def editor(dataframe1,
operation = '%',
dataframe2 = False,
sort_by = False,
keep_stats = True,
keep_top = False,
just_totals = False,
threshold = 'medium',
just_entries = False,
skip_entries = False,
merge_entries = False,
newname = 'combine',
just_subcorpora = False,
skip_subcorpora = False,
span_subcorpora = False,
merge_subcorpora = False,
new_subcorpus_name = False,
replace_names = False,
projection = False,
remove_above_p = False,
p = 0.05,
revert_year = True,
print_info = True,
convert_spelling = False,
selfdrop = True,
calc_all = True,
**kwargs
):
"""Edit results of corpus interrogation.
replace_names: give a tuple with (to_find, replacement)
give a raw string to delete"""
import pandas
import pandas as pd
import numpy as np
import re
import collections
from pandas import DataFrame, Series
from time import localtime, strftime
try:
get_ipython().getoutput()
except TypeError:
have_ipython = True
except NameError:
have_ipython = False
try:
from IPython.display import display, clear_output
except ImportError:
pass
pd.options.mode.chained_assignment = None
the_time_started = strftime("%Y-%m-%d %H:%M:%S")
pd.set_option('display.float_format', lambda x: '%.2f' % x)
from corpkit.tests import check_pytex
if check_pytex():
print_info = False
def combiney(df, df2, operation = '%', threshold = 'medium', prinf = True):
"""mash df and df2 together in appropriate way"""
# delete under threshold
if just_totals:
if using_totals:
if not single_totals:
to_drop = list(df2[df2['Combined total'] < threshold].index)
df = df.drop([e for e in to_drop if e in list(df.index)])
if prinf:
to_show = []
[to_show.append(w) for w in to_drop[:5]]
if len(to_drop) > 10:
to_show.append('...')
[to_show.append(w) for w in to_drop[-5:]]
if len(to_drop) > 0:
print 'Removing %d entries below threshold:\n %s' % (len(to_drop), '\n '.join(to_show))
if len(to_drop) > 10:
print '... and %d more ... \n' % (len(to_drop) - len(to_show) + 1)
else:
print ''
else:
denom = df2
else:
denom = list(df2)
if single_totals:
if operation == '%':
df = df * 100.0
df = df.div(denom, axis = 0)
elif operation == '+':
df = df.add(denom, axis = 0)
elif operation == '-':
df = df.sub(denom, axis = 0)
elif operation == '*':
df = df.mul(denom, axis = 0)
elif operation == '/':
df = df.div(denom, axis = 0)
return df
elif not single_totals:
#p = ProgressBar(len(list(df.columns)))
for index, entry in enumerate(list(df.columns)):
#p.animate(index)
if operation == '%':
try:
df[entry] = df[entry] * 100.0 / df2[entry]
except:
continue
#df.drop(entry, axis = 1, inplace = True)
#df[entry] = maths_done
elif operation == '+':
try:
df[entry] = df[entry] + df2[entry]
except:
continue
elif operation == '-':
try:
df[entry] = df[entry] - df2[entry]
except:
continue
elif operation == '*':
try:
df[entry] = df[entry] * df2[entry]
except:
continue
elif operation == '/':
try:
df[entry] = df[entry] / df2[entry]
except:
continue
#p.animate(len(list(df.columns)))
#if have_ipython:
#clear_output()
return df
def parse_input(df, input):
"""turn whatever has been passed in into list of words that can be used as pandas indices"""
import re
if input == 'all':
input = r'.*'
if type(input) == int:
input = [input]
elif type(input) == str:
try:
regex = re.compile(input)
parsed_input = [w for w in list(df) if re.search(regex, w)]
return parsed_input
except:
input = [input]
if type(input) == list:
if type(input[0]) == int:
parsed_input = [word for index, word in enumerate(list(df)) if index in input]
elif type(input[0]) == str or type(input[0]) == unicode:
parsed_input = [word for word in input if word in df.columns]
return parsed_input
def synonymise(df, pos = 'n'):
"""pass a df and a pos and convert df columns to most common synonyms"""
import nltk
from nltk.corpus import wordnet as wn
#from dictionaries.taxonomies import taxonomies
from collections import Counter
fixed = []
for w in list(df.columns):
try:
syns = []
for syns in wn.synsets(w, pos = pos):
for w in syns:
synonyms.append(w)
top_syn = Counter(syns).most_common(1)[0][0]
fixed.append(top_syn)
except:
fixed.append(w)
df.columns = fixed
return df
def convert_spell(df, convert_to = 'US', print_info = print_info):
"""turn dataframes into us/uk spelling"""
from dictionaries.word_transforms import usa_convert
if print_info:
print 'Converting spelling ... \n'
if convert_to == 'UK':
usa_convert = {v: k for k, v in usa_convert.items()}
fixed = []
for val in list(df.columns):
try:
fixed.append(usa_convert[val])
except:
fixed.append(val)
df.columns = fixed
return df
def merge_duplicates(df, print_info = print_info):
if print_info:
print 'Merging duplicate entries ... \n'
# now we have to merge all duplicates
for dup in df.columns.get_duplicates():
#num_dupes = len(list(df[dup].columns))
temp = df[dup].sum(axis = 1)
#df = df.drop([dup for d in range(num_dupes)], axis = 1)
df = df.drop(dup, axis = 1)
df[dup] = temp
return df
def name_replacer(df, replace_names, print_info = print_info):
import re
# double or single nest if need be
if type(replace_names) == str:
replace_names = [(replace_names, '')]
if type(replace_names[0]) == str:
replace_names = [replace_names]
"""replace entry names and merge"""
for to_find, replacement in replace_names:
if print_info:
try:
print 'Replacing "%s" with "%s" ...\n' % (to_find, replacement)
except:
print 'Deleting "%s" from entry names ...\n' % (to_find)
to_find = re.compile(to_find)
try:
replacement = replacement
except:
replacement = ''
df.columns = [re.sub(to_find, replacement, l) for l in list(df.columns)]
df = merge_duplicates(df, print_info = False)
return df
def just_these_entries(df, parsed_input, prinf = True):
entries = [word for word in list(df) if word not in parsed_input]
if prinf:
print 'Keeping %d entries:\n %s' % (len(parsed_input), '\n '.join(parsed_input[:10]))
if len(parsed_input) > 10:
print '... and %d more ... \n' % (len(parsed_input) - 10)
else:
print ''
df = df.drop(entries, axis = 1)
return df
def skip_these_entries(df, parsed_input, prinf = True):
if prinf:
print 'Skipping %d entries:\n %s' % (len(parsed_input), '\n '.join(parsed_input[:10]))
if len(parsed_input) > 10:
print '... and %d more ... \n' % (len(parsed_input) - 10)
else:
print ''
df = df.drop(parsed_input, axis = 1)
return df
def newname_getter(df, parsed_input, newname = 'combine', prinf = True, merging_subcorpora = False):
"""makes appropriate name for merged entries"""
if merging_subcorpora:
if newname is False:
newname = 'combine'
if type(newname) == int:
the_newname = list(df.columns)[newname]
elif type(newname) == str:
if newname == 'combine':
if len(parsed_input) <= 3:
the_newname = '/'.join(parsed_input)
elif len(parsed_input) > 3:
the_newname = '/'.join(parsed_input[:3]) + '...'
else:
the_newname = newname
if newname is False:
# revise this code
import operator
sumdict = {}
for item in parsed_input:
summed = sum(list(df[item]))
sumdict[item] = summed
the_newname = max(sumdict.iteritems(), key=operator.itemgetter(1))[0]
if type(the_newname) != unicode:
the_newname = unicode(the_newname, errors = 'ignore')
return the_newname
def merge_these_entries(df, parsed_input, the_newname, prinf = True, merging = 'entries'):
# make new entry with sum of parsed input
if prinf:
print 'Merging %d %s as "%s":\n %s' % (len(parsed_input), merging, the_newname, '\n '.join(parsed_input[:10]))
if len(parsed_input) > 10:
print '... and %d more ... \n' % (len(parsed_input) - 10)
else:
print ''
# remove old entries
temp = sum([df[i] for i in parsed_input])
df = df.drop(parsed_input, axis = 1)
df[the_newname] = temp
return df
def just_these_subcorpora(df, lst_of_subcorpora, prinf = True):
if type(lst_of_subcorpora[0]) == int:
lst_of_subcorpora = [str(l) for l in lst_of_subcorpora]
good_years = [subcorpus for subcorpus in list(df.index) if subcorpus in lst_of_subcorpora]
if prinf:
print 'Keeping %d subcorpora:\n %s' % (len(good_years), '\n '.join(good_years[:10]))
if len(good_years) > 10:
print '... and %d more ... \n' % (len(good_years) - 10)
else:
print ''
df = df.drop([subcorpus for subcorpus in list(df.index) if subcorpus not in good_years], axis = 0)
return df
def skip_these_subcorpora(df, lst_of_subcorpora, prinf = True):
if type(lst_of_subcorpora) == int:
lst_of_subcorpora = [lst_of_subcorpora]
if type(lst_of_subcorpora[0]) == int:
lst_of_subcorpora = [str(l) for l in lst_of_subcorpora]
bad_years = [subcorpus for subcorpus in list(df.index) if subcorpus in lst_of_subcorpora]
if prinf:
print 'Skipping %d subcorpora:\n %s' % (len(bad_years), '\n '.join([str(i) for i in bad_years[:10]]))
if len(bad_years) > 10:
print '... and %d more ... \n' % (len(bad_years) - 10)
else:
print ''
df = df.drop([subcorpus for subcorpus in list(df.index) if subcorpus in bad_years], axis = 0)
return df
def span_these_subcorpora(df, lst_of_subcorpora, prinf = True):
non_totals = [subcorpus for subcorpus in list(df.index)]
good_years = [subcorpus for subcorpus in non_totals if int(subcorpus) >= int(lst_of_subcorpora[0]) and int(subcorpus) <= int(lst_of_subcorpora[-1])]
if prinf:
print 'Keeping subcorpora:\n %d--%d\n' % (int(lst_of_subcorpora[0]), int(lst_of_subcorpora[-1]))
df = df.drop([subcorpus for subcorpus in list(df.index) if subcorpus not in good_years], axis = 0)
# retotal needed here
return df
def projector(df, list_of_tuples, prinf = True):
for subcorpus, projection_value in list_of_tuples:
if type(subcorpus) == int:
subcorpus = str(subcorpus)
df.ix[subcorpus] = df.ix[subcorpus] * projection_value
if prinf:
if type(projection_value) == float:
print 'Projection: %s * %s' % (subcorpus, projection_value)
if type(projection_value) == int:
print 'Projection: %s * %d' % (subcorpus, projection_value)
if prinf:
print ''
return df
def do_stats(df):
"""do linregress and add to df"""
from scipy.stats import linregress
entries = []
slopes = []
intercepts = []
rs = []
ps = []
stderrs = []
indices = list(df.index)
first_year = list(df.index)[0]
try:
x = [int(y) - int(first_year) for y in indices]
except ValueError:
x = range(len(indices))
statfields = ['slope', 'intercept', 'r', 'p', 'stderr']
for entry in list(df.columns):
entries.append(entry)
y = list(df[entry])
slope, intercept, r, p, stderr = linregress(x, y)
slopes.append(slope)
intercepts.append(intercept)
rs.append(r)
ps.append(p)
stderrs.append(stderr)
sl = pd.DataFrame([slopes, intercepts, rs, ps, stderrs],
index = statfields,
columns = list(df.columns))
df = df.append(sl)
# drop infinites and nans
df = df.replace([np.inf, -np.inf], np.nan)
df = df.fillna(0.0)
return df
def recalc(df, operation = '%'):
statfields = ['slope', 'intercept', 'r', 'p', 'stderr']
"""Add totals to the dataframe1"""
#df.drop('Total', axis = 0, inplace = True)
#df.drop('Total', axis = 1, inplace = True)
try:
df['temp-Total'] = df.drop(statfields).sum(axis = 1)
except:
df['temp-Total'] = df.sum(axis = 1)
df = df.T
try:
df['temp-Total'] = df.drop(statfields).sum(axis = 1)
except:
df['temp-Total'] = df.sum(axis = 1)
df = df.T
# make totals percentages if need be.
#if operation == '%':
# df['Total'] = df['Total'] * 100.0
# df['Total'] = df['Total'].div(list(df2), axis = 0)
#elif operation == '+':
# df['Total'] = df['Total'].add(list(df2), axis = 0)
#elif operation == '-':
# df['Total'] = df['Total'].sub(list(df2), axis = 0)
#elif operation == '*':
# df['Total'] = df['Total'].mul(list(df2), axis = 0)
#elif operation == '/':
# df['Total'] = df['Total'].div(list(df2), axis = 0)
return df
def resort(df, sort_by = False, keep_stats = False):
"""sort results"""
# translate options and make sure they are parseable
options = ['total', 'name', 'infreq', 'increase', 'turbulent',
'decrease', 'static', 'most', 'least', 'none']
if sort_by is True:
sort_by = 'total'
if sort_by == 'most':
sort_by = 'total'
if sort_by == 'least':
sort_by = 'infreq'
if sort_by not in options:
raise ValueError("sort_by parameter error: '%s' not recognised. Must be True, False, %s" % (sort_by, ', '.join(options)))
if operation.startswith('k'):
if type(df) == pandas.core.series.Series:
if sort_by == 'total':
df = df.order(ascending = False)
elif sort_by == 'infreq':
df = df.order(ascending = True)
elif sort_by == 'name':
df = df.sort_index()
return df
if just_totals:
if sort_by == 'infreq':
df = df.sort(columns = 'Combined total', ascending = True)
elif sort_by == 'total':
df = df.sort(columns = 'Combined total', ascending = False)
elif sort_by == 'name':
df = df.sort_index()
return df
# this is really shitty now that i know how to sort, like in the above
if keep_stats:
df = do_stats(df)
if sort_by == 'total':
if df1_istotals:
df = df.T
df = recalc(df, operation = operation)
tot = df.ix['temp-Total']
df = df[tot.argsort()[::-1]]
df = df.drop('temp-Total', axis = 0)
df = df.drop('temp-Total', axis = 1)
if df1_istotals:
df = df.T
elif sort_by == 'infreq':
if df1_istotals:
df = df.T
df = recalc(df, operation = operation)
tot = df.ix['temp-Total']
df = df[tot.argsort()]
df = df.drop('temp-Total', axis = 0)
df = df.drop('temp-Total', axis = 1)
if df1_istotals:
df = df.T
elif sort_by == 'name':
# currently case sensitive...
df = df.reindex_axis(sorted(df.columns), axis=1)
else:
statfields = ['slope', 'intercept', 'r', 'p', 'stderr']
if not keep_stats:
df = do_stats(df)
slopes = df.ix['slope']
if sort_by == 'increase':
df = df[slopes.argsort()[::-1]]
elif sort_by == 'decrease':
df = df[slopes.argsort()]
elif sort_by == 'static':
df = df[slopes.abs().argsort()]
elif sort_by == 'turbulent':
df = df[slopes.abs().argsort()[::-1]]
if remove_above_p:
# the easy way to do it!
df = df.T
df = df[df['p'] <= p]
df = df.T
# remove stats field by default
if not keep_stats:
df = df.drop(statfields, axis = 0)
return df
def set_threshold(big_list, threshold, prinf = True, for_keywords = False):
if type(threshold) == str:
if threshold.startswith('l'):
denominator = 10000
if threshold.startswith('m'):
denominator = 5000
if threshold.startswith('h'):
denominator = 2500
if type(big_list) == pandas.core.frame.DataFrame:
tot = big_list.sum().sum()
if type(big_list) == pandas.core.series.Series:
tot = big_list.sum()
the_threshold = float(tot) / float(denominator)
#if for_keywords:
#the_threshold = the_threshold / 2
else:
the_threshold = threshold
if prinf:
print 'Threshold: %d\n' % the_threshold
return the_threshold
#####################################################
# check if we're in concordance mode
try:
if list(dataframe1.columns) == ['l', 'm', 'r']:
conc_lines = True
else:
conc_lines = False
except:
conc_lines = False
# copy dataframe to be very safe
try:
df = dataframe1.copy()
except AttributeError:
no_good_dataframe1 = True
while no_good_dataframe1:
if 'interrogation' in str(type(dataframe1)):
sel = raw_input("\nIt looks like you're trying to edit an interrogation, " \
"rather than an interrogation's .results or .totals branch. You can:\n\n a) select .results branch\n b) select .totals branch\n c) exit\n\nYour choice: ")
if sel.startswith('a'):
try:
dataframe1 = dataframe1.results
no_good_dataframe1 = False
except:
pass
elif sel.startswith('b'):
try:
dataframe1 = dataframe1.totals
no_good_dataframe1 = False
except:
pass
else:
return
else:
raise ValueError("Thing to be edited (dataframe1) needs to be a Pandas DataFrame or Series. " \
"Right now, its type is: '%s'." % type(dataframe1).__name__)
df = dataframe1.copy()
# do concordance work
if conc_lines:
df = dataframe1.copy()
if just_entries:
if type(just_entries) == int:
just_entries = [just_entries]
if type(just_entries) == str:
df = df[df['m'].str.contains(just_entries)]
if type(just_entries) == list:
if type(just_entries[0]) == str:
regex = re.compile(r'(?i)^(' + r'|'.join(just_entries) + r')$')
df = df[df['m'].str.contains(regex)]
else:
df = df.ix[just_entries].reset_index(drop = True)
if skip_entries:
if type(skip_entries) == int:
skip_entries = [skip_entries]
if type(skip_entries) == str:
df = df[~df['m'].str.contains(skip_entries)]
if type(skip_entries) == list:
if type(skip_entries[0]) == str:
regex = re.compile(r'(?i)^(' + r'|'.join(skip_entries) + r')$')
df = df[~df['m'].str.contains(regex)]
else:
df = df.ix[[e for e in list(df.index) if e not in skip_entries]].reset_index(drop = True)
return df
if print_info:
print '\n***Processing results***\n========================\n'
df1_istotals = False
if type(df) == pandas.core.series.Series:
df1_istotals = True
df = pandas.DataFrame(df)
# if just a single result
else:
df = pandas.DataFrame(df)
if operation.startswith('k'):
if sort_by is False:
if not df1_istotals:
sort_by = 'turbulent'
if df1_istotals:
df = df.T
# figure out if there's a second list
# copy and remove totals if there is
single_totals = True
using_totals = False
outputmode = False
try:
if dataframe2.empty is False:
df2 = dataframe2.copy()
using_totals = True
if type(df2) == pandas.core.frame.DataFrame:
if len(df2.columns) > 1:
single_totals = False
else:
df2 = pd.Series(df2)
elif type(df2) == pandas.core.series.Series:
single_totals = True
else:
raise ValueError('dataframe2 not recognised.')
except AttributeError:
if dataframe2 == 'self':
outputmode = True
if projection:
# projection shouldn't do anything when working with '%', remember.
df = projector(df, projection)
if using_totals:
df2 = projector(df2, projection)
if convert_spelling:
df = convert_spell(df, convert_to = convert_spelling)
df = merge_duplicates(df)
if not single_totals:
df2 = convert_spell(df2, convert_to = convert_spelling, print_info = False)
df2 = merge_duplicates(df2, print_info = False)
if not df1_istotals:
sort_by = 'total'
if replace_names:
df = name_replacer(df, replace_names)
df = merge_duplicates(df)
if not single_totals:
df2 = name_replacer(df2, print_info = False)
df2 = merge_duplicates(df2, print_info = False)
if not sort_by:
sort_by = 'total'
# remove old stats if they're there:
statfields = ['slope', 'intercept', 'r', 'p', 'stderr']
try:
df = df.drop(statfields, axis = 0)
except:
pass
if using_totals:
try:
df2 = df2.drop(statfields, axis = 0)
except:
pass
# merging
if merge_entries:
the_newname = newname_getter(df, parse_input(df, merge_entries), newname = newname, prinf = print_info)
df = merge_these_entries(df, parse_input(df, merge_entries), the_newname, prinf = print_info)
if not single_totals:
df2 = merge_these_entries(df2, parse_input(df2, merge_entries), the_newname, prinf = False)
if merge_subcorpora:
the_newname = newname_getter(df.T, parse_input(df.T, merge_subcorpora),
newname = new_subcorpus_name,
merging_subcorpora = True,
prinf = print_info)
df = merge_these_entries(df.T, parse_input(df.T, merge_subcorpora), the_newname, merging = 'subcorpora', prinf = print_info).T
if using_totals:
df2 = merge_these_entries(df2.T, parse_input(df2.T, merge_subcorpora), the_newname, merging = 'subcorpora', prinf = print_info).T
if just_subcorpora:
df = just_these_subcorpora(df, just_subcorpora, prinf = print_info)
if using_totals:
df2 = just_these_subcorpora(df2, just_subcorpora, prinf = False)
if skip_subcorpora:
df = skip_these_subcorpora(df, skip_subcorpora, prinf = print_info)
if using_totals:
df2 = skip_these_subcorpora(df2, skip_subcorpora, prinf = False)
if span_subcorpora:
df = span_these_subcorpora(df, span_subcorpora, prinf = print_info)
if using_totals:
df2 = span_these_subcorpora(df2, span_subcorpora, prinf = False)
if just_entries:
df = just_these_entries(df, parse_input(df, just_entries), prinf = print_info)
if not single_totals:
df2 = just_these_entries(df2, parse_input(df2, just_entries), prinf = False)
if skip_entries:
df = skip_these_entries(df, parse_input(df, skip_entries), prinf = print_info)
if not single_totals:
df2 = skip_these_entries(df2, parse_input(df2, skip_entries), prinf = False)
# drop infinites and nans
df = df.replace([np.inf, -np.inf], np.nan)
df = df.fillna(0.0)
# make just_totals as dataframe
just_one_total_number = False
if just_totals:
df = pd.DataFrame(df.sum(), columns = ['Combined total'])
if using_totals:
if not single_totals:
df2 = pd.DataFrame(df2.sum(), columns = ['Combined total'])
else:
just_one_total_number = True
df2 = df2.sum()
# generate '%' totals here ...
if using_totals:
if operation == '%':
tot1 = df.T.sum()
if single_totals:
tot2 = df2
else:
tot2 = df2.T.sum()
total = tot1 * 100.0 / tot2
# combine lists
if using_totals or outputmode:
if not operation.startswith('k'):
the_threshold = 0
# set a threshold if just_totals
if outputmode is True:
df2 = df.T.sum()
if not just_totals:
df2.name = 'Total'
else:
df2.name = 'Combined total'
using_totals = True
single_totals = True
if just_totals:
if not single_totals:
the_threshold = set_threshold(df2, threshold, prinf = print_info)
df = combiney(df, df2, operation = operation, threshold = the_threshold, prinf = print_info)
# if doing keywording...
if operation.startswith('k'):
from corpkit.keys import keywords
# allow saved dicts to be df2, etc
try:
if dataframe2 == 'self':
df2 = df.copy()
except TypeError:
pass
if type(dataframe2) == str:
if dataframe2 != 'self':
df2 = dataframe2
else:
the_threshold = False
df = keywords(df, df2,
selfdrop = selfdrop,
threshold = threshold,
printstatus = print_info,
editing = True,
calc_all = calc_all,
**kwargs)
# eh?
df = df.T
# drop infinites and nans
df = df.replace([np.inf, -np.inf], np.nan)
df = df.fillna(0.0)
# resort data
if sort_by:
df = resort(df, keep_stats = keep_stats, sort_by = sort_by)
if keep_top:
if not just_totals:
df = df[list(df.columns)[:keep_top]]
else:
df = df.head(keep_top)
if just_totals:
# turn just_totals into series:
df = pd.Series(df['Combined total'], name = 'Combined total')
if df1_istotals:
if operation.startswith('k'):
try:
df = pd.Series(df.ix[dataframe1.name])
df.name = '%s: keyness' % df.name
except:
df = df.iloc[0,:]
df.name = 'keyness' % df.name
# generate totals branch if not percentage results:
if df1_istotals or operation.startswith('k'):
try:
total = pd.Series(df['Total'], name = 'Total')
except:
total = 'none'
#total = df.copy()
else:
# might be wrong if using division or something...
total = df.T.sum()
if type(df) == pandas.core.frame.DataFrame:
datatype = df.ix[0].dtype
else:
datatype = df.dtype
#make named_tuple
the_operation = 'none'
if using_totals:
the_operation = operation
the_options = {}
the_options['time_started'] = the_time_started
the_options['function'] = 'editor'
the_options['dataframe1'] = dataframe1
the_options['operation'] = the_operation
the_options['dataframe2'] = dataframe2
the_options['datatype'] = datatype
the_options['sort_by'] = sort_by
the_options['keep_stats'] = keep_stats
the_options['just_totals'] = just_totals
the_options['threshold'] = threshold # can be wrong!
the_options['just_entries'] = just_entries
the_options['just_entries'] = just_entries
the_options['skip_entries'] = skip_entries
the_options['merge_entries'] = merge_entries
the_options['newname'] = newname
the_options['just_subcorpora'] = just_subcorpora
the_options['skip_subcorpora'] = skip_subcorpora
the_options['span_subcorpora'] = span_subcorpora
the_options['merge_subcorpora'] = merge_subcorpora
the_options['new_subcorpus_name'] = new_subcorpus_name
the_options['projection'] = projection
the_options['remove_above_p'] = remove_above_p
the_options['p'] = p
the_options['revert_year'] = revert_year
the_options['print_info'] = print_info
outputnames = collections.namedtuple('edited_interrogation', ['query', 'results', 'totals'])
output = outputnames(the_options, df, total)
#print '\nResult (sample)\n'
if print_info:
#if merge_entries or merge_subcorpora or span_subcorpora or just_subcorpora or \
#just_entries or skip_entries or skip_subcorpora or printed_th or projection:
print '***Done!***\n========================\n'
#print df.head().T
#print ''
if operation.startswith('k') or just_totals or df1_istotals:
pd.set_option('display.max_rows', 30)
else:
pd.set_option('display.max_rows', 15)
pd.set_option('display.max_columns', 8)
pd.set_option('max_colwidth',70)
pd.set_option('display.width', 800)
pd.set_option('expand_frame_repr', False)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
return output
def editor(interrogation,
operation=None,
denominator=False,
sort_by=False,
keep_stats=False,
keep_top=False,
just_totals=False,
threshold='medium',
just_entries=False,
skip_entries=False,
span_entries=False,
merge_entries=False,
just_subcorpora=False,
skip_subcorpora=False,
span_subcorpora=False,
merge_subcorpora=False,
replace_names=False,
replace_subcorpus_names=False,
projection=False,
remove_above_p=False,
p=0.05,
print_info=False,
spelling=False,
selfdrop=True,
calc_all=True,
keyword_measure='ll',
**kwargs):
"""
See corpkit.interrogation.Interrogation.edit() for docstring
"""
# grab arguments, in case we get dict input and have to iterate
locs = locals()
import corpkit
import re
import collections
import pandas as pd
import numpy as np
from pandas import DataFrame, Series
from time import localtime, strftime
try:
get_ipython().getoutput()
except TypeError:
have_ipython = True
except NameError:
have_ipython = False
try:
from IPython.display import display, clear_output
except ImportError:
pass
# new ipython error
except AttributeError:
have_ipython = False
pass
# to use if we also need to worry about concordance lines
return_conc = False
from corpkit.interrogation import Interrodict, Interrogation, Concordance
if interrogation.__class__ == Interrodict:
locs.pop('interrogation', None)
from collections import OrderedDict
outdict = OrderedDict()
for i, (k, v) in enumerate(interrogation.items()):
# only print the first time around
if i != 0:
locs['print_info'] = False
if isinstance(denominator,
STRINGTYPE) and denominator.lower() == 'self':
denominator = interrogation
# if df2 is also a dict, get the relevant entry
if isinstance(denominator, (dict, Interrodict)):
#if sorted(set([i.lower() for i in list(dataframe1.keys())])) == \
# sorted(set([i.lower() for i in list(denominator.keys())])):
# locs['denominator'] = denominator[k]
# fix: this repeats itself for every key, when it doesn't need to
# denominator_sum:
if kwargs.get('denominator_sum'):
locs['denominator'] = denominator.collapse(axis='key')
if kwargs.get('denominator_totals'):
locs['denominator'] = denominator[k].totals
else:
locs['denominator'] = denominator[k].results
outdict[k] = v.results.edit(**locs)
if print_info:
thetime = strftime("%H:%M:%S", localtime())
print(
"\n%s: Finished! Output is a dictionary with keys:\n\n '%s'\n"
% (thetime, "'\n '".join(sorted(outdict.keys()))))
return Interrodict(outdict)
elif isinstance(interrogation, (DataFrame, Series)):
dataframe1 = interrogation
elif isinstance(interrogation, Interrogation):
#if interrogation.__dict__.get('concordance', None) is not None:
# concordances = interrogation.concordance
branch = kwargs.pop('branch', 'results')
if branch.lower().startswith('r'):
dataframe1 = interrogation.results
elif branch.lower().startswith('t'):
dataframe1 = interrogation.totals
elif branch.lower().startswith('c'):
dataframe1 = interrogation.concordance
return_conc = True
else:
dataframe1 = interrogation.results
elif isinstance(interrogation, Concordance) or \
all(x in list(dataframe1.columns) for x in [ 'l', 'm', 'r']):
return_conc = True
print('heree')
dataframe1 = interrogation
# hope for the best
else:
dataframe1 = interrogation
the_time_started = strftime("%Y-%m-%d %H:%M:%S")
pd.options.mode.chained_assignment = None
try:
from process import checkstack
except ImportError:
from corpkit.process import checkstack
if checkstack('pythontex'):
print_info = False
def combiney(df, df2, operation='%', threshold='medium', prinf=True):
"""
Mash df and df2 together in appropriate way
"""
totals = False
# delete under threshold
if just_totals:
if using_totals:
if not single_totals:
to_drop = list(
df2[df2['Combined total'] < threshold].index)
df = df.drop([e for e in to_drop if e in list(df.index)])
if prinf:
to_show = []
[to_show.append(w) for w in to_drop[:5]]
if len(to_drop) > 10:
to_show.append('...')
[to_show.append(w) for w in to_drop[-5:]]
if len(to_drop) > 0:
print(
'Removing %d entries below threshold:\n %s'
% (len(to_drop), '\n '.join(to_show)))
if len(to_drop) > 10:
print('... and %d more ... \n' %
(len(to_drop) - len(to_show) + 1))
else:
print('')
else:
denom = df2
else:
denom = list(df2)
if single_totals:
if operation == '%':
totals = df.sum() * 100.0 / float(df.sum().sum())
df = df * 100.0
try:
df = df.div(denom, axis=0)
except ValueError:
thetime = strftime("%H:%M:%S", localtime())
print(
'%s: cannot combine DataFrame 1 and 2: different shapes'
% thetime)
elif operation == '+':
try:
df = df.add(denom, axis=0)
except ValueError:
thetime = strftime("%H:%M:%S", localtime())
print(
'%s: cannot combine DataFrame 1 and 2: different shapes'
% thetime)
elif operation == '-':
try:
df = df.sub(denom, axis=0)
except ValueError:
thetime = strftime("%H:%M:%S", localtime())
print(
'%s: cannot combine DataFrame 1 and 2: different shapes'
% thetime)
elif operation == '*':
totals = df.sum() * float(df.sum().sum())
try:
df = df.mul(denom, axis=0)
except ValueError:
thetime = strftime("%H:%M:%S", localtime())
print(
'%s: cannot combine DataFrame 1 and 2: different shapes'
% thetime)
elif operation == '/':
try:
totals = df.sum() / float(df.sum().sum())
df = df.div(denom, axis=0)
except ValueError:
thetime = strftime("%H:%M:%S", localtime())
print(
'%s: cannot combine DataFrame 1 and 2: different shapes'
% thetime)
elif operation == 'a':
for c in [c for c in list(df.columns) if int(c) > 1]:
df[c] = df[c] * (1.0 / int(c))
df = df.sum(axis=1) / df2
elif operation.startswith('c'):
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
df = pandas.concat([df, df2], axis=1)
return df, totals
elif not single_totals:
if not operation.startswith('a'):
# generate totals
if operation == '%':
totals = df.sum() * 100.0 / float(df2.sum().sum())
if operation == '*':
totals = df.sum() * float(df2.sum().sum())
if operation == '/':
totals = df.sum() / float(df2.sum().sum())
if operation.startswith('c'):
# add here the info that merging will not work
# with identical colnames
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
d = pd.concat([df.T, df2.T])
# make index nums
d = d.reset_index()
# sum and remove duplicates
d = d.groupby('index').sum()
dx = d.reset_index('index')
dx.index = list(dx['index'])
df = dx.drop('index', axis=1).T
def editf(datum):
meth = {
'%': datum.div,
'*': datum.mul,
'/': datum.div,
'+': datum.add,
'-': datum.sub
}
if datum.name in list(df2.columns):
method = meth[operation]
mathed = method(df2[datum.name], fill_value=0.0)
if operation == '%':
return mathed * 100.0
else:
return mathed
else:
return datum * 0.0
df = df.apply(editf)
else:
for c in [c for c in list(df.columns) if int(c) > 1]:
df[c] = df[c] * (1.0 / int(c))
df = df.sum(axis=1) / df2.T.sum()
return df, totals
def skip_keep_merge_span(df):
"""
Do all skipping, keeping, merging and spanning
"""
from corpkit.dictionaries.process_types import Wordlist
if skip_entries:
if isinstance(skip_entries, (list, Wordlist)):
df = df.drop(list(skip_entries), axis=1, errors='ignore')
else:
df = df.loc[:, ~df.columns.str.contains(skip_entries)]
if just_entries:
if isinstance(just_entries, (list, Wordlist)):
je = [i for i in list(just_entries) if i in list(df.columns)]
df = df[je]
else:
df = df.loc[:, df.columns.str.contains(just_entries)]
if merge_entries:
for newname, crit in merge_entries.items():
if isinstance(crit, (list, Wordlist)):
crit = [i for i in list(crit) if i in list(df.columns)]
cr = [i for i in list(crit) if i in list(df.columns)]
summed = df[cr].sum(axis=1)
df = df.drop(list(cr), axis=1, errors='ignore')
else:
summed = df.loc[:,
df.columns.str.contains(crit)].sum(axis=1)
df = df.loc[:, ~df.columns.str.contains(crit)]
df.insert(0, newname, summed, allow_duplicates=True)
if span_entries:
df = df.iloc[:, span_entries[0]:span_entries[1]]
if skip_subcorpora:
if isinstance(skip_subcorpora, (list, Wordlist)):
df = df.drop(list(skip_subcorpora), axis=0, errors='ignore')
else:
df = df[~df.index.str.contains(skip_subcorpora)]
if just_subcorpora:
if isinstance(just_subcorpora, (list, Wordlist)):
js = [i for i in list(just_subcorpora) if i in list(df.index)]
df = df.loc[js]
else:
df = df[df.index.str.contains(just_subcorpora)]
if merge_subcorpora:
df = df.T
for newname, crit in merge_subcorpora.items():
if isinstance(crit, (list, Wordlist)):
crit = [i for i in list(crit) if i in list(df.columns)]
summed = df[list(crit)].sum(axis=1)
df = df.drop(list(crit), axis=1, errors='ignore')
else:
summed = df.loc[:,
df.columns.str.contains(crit)].sum(axis=1)
df = df.loc[:, ~df.columns.str.contains(crit)]
df.insert(0, newname, summed, allow_duplicates=True)
df = df.T
if span_subcorpora:
df = df.iloc[span_subcorpora[0]:span_subcorpora[1], :]
return df
def parse_input(df, the_input):
"""turn whatever has been passed in into list of words that can
be used as pandas indices---maybe a bad way to go about it"""
parsed_input = False
import re
if the_input == 'all':
the_input = r'.*'
if isinstance(the_input, int):
try:
the_input = str(the_input)
except:
pass
the_input = [the_input]
elif isinstance(the_input, STRINGTYPE):
regex = re.compile(the_input)
parsed_input = [w for w in list(df) if re.search(regex, w)]
return parsed_input
from corpkit.dictionaries.process_types import Wordlist
if isinstance(the_input, Wordlist) or the_input.__class__ == Wordlist:
the_input = list(the_input)
if isinstance(the_input, list):
if isinstance(the_input[0], int):
parsed_input = [
word for index, word in enumerate(list(df))
if index in the_input
]
elif isinstance(the_input[0], STRINGTYPE):
try:
parsed_input = [
word for word in the_input if word in df.columns
]
except AttributeError: # if series
parsed_input = [
word for word in the_input if word in df.index
]
return parsed_input
def synonymise(df, pos='n'):
"""pass a df and a pos and convert df columns to most common synonyms"""
from nltk.corpus import wordnet as wn
#from dictionaries.taxonomies import taxonomies
from collections import Counter
fixed = []
for w in list(df.columns):
try:
syns = []
for syns in wn.synsets(w, pos=pos):
for w in syns:
synonyms.append(w)
top_syn = Counter(syns).most_common(1)[0][0]
fixed.append(top_syn)
except:
fixed.append(w)
df.columns = fixed
return df
def convert_spell(df, convert_to='US', print_info=print_info):
"""turn dataframes into us/uk spelling"""
from dictionaries.word_transforms import usa_convert
if print_info:
print('Converting spelling ... \n')
if convert_to == 'UK':
usa_convert = {v: k for k, v in list(usa_convert.items())}
fixed = []
for val in list(df.columns):
try:
fixed.append(usa_convert[val])
except:
fixed.append(val)
df.columns = fixed
return df
def merge_duplicates(df, print_info=print_info):
if print_info:
print('Merging duplicate entries ... \n')
# now we have to merge all duplicates
for dup in df.columns.get_duplicates():
#num_dupes = len(list(df[dup].columns))
temp = df[dup].sum(axis=1)
#df = df.drop([dup for d in range(num_dupes)], axis=1)
df = df.drop(dup, axis=1)
df[dup] = temp
return df
def name_replacer(df, replace_names, print_info=print_info):
"""replace entry names and merge"""
import re
# get input into list of tuples
# if it's a string, we want to delete it
if isinstance(replace_names, STRINGTYPE):
replace_names = [(replace_names, '')]
# this is for some malformed list
if not isinstance(replace_names, dict):
if isinstance(replace_names[0], STRINGTYPE):
replace_names = [replace_names]
# if dict, make into list of tupes
if isinstance(replace_names, dict):
replace_names = [(v, k) for k, v in replace_names.items()]
for to_find, replacement in replace_names:
if print_info:
if replacement:
print('Replacing "%s" with "%s" ...\n' %
(to_find, replacement))
else:
print('Deleting "%s" from entry names ...\n' % to_find)
to_find = re.compile(to_find)
if not replacement:
replacement = ''
df.columns = [
re.sub(to_find, replacement, l) for l in list(df.columns)
]
df = merge_duplicates(df, print_info=False)
return df
def newname_getter(df,
parsed_input,
newname='combine',
prinf=True,
merging_subcorpora=False):
"""makes appropriate name for merged entries"""
if merging_subcorpora:
if newname is False:
newname = 'combine'
if isinstance(newname, int):
the_newname = list(df.columns)[newname]
elif isinstance(newname, STRINGTYPE):
if newname == 'combine':
if len(parsed_input) <= 3:
the_newname = '/'.join(parsed_input)
elif len(parsed_input) > 3:
the_newname = '/'.join(parsed_input[:3]) + '...'
else:
the_newname = newname
if not newname:
# revise this code
import operator
sumdict = {}
for item in parsed_input:
summed = sum(list(df[item]))
sumdict[item] = summed
the_newname = max(iter(sumdict.items()),
key=operator.itemgetter(1))[0]
if not isinstance(the_newname, STRINGTYPE):
the_newname = str(the_newname, errors='ignore')
return the_newname
def projector(df, list_of_tuples, prinf=True):
"""project abs values"""
if isinstance(list_of_tuples, list):
tdict = {}
for a, b in list_of_tuples:
tdict[a] = b
list_of_tuples = tdict
for subcorpus, projection_value in list(list_of_tuples.items()):
if isinstance(subcorpus, int):
subcorpus = str(subcorpus)
df.ix[subcorpus] = df.ix[subcorpus] * projection_value
if prinf:
if isinstance(projection_value, float):
print('Projection: %s * %s' %
(subcorpus, projection_value))
if isinstance(projection_value, int):
print('Projection: %s * %d' %
(subcorpus, projection_value))
if prinf:
print('')
return df
def lingres(ser, index):
from scipy.stats import linregress
from pandas import Series
ix = ['slope', 'intercept', 'r', 'p', 'stderr']
return Series(linregress(index, ser.values), index=ix)
def do_stats(df):
"""do linregress and add to df"""
try:
from scipy.stats import linregress
except ImportError:
thetime = strftime("%H:%M:%S", localtime())
print('%s: sort type not available in this version of corpkit.' %
thetime)
return False
indices = list(df.index)
first_year = list(df.index)[0]
try:
x = [int(y) - int(first_year) for y in indices]
except ValueError:
x = list(range(len(indices)))
stats = df.apply(lingres, axis=0, index=x)
df = df.append(stats)
df = df.replace([np.inf, -np.inf], 0.0)
return df
def resort(df, sort_by=False, keep_stats=False):
"""
Sort results, potentially using scipy's linregress
"""
# translate options and make sure they are parseable
stat_field = ['slope', 'intercept', 'r', 'p', 'stderr']
easy_sorts = ['total', 'infreq', 'name', 'most', 'least', 'reverse']
stat_sorts = ['increase', 'decrease', 'static', 'turbulent']
options = stat_field + easy_sorts + stat_sorts
sort_by_convert = {'most': 'total', True: 'total', 'least': 'infreq'}
sort_by = sort_by_convert.get(sort_by, sort_by)
# probably broken :(
if just_totals:
if sort_by == 'name':
return df.sort_index()
else:
return df.sort_values(by='Combined total',
ascending=sort_by != 'total',
axis=1)
stats_done = False
if keep_stats or sort_by in stat_field + stat_sorts:
df = do_stats(df)
stats_done = True
if isinstance(df, bool):
if df is False:
return False
if isinstance(df, Series):
if stats_done:
stats = df.ix[range(-5, 0)]
df = df.drop(list(stats.index))
if sort_by == 'name':
df = df.sort_index()
elif sort_by == 'reverse':
df = df[::-1]
else:
df = df.sort_values(ascending=sort_by != 'total')
if stats_done:
df = df.append(stats)
return df
if sort_by == 'name':
# currently case sensitive
df = df.reindex_axis(sorted(df.columns), axis=1)
elif sort_by in ['total', 'infreq']:
if df1_istotals:
df = df.T
df = df[list(
df.sum().sort_values(ascending=sort_by != 'total').index)]
elif sort_by == 'reverse':
df = df.T[::-1].T
# sort by slope etc., or search by subcorpus name
if sort_by in stat_field or sort_by not in options:
asc = kwargs.get('reverse', False)
df = df.T.sort_values(by=sort_by, ascending=asc).T
if sort_by in ['increase', 'decrease', 'static', 'turbulent']:
slopes = df.ix['slope']
if sort_by == 'increase':
df = df[slopes.argsort()[::-1]]
elif sort_by == 'decrease':
df = df[slopes.argsort()]
elif sort_by == 'static':
df = df[slopes.abs().argsort()]
elif sort_by == 'turbulent':
df = df[slopes.abs().argsort()[::-1]]
if remove_above_p:
df = df.T
df = df[df['p'] <= p]
df = df.T
# remove stats field by default
if not keep_stats:
df = df.drop(stat_field, axis=0, errors='ignore')
return df
def set_threshold(big_list, threshold, prinf=True):
if isinstance(threshold, STRINGTYPE):
if threshold.startswith('l'):
denominator = 10000
if threshold.startswith('m'):
denominator = 5000
if threshold.startswith('h'):
denominator = 2500
if isinstance(big_list, DataFrame):
tot = big_list.sum().sum()
if isinstance(big_list, Series):
tot = big_list.sum()
tshld = float(tot) / float(denominator)
else:
tshld = threshold
if prinf:
print('Threshold: %d\n' % tshld)
return tshld
# copy dataframe to be very safe
df = dataframe1.copy()
# make cols into strings
try:
df.columns = [str(c) for c in list(df.columns)]
except:
pass
if operation is None:
operation = 'None'
if isinstance(interrogation, Concordance):
return_conc = True
# do concordance work
if return_conc:
if just_entries:
if isinstance(just_entries, int):
just_entries = [just_entries]
if isinstance(just_entries, STRINGTYPE):
df = df[df['m'].str.contains(just_entries)]
if isinstance(just_entries, list):
if all(isinstance(e, STRINGTYPE) for e in just_entries):
mp = df['m'].map(lambda x: x in just_entries)
df = df[mp]
else:
df = df.ix[just_entries]
if skip_entries:
if isinstance(skip_entries, int):
skip_entries = [skip_entries]
if isinstance(skip_entries, STRINGTYPE):
df = df[~df['m'].str.contains(skip_entries)]
if isinstance(skip_entries, list):
if all(isinstance(e, STRINGTYPE) for e in skip_entries):
mp = df['m'].map(lambda x: x not in skip_entries)
df = df[mp]
else:
df = df.drop(skip_entries, axis=0)
if just_subcorpora:
if isinstance(just_subcorpora, int):
just_subcorpora = [just_subcorpora]
if isinstance(just_subcorpora, STRINGTYPE):
df = df[df['c'].str.contains(just_subcorpora)]
if isinstance(just_subcorpora, list):
if all(isinstance(e, STRINGTYPE) for e in just_subcorpora):
mp = df['c'].map(lambda x: x in just_subcorpora)
df = df[mp]
else:
df = df.ix[just_subcorpora]
if skip_subcorpora:
if isinstance(skip_subcorpora, int):
skip_subcorpora = [skip_subcorpora]
if isinstance(skip_subcorpora, STRINGTYPE):
df = df[~df['c'].str.contains(skip_subcorpora)]
if isinstance(skip_subcorpora, list):
if all(isinstance(e, STRINGTYPE) for e in skip_subcorpora):
mp = df['c'].map(lambda x: x not in skip_subcorpora)
df = df[mp]
else:
df = df.drop(skip_subcorpora, axis=0)
return Concordance(df)
if print_info:
print('\n***Processing results***\n========================\n')
df1_istotals = False
if isinstance(df, Series):
df1_istotals = True
df = DataFrame(df)
# if just a single result
else:
df = DataFrame(df)
if operation.startswith('k'):
if sort_by is False:
if not df1_istotals:
sort_by = 'turbulent'
if df1_istotals:
df = df.T
# figure out if there's a second list
# copy and remove totals if there is
single_totals = True
using_totals = False
outputmode = False
if denominator.__class__ == Interrogation:
try:
denominator = denominator.results
except AttributeError:
denominator = denominator.totals
if denominator is not False and not isinstance(denominator, STRINGTYPE):
df2 = denominator.copy()
using_totals = True
if isinstance(df2, DataFrame):
if len(df2.columns) > 1:
single_totals = False
else:
df2 = Series(df2.iloc[:, 0])
elif isinstance(df2, Series):
single_totals = True
#if operation == 'k':
#raise ValueError('Keywording requires a DataFrame for denominator. Use "self"?')
else:
if operation in ['k', 'a', '%', '/', '*', '-', '+']:
denominator = 'self'
if denominator == 'self':
outputmode = True
if operation.startswith('a') or operation.startswith('A'):
if list(df.columns)[0] != '0' and list(df.columns)[0] != 0:
df = df.T
if using_totals:
if not single_totals:
df2 = df2.T
if projection:
# projection shouldn't do anything when working with '%', remember.
df = projector(df, projection)
if using_totals:
df2 = projector(df2, projection)
if spelling:
df = convert_spell(df, convert_to=spelling)
df = merge_duplicates(df, print_info=False)
if not single_totals:
df2 = convert_spell(df2, convert_to=spelling, print_info=False)
df2 = merge_duplicates(df2, print_info=False)
if not df1_istotals:
sort_by = 'total'
if replace_names:
df = name_replacer(df, replace_names)
df = merge_duplicates(df)
if not single_totals:
df2 = name_replacer(df2, replace_names, print_info=False)
df2 = merge_duplicates(df2, print_info=False)
if not sort_by:
sort_by = 'total'
if replace_subcorpus_names:
df = name_replacer(df.T, replace_subcorpus_names)
df = merge_duplicates(df).T
df = df.sort_index()
if not single_totals:
if isinstance(df2, DataFrame):
df2 = df2.T
df2 = name_replacer(df2, replace_subcorpus_names, print_info=False)
df2 = merge_duplicates(df2, print_info=False)
if isinstance(df2, DataFrame):
df2 = df2.T
df2 = df2.sort_index()
if not sort_by:
sort_by = 'total'
# remove old stats if they're there:
statfields = ['slope', 'intercept', 'r', 'p', 'stderr']
try:
df = df.drop(statfields, axis=0)
except:
pass
if using_totals:
try:
df2 = df2.drop(statfields, axis=0)
except:
pass
# remove totals and tkinter order
for name, ax in zip(['Total'] * 2 + ['tkintertable-order'] * 2,
[0, 1, 0, 1]):
if name == 'Total' and df1_istotals:
continue
try:
df = df.drop(name, axis=ax, errors='ignore')
except:
pass
for name, ax in zip(['Total'] * 2 + ['tkintertable-order'] * 2,
[0, 1, 0, 1]):
if name == 'Total' and single_totals:
continue
try:
df2 = df2.drop(name, axis=ax, errors='ignore')
except:
pass
df = skip_keep_merge_span(df)
try:
df2 = skip_keep_merge_span(df2)
except:
pass
# drop infinites and nans
df = df.replace([np.inf, -np.inf], np.nan)
df = df.fillna(0.0)
if just_totals:
df = DataFrame(df.sum(), columns=['Combined total'])
if using_totals:
if not single_totals:
df2 = DataFrame(df2.sum(), columns=['Combined total'])
else:
df2 = df2.sum()
tots = df.sum(axis=1)
if using_totals or outputmode:
if not operation.startswith('k'):
tshld = 0
# set a threshold if just_totals
if outputmode is True:
df2 = df.T.sum()
if not just_totals:
df2.name = 'Total'
else:
df2.name = 'Combined total'
using_totals = True
single_totals = True
if just_totals:
if not single_totals:
tshld = set_threshold(df2, threshold, prinf=print_info)
df, tots = combiney(df,
df2,
operation=operation,
threshold=tshld,
prinf=print_info)
# if doing keywording...
if operation.startswith('k'):
if isinstance(denominator, STRINGTYPE):
if denominator == 'self':
df2 = df.copy()
else:
df2 = denominator
from corpkit.keys import keywords
df = keywords(df,
df2,
selfdrop=selfdrop,
threshold=threshold,
print_info=print_info,
editing=True,
calc_all=calc_all,
sort_by=sort_by,
measure=keyword_measure,
**kwargs)
# drop infinites and nans
df = df.replace([np.inf, -np.inf], np.nan)
df = df.fillna(0.0)
# resort data
if sort_by or keep_stats:
df = resort(df, keep_stats=keep_stats, sort_by=sort_by)
if isinstance(df, bool):
if df is False:
return 'linregress'
if keep_top:
if not just_totals:
df = df[list(df.columns)[:keep_top]]
else:
df = df.head(keep_top)
if just_totals:
# turn just_totals into series:
df = Series(df['Combined total'], name='Combined total')
if df1_istotals:
if operation.startswith('k'):
try:
df = Series(df.ix[dataframe1.name])
df.name = '%s: keyness' % df.name
except:
df = df.iloc[0, :]
df.name = 'keyness' % df.name
# generate totals branch if not percentage results:
# fix me
if df1_istotals or operation.startswith('k'):
if not just_totals:
try:
total = Series(df['Total'], name='Total')
except:
total = 'none'
pass
#total = df.copy()
else:
total = 'none'
else:
# might be wrong if using division or something...
try:
total = df.T.sum(axis=1)
except:
total = 'none'
if not isinstance(tots, DataFrame) and not isinstance(tots, Series):
total = df.sum(axis=1)
else:
total = tots
if isinstance(df, DataFrame):
if df.empty:
datatype = 'object'
else:
datatype = df.iloc[0].dtype
else:
datatype = df.dtype
locs['datatype'] = datatype
# TURN INT COL NAMES INTO STR
try:
df.results.columns = [str(d) for d in list(df.results.columns)]
except:
pass
def add_tkt_index(df):
"""add an order for tkintertable if using gui"""
if isinstance(df, Series):
df = df.T
df = df.drop('tkintertable-order', errors='ignore', axis=0)
df = df.drop('tkintertable-order', errors='ignore', axis=1)
dat = [i for i in range(len(df.index))]
df['tkintertable-order'] = Series(dat, index=list(df.index))
df = df.T
return df
# while tkintertable can't sort rows
if checkstack('tkinter'):
df = add_tkt_index(df)
if kwargs.get('df1_always_df'):
if isinstance(df, Series):
df = DataFrame(df)
# delete non-appearing conc lines
lns = None
if isinstance(getattr(interrogation, 'concordance', None), Concordance):
try:
col_crit = interrogation.concordance['m'].map(
lambda x: x in list(df.columns))
ind_crit = interrogation.concordance['c'].map(
lambda x: x in list(df.index))
lns = interrogation.concordance[col_crit]
lns = lns.loc[ind_crit]
lns = Concordance(lns)
except ValueError:
lns = None
output = Interrogation(results=df,
totals=total,
query=locs,
concordance=lns)
if print_info:
print('***Done!***\n========================\n')
return output
def editor(interrogation,
operation=None,
denominator=False,
sort_by=False,
keep_stats=False,
keep_top=False,
just_totals=False,
threshold='medium',
just_entries=False,
skip_entries=False,
merge_entries=False,
just_subcorpora=False,
skip_subcorpora=False,
span_subcorpora=False,
merge_subcorpora=False,
replace_names=False,
replace_subcorpus_names=False,
projection=False,
remove_above_p=False,
p=0.05,
print_info=False,
spelling=False,
selfdrop=True,
calc_all=True,
keyword_measure='ll',
**kwargs
):
"""
See corpkit.interrogation.Interrogation.edit() for docstring
"""
# grab arguments, in case we get dict input and have to iterate
locs = locals()
import corpkit
import re
import collections
import pandas as pd
import numpy as np
from pandas import DataFrame, Series
from time import localtime, strftime
try:
get_ipython().getoutput()
except TypeError:
have_ipython = True
except NameError:
have_ipython = False
try:
from IPython.display import display, clear_output
except ImportError:
pass
# to use if we also need to worry about concordance lines
return_conc = False
from corpkit.interrogation import Interrodict, Interrogation, Concordance
if interrogation.__class__ == Interrodict:
locs.pop('interrogation', None)
from collections import OrderedDict
outdict = OrderedDict()
for i, (k, v) in enumerate(interrogation.items()):
# only print the first time around
if i != 0:
locs['print_info'] = False
if isinstance(denominator, STRINGTYPE) and denominator.lower() == 'self':
denominator = interrogation
# if df2 is also a dict, get the relevant entry
if isinstance(denominator, (dict, Interrodict)):
#if sorted(set([i.lower() for i in list(dataframe1.keys())])) == \
# sorted(set([i.lower() for i in list(denominator.keys())])):
# locs['denominator'] = denominator[k]
# fix: this repeats itself for every key, when it doesn't need to
# denominator_sum:
if kwargs.get('denominator_sum'):
locs['denominator'] = denominator.collapse(axis='key')
if kwargs.get('denominator_totals'):
locs['denominator'] = denominator[k].totals
else:
locs['denominator'] = denominator[k].results
outdict[k] = v.results.edit(**locs)
if print_info:
thetime = strftime("%H:%M:%S", localtime())
print("\n%s: Finished! Output is a dictionary with keys:\n\n '%s'\n" % (thetime, "'\n '".join(sorted(outdict.keys()))))
return Interrodict(outdict)
elif isinstance(interrogation, (DataFrame, Series)):
dataframe1 = interrogation
elif isinstance(interrogation, Interrogation):
#if interrogation.__dict__.get('concordance', None) is not None:
# concordances = interrogation.concordance
branch = kwargs.pop('branch', 'results')
if branch.lower().startswith('r') :
dataframe1 = interrogation.results
elif branch.lower().startswith('t'):
dataframe1 = interrogation.totals
elif branch.lower().startswith('c'):
dataframe1 = interrogation.concordance
return_conc = True
else:
dataframe1 = interrogation.results
elif isinstance(interrogation, Concordance) or \
all(x in list(dataframe1.columns) for x in [ 'l', 'm', 'r']):
return_conc = True
print('heree')
dataframe1 = interrogation
# hope for the best
else:
dataframe1 = interrogation
the_time_started = strftime("%Y-%m-%d %H:%M:%S")
pd.options.mode.chained_assignment = None
try:
from process import checkstack
except ImportError:
from corpkit.process import checkstack
if checkstack('pythontex'):
print_info=False
def combiney(df, df2, operation='%', threshold='medium', prinf=True):
"""mash df and df2 together in appropriate way"""
totals = False
# delete under threshold
if just_totals:
if using_totals:
if not single_totals:
to_drop = list(df2[df2['Combined total'] < threshold].index)
df = df.drop([e for e in to_drop if e in list(df.index)])
if prinf:
to_show = []
[to_show.append(w) for w in to_drop[:5]]
if len(to_drop) > 10:
to_show.append('...')
[to_show.append(w) for w in to_drop[-5:]]
if len(to_drop) > 0:
print('Removing %d entries below threshold:\n %s' % (len(to_drop), '\n '.join(to_show)))
if len(to_drop) > 10:
print('... and %d more ... \n' % (len(to_drop) - len(to_show) + 1))
else:
print('')
else:
denom = df2
else:
denom = list(df2)
if single_totals:
if operation == '%':
totals = df.sum() * 100.0 / float(df.sum().sum())
df = df * 100.0
try:
df = df.div(denom, axis=0)
except ValueError:
thetime = strftime("%H:%M:%S", localtime())
print('%s: cannot combine DataFrame 1 and 2: different shapes' % thetime)
elif operation == '+':
try:
df = df.add(denom, axis=0)
except ValueError:
thetime = strftime("%H:%M:%S", localtime())
print('%s: cannot combine DataFrame 1 and 2: different shapes' % thetime)
elif operation == '-':
try:
df = df.sub(denom, axis=0)
except ValueError:
thetime = strftime("%H:%M:%S", localtime())
print('%s: cannot combine DataFrame 1 and 2: different shapes' % thetime)
elif operation == '*':
totals = df.sum() * float(df.sum().sum())
try:
df = df.mul(denom, axis=0)
except ValueError:
thetime = strftime("%H:%M:%S", localtime())
print('%s: cannot combine DataFrame 1 and 2: different shapes' % thetime)
elif operation == '/':
try:
totals = df.sum() / float(df.sum().sum())
df = df.div(denom, axis=0)
except ValueError:
thetime = strftime("%H:%M:%S", localtime())
print('%s: cannot combine DataFrame 1 and 2: different shapes' % thetime)
elif operation == 'a':
for c in [c for c in list(df.columns) if int(c) > 1]:
df[c] = df[c] * (1.0 / int(c))
df = df.sum(axis=1) / df2
elif operation.startswith('c'):
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
df = pandas.concat([df, df2], axis=1)
return df, totals
elif not single_totals:
if not operation.startswith('a'):
# generate totals
if operation == '%':
totals = df.sum() * 100.0 / float(df2.sum().sum())
if operation == '*':
totals = df.sum() * float(df2.sum().sum())
if operation == '/':
totals = df.sum() / float(df2.sum().sum())
if operation.startswith('c'):
# add here the info that merging will not work
# with identical colnames
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
d = pd.concat([df.T, df2.T])
# make index nums
d = d.reset_index()
# sum and remove duplicates
d = d.groupby('index').sum()
dx = d.reset_index('index')
dx.index = list(dx['index'])
df = dx.drop('index', axis=1).T
def editf(datum):
meth = {'%': datum.div,
'*': datum.mul,
'/': datum.div,
'+': datum.add,
'-': datum.sub}
if datum.name in list(df2.columns):
method = meth[operation]
mathed = method(df2[datum.name], fill_value=0.0)
if operation == '%':
return mathed * 100.0
else:
return mathed
else:
return datum * 0.0
df = df.apply(editf)
else:
for c in [c for c in list(df.columns) if int(c) > 1]:
df[c] = df[c] * (1.0 / int(c))
df = df.sum(axis=1) / df2.T.sum()
return df, totals
def parse_input(df, the_input):
"""turn whatever has been passed in into list of words that can
be used as pandas indices---maybe a bad way to go about it"""
parsed_input = False
import re
if the_input == 'all':
the_input = r'.*'
if isinstance(the_input, int):
try:
the_input = str(the_input)
except:
pass
the_input = [the_input]
elif isinstance(the_input, STRINGTYPE):
regex = re.compile(the_input)
parsed_input = [w for w in list(df) if re.search(regex, w)]
return parsed_input
from corpkit.dictionaries.process_types import Wordlist
if isinstance(the_input, Wordlist) or the_input.__class__ == Wordlist:
the_input = list(the_input)
if isinstance(the_input, list):
if isinstance(the_input[0], int):
parsed_input = [word for index, word in enumerate(list(df)) if index in the_input]
elif isinstance(the_input[0], STRINGTYPE):
try:
parsed_input = [word for word in the_input if word in df.columns]
except AttributeError: # if series
parsed_input = [word for word in the_input if word in df.index]
return parsed_input
def synonymise(df, pos='n'):
"""pass a df and a pos and convert df columns to most common synonyms"""
from nltk.corpus import wordnet as wn
#from dictionaries.taxonomies import taxonomies
from collections import Counter
fixed = []
for w in list(df.columns):
try:
syns = []
for syns in wn.synsets(w, pos=pos):
for w in syns:
synonyms.append(w)
top_syn = Counter(syns).most_common(1)[0][0]
fixed.append(top_syn)
except:
fixed.append(w)
df.columns = fixed
return df
def convert_spell(df, convert_to='US', print_info=print_info):
"""turn dataframes into us/uk spelling"""
from dictionaries.word_transforms import usa_convert
if print_info:
print('Converting spelling ... \n')
if convert_to == 'UK':
usa_convert = {v: k for k, v in list(usa_convert.items())}
fixed = []
for val in list(df.columns):
try:
fixed.append(usa_convert[val])
except:
fixed.append(val)
df.columns = fixed
return df
def merge_duplicates(df, print_info=print_info):
if print_info:
print('Merging duplicate entries ... \n')
# now we have to merge all duplicates
for dup in df.columns.get_duplicates():
#num_dupes = len(list(df[dup].columns))
temp = df[dup].sum(axis=1)
#df = df.drop([dup for d in range(num_dupes)], axis=1)
df = df.drop(dup, axis=1)
df[dup] = temp
return df
def name_replacer(df, replace_names, print_info=print_info):
"""replace entry names and merge"""
import re
# get input into list of tuples
# if it's a string, we want to delete it
if isinstance(replace_names, STRINGTYPE):
replace_names = [(replace_names, '')]
# this is for some malformed list
if not isinstance(replace_names, dict):
if isinstance(replace_names[0], STRINGTYPE):
replace_names = [replace_names]
# if dict, make into list of tupes
if isinstance(replace_names, dict):
replace_names = [(v, k) for k, v in replace_names.items()]
for to_find, replacement in replace_names:
if print_info:
if replacement:
print('Replacing "%s" with "%s" ...\n' % (to_find, replacement))
else:
print('Deleting "%s" from entry names ...\n' % to_find)
to_find = re.compile(to_find)
if not replacement:
replacement = ''
df.columns = [re.sub(to_find, replacement, l) for l in list(df.columns)]
df = merge_duplicates(df, print_info=False)
return df
def just_these_entries(df, parsed_input, prinf=True):
entries = [word for word in list(df) if word not in parsed_input]
if prinf:
print('Keeping %d entries:\n %s' % \
(len(parsed_input), '\n '.join(parsed_input[:10])))
if len(parsed_input) > 10:
print('... and %d more ... \n' % (len(parsed_input) - 10))
else:
print('')
df = df.drop(entries, axis=1)
return df
def skip_these_entries(df, parsed_input, prinf=True):
if prinf:
print('Skipping %d entries:\n %s' % \
(len(parsed_input), '\n '.join(parsed_input[:10])))
if len(parsed_input) > 10:
print('... and %d more ... \n' % (len(parsed_input) - 10))
else:
print('')
df = df.drop(parsed_input, axis=1)
return df
def newname_getter(df, parsed_input, newname='combine', prinf=True, merging_subcorpora=False):
"""makes appropriate name for merged entries"""
if merging_subcorpora:
if newname is False:
newname = 'combine'
if isinstance(newname, int):
the_newname = list(df.columns)[newname]
elif isinstance(newname, STRINGTYPE):
if newname == 'combine':
if len(parsed_input) <= 3:
the_newname = '/'.join(parsed_input)
elif len(parsed_input) > 3:
the_newname = '/'.join(parsed_input[:3]) + '...'
else:
the_newname = newname
if not newname:
# revise this code
import operator
sumdict = {}
for item in parsed_input:
summed = sum(list(df[item]))
sumdict[item] = summed
the_newname = max(iter(sumdict.items()), key=operator.itemgetter(1))[0]
if not isinstance(the_newname, STRINGTYPE):
the_newname = str(the_newname, errors='ignore')
return the_newname
def merge_these_entries(df, parsed_input, the_newname, prinf=True, merging='entries'):
# make new entry with sum of parsed input
if len(parsed_input) == 0:
import warnings
warnings.warn('No %s could be automatically merged.\n' % merging)
else:
if prinf:
print('Merging %d %s as "%s":\n %s' % \
(len(parsed_input), merging, the_newname, '\n '.join(parsed_input[:10])))
if len(parsed_input) > 10:
print('... and %d more ... \n' % (len(parsed_input) - 10))
else:
print('')
# remove old entries
temp = sum([df[i] for i in parsed_input])
if isinstance(df, Series):
df = df.drop(parsed_input, errors='ignore')
nms = list(df.index)
else:
df = df.drop(parsed_input, axis=1, errors='ignore')
nms = list(df.columns)
if the_newname in nms:
df[the_newname] = df[the_newname] + temp
else:
df[the_newname] = temp
return df
def just_these_subcorpora(df, lst_of_subcorpora, prinf=True):
if isinstance(lst_of_subcorpora[0], int):
lst_of_subcorpora = [str(l) for l in lst_of_subcorpora]
good_years = [subcorpus for subcorpus in list(df.index) if subcorpus in lst_of_subcorpora]
if prinf:
print('Keeping %d subcorpora:\n %s' % (len(good_years), '\n '.join(good_years[:10])))
if len(good_years) > 10:
print('... and %d more ... \n' % (len(good_years) - 10))
else:
print('')
df = df.drop([subcorpus for subcorpus in list(df.index) if subcorpus not in good_years], axis=0)
return df
def skip_these_subcorpora(df, lst_of_subcorpora, prinf=True):
if isinstance(lst_of_subcorpora, int):
lst_of_subcorpora = [lst_of_subcorpora]
if isinstance(lst_of_subcorpora[0], int):
lst_of_subcorpora = [str(l) for l in lst_of_subcorpora]
bad_years = [subcorpus for subcorpus in list(df.index) if subcorpus in lst_of_subcorpora]
if len(bad_years) == 0:
import warnings
warnings.warn('No subcorpora skipped.\n')
else:
if prinf:
print('Skipping %d subcorpora:\n %s' % (len(bad_years), '\n '.join([str(i) for i in bad_years[:10]])))
if len(bad_years) > 10:
print('... and %d more ... \n' % (len(bad_years) - 10))
else:
print('')
df = df.drop([subcorpus for subcorpus in list(df.index) if subcorpus in bad_years], axis=0)
return df
def span_these_subcorpora(df, lst_of_subcorpora, prinf=True):
"""select only a span of suborpora (first, last)"""
fir, sec = lst_of_subcorpora
if len(lst_of_subcorpora) == 0:
import warnings
warnings.warn('Span not identified.\n')
else:
if prinf:
print('Keeping subcorpora:\n %d--%d\n' % (int(fir), int(sec)))
sbs = list(df.index)
df = df.ix[sbs.index(fir):sbs.index(sec) + 1]
return df
def projector(df, list_of_tuples, prinf=True):
"""project abs values"""
if isinstance(list_of_tuples, list):
tdict = {}
for a, b in list_of_tuples:
tdict[a] = b
list_of_tuples = tdict
for subcorpus, projection_value in list(list_of_tuples.items()):
if isinstance(subcorpus, int):
subcorpus = str(subcorpus)
df.ix[subcorpus] = df.ix[subcorpus] * projection_value
if prinf:
if isinstance(projection_value, float):
print('Projection: %s * %s' % (subcorpus, projection_value))
if isinstance(projection_value, int):
print('Projection: %s * %d' % (subcorpus, projection_value))
if prinf:
print('')
return df
def do_stats(df):
"""do linregress and add to df"""
try:
from scipy.stats import linregress
except ImportError:
thetime = strftime("%H:%M:%S", localtime())
print('%s: sort type not available in this verion of corpkit.' % thetime)
return False
indices = list(df.index)
first_year = list(df.index)[0]
try:
x = [int(y) - int(first_year) for y in indices]
except ValueError:
x = list(range(len(indices)))
statfields = ['slope', 'intercept', 'r', 'p', 'stderr']
stats = []
if isinstance(df, Series):
y = list(df.values)
sl = Series(list(linregress(x, y)), index=statfields)
else:
for entry in list(df.columns):
y = list(df[entry])
stats.append(list(linregress(x, y)))
sl = DataFrame(zip(*stats), index=statfields, columns=list(df.columns))
df = df.append(sl)
# drop infinites and nans
df = df.replace([np.inf, -np.inf], np.nan)
df = df.fillna(0.0)
return df
def resort(df, sort_by = False, keep_stats = False):
"""sort results, potentially using scipy's linregress"""
# translate options and make sure they are parseable
stat_field = ['slope', 'intercept', 'r', 'p', 'stderr']
easy_sorts = ['total', 'infreq', 'name', 'most', 'least']
stat_sorts = ['increase', 'decrease', 'static', 'turbulent']
options = stat_field + easy_sorts + stat_sorts
sort_by_convert = {'most': 'total', True: 'total', 'least': 'infreq'}
sort_by = sort_by_convert.get(sort_by, sort_by)
# probably broken :(
if just_totals:
if sort_by == 'name':
return df.sort_index()
else:
return df.sort_values(by='Combined total', ascending=sort_by != 'total', axis=1)
stats_done = False
if keep_stats or sort_by in stat_field + stat_sorts:
df = do_stats(df)
stats_done = True
if isinstance(df, bool):
if df is False:
return False
if isinstance(df, Series):
if stats_done:
stats = df.ix[range(-5, 0)]
df = df.drop(list(stats.index))
if sort_by == 'name':
df = df.sort_index()
else:
df = df.sort_values(ascending=sort_by != 'total')
if stats_done:
df = df.append(stats)
return df
if sort_by == 'name':
# currently case sensitive
df = df.reindex_axis(sorted(df.columns), axis=1)
elif sort_by in ['total', 'infreq']:
if df1_istotals:
df = df.T
df = df[list(df.sum().sort_values(ascending=sort_by != 'total').index)]
# sort by slope etc., or search by subcorpus name
if sort_by in stat_field or sort_by not in options:
asc = kwargs.get('reverse', False)
df = df.T.sort_values(by=sort_by, ascending=asc).T
if sort_by in ['increase', 'decrease', 'static', 'turbulent']:
slopes = df.ix['slope']
if sort_by == 'increase':
df = df[slopes.argsort()[::-1]]
elif sort_by == 'decrease':
df = df[slopes.argsort()]
elif sort_by == 'static':
df = df[slopes.abs().argsort()]
elif sort_by == 'turbulent':
df = df[slopes.abs().argsort()[::-1]]
if remove_above_p:
df = df.T
df = df[df['p'] <= p]
df = df.T
# remove stats field by default
if not keep_stats:
df = df.drop(stat_field, axis=0, errors='ignore')
return df
def set_threshold(big_list, threshold, prinf=True):
if isinstance(threshold, STRINGTYPE):
if threshold.startswith('l'):
denominator = 10000
if threshold.startswith('m'):
denominator = 5000
if threshold.startswith('h'):
denominator = 2500
if isinstance(big_list, DataFrame):
tot = big_list.sum().sum()
if isinstance(big_list, Series):
tot = big_list.sum()
tshld = float(tot) / float(denominator)
else:
tshld = threshold
if prinf:
print('Threshold: %d\n' % tshld)
return tshld
# copy dataframe to be very safe
df = dataframe1.copy()
# make cols into strings
try:
df.columns = [str(c) for c in list(df.columns)]
except:
pass
if operation is None:
operation = 'None'
if isinstance(interrogation, Concordance):
return_conc = True
# do concordance work
if return_conc:
if just_entries:
if isinstance(just_entries, int):
just_entries = [just_entries]
if isinstance(just_entries, STRINGTYPE):
df = df[df['m'].str.contains(just_entries)]
if isinstance(just_entries, list):
if all(isinstance(e, STRINGTYPE) for e in just_entries):
mp = df['m'].map(lambda x: x in just_entries)
df = df[mp]
else:
df = df.ix[just_entries]
if skip_entries:
if isinstance(skip_entries, int):
skip_entries = [skip_entries]
if isinstance(skip_entries, STRINGTYPE):
df = df[~df['m'].str.contains(skip_entries)]
if isinstance(skip_entries, list):
if all(isinstance(e, STRINGTYPE) for e in skip_entries):
mp = df['m'].map(lambda x: x not in skip_entries)
df = df[mp]
else:
df = df.drop(skip_entries, axis=0)
if just_subcorpora:
if isinstance(just_subcorpora, int):
just_subcorpora = [just_subcorpora]
if isinstance(just_subcorpora, STRINGTYPE):
df = df[df['c'].str.contains(just_subcorpora)]
if isinstance(just_subcorpora, list):
if all(isinstance(e, STRINGTYPE) for e in just_subcorpora):
mp = df['c'].map(lambda x: x in just_subcorpora)
df = df[mp]
else:
df = df.ix[just_subcorpora]
if skip_subcorpora:
if isinstance(skip_subcorpora, int):
skip_subcorpora = [skip_subcorpora]
if isinstance(skip_subcorpora, STRINGTYPE):
df = df[~df['c'].str.contains(skip_subcorpora)]
if isinstance(skip_subcorpora, list):
if all(isinstance(e, STRINGTYPE) for e in skip_subcorpora):
mp = df['c'].map(lambda x: x not in skip_subcorpora)
df = df[mp]
else:
df = df.drop(skip_subcorpora, axis=0)
return Concordance(df)
if print_info:
print('\n***Processing results***\n========================\n')
df1_istotals = False
if isinstance(df, Series):
df1_istotals = True
df = DataFrame(df)
# if just a single result
else:
df = DataFrame(df)
if operation.startswith('k'):
if sort_by is False:
if not df1_istotals:
sort_by = 'turbulent'
if df1_istotals:
df = df.T
# figure out if there's a second list
# copy and remove totals if there is
single_totals = True
using_totals = False
outputmode = False
if denominator.__class__ == Interrogation:
try:
denominator = denominator.results
except AttributeError:
denominator = denominator.totals
if denominator is not False and not isinstance(denominator, STRINGTYPE):
df2 = denominator.copy()
using_totals = True
if isinstance(df2, DataFrame):
if len(df2.columns) > 1:
single_totals = False
else:
df2 = Series(df2)
elif isinstance(df2, Series):
single_totals = True
#if operation == 'k':
#raise ValueError('Keywording requires a DataFrame for denominator. Use "self"?')
else:
if operation in ['k', 'a', '%', '/', '*', '-', '+']:
denominator = 'self'
if denominator == 'self':
outputmode = True
if operation.startswith('a') or operation.startswith('A'):
if list(df.columns)[0] != '0' and list(df.columns)[0] != 0:
df = df.T
if using_totals:
if not single_totals:
df2 = df2.T
if projection:
# projection shouldn't do anything when working with '%', remember.
df = projector(df, projection)
if using_totals:
df2 = projector(df2, projection)
if spelling:
df = convert_spell(df, convert_to=spelling)
df = merge_duplicates(df, print_info=False)
if not single_totals:
df2 = convert_spell(df2, convert_to=spelling, print_info=False)
df2 = merge_duplicates(df2, print_info=False)
if not df1_istotals:
sort_by = 'total'
if replace_names:
df = name_replacer(df, replace_names)
df = merge_duplicates(df)
if not single_totals:
df2 = name_replacer(df2, replace_names, print_info=False)
df2 = merge_duplicates(df2, print_info=False)
if not sort_by:
sort_by = 'total'
if replace_subcorpus_names:
df = name_replacer(df.T, replace_subcorpus_names)
df = merge_duplicates(df).T
df = df.sort_index()
if not single_totals:
if isinstance(df2, DataFrame):
df2 = df2.T
df2 = name_replacer(df2, replace_subcorpus_names, print_info=False)
df2 = merge_duplicates(df2, print_info=False)
if isinstance(df2, DataFrame):
df2 = df2.T
df2 = df2.sort_index()
if not sort_by:
sort_by = 'total'
# remove old stats if they're there:
statfields = ['slope', 'intercept', 'r', 'p', 'stderr']
try:
df = df.drop(statfields, axis=0)
except:
pass
if using_totals:
try:
df2 = df2.drop(statfields, axis=0)
except:
pass
# remove totals and tkinter order
for name, ax in zip(['Total'] * 2 + ['tkintertable-order'] * 2, [0, 1, 0, 1]):
if name == 'Total' and df1_istotals:
continue
try:
df = df.drop(name, axis=ax, errors='ignore')
except:
pass
for name, ax in zip(['Total'] * 2 + ['tkintertable-order'] * 2, [0, 1, 0, 1]):
if name == 'Total' and single_totals:
continue
try:
df2 = df2.drop(name, axis=ax, errors='ignore')
except:
pass
# merging: make dicts if they aren't already, so we can iterate
if merge_entries:
if not isinstance(merge_entries, list):
if isinstance(merge_entries, STRINGTYPE):
merge_entries = {'combine': merge_entries}
# for newname, criteria
for name, the_input in sorted(merge_entries.items()):
pin = parse_input(df, the_input)
the_newname = newname_getter(df, pin, newname=name, prinf=print_info)
df = merge_these_entries(df, pin, the_newname, prinf=print_info)
if not single_totals:
pin2 = parse_input(df2, the_input)
df2 = merge_these_entries(df2, pin2, the_newname, prinf=False)
else:
for i in merge_entries:
pin = parse_input(df, merge_entries)
the_newname = newname_getter(df, pin, prinf=print_info)
df = merge_these_entries(df, pin, the_newname, prinf=print_info)
if not single_totals:
pin2 = parse_input(df2, merge_entries)
df2 = merge_these_entries(df2, pin2, the_newname, prinf=False)
if merge_subcorpora:
if not isinstance(merge_subcorpora, dict):
if isinstance(merge_subcorpora, list):
if isinstance(merge_subcorpora[0], tuple):
merge_subcorpora = {x: y for x, y in merge_subcorpora}
elif isinstance(merge_subcorpora[0], STRINGTYPE):
merge_subcorpora = {'combine': [x for x in merge_subcorpora]}
elif isinstance(merge_subcorpora[0], int):
merge_subcorpora = {'combine': [str(x) for x in merge_subcorpora]}
else:
merge_subcorpora = {'combine': merge_subcorpora}
for name, the_input in sorted(merge_subcorpora.items()):
pin = parse_input(df.T, the_input)
the_newname = newname_getter(df.T, pin, newname=name, \
merging_subcorpora=True, prinf=print_info)
df = merge_these_entries(df.T, pin, the_newname, merging='subcorpora',
prinf=print_info).T
if using_totals:
pin2 = parse_input(df2.T, the_input)
df2 = merge_these_entries(df2.T, pin2, the_newname, merging='subcorpora',
prinf=False).T
if just_subcorpora:
df = just_these_subcorpora(df, just_subcorpora, prinf=print_info)
if using_totals:
df2 = just_these_subcorpora(df2, just_subcorpora, prinf=False)
if skip_subcorpora:
df = skip_these_subcorpora(df, skip_subcorpora, prinf=print_info)
if using_totals:
df2 = skip_these_subcorpora(df2, skip_subcorpora, prinf=False)
if span_subcorpora:
df = span_these_subcorpora(df, span_subcorpora, prinf=print_info)
if using_totals:
df2 = span_these_subcorpora(df2, span_subcorpora, prinf=False)
if just_entries:
df = just_these_entries(df, parse_input(df, just_entries), prinf=print_info)
if not single_totals:
df2 = just_these_entries(df2, parse_input(df2, just_entries), prinf=False)
if skip_entries:
df = skip_these_entries(df, parse_input(df, skip_entries), prinf=print_info)
if not single_totals:
df2 = skip_these_entries(df2, parse_input(df2, skip_entries), prinf=False)
# drop infinites and nans
df = df.replace([np.inf, -np.inf], np.nan)
df = df.fillna(0.0)
if just_totals:
df = DataFrame(df.sum(), columns=['Combined total'])
if using_totals:
if not single_totals:
df2 = DataFrame(df2.sum(), columns=['Combined total'])
else:
df2 = df2.sum()
tots = df.sum(axis=1)
if using_totals or outputmode:
if not operation.startswith('k'):
tshld = 0
# set a threshold if just_totals
if outputmode is True:
df2 = df.T.sum()
if not just_totals:
df2.name = 'Total'
else:
df2.name = 'Combined total'
using_totals = True
single_totals = True
if just_totals:
if not single_totals:
tshld = set_threshold(df2, threshold, prinf=print_info)
df, tots = combiney(df, df2, operation=operation, threshold=tshld, prinf=print_info)
# if doing keywording...
if operation.startswith('k'):
if isinstance(denominator, STRINGTYPE):
if denominator == 'self':
df2 = df.copy()
else:
df2 = denominator
from corpkit.keys import keywords
df = keywords(df, df2,
selfdrop=selfdrop,
threshold=threshold,
print_info=print_info,
editing=True,
calc_all=calc_all,
sort_by=sort_by,
measure=keyword_measure,
**kwargs)
# drop infinites and nans
df = df.replace([np.inf, -np.inf], np.nan)
df = df.fillna(0.0)
# resort data
if sort_by or keep_stats:
df = resort(df, keep_stats=keep_stats, sort_by=sort_by)
if isinstance(df, bool):
if df is False:
return 'linregress'
if keep_top:
if not just_totals:
df = df[list(df.columns)[:keep_top]]
else:
df = df.head(keep_top)
if just_totals:
# turn just_totals into series:
df = Series(df['Combined total'], name='Combined total')
if df1_istotals:
if operation.startswith('k'):
try:
df = Series(df.ix[dataframe1.name])
df.name = '%s: keyness' % df.name
except:
df = df.iloc[0, :]
df.name = 'keyness' % df.name
# generate totals branch if not percentage results:
# fix me
if df1_istotals or operation.startswith('k'):
if not just_totals:
try:
total = Series(df['Total'], name='Total')
except:
total = 'none'
pass
#total = df.copy()
else:
total = 'none'
else:
# might be wrong if using division or something...
try:
total = df.T.sum(axis=1)
except:
total = 'none'
if not isinstance(tots, DataFrame) and not isinstance(tots, Series):
total = df.sum(axis=1)
else:
total = tots
if isinstance(df, DataFrame):
datatype = df.iloc[0].dtype
else:
datatype = df.dtype
locs['datatype'] = datatype
# TURN INT COL NAMES INTO STR
try:
df.results.columns = [str(d) for d in list(df.results.columns)]
except:
pass
def add_tkt_index(df):
"""add an order for tkintertable if using gui"""
if isinstance(df, Series):
df = df.T
df = df.drop('tkintertable-order', errors='ignore', axis=0)
df = df.drop('tkintertable-order', errors='ignore', axis=1)
dat = [i for i in range(len(df.index))]
df['tkintertable-order'] = Series(dat, index=list(df.index))
df = df.T
return df
# while tkintertable can't sort rows
if checkstack('tkinter'):
df = add_tkt_index(df)
if kwargs.get('df1_always_df'):
if isinstance(df, Series):
df = DataFrame(df)
# delete non-appearing conc lines
if not hasattr(interrogation, 'concordance'):
lns = None
elif hasattr(interrogation, 'concordance') and interrogation.concordance is None:
lns = None
else:
col_crit = interrogation.concordance['m'].map(lambda x: x in list(df.columns))
ind_crit = interrogation.concordance['c'].map(lambda x: x in list(df.index))
lns = interrogation.concordance[col_crit]
lns = lns.loc[ind_crit]
lns = Concordance(lns)
output = Interrogation(results=df, totals=total, query=locs, concordance=lns)
if print_info:
print('***Done!***\n========================\n')
return output
def editor(dataframe1,
operation = None,
dataframe2 = False,
sort_by = False,
keep_stats = False,
keep_top = False,
just_totals = False,
threshold = 'medium',
just_entries = False,
skip_entries = False,
merge_entries = False,
newname = 'combine',
multiple_merge = False,
just_subcorpora = False,
skip_subcorpora = False,
span_subcorpora = False,
merge_subcorpora = False,
new_subcorpus_name = False,
replace_names = False,
projection = False,
remove_above_p = False,
p = 0.05,
revert_year = True,
print_info = True,
spelling = False,
selfdrop = True,
calc_all = True,
**kwargs
):
"""
Edit results of interrogations, calculate slopes, do keywording, sort, etc.
dataframe1: list of results or totals to edit
operation: kind of maths to do on inputted lists:
'+', '-', '/', '*', '%': self explanatory
'k': log likelihood (keywords)
'a': get distance metric (for use with interrogator 'a' option)
'd': get percent difference (alternative approach to keywording)
dataframe2: list of results or totals
if list of results, for each entry in dataframe 1, locate
entry with same name in dataframe 2, and do maths there
sort_by: calculate slope, stderr, r, p values, then sort by:
increase: highest to lowest slope value
decrease: lowest to highest slope value
turbulent: most change in y axis values
static: least change in y axis values
total/most: largest number first
infreq/least: smallest number first
name: alphabetically
keep_stats: keep/drop stats values from dataframe after sorting
keep_top: after sorting, remove all but the top n results
just_totals: sum each column and work with sums
threshold: when using results list as dataframe 2, drop values occurring
fewer than n times. If not keywording, you can use:
'high': dataframe2 total / 2500
'medium': dataframe2 total / 5000
'low': dataframe2 total / 10000
If keywording, there are smaller default thresholds
just_entries: keep only entries specified using:
regex, list of indices, list of words
skip_entries: as above
merge_entries: as above, or can take a dictionary of:
{newname: regex/wordlist/indexlist}
newname: new name for merged entries
just_subcorpora: as above
skip_subcorpora: as above
span_subcorpora: If numerical subcorpus names, give a tuple and get all
numbers in between
merge_subcorpora: as above
new_subcorpus_name: as above
replace_names: edit result names and then merge duplicate names. Use either:
a tuple: (r'regex-to-match', 'replacement text')
a string: a regex to delete
projection: a tuple of ('subcorpus-name', n) to multiply results by n
remove_above_p: delete any result over p
p: set the p value
revert_year: when doing linear regression on years, turn them into 1, 2 ...
print_info: print stuff to console showing what's being edited
spelling: convert/normalise spelling:
'US' or 'UK'
selfdrop: when keywording, try to remove target corpus from reference corpus
calc_all: when keywording, calculate words that appear in either corpus
"""
import pandas
import pandas as pd
import numpy as np
import re
import collections
from pandas import DataFrame, Series
from time import localtime, strftime
try:
get_ipython().getoutput()
except TypeError:
have_ipython = True
except NameError:
have_ipython = False
try:
from IPython.display import display, clear_output
except ImportError:
pass
pd.options.mode.chained_assignment = None
the_time_started = strftime("%Y-%m-%d %H:%M:%S")
pd.set_option('display.float_format', lambda x: '%.2f' % x)
from corpkit.tests import check_pytex
if check_pytex():
print_info = False
import signal
def signal_handler(signal, frame):
import signal
"""pause on ctrl+c, rather than just stop loop"""
#def subsig(signal, frame):
# """exit on ctrl c"""
# import sys
# sys.exit(0)
#
#signal.signal(signal.SIGINT, subsig)
import sys
from time import localtime, strftime
time = strftime("%H:%M:%S", localtime())
sel = raw_input('\n\n%s: Paused. Press return to resume, or type exit to quit: \n' % time)
if sel.startswith('e') or sel.startswith('E'):
sys.exit(0)
else:
time = strftime("%H:%M:%S", localtime())
print '%s: Interrogation resumed.\n' % time
signal.signal(signal.SIGINT, signal_handler)
def combiney(df, df2, operation = '%', threshold = 'medium', prinf = True):
"""mash df and df2 together in appropriate way"""
totals = False
# delete under threshold
if just_totals:
if using_totals:
if not single_totals:
to_drop = list(df2[df2['Combined total'] < threshold].index)
df = df.drop([e for e in to_drop if e in list(df.index)])
if prinf:
to_show = []
[to_show.append(w) for w in to_drop[:5]]
if len(to_drop) > 10:
to_show.append('...')
[to_show.append(w) for w in to_drop[-5:]]
if len(to_drop) > 0:
print 'Removing %d entries below threshold:\n %s' % (len(to_drop), '\n '.join(to_show))
if len(to_drop) > 10:
print '... and %d more ... \n' % (len(to_drop) - len(to_show) + 1)
else:
print ''
else:
denom = df2
else:
denom = list(df2)
if single_totals:
if operation == '%':
totals = df.sum() * 100.0 / float(df.sum().sum())
df = df * 100.0
df = df.div(denom, axis = 0)
elif operation == '+':
df = df.add(denom, axis = 0)
elif operation == '-':
df = df.sub(denom, axis = 0)
elif operation == '*':
totals = df.sum() * float(df.sum().sum())
df = df.mul(denom, axis = 0)
elif operation == '/':
totals = df.sum() / float(df.sum().sum())
df = df.div(denom, axis = 0)
elif operation == 'd':
#df.ix['Combined total'] = df.sum()
#to_drop = to_drop = list(df.T[df.T['Combined total'] < threshold].index)
to_drop = [n for n in list(df.columns) if df[n].sum() < threshold]
df = df.drop([e for e in to_drop if e in list(df.columns)], axis = 1)
#df.drop('Combined total')
if prinf:
to_show = []
[to_show.append(w) for w in to_drop[:5]]
if len(to_drop) > 10:
to_show.append('...')
[to_show.append(w) for w in to_drop[-5:]]
if len(to_drop) > 0:
print 'Removing %d entries below threshold:\n %s' % (len(to_drop), '\n '.join(to_show))
if len(to_drop) > 10:
print '... and %d more ... \n' % (len(to_drop) - len(to_show) + 1)
else:
print ''
# get normalised num in target corpus
norm_in_target = df.div(denom, axis = 0)
# get normalised num in reference corpus, with or without selfdrop
tot_in_ref = df.copy()
for c in list(tot_in_ref.index):
if selfdrop:
tot_in_ref.ix[c] = df.sum() - tot_in_ref.ix[c]
else:
tot_in_ref.ix[c] = df.sum()
norm_in_ref = tot_in_ref.div(df.sum().sum())
df = (norm_in_target - norm_in_ref) / norm_in_ref * 100.0
df = df.replace(float(-100.00), np.nan)
elif operation == 'a':
for c in [c for c in list(df.columns) if int(c) > 1]:
df[c] = df[c] * (1.0 / int(c))
df = df.sum(axis = 1) / df2
return df, totals
elif not single_totals:
if not operation.startswith('a'):
# generate totals
if operation == '%':
totals = df.sum() * 100.0 / float(df2.sum().sum())
if operation == '*':
totals = df.sum() * float(df2.sum().sum())
if operation == '/':
totals = df.sum() / float(df2.sum().sum())
for index, entry in enumerate(list(df.columns)):
#p.animate(index)
if operation == '%':
try:
df[entry] = df[entry] * 100.0 / df2[entry]
except:
continue
#df.drop(entry, axis = 1, inplace = True)
#df[entry] = maths_done
elif operation == '+':
try:
df[entry] = df[entry] + df2[entry]
except:
continue
elif operation == '-':
try:
df[entry] = df[entry] - df2[entry]
except:
continue
elif operation == '*':
try:
df[entry] = df[entry] * df2[entry]
except:
continue
elif operation == '/':
try:
df[entry] = df[entry] / df2[entry]
except:
continue
else:
for c in [c for c in list(df.columns) if int(c) > 1]:
df[c] = df[c] * (1.0 / int(c))
df = df.sum(axis = 1) / df2.T.sum()
#p.animate(len(list(df.columns)))
#if have_ipython:
#clear_output()
return df, totals
def parse_input(df, input):
"""turn whatever has been passed in into list of words that can
be used as pandas indices---maybe a bad way to go about it"""
import re
if input == 'all':
input = r'.*'
if type(input) == int:
try:
input = str(input)
except:
pass
input = [input]
elif type(input) == str:
try:
regex = re.compile(input)
parsed_input = [w for w in list(df) if re.search(regex, w)]
return parsed_input
except:
input = [input]
if type(input) == list:
if type(input[0]) == int:
parsed_input = [word for index, word in enumerate(list(df)) if index in input]
elif type(input[0]) == str or type(input[0]) == unicode:
try:
parsed_input = [word for word in input if word in df.columns]
except AttributeError: # if series
parsed_input = [word for word in input if word in df.index]
return parsed_input
def synonymise(df, pos = 'n'):
"""pass a df and a pos and convert df columns to most common synonyms"""
import nltk
from nltk.corpus import wordnet as wn
#from dictionaries.taxonomies import taxonomies
from collections import Counter
fixed = []
for w in list(df.columns):
try:
syns = []
for syns in wn.synsets(w, pos = pos):
for w in syns:
synonyms.append(w)
top_syn = Counter(syns).most_common(1)[0][0]
fixed.append(top_syn)
except:
fixed.append(w)
df.columns = fixed
return df
def convert_spell(df, convert_to = 'US', print_info = print_info):
"""turn dataframes into us/uk spelling"""
from dictionaries.word_transforms import usa_convert
if print_info:
print 'Converting spelling ... \n'
if convert_to == 'UK':
usa_convert = {v: k for k, v in usa_convert.items()}
fixed = []
for val in list(df.columns):
try:
fixed.append(usa_convert[val])
except:
fixed.append(val)
df.columns = fixed
return df
def merge_duplicates(df, print_info = print_info):
if print_info:
print 'Merging duplicate entries ... \n'
# now we have to merge all duplicates
for dup in df.columns.get_duplicates():
#num_dupes = len(list(df[dup].columns))
temp = df[dup].sum(axis = 1)
#df = df.drop([dup for d in range(num_dupes)], axis = 1)
df = df.drop(dup, axis = 1)
df[dup] = temp
return df
def name_replacer(df, replace_names, print_info = print_info):
"""replace entry names and merge"""
import re
# double or single nest if need be
if type(replace_names) == str:
replace_names = [(replace_names, '')]
if type(replace_names) != dict:
if type(replace_names[0]) == str:
replace_names = [replace_names]
if type(replace_names) == dict:
replace_names = [(v, k) for k, v in replace_names.items()]
for to_find, replacement in replace_names:
if print_info:
try:
print 'Replacing "%s" with "%s" ...\n' % (to_find, replacement)
except:
print 'Deleting "%s" from entry names ...\n' % (to_find)
to_find = re.compile(to_find)
try:
replacement = replacement
except:
replacement = ''
df.columns = [re.sub(to_find, replacement, l) for l in list(df.columns)]
df = merge_duplicates(df, print_info = False)
return df
def just_these_entries(df, parsed_input, prinf = True):
entries = [word for word in list(df) if word not in parsed_input]
if prinf:
print 'Keeping %d entries:\n %s' % (len(parsed_input), '\n '.join(parsed_input[:10]))
if len(parsed_input) > 10:
print '... and %d more ... \n' % (len(parsed_input) - 10)
else:
print ''
df = df.drop(entries, axis = 1)
return df
def skip_these_entries(df, parsed_input, prinf = True):
if prinf:
print 'Skipping %d entries:\n %s' % (len(parsed_input), '\n '.join(parsed_input[:10]))
if len(parsed_input) > 10:
print '... and %d more ... \n' % (len(parsed_input) - 10)
else:
print ''
df = df.drop(parsed_input, axis = 1)
return df
def newname_getter(df, parsed_input, newname = 'combine', prinf = True, merging_subcorpora = False):
"""makes appropriate name for merged entries"""
if merging_subcorpora:
if newname is False:
newname = 'combine'
if type(newname) == int:
the_newname = list(df.columns)[newname]
elif type(newname) == str:
if newname == 'combine':
if len(parsed_input) <= 3:
the_newname = '/'.join(parsed_input)
elif len(parsed_input) > 3:
the_newname = '/'.join(parsed_input[:3]) + '...'
else:
the_newname = newname
if newname is False:
# revise this code
import operator
sumdict = {}
for item in parsed_input:
summed = sum(list(df[item]))
sumdict[item] = summed
the_newname = max(sumdict.iteritems(), key=operator.itemgetter(1))[0]
if type(the_newname) != unicode:
the_newname = unicode(the_newname, errors = 'ignore')
return the_newname
def merge_these_entries(df, parsed_input, the_newname, prinf = True, merging = 'entries'):
# make new entry with sum of parsed input
if len(parsed_input) == 0:
import warnings
warnings.warn('No %s could be automatically merged.\n' % merging)
else:
if prinf:
print 'Merging %d %s as "%s":\n %s' % (len(parsed_input), merging, the_newname, '\n '.join(parsed_input[:10]))
if len(parsed_input) > 10:
print '... and %d more ... \n' % (len(parsed_input) - 10)
else:
print ''
# remove old entries
temp = sum([df[i] for i in parsed_input])
if not multiple_merge:
if type(df) == pandas.core.series.Series:
df = df.drop(parsed_input)
else:
df = df.drop(parsed_input, axis = 1)
df[the_newname] = temp
return df
def just_these_subcorpora(df, lst_of_subcorpora, prinf = True):
if type(lst_of_subcorpora[0]) == int:
lst_of_subcorpora = [str(l) for l in lst_of_subcorpora]
good_years = [subcorpus for subcorpus in list(df.index) if subcorpus in lst_of_subcorpora]
if prinf:
print 'Keeping %d subcorpora:\n %s' % (len(good_years), '\n '.join(good_years[:10]))
if len(good_years) > 10:
print '... and %d more ... \n' % (len(good_years) - 10)
else:
print ''
df = df.drop([subcorpus for subcorpus in list(df.index) if subcorpus not in good_years], axis = 0)
return df
def skip_these_subcorpora(df, lst_of_subcorpora, prinf = True):
if type(lst_of_subcorpora) == int:
lst_of_subcorpora = [lst_of_subcorpora]
if type(lst_of_subcorpora[0]) == int:
lst_of_subcorpora = [str(l) for l in lst_of_subcorpora]
bad_years = [subcorpus for subcorpus in list(df.index) if subcorpus in lst_of_subcorpora]
if len(bad_years) == 0:
import warnings
warnings.warn('No subcorpora skipped.\n')
else:
if prinf:
print 'Skipping %d subcorpora:\n %s' % (len(bad_years), '\n '.join([str(i) for i in bad_years[:10]]))
if len(bad_years) > 10:
print '... and %d more ... \n' % (len(bad_years) - 10)
else:
print ''
df = df.drop([subcorpus for subcorpus in list(df.index) if subcorpus in bad_years], axis = 0)
return df
def span_these_subcorpora(df, lst_of_subcorpora, prinf = True):
non_totals = [subcorpus for subcorpus in list(df.index)]
good_years = [subcorpus for subcorpus in non_totals if int(subcorpus) >= int(lst_of_subcorpora[0]) and int(subcorpus) <= int(lst_of_subcorpora[-1])]
if len(lst_of_subcorpora) == 0:
import warnings
warnings.warn('Span not identified.\n')
else:
if prinf:
print 'Keeping subcorpora:\n %d--%d\n' % (int(lst_of_subcorpora[0]), int(lst_of_subcorpora[-1]))
df = df.drop([subcorpus for subcorpus in list(df.index) if subcorpus not in good_years], axis = 0)
# retotal needed here
return df
def projector(df, list_of_tuples, prinf = True):
for subcorpus, projection_value in list_of_tuples:
if type(subcorpus) == int:
subcorpus = str(subcorpus)
df.ix[subcorpus] = df.ix[subcorpus] * projection_value
if prinf:
if type(projection_value) == float:
print 'Projection: %s * %s' % (subcorpus, projection_value)
if type(projection_value) == int:
print 'Projection: %s * %d' % (subcorpus, projection_value)
if prinf:
print ''
return df
def do_stats(df):
"""do linregress and add to df"""
from scipy.stats import linregress
entries = []
slopes = []
intercepts = []
rs = []
ps = []
stderrs = []
indices = list(df.index)
first_year = list(df.index)[0]
try:
x = [int(y) - int(first_year) for y in indices]
except ValueError:
x = range(len(indices))
statfields = ['slope', 'intercept', 'r', 'p', 'stderr']
for entry in list(df.columns):
entries.append(entry)
y = list(df[entry])
slope, intercept, r, p, stderr = linregress(x, y)
slopes.append(slope)
intercepts.append(intercept)
rs.append(r)
ps.append(p)
stderrs.append(stderr)
sl = pd.DataFrame([slopes, intercepts, rs, ps, stderrs],
index = statfields,
columns = list(df.columns))
df = df.append(sl)
# drop infinites and nans
if operation != 'd':
df = df.replace([np.inf, -np.inf], np.nan)
df = df.fillna(0.0)
return df
def recalc(df, operation = '%'):
statfields = ['slope', 'intercept', 'r', 'p', 'stderr']
"""Add totals to the dataframe1"""
#df.drop('Total', axis = 0, inplace = True)
#df.drop('Total', axis = 1, inplace = True)
try:
df['temp-Total'] = df.drop(statfields).sum(axis = 1)
except:
df['temp-Total'] = df.sum(axis = 1)
df = df.T
try:
df['temp-Total'] = df.drop(statfields).sum(axis = 1)
except:
df['temp-Total'] = df.sum(axis = 1)
df = df.T
return df
def resort(df, sort_by = False, keep_stats = False):
"""sort results"""
# translate options and make sure they are parseable
options = ['total', 'name', 'infreq', 'increase', 'turbulent',
'decrease', 'static', 'most', 'least', 'none']
if sort_by is True:
sort_by = 'total'
if sort_by == 'most':
sort_by = 'total'
if sort_by == 'least':
sort_by = 'infreq'
if sort_by not in options:
raise ValueError("sort_by parameter error: '%s' not recognised. Must be True, False, %s" % (sort_by, ', '.join(options)))
if operation.startswith('k'):
if type(df) == pandas.core.series.Series:
if sort_by == 'total':
df = df.order(ascending = False)
elif sort_by == 'infreq':
df = df.order(ascending = True)
elif sort_by == 'name':
df = df.sort_index()
return df
if just_totals:
if sort_by == 'infreq':
df = df.sort(columns = 'Combined total', ascending = True)
elif sort_by == 'total':
df = df.sort(columns = 'Combined total', ascending = False)
elif sort_by == 'name':
df = df.sort_index()
return df
# this is really shitty now that i know how to sort, like in the above
if keep_stats:
df = do_stats(df)
if sort_by == 'total':
if df1_istotals:
df = df.T
df = recalc(df, operation = operation)
tot = df.ix['temp-Total']
df = df[tot.argsort()[::-1]]
df = df.drop('temp-Total', axis = 0)
df = df.drop('temp-Total', axis = 1)
if df1_istotals:
df = df.T
elif sort_by == 'infreq':
if df1_istotals:
df = df.T
df = recalc(df, operation = operation)
tot = df.ix['temp-Total']
df = df[tot.argsort()]
df = df.drop('temp-Total', axis = 0)
df = df.drop('temp-Total', axis = 1)
if df1_istotals:
df = df.T
elif sort_by == 'name':
# currently case sensitive...
df = df.reindex_axis(sorted(df.columns), axis=1)
else:
statfields = ['slope', 'intercept', 'r', 'p', 'stderr']
if not keep_stats:
df = do_stats(df)
slopes = df.ix['slope']
if sort_by == 'increase':
df = df[slopes.argsort()[::-1]]
elif sort_by == 'decrease':
df = df[slopes.argsort()]
elif sort_by == 'static':
df = df[slopes.abs().argsort()]
elif sort_by == 'turbulent':
df = df[slopes.abs().argsort()[::-1]]
if remove_above_p:
# the easy way to do it!
df = df.T
df = df[df['p'] <= p]
df = df.T
# remove stats field by default
if not keep_stats:
df = df.drop(statfields, axis = 0)
return df
def set_threshold(big_list, threshold, prinf = True, for_keywords = False):
if type(threshold) == str:
if threshold.startswith('l'):
denominator = 10000
if threshold.startswith('m'):
denominator = 5000
if threshold.startswith('h'):
denominator = 2500
if type(big_list) == pandas.core.frame.DataFrame:
tot = big_list.sum().sum()
if type(big_list) == pandas.core.series.Series:
tot = big_list.sum()
the_threshold = float(tot) / float(denominator)
#if for_keywords:
#the_threshold = the_threshold / 2
else:
the_threshold = threshold
if prinf:
print 'Threshold: %d\n' % the_threshold
return the_threshold
#####################################################
# check if we're in concordance mode
try:
if list(dataframe1.columns) == ['l', 'm', 'r']:
conc_lines = True
else:
conc_lines = False
except:
conc_lines = False
# copy dataframe to be very safe
try:
df = dataframe1.copy()
except AttributeError:
no_good_dataframe1 = True
while no_good_dataframe1:
if 'interrogation' in str(type(dataframe1)):
sel = raw_input("\nIt looks like you're trying to edit an interrogation, " \
"rather than an interrogation's .results or .totals branch. You can:\n\n a) select .results branch\n b) select .totals branch\n c) exit\n\nYour choice: ")
if sel.startswith('a'):
try:
dataframe1 = dataframe1.results
no_good_dataframe1 = False
except:
pass
elif sel.startswith('b'):
try:
dataframe1 = dataframe1.totals
no_good_dataframe1 = False
except:
pass
else:
return
else:
raise ValueError("Thing to be edited (dataframe1) needs to be a Pandas DataFrame or Series. " \
"Right now, its type is: '%s'." % type(dataframe1).__name__)
df = dataframe1.copy()
# make cols into strings
try:
df.columns = [str(c) for c in list(df.columns)]
except:
pass
if operation is None:
operation = 'None'
# do concordance work
if conc_lines:
df = dataframe1.copy()
if just_entries:
if type(just_entries) == int:
just_entries = [just_entries]
if type(just_entries) == str:
df = df[df['m'].str.contains(just_entries)]
if type(just_entries) == list:
if type(just_entries[0]) == str:
regex = re.compile(r'(?i)^(' + r'|'.join(just_entries) + r')$')
df = df[df['m'].str.contains(regex)]
else:
df = df.ix[just_entries].reset_index(drop = True)
if skip_entries:
if type(skip_entries) == int:
skip_entries = [skip_entries]
if type(skip_entries) == str:
df = df[~df['m'].str.contains(skip_entries)]
if type(skip_entries) == list:
if type(skip_entries[0]) == str:
regex = re.compile(r'(?i)^(' + r'|'.join(skip_entries) + r')$')
df = df[~df['m'].str.contains(regex)]
else:
df = df.ix[[e for e in list(df.index) if e not in skip_entries]].reset_index(drop = True)
return df
if print_info:
print '\n***Processing results***\n========================\n'
df1_istotals = False
if type(df) == pandas.core.series.Series:
df1_istotals = True
df = pandas.DataFrame(df)
# if just a single result
else:
df = pandas.DataFrame(df)
if operation.startswith('k'):
if sort_by is False:
if not df1_istotals:
sort_by = 'turbulent'
if df1_istotals:
df = df.T
# figure out if there's a second list
# copy and remove totals if there is
single_totals = True
using_totals = False
outputmode = False
try:
if dataframe2.empty is False:
df2 = dataframe2.copy()
using_totals = True
if type(df2) == pandas.core.frame.DataFrame:
if len(df2.columns) > 1:
single_totals = False
else:
df2 = pd.Series(df2)
if operation == 'd':
df2 = df2.sum(axis = 1)
single_totals = True
elif type(df2) == pandas.core.series.Series:
single_totals = True
#if operation == 'k':
#raise ValueError('Keywording requires a DataFrame for dataframe2. Use "self"?')
else:
raise ValueError('dataframe2 not recognised.')
except AttributeError:
if operation in ['k', 'd', 'a', '%', '/', '*']:
dataframe2 = 'self'
if dataframe2 == 'self':
outputmode = True
else:
if operation in ['-', '+']:
raise ValueError('dataframe2 needed for +/- operations.')
if operation.startswith('a') or operation.startswith('A'):
if list(df.columns)[0] != '0' and list(df.columns)[0] != 0:
df = df.T
if using_totals:
if not single_totals:
df2 = df2.T
if projection:
# projection shouldn't do anything when working with '%', remember.
df = projector(df, projection)
if using_totals:
df2 = projector(df2, projection)
if spelling:
df = convert_spell(df, convert_to = spelling)
df = merge_duplicates(df, print_info = False)
if not single_totals:
df2 = convert_spell(df2, convert_to = spelling, print_info = False)
df2 = merge_duplicates(df2, print_info = False)
if not df1_istotals:
sort_by = 'total'
if replace_names:
df = name_replacer(df, replace_names)
df = merge_duplicates(df)
if not single_totals:
df2 = name_replacer(df2, print_info = False)
df2 = merge_duplicates(df2, print_info = False)
if not sort_by:
sort_by = 'total'
# remove old stats if they're there:
statfields = ['slope', 'intercept', 'r', 'p', 'stderr']
try:
df = df.drop(statfields, axis = 0)
except:
pass
if using_totals:
try:
df2 = df2.drop(statfields, axis = 0)
except:
pass
# merging: make dicts if they aren't already, so we can iterate
if merge_entries:
if type(merge_entries) != dict:
merge_entries = {newname: merge_entries}
for name, input in sorted(merge_entries.items()):
the_newname = newname_getter(df, parse_input(df, input), newname = name, prinf = print_info)
df = merge_these_entries(df, parse_input(df, input), the_newname, prinf = print_info)
if not single_totals:
df2 = merge_these_entries(df2, parse_input(df2, input), the_newname, prinf = False)
if merge_subcorpora:
print merge_subcorpora
print new_subcorpus_name
if type(merge_subcorpora) != dict:
if type(merge_subcorpora) == list:
if type(merge_subcorpora[0]) == tuple:
merge_subcorpora = {x: y for x, y in merge_subcorpora}
elif type(merge_subcorpora[0]) == str or type(merge_subcorpora[0]) == unicode:
merge_subcorpora = {new_subcorpus_name: [x for x in merge_subcorpora]}
elif type(merge_subcorpora[0]) == int:
merge_subcorpora = {new_subcorpus_name: [str(x) for x in merge_subcorpora]}
else:
merge_subcorpora = {new_subcorpus_name: merge_subcorpora}
for name, input in sorted(merge_subcorpora.items()):
the_newname = newname_getter(df.T, parse_input(df.T, input),
newname = name,
merging_subcorpora = True,
prinf = print_info)
df = merge_these_entries(df.T, parse_input(df.T, input), the_newname, merging = 'subcorpora', prinf = print_info).T
if using_totals:
df2 = merge_these_entries(df2.T, parse_input(df2.T, input), the_newname, merging = 'subcorpora', prinf = False).T
if just_subcorpora:
df = just_these_subcorpora(df, just_subcorpora, prinf = print_info)
if using_totals:
df2 = just_these_subcorpora(df2, just_subcorpora, prinf = False)
if skip_subcorpora:
df = skip_these_subcorpora(df, skip_subcorpora, prinf = print_info)
if using_totals:
df2 = skip_these_subcorpora(df2, skip_subcorpora, prinf = False)
if span_subcorpora:
df = span_these_subcorpora(df, span_subcorpora, prinf = print_info)
if using_totals:
df2 = span_these_subcorpora(df2, span_subcorpora, prinf = False)
if just_entries:
df = just_these_entries(df, parse_input(df, just_entries), prinf = print_info)
if not single_totals:
df2 = just_these_entries(df2, parse_input(df2, just_entries), prinf = False)
if skip_entries:
df = skip_these_entries(df, parse_input(df, skip_entries), prinf = print_info)
if not single_totals:
df2 = skip_these_entries(df2, parse_input(df2, skip_entries), prinf = False)
# drop infinites and nans
if operation != 'd':
df = df.replace([np.inf, -np.inf], np.nan)
df = df.fillna(0.0)
# make just_totals as dataframe
just_one_total_number = False
if just_totals:
df = pd.DataFrame(df.sum(), columns = ['Combined total'])
if using_totals:
if not single_totals:
df2 = pd.DataFrame(df2.sum(), columns = ['Combined total'])
else:
just_one_total_number = True
df2 = df2.sum()
# combine lists
tots = df.sum(axis = 1)
if using_totals or outputmode:
if not operation.startswith('k'):
the_threshold = 0
# set a threshold if just_totals
if outputmode is True:
df2 = df.T.sum()
if not just_totals:
df2.name = 'Total'
else:
df2.name = 'Combined total'
using_totals = True
single_totals = True
if just_totals:
if not single_totals:
the_threshold = set_threshold(df2, threshold, prinf = print_info)
if operation == 'd':
the_threshold = set_threshold(df2, threshold, prinf = print_info)
df, tots = combiney(df, df2, operation = operation, threshold = the_threshold, prinf = print_info)
# if doing keywording...
if operation.startswith('k'):
from corpkit.keys import keywords
# allow saved dicts to be df2, etc
try:
if dataframe2 == 'self':
df2 = df.copy()
except TypeError:
pass
if type(dataframe2) == str:
if dataframe2 != 'self':
df2 = dataframe2
else:
the_threshold = False
df = keywords(df, df2,
selfdrop = selfdrop,
threshold = threshold,
printstatus = print_info,
editing = True,
calc_all = calc_all,
**kwargs)
# eh?
df = df.T
# drop infinites and nans
if operation != 'd':
df = df.replace([np.inf, -np.inf], np.nan)
df = df.fillna(0.0)
# resort data
if sort_by:
df = resort(df, keep_stats = keep_stats, sort_by = sort_by)
if keep_top:
if not just_totals:
df = df[list(df.columns)[:keep_top]]
else:
df = df.head(keep_top)
if just_totals:
# turn just_totals into series:
df = pd.Series(df['Combined total'], name = 'Combined total')
if df1_istotals:
if operation.startswith('k'):
try:
df = pd.Series(df.ix[dataframe1.name])
df.name = '%s: keyness' % df.name
except:
df = df.iloc[0,:]
df.name = 'keyness' % df.name
# generate totals branch if not percentage results:
if df1_istotals or operation.startswith('k'):
try:
total = pd.Series(df['Total'], name = 'Total')
except:
total = 'none'
#total = df.copy()
else:
# might be wrong if using division or something...
total = df.T.sum()
if type(tots) != pandas.core.frame.DataFrame and type(tots) != pandas.core.series.Series:
total = df.sum(axis = 1)
else:
total = tots
if type(df) == pandas.core.frame.DataFrame:
datatype = df.ix[0].dtype
else:
datatype = df.dtype
# TURN INT COL NAMES INTO STR
try:
df.results.columns = [str(d) for d in list(df.results.columns)]
except:
pass
#make named_tuple
the_operation = 'none'
if using_totals:
the_operation = operation
the_options = {}
the_options['time_started'] = the_time_started
the_options['function'] = 'editor'
the_options['dataframe1'] = dataframe1
the_options['operation'] = the_operation
the_options['dataframe2'] = dataframe2
the_options['datatype'] = datatype
the_options['sort_by'] = sort_by
the_options['keep_stats'] = keep_stats
the_options['just_totals'] = just_totals
the_options['threshold'] = threshold # can be wrong!
the_options['just_entries'] = just_entries
the_options['just_entries'] = just_entries
the_options['skip_entries'] = skip_entries
the_options['merge_entries'] = merge_entries
the_options['newname'] = newname
the_options['just_subcorpora'] = just_subcorpora
the_options['skip_subcorpora'] = skip_subcorpora
the_options['span_subcorpora'] = span_subcorpora
the_options['merge_subcorpora'] = merge_subcorpora
the_options['new_subcorpus_name'] = new_subcorpus_name
the_options['projection'] = projection
the_options['remove_above_p'] = remove_above_p
the_options['p'] = p
the_options['revert_year'] = revert_year
the_options['print_info'] = print_info
outputnames = collections.namedtuple('edited_interrogation', ['query', 'results', 'totals'])
output = outputnames(the_options, df, total)
#print '\nResult (sample)\n'
if print_info:
#if merge_entries or merge_subcorpora or span_subcorpora or just_subcorpora or \
#just_entries or skip_entries or skip_subcorpora or printed_th or projection:
print '***Done!***\n========================\n'
#print df.head().T
#print ''
if operation.startswith('k') or just_totals or df1_istotals:
pd.set_option('display.max_rows', 30)
else:
pd.set_option('display.max_rows', 15)
pd.set_option('display.max_columns', 8)
pd.set_option('max_colwidth',70)
pd.set_option('display.width', 800)
pd.set_option('expand_frame_repr', False)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
return output
