def mathify_column(x: pd.Series, **kwargs) -> pd.Series:
"""
Mathify (when one could) a pd.Series iteratively
Parameters
----------
x : pd.Series
The input series
**kwargs:
num_digit : int, optional
Number of digits after the decimal point represented, by default 3.
Integers that are > 1 are ignored.
large : int, optional
The upper limit until scientific notation; scientific notation is activated if
s > 10 ** large; the parameter is ignored if auto_scientific is False, by default 9
na_rep : str, optional
Representation of missing values (NaN, nan, None, etc.), by default "---"
additional_enclosures : Iterable[Enclosure], optional
Additional enclosures to be checked by mathify, by default []
auto_scientific : bool, optional
Whether to convert automatically into scientific notation, by default True.
Scientific notation is activated if number is outside of
[10 ** -num_digit, 10 ** large]
Returns
-------
pd.Series
The output series
"""
return x.apply(skiperror(lambda y: mathify(y, **kwargs), return_x=True))
def mathify_table(df: pd.DataFrame,
texttt_index: bool = False,
texttt_column: bool = False,
**kwargs) -> pd.DataFrame:
"""
Mathify a pd.DataFrame iteratively
Parameters
----------
df : pd.DataFrame
The input DataFrame
texttt_index : bool, optional
Whether or not to escape index and convert index labels to \\texttt,
by default False
texttt_column : bool, optional
Whether or not to escape index and convert column labels to \\texttt,
by default False
**kwargs:
num_digit : int, optional
Number of digits after the decimal point represented, by default 3.
Integers that are > 1 are ignored.
large : int, optional
The upper limit until scientific notation; scientific notation is activated if
s > 10 ** large; the parameter is ignored if auto_scientific is False, by default 9
na_rep : str, optional
Representation of missing values (NaN, nan, None, etc.), by default "---"
additional_enclosures : Iterable[Enclosure], optional
Additional enclosures to be checked by mathify, by default []
auto_scientific : bool, optional
Whether to convert automatically into scientific notation, by default True.
Scientific notation is activated if number is outside of
[10 ** -num_digit, 10 ** large]
Returns
-------
d : pd.DataFrame
The output DataFrame
"""
d = df.apply(
skiperror(lambda x: mathify_column(x, **kwargs), return_x=True))
if texttt_index:
d.index = [
"\\texttt{{{}}}".format(s.replace("_", "\\_")) if
(len(s) > 0 and s[0] != "$" and s[-1] != "$") else s
for s in d.index.astype(str)
]
if texttt_column:
d.columns = [
"\\texttt{{{}}}".format(s.replace("_", "\\_")) if
(len(s) > 0 and s[0] != "$" and s[-1] != "$") else s
for s in np.array(d.columns).astype(str)
]
return d
def test_skiperror():
df = pd.DataFrame({"x": [1, 2, 3, "a"], "y": [1, 2, 3, "b"]})
def func(s):
return s + 1
# Verify that applying function causes error
with pytest.raises(Exception):
df["x"].apply(func)
result = df["x"].apply(skiperror(func))
assert (result.values[:-1] == np.array([2, 3, 4])).all() and np.isnan(
result.values[-1])
result = df["x"].apply(skiperror(func, return_x=True))
assert (result.values == np.array([2, 3, 4, "a"], dtype=object)).all()
result = df["x"].apply(skiperror(func, return_x=False, return_val=5))
assert (result.values == np.array([2, 3, 4, 5])).all()