Code

pyTruthTable.pyTruthTable¶

PyTruthTable¶

class PyTruthTable():
 |  PyTruthTable(names=[], df=pd.DataFrame())

The constructor initialize a Truth Table with a list of names or with a custom dataframe.

Initializing with a list of names means generating automatically all possible combination of True and False

Arguments:

names: Name of the columns.
df: Initial Pandas' dataframe.

Raises:

UserWarning: If the initial df and names are specified simultaneously, the table generation is ignored due to possible table dimention conflicts. Only the initial dataframe is used.

Example

t_table = ptt.PyTruthTable(["A", "B", "C"])
t_table.table_df

A	B	C
True	True	True
True	True	False
True	False	True
True	False	False
False	True	True
False	True	False
False	False	True
False	False	False

set_default_spacing¶

 | set_default_spacing()

By default, all symbols are spaced from the variable name.

set_no_spacing¶

 | set_no_spacing()

Remove spaces from column's name. By default, all symbols are spaced from the variable name.

Examples:

t_table = ptt.PyTruthTable(["A"])

t_table.append("or", 0, 0)   # Simple 'or' operation
t_table.set_no_spacing()             # Remove spaces
t_table.append("or", 0, 0)   # Same 'or' operation

t_table.table_df

A	A v A	AvA
True	True	True
False	False	False

set_default_symbols¶

 | set_default_symbols()

Name the new column using the default symbols.

Notice that some operations does not contain symbols because they are a combination of other symbols.

Operation	Symbol
implies	"→"
nimplies	"↛"
converse	"←"
nconverse	"↚"
not	"¬"
and	"^"
or	"v"
nor	""
xor	"⊕"
xnor	""
nand	""
equals	"↔"
nequals	"↮"

set_text_symbols¶

 | set_text_symbols()

Name the new column using the operation name instead of symbol.

i.e: When using and operation your new column will be called A and B instead of A ^ B.

Operation	Text
implies	"implies"
nimplies	"not implies"
converse	"converse"
nconverse	"not converse"
not	"not"
and	"and",
or	"or"
nor	"nor"
xor	"xor"
xnor	"xnor"
nand	"nand"
equals	"equals"
nequals	"not equals"

Examples:

df = pd.DataFrame([A])

t_table = ptt.PyTruthTable(df=df)

t_table.append("or", 0, 0)   # Simple 'or' operation
t_table.set_text_symbols()   # Change to text
t_table.append("or", 0, 0)   # Same 'or' operation

t_table.table_df

A	A v A	A or A
True	True	True
False	False	False

generator¶

 | generator(names=[])

Generate a combination of True and False columns given a list of names.

Arguments:

names: Name of the columns.

Returns:

A dataframe with a binary combination using the list names .

Example:

t_table = ptt.PyTruthTable()
t_table.generator(["First", "Second"])

First	Second
True	True
True	False
False	True
False	False

t_table = ptt.PyTruthTable()
t_table.generator(["First", "Second", "Third"])

First	Second	Third
True	True	True
True	True	False
True	False	True
True	False	False
False	True	True
False	True	False
False	False	True
False	False	False

append¶

 | append(operation, a_in, b_in="", name="")

Make a logical operation with column a_in and b_in. The new column will automatically append to the main dataframe. The new column name can be specified with name.

a_in and b_in can be either a column name or number.

Arguments:

operation: Logical operation to be executed
a_in: First column. (Left side)
b_in: Second column. (Right side)

Raises:

NameError: Input column does not exist.
NameError: Operation does not exist.
ValueError: The operation needs b_in to also be specified

append_df¶

 | append_df(a_dataframe)

Append a custom dataframe to your truth table. The number of rows of a_dataframe must always match the current truth table.

Arguments:

a_dataframe: Dataframe to append

Raises:

TypeError: The parameter should be a dataframe
RuntimeError: Number of rows of dataframe must match with current truth table

column¶

 | column(operation, a_in, b_in="", name="")

Return a column of logical operation with column a_in and b_in. The new column will NOT append to the main dataframe. The new column name can be specified with name.

a_in and b_in can be either a column name or number.

Arguments:

operation: Logical operation to be executed
a_in: First column. (Left side)
b_in: Second column. (Right side)

Raises:

NameError: Input column does not exist.
NameError: Operation does not exist.
ValueError: The operation needs b_in to also be specified