def _levels_dict(demographics):
"""Create a dictionary of indices and
corresponding demographic levels
Parameters
----------
demographics : pd.Series
Demographic labels
Returns
-------
levels_dict : dict
"""
levels = _levels(demographics)
levels_dict = {k : v for k, v in enumerate(levels)}
return levels_dict
def _arrs_pos(df_orig,
df_pos,
demographic,
pos,
d_levels=None,
print_levels=False):
"""Individual part-of-speech
arrays for a particular demographic
Parameters
----------
df_orig : pd.DataFrame
The DataFrame from which `df_pos` was created
df_pos : pd.DataFrame
The part-of-speech DataFrame
demographic : str
A valid demographic-data column in `df_orig`
pos : str
A column in `df_pos` corresponding
to a part of speech
d_levels : list, default None
The specific demographic levels desired
print_levels : bool, default False
Whether to print the demographic levels
Returns
-------
arrs : tuple of np.arrays
The corresponding `pos` values for each `demographic`
"""
df_pos = df_pos.copy() # so we don't modify it
df_pos[demographic] = df_orig[demographic].values
levels = _levels(df_orig[demographic], d_levels, print_levels)
arrs = []
for d in levels:
arr = df_pos[df_pos[demographic] == d][pos].values
n = arr.shape[0]
if n < 0.1 * df_pos.shape[0]:
print("Warning: '" + d +
"' category has less than 10% of observations (" + str(n) +
")")
arrs.append(arr)
return tuple(arrs)
def _arrs_pos(df_orig, df_pos, demographic, pos,
d_levels=None, print_levels=False):
"""Individual part-of-speech
arrays for a particular demographic
Parameters
----------
df_orig : pd.DataFrame
The DataFrame from which `df_pos` was created
df_pos : pd.DataFrame
The part-of-speech DataFrame
demographic : str
A valid demographic-data column in `df_orig`
pos : str
A column in `df_pos` corresponding
to a part of speech
d_levels : list, default None
The specific demographic levels desired
print_levels : bool, default False
Whether to print the demographic levels
Returns
-------
arrs : tuple of np.arrays
The corresponding `pos` values for each `demographic`
"""
df_pos = df_pos.copy() # so we don't modify it
df_pos[demographic] = df_orig[demographic].values
levels = _levels(df_orig[demographic], d_levels, print_levels)
arrs = []
for d in levels:
arr = df_pos[df_pos[demographic] == d][pos].values
n = arr.shape[0]
if n < 0.1 * df_pos.shape[0]:
print("Warning: '" + d +
"' category has less than 10% of observations (" +
str(n) + ")")
arrs.append(arr)
return tuple(arrs)
