the `fn` property of the `sheet` object.
            Usage: `sheet.iloc[i,j] = sheet.fn.sqrt(OPERAND)`
        6. **Logarithm function**: returns the natural logarithm of the input value.
            For example ln(11) = 2.3978952728.
            Available in the `fn` property of the `sheet` object.
            Usage: `sheet.iloc[i,j] = sheet.fn.ln(OPERAND)`
        7. **Exponential function**: returns the exponential of the input value.
            For example exp(1) = _e_ power to 1 = 2.71828182846.
            Available in the `fn` property of the `sheet` object.
            Usage: `sheet.iloc[i,j] = sheet.fn.exp(OPERAND)`
        8. **Logical negation**: returns the logical negation of the input value.
            For example neg(false) = true.
            Available as the overloaded operator `~`.
            Usage: `sheet.iloc[i,j] = ~OPERAND`.
            _Also available in the `fn` property of the `sheet` object.
            Usage: `sheet.iloc[i,j] = sheet.fn.neg(OPERAND)`_
        9. **Signum function**: returns the signum of the input value.
            For example sign(-4.5) = -1, sign(5) = 1, sign(0) = 0.
            Available in the `fn` property of the `sheet` object.
            Usage: `sheet.iloc[i,j] = sheet.fn.sign(OPERAND)`
            
        All unary operators are defined in the `fn` property of the Sheet
        object (together with brackets, that works exactly the same - see bellow).
        
        #### Binary operations
        There are the following binary operations (in the following the `OPERAND_1`
        and `OPERAND_2` are the instances of the Cell class):
        
        1. **Addition**: return the sum of two numbers. 
            For example: 5 + 2 = 7.
            Available as the overloaded operator `+`.
            Usage: `sheet.iloc[i,j] = OPERAND_1 + OPERAND_2`
        2. **Subtraction**: return the difference of two numbers. 
            For example: 5 - 2 = 3.
            Available as the overloaded operator `-`.
            Usage: `sheet.iloc[i,j] = OPERAND_1 - OPERAND_2`
        3. **Multiplication**: return the product of two numbers. 
            For example: 5 * 2 = 10.
            Available as the overloaded operator `*`.
            Usage: `sheet.iloc[i,j] = OPERAND_1 * OPERAND_2`
        4. **Division**: return the quotient of two numbers. 
            For example: 5 / 2 = 2.5.
            Available as the overloaded operator `/`.
            Usage: `sheet.iloc[i,j] = OPERAND_1 / OPERAND_2`
        5. **Exponentiation**: return the power of two numbers. 
            For example: 5 ** 2 = 25.
            Available as the overloaded operator `**`.
            Usage: `sheet.iloc[i,j] = OPERAND_1 ** OPERAND_2`
        6. **Logical equality**: return true if inputs are equals, false otherwise. 
            For example: 5 = 2 <=> false.
            Available as the overloaded operator `==`.
            Usage: `sheet.iloc[i,j] = OPERAND_1 == OPERAND_2`
        7. **Logical inequality**: return true if inputs are not equals,
        false otherwise. 
            For example: 5 ≠ 2 <=> true.
            Available as the overloaded operator `!=`.
            Usage: `sheet.iloc[i,j] = OPERAND_1 != OPERAND_2`
        8. **Relational greater than operator**: return true if the first operand is
        greater than another operand, false otherwise. 
            For example: 5 > 2 <=> true.
            Available as the overloaded operator `>`.
            Usage: `sheet.iloc[i,j] = OPERAND_1 > OPERAND_2`
        9. **Relational greater than or equal to operator**: return true if the first
        operand is greater than or equal to another operand, false otherwise. 
            For example: 5 ≥ 2 <=> true.
            Available as the overloaded operator `>=`.
            Usage: `sheet.iloc[i,j] = OPERAND_1 >= OPERAND_2`
        10. **Relational less than operator**: return true if the first operand is
        less than another operand, false otherwise. 
            For example: 5 < 2 <=> false.
            Available as the overloaded operator `<`.
            Usage: `sheet.iloc[i,j] = OPERAND_1 < OPERAND_2`
        11. **Relational less than or equal to operator**: return true if the first
        operand is less than or equal to another operand, false otherwise. 
            For example: 5 ≤ 2 <=> false.
            Available as the overloaded operator `<=`.
            Usage: `sheet.iloc[i,j] = OPERAND_1 <= OPERAND_2`
        12. **Logical conjunction operator**: return true if the first
        operand is true and another operand is true, false otherwise. 
            For example: true ∧ false <=> false.
            Available as the overloaded operator `&`.
            Usage: `sheet.iloc[i,j] = OPERAND_1 & OPERAND_2`.
            **_BEWARE that operator `and` IS NOT OVERLOADED! Because it is not
            technically possible._**
        13. **Logical disjunction operator**: return true if the first
        operand is true or another operand is true, false otherwise. 
            For example: true ∨ false <=> true.
            Available as the overloaded operator `|`.
            Usage: `sheet.iloc[i,j] = OPERAND_1 | OPERAND_2`.
            **_BEWARE that operator `or` IS NOT OVERLOADED! Because it is not
            technically possible._**
        14. **Concatenate strings**: return string concatenation of inputs.
            For example: CONCATENATE(7, "Hello") <=> "7Hello".
            Available as the overloaded operator `<<`.
            Usage: `sheet.iloc[i,j] = OPERAND_1 << OPERAND_2`
        
        Operations can be chained in the string:
        ```python
        sheet.iloc[i,j] = OPERAND_1 + OPERAND_2 * OPERAND_3 ** OPERAND_4
        ```
        The priority of the operators is the same as in normal mathematics. If
        you need to modify priority, you need to use brackets, for example:
        ```python
        sheet.iloc[i,j] = sheet.fn.brackets(OPERAND_1 + OPERAND_2) \
            * OPERAND_3 ** OPERAND_4
        ```
        #### Brackets for computation
        Brackets are technically speaking just another unary operator. They are
        defined in the `fn` property. They can be used like:
        ```python
        sheet.iloc[i,j] = sheet.fn.brackets(OPERAND_1 + OPERAND_2) \ 
            * OPERAND_3 ** OPERAND_4
        ```
        #### Example
        For example
        ```python
        # Equivalent of: value at [1,0] * (value at [2,1] + value at [3,1]) * exp(9)
        sheet.iloc[0,0] = sheet.iloc[1,0] * sheet.fn.brackets(
                sheet.iloc[2,1] + sheet.iloc[3,1]
            ) * sheet.fn.exp(sheet.fn.const(9))
        ```
        ### Accessing the computed values
        You can access either to the actual numerical value of the cell or to the
        word that is created in all the languages. The numerical value is accessible
        using the `value` property, whereas the words are accessible using
        the `parse` property (it returns a dictionary with languages as keys
        and word as values).
        ```python
        # Access the value of the cell
        value_of_cell: float = sheet.iloc[i, j].value
        # Access all the words in the cell
        word: dict = sheet.iloc[i, j].parse
        # Access the word in language 'lang'
        word_in_language_lang = word['lang']
        ```
        
        ### Exporting the results
        There are various methods available for exporting the results. All these
        methods can be used either to a whole sheet (instance of Sheet)
        or to any slice (CellSlice instance):
        
        1. **Excel format**, method `to_excel`:
        Export the sheet to the Excel-compatible file.
        2. **Dictionary of values**, method `to_dictionary`:
        Export the sheet to the dictionary (`dict` type).
        3. **JSON format**, method `to_json`:
        Export the sheet to the JSON format (serialize output of `to_dictionary`).
        3. **2D array as a string**, method: `to_string_of_values`:
        Export values to the string that looks like Python array definition string.
        4. **CSV**, method `to_csv`:
        Export the values to the CSV compatible string (that can be saved to the file)
        5. **Markdown (MD)**, method `to_markdown`:
        Export the values to MD (Markdown) file format string.
        Defined as a table.
        6. **NumPy ndarray**, method `to_numpy`:
        Export the sheet as a `numpy.ndarray` object.
        7. **Python 2D list**, method `to_2d_list`: 
        Export values 2 dimensional Python array (list of the list of the values).
        8. **HTML table**, method `to_html_table`:
        Export values to HTML table.
        
        #### Description field
        There is a possibility to add a description to a cell in the sheet
        (or to the whole slice of the sheet). It can be done using the property
        `description` on the cell or slice object. It should be done just before
        the export is done (together with defining Excel styles, see below)
        because once you rewrite the value of the cell on a given location,
        the description is lost.
        
        Example of using the description field:
        ```python
        # Setting the description of a single cell
        sheet.iloc[i, j].description = "Some text describing a cell"
        # Seting the description to a slice (propagate its value to each cell)
        sheet.iloc[i:j, k:l].description = "Text describing each cell in the slice"
        ```
        #### Exporting to Excel
        It can be done using the interface:
        ```python
        sheet.to_excel(
            file_path: str,
            /, *,
            spaces_replacement: str = ' ',
            label_row_format: dict = {'bold': True},
            label_column_format: dict = {'bold': True},
            variables_sheet_name: Optional[str] = None,
            variables_sheet_header: Dict[str, str] = MappingProxyType(
            {
                "name": "Name",
                "value": "Value",
                "description": "Description"
            }),
            values_only: bool = False,
            skipped_label_replacement: str = '',
            row_height: List[float] = [],
            column_width: List[float] = [],
            top_left_corner_text: str = ""
        )
        ```
        The only required argument is the path to the destination file (positional
        only parameter). Other parameters are passed as keywords (non-positional only). 
        * `file_path (str)`: Path to the target .xlsx file. (**REQUIRED**, only
        positional)
        * `spaces_replacement (str)`: All the spaces in the rows and columns
        descriptions (labels) are replaced with this string.
        * `label_row_format (dict)`: Excel styles for the label of rows,
        documentation: https://xlsxwriter.readthedocs.io/format.html
        * `label_column_format (dict)`: Excel styles for the label of columns,
        documentation: https://xlsxwriter.readthedocs.io/format.html
        * `variables_sheet_name (Optional[str])`: If set, creates the new
        sheet with variables and their description and possibility
        to set them up (directly from the sheet).
        * `variables_sheet_header (Dict[str, str])`: Define the labels (header)
        for the sheet with variables (first row in the sheet). Dictionary should look
        like: `{"name": "Name", "value": "Value", "description": "Description"}`.
        * `values_only (bool)`: If true, only values (and not formulas) are
        exported.
        * `skipped_label_replacement (str)`: Replacement for the SkippedLabel
        instances.
        * `row_height (List[float])`: List of row heights, or empty for the
        default height (or `None` for default height in the series).
        If row labels are included, there is a label row height on the
        first position in array.
        * `column_width (List[float])`: List of column widths, or empty for the
        default widths (or `None` for the default width in the series).
        If column labels are included, there is a label column width
        on the first position in array.
        * `top_left_corner_text (str)`: Text in the top left corner. Apply
        only when the row and column labels are included.
        
        ##### Setting the format/style for Excel cells
        There is a possibility to set the style/format of each cell in the grid
        or the slice of the gird using property `excel_format`. Style assignment
        should be done just before the export to the file because each new
        assignment of values to the cell overrides its style. Format/style can
        be set for both slice and single value. 
        
        Example of setting Excel format/style for cells and slices:
        ```python
        # Set the format of the cell on the position [i, j] (use bold value)
        sheet.iloc[i, j].excel_format = {'bold': True}
        # Set the format of the cell slice (use bold value and red color)
        sheet.iloc[i:j, k:l].excel_format = {'bold': True, 'color': 'red'}
        ```
        ##### Appending to existing Excel file
        Appending to existing Excel (`.xlsx`) format **is currently not supported** due
        to the missing functionality of the package XlsxWriter on which this
        library relies.
        
        #### Exporting to the dictionary (and JSON)
        It can be done using the interface:
        ```python
        sheet.to_dictionary(languages: List[str] = None,
                            use_language_for_description: Optional[str] = None, 
                            /, *, 
                            by_row: bool = True,
                            languages_pseudonyms: List[str] = None,
                            spaces_replacement: str = ' ',
                            skip_nan_cell: bool = False,
                            nan_replacement: object = None,
                            append_dict: dict = {})
        ```
        **Parameters are (all optional):**
        
        _Positional only:_
        * `languages (List[str])`: List of languages that should be exported.
        * `use_language_for_description (Optional[str])`: If set-up (using the language
        name), description field is set to be either the description value 
        (if defined) or the value of this language. 
        
        _Key-value only:_
        * `by_row (bool)`: If True, rows are the first indices and columns are the
        second in the order. If False it is vice-versa.
        * `languages_pseudonyms (List[str])`: Rename languages to the strings inside
        this list.
        * `spaces_replacement (str)`: All the spaces in the rows and columns
        descriptions (labels) are replaced with this string.
        * `skip_nan_cell (bool)`: If true, `None` (NaN, empty cells) values are
        skipped, default value is false (NaN values are included).
        * `nan_replacement (object)`: Replacement for the `None` (NaN) value.
        * `error_replacement (object)`: Replacement for the error value.
        * `append_dict (dict)`: Append this dictionary to output.
        * `generate_schema (bool)`: If true, returns the JSON schema.
        
        All the rows and columns with labels that are instances of SkippedLabel are
        entirely skipped. 
        
        **The return value is:** 
        
        Dictionary with keys: 1. column/row, 2. row/column, 3. language or
        language pseudonym or 'value' keyword for values -> value as a value or
        as a cell building string.
        
        ##### Exporting to JSON
        Exporting to JSON string is available using `to_json` method with exactly the
        same interface. The return value is the string.
        
        The reason why this method is separate is because of some values inserted
        from NumPy arrays cannot be serialized using native serializer.
        
        To get JSON schema you can use either `generate_schema (bool)` parameter or
        directly use static method `generate_json_schema` of the `Sheet` class.
        
        ##### Output example
        Output of the JSON format
        ```json
        {
           "table":{
              "data":{
                 "rows":{
                    "R_0":{
                       "columns":{
                          "NL_C_0":{
                             "excel":"1",
                             "python_numpy":"1",
                             "native":"1",
                             "value":1,
                             "description":"DescFor0,0"
                          },
                          "NL_C_1":{
                             "excel":"2",
                             "python_numpy":"2",
                             "native":"2",
                             "value":2,
                             "description":"DescFor0,1"
                          },
                          "NL_C_2":{
                             "excel":"3",
                             "python_numpy":"3",
                             "native":"3",
                             "value":3,
                             "description":"DescFor0,2"
                          },
                          "NL_C_3":{
                             "excel":"4",
                             "python_numpy":"4",
                             "native":"4",
                             "value":4,
                             "description":"DescFor0,3"
                          }
                       }
                    },
                    "R_1":{
                       "columns":{
                          "NL_C_0":{
                             "excel":"5",
                             "python_numpy":"5",
                             "native":"5",
                             "value":5,
                             "description":"DescFor1,0"
                          },
                          "NL_C_1":{
                             "excel":"6",
                             "python_numpy":"6",
                             "native":"6",
                             "value":6,
                             "description":"DescFor1,1"
                          },
                          "NL_C_2":{
                             "excel":"7",
                             "python_numpy":"7",
                             "native":"7",
                             "value":7,
                             "description":"DescFor1,2"
                          },
                          "NL_C_3":{
                             "excel":"8",
                             "python_numpy":"8",
                             "native":"8",
                             "value":8,
                             "description":"DescFor1,3"
                          }
                       }
                    },
                    "R_2":{
                       "columns":{
                          "NL_C_0":{
                             "excel":"9",
                             "python_numpy":"9",
                             "native":"9",
                             "value":9,
                             "description":"DescFor2,0"
                          },
                          "NL_C_1":{
                             "excel":"10",
                             "python_numpy":"10",
                             "native":"10",
                             "value":10,
                             "description":"DescFor2,1"
                          },
                          "NL_C_2":{
                             "excel":"11",
                             "python_numpy":"11",
                             "native":"11",
                             "value":11,
                             "description":"DescFor2,2"
                          },
                          "NL_C_3":{
                             "excel":"12",
                             "python_numpy":"12",
                             "native":"12",
                             "value":12,
                             "description":"DescFor2,3"
                          }
                       }
                    },
                    "R_3":{
                       "columns":{
                          "NL_C_0":{
                             "excel":"13",
                             "python_numpy":"13",
                             "native":"13",
                             "value":13,
                             "description":"DescFor3,0"
                          },
                          "NL_C_1":{
                             "excel":"14",
                             "python_numpy":"14",
                             "native":"14",
                             "value":14,
                             "description":"DescFor3,1"
                          },
                          "NL_C_2":{
                             "excel":"15",
                             "python_numpy":"15",
                             "native":"15",
                             "value":15,
                             "description":"DescFor3,2"
                          },
                          "NL_C_3":{
                             "excel":"16",
                             "python_numpy":"16",
                             "native":"16",
                             "value":16,
                             "description":"DescFor3,3"
                          }
                       }
                    },
                    "R_4":{
                       "columns":{
                          "NL_C_0":{
                             "excel":"17",
                             "python_numpy":"17",
                             "native":"17",
                             "value":17,
                             "description":"DescFor4,0"
                          },
                          "NL_C_1":{
                             "excel":"18",
                             "python_numpy":"18",
                             "native":"18",
                             "value":18,
                             "description":"DescFor4,1"
                          },
                          "NL_C_2":{
                             "excel":"19",
                             "python_numpy":"19",
                             "native":"19",
                             "value":19,
                             "description":"DescFor4,2"
                          },
                          "NL_C_3":{
                             "excel":"20",
                             "python_numpy":"20",
                             "native":"20",
                             "value":20,
                             "description":"DescFor4,3"
                          }
                       }
                    }
                 }
              },
              "variables":{
        
              },
              "rows":[
                 {
                    "name":"R_0",
                    "description":"HT_R_0"
                 },
                 {
                    "name":"R_1",
                    "description":"HT_R_1"
                 },
                 {
                    "name":"R_2",
                    "description":"HT_R_2"
                 },
                 {
                    "name":"R_3",
                    "description":"HT_R_3"
                 },
                 {
                    "name":"R_4",
                    "description":"HT_R_4"
                 }
              ],
              "columns":[
                 {
                    "name":"NL_C_0",
                    "description":"HT_C_0"
                 },
                 {
                    "name":"NL_C_1",
                    "description":"HT_C_1"
                 },
                 {
                    "name":"NL_C_2",
                    "description":"HT_C_2"
                 },
                 {
                    "name":"NL_C_3",
                    "description":"HT_C_3"
                 }
              ]
           }
        }
        ```
        
        #### Exporting to the CSV
        It can be done using the interface:
        ```python
        sheet.to_csv(*,
            language: Optional[str] = None,
            spaces_replacement: str = ' ',
            top_left_corner_text: str = "Sheet",
            sep: str = ',',
            line_terminator: str = '\n',
            na_rep: str = '',
            skip_labels: bool = False,
            skipped_label_replacement: str = ''
        ) -> str
        ```
        Parameters are (all optional and key-value only):
        
        * `language (Optional[str])`: If set-up, export the word in this
        language in each cell instead of values.
        * `spaces_replacement (str)`: All the spaces in the rows and columns
         descriptions (labels) are replaced with this string.
        * `top_left_corner_text (str)`: Text in the top left corner.
        * `sep (str)`: Separator of values in a row.
        * `line_terminator (str)`: Ending sequence (character) of a row.
        * `na_rep (str)`: Replacement for the missing data.
        * `skip_labels (bool)`: If true, first row and column with labels is
         skipped
        * `skipped_label_replacement (str)`: Replacement for the SkippedLabel
        instances.
        
        **The return value is:** 
        
        CSV of the values as a string.
        
        ##### Output example
        ```text
        Sheet,NL_C_0,NL_C_1,NL_C_2,NL_C_3
        R_0,1,2,3,4
        R_1,5,6,7,8
        R_2,9,10,11,12
        R_3,13,14,15,16
        R_4,17,18,19,20
        ```
        
        #### Exporting to Markdown (MD) format
        It can be done using the interface:
        ```python
        sheet.to_markdown(*,
            language: Optional[str] = None,
            spaces_replacement: str = ' ',
            top_left_corner_text: str = "Sheet",
            na_rep: str = '',
            skip_labels: bool = False,
            skipped_label_replacement: str = ''
        )
        ```
        Parameters are (all optional, all key-value only):
        
        * `language (Optional[str])`: If set-up, export the word in this
        language in each cell instead of values.
        * `spaces_replacement (str)`: All the spaces in the rows and columns
        descriptions (labels) are replaced with this string.
        * `top_left_corner_text (str)`: Text in the top left corner.
        * `na_rep (str)`: Replacement for the missing data.
        * `skip_labels (bool)`: If true, first row and column with labels is
        skipped
        * `skipped_label_replacement (str)`: Replacement for the SkippedLabel
        instances.
                        
        **The return value is:** 
        
        Markdown (MD) compatible table of the values as a string.
        
        ##### Output example
        ```markdown
        | Sheet |*NL_C_0* | *NL_C_1* | *NL_C_2* | *NL_C_3* |
        |----|----|----|----|----|
        | *R_0* | 1 | 2 | 3 | 4 |
        | *R_1* | 5 | 6 | 7 | 8 |
        | *R_2* | 9 | 10 | 11 | 12 |
        | *R_3* | 13 | 14 | 15 | 16 |
        | *R_4* | 17 | 18 | 19 | 20 |
        ```
        
        #### Exporting to HTML table format
        It can be done using the interface:
        ```python
        sheet.to_html_table(*,
            spaces_replacement: str = ' ',
            top_left_corner_text: str = "Sheet",
            na_rep: str = '',
            language_for_description: str = None,
            skip_labels: bool = False,
            skipped_label_replacement: str = ''
        )
        ```
        Parameters are (all optional, all key-value only):
        
        * `spaces_replacement (str)`: All the spaces in the rows and columns
        descriptions (labels) are replaced with this string.
        * `top_left_corner_text (str)`: Text in the top left corner.
        * `na_rep (str)`: Replacement for the missing data.
        * `language_for_description (str)`: If not `None`, the description
        of each computational cell is inserted as word of this language
        (if the property description is not set).
        * `skip_labels (bool)`: If true, first row and column with labels is
        skipped
        * `skipped_label_replacement (str)`: Replacement for the SkippedLabel
        instances.
        
        **The return value is:** 
        
        HTML table of the values as a string. Table is usable mainly for debugging
        purposes.
        
        ##### Output example
        ```html
        <table>
           <tr>
              <th>Sheet</th>
              <th><a href="javascript:;"  title="HT_C_0">NL_C_0</a></th>
              <th><a href="javascript:;"  title="HT_C_1">NL_C_1</a></th>
              <th><a href="javascript:;"  title="HT_C_2">NL_C_2</a></th>
              <th><a href="javascript:;"  title="HT_C_3">NL_C_3</a></th>
           </tr>
           <tr>
              <td><a href="javascript:;"  title="HT_R_0">R_0</a></td>
              <td><a href="javascript:;"  title="DescFor0,0">1</a></td>
              <td><a href="javascript:;"  title="DescFor0,1">2</a></td>
              <td><a href="javascript:;"  title="DescFor0,2">3</a></td>
              <td><a href="javascript:;"  title="DescFor0,3">4</a></td>
           </tr>
           <tr>
              <td><a href="javascript:;"  title="HT_R_1">R_1</a></td>
              <td><a href="javascript:;"  title="DescFor1,0">5</a></td>
              <td><a href="javascript:;"  title="DescFor1,1">6</a></td>
              <td><a href="javascript:;"  title="DescFor1,2">7</a></td>
              <td><a href="javascript:;"  title="DescFor1,3">8</a></td>
           </tr>
           <tr>
              <td><a href="javascript:;"  title="HT_R_2">R_2</a></td>
              <td><a href="javascript:;"  title="DescFor2,0">9</a></td>
              <td><a href="javascript:;"  title="DescFor2,1">10</a></td>
              <td><a href="javascript:;"  title="DescFor2,2">11</a></td>
              <td><a href="javascript:;"  title="DescFor2,3">12</a></td>
           </tr>
           <tr>
              <td><a href="javascript:;"  title="HT_R_3">R_3</a></td>
              <td><a href="javascript:;"  title="DescFor3,0">13</a></td>
              <td><a href="javascript:;"  title="DescFor3,1">14</a></td>
              <td><a href="javascript:;"  title="DescFor3,2">15</a></td>
              <td><a href="javascript:;"  title="DescFor3,3">16</a></td>
           </tr>
           <tr>
              <td><a href="javascript:;"  title="HT_R_4">R_4</a></td>
              <td><a href="javascript:;"  title="DescFor4,0">17</a></td>
              <td><a href="javascript:;"  title="DescFor4,1">18</a></td>
              <td><a href="javascript:;"  title="DescFor4,2">19</a></td>
              <td><a href="javascript:;"  title="DescFor4,3">20</a></td>
           </tr>
        </table>
        ```
        
        ## Remarks and definitions
        * **Anchored cell** is a cell that is located in the sheet and can be
        accessed using position.
        * **Un-anchored cell** is a cell that is the result of some computation or
        a constant defined by the user for some computation (and does not have
        any position in the sheet grid yet).
        
        **Example:**
        ```python
        anchored_cell = sheet.iloc[4,2]
        unanchored_cell_1 = sheet.iloc[4,2] * sheet.iloc[5,2]
        unanchored_cell_2 = sheet.fn.const(9)
        ```
        
        ## Software User Manual (SUM), how to use it?
        ### Installation
        To install the most actual package, use the command:
        ```commandline
        git clone https://github.com/david-salac/Portable-spreadsheet-generator
        cd Portable-spreadsheet-generator/
        python setup.py install
        ```
        or simply install using PIP:
        ```commandline
        pip install portable-spreadsheet
        ```
        #### Running of the unit-tests
        For running package unit-tests, use command:
        ```commandline
        python setup.py test
        ```
        In order to run package unit-tests you need to clone package first.
        ### Demo
        The following demo contains a simple example with aggregations.
        
        ```python
        import portable_spreadsheet as ps
        import numpy as np
        
        # This is a simple demo that represents the possibilities of the package
        #   The purpose of this demo is to create a class rooms and monitor students
        
        sheet = ps.Sheet.create_new_sheet(
            # Size of the table (rows, columns):
            24, 8,
            rows_labels=['Adam', 'Oliver', 'Harry', 'George', 'John', 'Jack', 'Jacob',
                         'Leo', 'Oscar', 'Charlie', 'Peter', 'Olivia', 'Amelia',
                         'Isla', 'Ava', 'Emily', 'Isabella', 'Mia', 'Poppy',
                         'Ella', 'Lily', 'Average of all', 'Average of boys',
                         'Average of girls'],
            columns_labels=['Biology', 'Physics', 'Math', 'English', 'French',
                            'Best performance', 'Worst performance', 'Mean'],
            columns_help_text=[
                'Annual performance', 'Annual performance', 'Annual performance',
                'Annual performance', 'Annual performance',
                'Best performance of all subjects',
                'Worst performance of all subjects',
                'Mean performance of all subjects',
            ]
        )
        
        # === Insert some percentiles to students performance: ===
        # A) In this case insert random values in the first row to the 3rd row from the
        #   end, and in the first column.
        sheet.iloc[:-3, 0] = np.random.random(21) * 100
        # B) Same can be achieved using the label indices:
        sheet.loc["Adam":'Average of all', 'Physics'] = np.random.random(21) * 100
        # C) Or by using the cell by cell approach:
        for row_idx in range(21):
            # I) Again by the simple integer index
            sheet.iloc[row_idx, 2] = np.random.random() * 100
            # II) Or by the label
            row_label: str = sheet.cell_indices.rows_labels[row_idx]
            sheet.loc[row_label, 'English'] = np.random.random() * 100
        # Insert values to last column
        sheet.iloc[:21, 4] = np.random.random(21) * 100
        
        # === Insert computations ===
        # Insert the computations on the row
        for row_idx in range(21):
            # I) Maximal value
            sheet.iloc[row_idx, 5] = sheet.iloc[row_idx, 0:5].max()
            # II) Minimal value
            sheet.iloc[row_idx, 6] = sheet.iloc[row_idx, 0:5].min()
            # III) Mean value
            sheet.iloc[row_idx, 7] = sheet.iloc[row_idx, 0:5].mean()
        # Insert the similar to rows:
        for col_idx in range(8):
            # I) Values of all
            sheet.iloc[21, col_idx] = sheet.iloc[0:21, col_idx].average()
            # II) Values of boys
            sheet.iloc[22, col_idx] = sheet.iloc[0:11, col_idx].average()
            # III) Values of girls
            sheet.iloc[23, col_idx] = sheet.iloc[11:21, col_idx].average()
        
        # Export results to Excel file, TODO: change the target directory:
        sheet.to_excel("OUTPUTS/student_marks.xlsx", sheet_name="Marks")
        
        # Top print table as Markdown
        print(sheet.to_markdown())
        ```
        
        ## Multiple sheets
        If you need to have an application that uses multiple sheets
        simultaneously - there is a special class for these purposes
        called WorkBook. It allows you to create multiple sheets, have
        references from one sheet to another and export to various formats.
        
        ### Creation of the WorkBook
        To create a new workbook, you need to have all sheets prepared. The
        constructor accepts just a list of all sheets (Sheet instance).
        ```python
        def __init__(self, *sheets: Iterable[Sheet])
        ```
        
        ### Special sheet for variables
        There is a way how to create a customized sheet for variables. 
        To do so, use the function `create_variable_sheet`. It
        has the following syntax:
        ```python
        def create_variable_sheet(self,
                                  *,
                                  nr_rows_prefix: int = 0,
                                  nr_rows_suffix: int = 0,
                                  nr_columns_prefix: int = 0,
                                  nr_columns_suffix: int = 0,
                                  sheet_name: str = "config",
                                  position: int = 0
                                  ) -> Sheet
        ```
        parameters have the following logic:
         * `nr_rows_prefix (int)`: defines the number of rows that are prefix
        for variables definition segment.
         * `nr_rows_suffix (int)`: defines the number of rows that are suffix
        for variables definition segment.
         * `nr_columns_prefix (int)`: defines the number of columns that are
        prefix for variables definition segment.
         * `nr_columns_suffix (int)`: defines the number of columns that are
        suffix for variables definition segment.
         * `sheet_name (str)`: Name of the sheet for variables.
         * `position (int)`: Relative position in the workbook (indexed from 0).
        
        ### Exporting to Excel (xlsx) format
        For exporting to Excel (.xlsx) format, there is a function called
        `to_excel`. It takes two parameters. The first one is the path to
        the file; another is the set of parameters for exporting each sheet.
        ```python
        def to_excel(self,
                     file_path: Union[str, pathlib.Path],
                     /, *,  # noqa: E225
                     export_parameters: Tuple[ExcelParameters]
                     ) -> None
        ```
        the first parameter is always positional; another is keyword type.
        
        Data class `ExcelParameters` has the structure motivated by the
        parameters required by `Sheet.to_excel`:
        ```python
        class ExcelParameters(ClassVarsToDict):
            spaces_replacement: str = ' '
            label_row_format: dict = MappingProxyType({'bold': True})
            label_column_format: dict = MappingProxyType({'bold': True})
            values_only: bool = False
            skipped_label_replacement: str = ''
            row_height: Tuple[float] = tuple([])
            column_width: Tuple[float] = tuple([])
            top_left_corner_text: str = ""
        ```
        
        ### Exporting to dictionary
        It is possible to export sheets to the dictionary
        by using `to_dictionary` function:
        ```python
        def to_dictionary(self,
                          *,
                          export_parameters: Tuple[DictionaryParameters]
                          ) -> dict:
        ```
        the second parameter is keyword type.
        
        Data class `DictionaryParameters` has the structure motivated by the
        parameters required by `Sheet.to_dictionary`:
        ```python
        class DictionaryParameters(ClassVarsToDict):
            languages: List[str] = None
            use_language_for_description: Optional[str] = None
            by_row: bool = True
            languages_pseudonyms: List[str] = None
            spaces_replacement: str = ' '
            skip_nan_cell: bool = False
            nan_replacement: object = None
            error_replacement: object = None
            append_dict: dict = MappingProxyType({})
            generate_schema: bool = False
        ```
        
        ### Export to JSON
        Exporting to JSON has the same logic as exporting to dictionary.
        ```python
        def to_json(self,
                    *,
                    export_parameters: Tuple[DictionaryParameters]) -> str:
        ```
        The logic is the same as above.
        
        The JSON schema can be generated using the static `generate_json_schema`
        function.
        
        ### Export to list
        It is possible to export sheets to the 3D list
        by using `to_list` function:
        ```python
        def to_list(self,
                    *,
                    export_parameters: Tuple[ListParameters]) -> list:
        ```
        the second parameter is keyword type.
        
        Data class `ListParameters` has the structure motivated by the
        parameters required by `Sheet.to_list`:
        ```python
        class ListParameters(ClassVarsToDict):
            language: Optional[str] = None
            skip_labels: bool = False
            na_rep: Optional[object] = None
            spaces_replacement: str = ' '
            skipped_label_replacement: str = ''
        ```
        
        ### Export to string
        There is a simple way how to export to string by using 
        `to_string_of_values` function (no parameters are required).
        
        ### Accessing sheet in workbook
        Each sheet in the workbook can be accessed by using `[]` operator:
        ```python
        sheet = workbook[NAME_OF_THE_SHEET]
        ```
        where `NAME_OF_THE_SHEET` is the string representing the name of the sheet.
        
        ### Cross-referencing
        In order to access the value in a different sheet, you have to use:
        ```python
        sheet_a.iloc[x1, y1] = sheet.fn.cross_reference(sheet.iloc[x2, y2],
                                                        sheet_b)
        ```
        The function `cross_reference` takes two parameters, the first one is the
        cell that is the target, the second one is the whole target sheet.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
