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, 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, do keywording, sort, etc. ``just/skip_entries`` and ``just/skip_subcorpora`` can take a few different kinds of input: * str: treated as regular expression to match * list: * of integers: indices to match * of strings: entries/subcorpora to match ``merge_entries`` and ``merge_subcorpora``, however, are best entered as dicts: ``{newname: criteria, newname2: criteria2}``` where criteria is a string, list, etc. :param interrogation: Results to edit :type interrogation: pandas.core.frame.DataFrame :param 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) :type operation: str :param denominator: 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 if 'self', do all merging/keeping operations, then use edited interrogation as denominator :type denominator: pandas.core.series.Series/pandas.core.frame.DataFrame/dict/'self' :param 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 :type sort_by: str :param keep_stats: Keep/drop stats values from dataframe after sorting :type keep_stats: bool :param keep_top: After sorting, remove all but the top *keep_top* results :type keep_top: int :param just_totals: Sum each column and work with sums :type just_totals: bool :param threshold: When using results list as denominator, drop values occurring fewer than n times. If not keywording, you can use: ``'high'``: denominator total / 2500 ``'medium'``: denominator total / 5000 ``'low'``: denominator total / 10000 Note: if keywording, there are smaller default thresholds :type threshold: int/bool :param just_entries: Keep matching entries :type just_entries: see above :param skip_entries: Skip matching entries :type skip_entries: see above :param merge_entries: Merge matching entries :type merge_entries: see above :param newname: New name for merged entries :type newname: str/'combine' :param just_subcorpora: Keep matching subcorpora :type just_subcorpora: see above :param skip_subcorpora: Skip matching subcorpora :type skip_subcorpora: see above :param span_subcorpora: If subcorpora are numerically named, span all from *int* to *int2*, inclusive :type span_subcorpora: tuple -- ``(int, int2)`` :param merge_subcorpora: Merge matching subcorpora :type merge_subcorpora: see above :param new_subcorpus_name: Name for merged subcorpora :type new_subcorpus_name: str/``'combine'`` :param replace_names: Edit result names and then merge duplicate names. :type replace_names: dict -- ``{criteria: replacement_text}``; str -- a regex to delete from names :param projection: a to multiply results in subcorpus by n :type projection: tuple -- ``(subcorpus_name, n)`` :param remove_above_p: Delete any result over p :type remove_above_p: bool :param p: set the p value :type p: float :param revert_year: when doing linear regression on years, turn annual subcorpora into 1, 2 ... :type revert_year: bool :param print_info: Print stuff to console showing what's being edited :type print_info: bool :param spelling: Convert/normalise spelling: :type spelling: str -- ``'US'``/``'UK'`` :param selfdrop: When keywording, try to remove target corpus from reference corpus :type selfdrop: bool :param calc_all: When keywording, calculate words that appear in either corpus :type calc_all: bool :returns: corpkit.interrogation.Interrogation """ # grab arguments, in case we get dict input and have to iterate locs = locals() import corpkit import pandas import signal 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 return_conc = False from interrogation import Interrodict, Interrogation, Concordance if interrogation.__class__ == Interrodict: from collections import OrderedDict outdict = OrderedDict() from editor import editor for i, (k, v) in enumerate(interrogation.items()): # only print the first time around if i == 0: pass # saved_args['print_info'] = True else: locs["print_info"] = False # if df2 is also a dict, get the relevant entry if type(denominator) == dict: 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] if kwargs.get("use_df2_totals"): saved_args["denominator"] = denominator[k].totals outdict[k] = editor(v.results, **saved_args) if print_info: from time import localtime, strftime 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 type(interrogation) in [pandas.core.frame.DataFrame, pandas.core.series.Series]: dataframe1 = interrogation elif interrogation.__class__ == 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 interrogation.__class__ == Concordance or all(x in list(dataframe1.columns) for x in ["l", "m", "r"]): return_conc = True 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 pd.set_option("display.float_format", lambda x: "%.2f" % x) from 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""" 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: from time import localtime, strftime 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: from time import localtime, strftime 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: from time import localtime, strftime 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: from time import localtime, strftime 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: from time import localtime, strftime thetime = strftime("%H:%M:%S", localtime()) print("%s: cannot combine DataFrame 1 and 2: different shapes" % thetime) 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 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 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() 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""" # FIX MERGE ERROR HERE parsed_input = False import re if the_input == "all": the_input = r".*" if type(the_input) == int: try: the_input = str(the_input) except: pass the_input = [the_input] elif type(the_input) == str or type(the_input) == str: try: regex = re.compile(the_input) parsed_input = [w for w in list(df) if re.search(regex, w)] return parsed_input except: the_input = [the_input] if type(the_input) == list: if type(the_input[0]) == int: parsed_input = [word for index, word in enumerate(list(df)) if index in the_input] elif type(the_input[0]) == str or type(the_input[0]) == str: 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 # 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 list(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(iter(sumdict.items()), key=operator.itemgetter(1))[0] if type(the_newname) != str: 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 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): """select only a span of numerical suborpora (first, last)""" 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): """project abs values""" if type(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 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""" try: from scipy.stats import linregress except ImportError: from time import localtime, strftime thetime = strftime("%H:%M:%S", localtime()) print("%s: sort type not available in this verion of corpkit." % thetime) return False # from stats.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 = list(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, potentially using scipy's linregress""" # translate options and make sure they are parseable options = [ "total", "name", "infreq", "increase", "turbulent", "decrease", "static", "most", "least", "none", "p", ] 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 and sort_by: 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_values(by="Combined total", ascending=True, axis=1) elif sort_by == "total": df = df.sort_values(by="Combined total", ascending=False, axis=1) 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 type(df) == bool: if df is False: return False 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) elif sort_by == "p": df = df.T.sort_values(by="p").T else: statfields = ["slope", "intercept", "r", "p", "stderr"] if not keep_stats: df = do_stats(df) if type(df) == bool: if df is False: return False 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 # 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" # do concordance work if return_conc: 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 all(type(e) == str 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 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 all(type(e) == str 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 type(just_subcorpora) == int: just_subcorpora = [just_subcorpora] if type(just_subcorpora) == str: df = df[df["c"].str.contains(just_subcorpora)] if type(just_subcorpora) == list: if all(type(e) == str 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 type(skip_subcorpora) == int: skip_subcorpora = [skip_subcorpora] if type(skip_subcorpora) == str: df = df[~df["c"].str.contains(skip_subcorpora)] if type(skip_subcorpora) == list: if all(type(e) == str 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 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 if denominator is not False and type(denominator) != str: df2 = denominator.copy() using_totals = True if type(df2) == pandas.core.frame.DataFrame: if len(df2.columns) > 1: single_totals = False else: df2 = pandas.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 denominator. Use "self"?') else: raise ValueError("Denominator not recognised.") else: if operation in ["k", "d", "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, 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 # 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 type(merge_entries) != list: if type(merge_entries) == str or type(merge_entries) == str: merge_entries = {newname: merge_entries} # for newname, criteria for name, the_input in sorted(merge_entries.items()): the_newname = newname_getter(df, parse_input(df, the_input), newname=name, prinf=print_info) df = merge_these_entries(df, parse_input(df, the_input), the_newname, prinf=print_info) if not single_totals: df2 = merge_these_entries(df2, parse_input(df2, the_input), the_newname, prinf=False) else: for i in 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: 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]) == str: 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, the_input in sorted(merge_subcorpora.items()): the_newname = newname_getter( df.T, parse_input(df.T, the_input), newname=name, merging_subcorpora=True, prinf=print_info ) df = merge_these_entries( df.T, parse_input(df.T, the_input), the_newname, merging="subcorpora", prinf=print_info ).T if using_totals: df2 = merge_these_entries( df2.T, parse_input(df2.T, the_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() 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 keys import keywords # allow saved dicts to be df2, etc try: if denominator == "self": df2 = df.copy() except TypeError: pass if type(denominator) == str: if denominator != "self": df2 = denominator 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 or keep_stats: df = resort(df, keep_stats=keep_stats, sort_by=sort_by) if type(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 = 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: # fix me if df1_istotals or operation.startswith("k"): if not just_totals: try: total = pd.Series(df["Total"], name="Total") except: pass total = "none" # 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 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 def add_tkt_index(df): if type(df) != pandas.core.series.Series: df = df.T df = df.drop("tkintertable-order", errors="ignore", axis=0) df = df.drop("tkintertable-order", errors="ignore", axis=1) df["tkintertable-order"] = pd.Series( [index for index, data in enumerate(list(df.index))], index=list(df.index) ) df = df.T return df # while tkintertable can't sort rows from tests import check_t_kinter tk = check_t_kinter() if tk: df = add_tkt_index(df) if kwargs.get("df1_always_df"): if type(df) == pandas.core.series.Series: df = pandas.DataFrame(df) # outputnames = collections.namedtuple('edited_interrogation', ['query', 'results', 'totals']) # output = outputnames(the_options, df, total) # delete non-appearing conc lines if interrogation.__dict__.get("concordance", None) 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) # 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(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, do keywording, sort, etc. ``just/skip_entries`` and ``just/skip_subcorpora`` can take a few different kinds of input: * str: treated as regular expression to match * list: * of integers: indices to match * of strings: entries/subcorpora to match ``merge_entries`` and ``merge_subcorpora``, however, are best entered as dicts: ``{newname: criteria, newname2: criteria2}``` where criteria is a string, list, etc. :param dataframe1: Results to edit :type dataframe1: pandas.core.frame.DataFrame :param 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) :type operation: str :param 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 if 'self', do all merging/keeping operations, then use edited dataframe1 as dataframe2 :type dataframe2: pandas.core.series.Series/pandas.core.frame.DataFrame/dict/'self' :param 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 :type sort_by: str :param keep_stats: Keep/drop stats values from dataframe after sorting :type keep_stats: bool :param keep_top: After sorting, remove all but the top *keep_top* results :type keep_top: int :param just_totals: Sum each column and work with sums :type just_totals: bool :param 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 Note: if keywording, there are smaller default thresholds :type threshold: int/bool :param just_entries: Keep matching entries :type just_entries: see above :param skip_entries: Skip matching entries :type skip_entries: see above :param merge_entries: Merge matching entries :type merge_entries: see above :param newname: New name for merged entries :type newname: str/'combine' :param just_subcorpora: Keep matching subcorpora :type just_subcorpora: see above :param skip_subcorpora: Skip matching subcorpora :type skip_subcorpora: see above :param span_subcorpora: If subcorpora are numerically named, span all from *int* to *int2*, inclusive :type span_subcorpora: tuple -- ``(int, int2)`` :param merge_subcorpora: Merge matching subcorpora :type merge_subcorpora: see above :param new_subcorpus_name: Name for merged subcorpora :type new_subcorpus_name: str/``'combine'`` :param replace_names: Edit result names and then merge duplicate names. :type replace_names: dict -- ``{criteria: replacement_text}``; str -- a regex to delete from names :param projection: a to multiply results in subcorpus by n :type projection: tuple -- ``(subcorpus_name, n)`` :param remove_above_p: Delete any result over p :type remove_above_p: bool :param p: set the p value :type p: float :param revert_year: when doing linear regression on years, turn annual subcorpora into 1, 2 ... :type revert_year: bool :param print_info: Print stuff to console showing what's being edited :type print_info: bool :param spelling: Convert/normalise spelling: :type spelling: str -- ``'US'``/``'UK'`` :param selfdrop: When keywording, try to remove target corpus from reference corpus :type selfdrop: bool :param calc_all: When keywording, calculate words that appear in either corpus :type calc_all: bool :returns: Edited interrogation """ # grab arguments, in case we get dict input and have to iterate saved_args = locals() import corpkit import pandas import signal 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 # if passing a multiquery, do each result separately and return if type(dataframe1) == dict: outdict = {} from corpkit.editor import editor del saved_args['dataframe1'] for i, (k, v) in enumerate(dataframe1.items()): # only print the first time around if i == 0: pass #saved_args['print_info'] = True else: saved_args['print_info'] = False # if df2 is also a dict, get the relevant entry if type(dataframe2) == dict: if sorted(set([i.lower() for i in dataframe1.keys()])) == \ sorted(set([i.lower() for i in dataframe2.keys()])): saved_args['dataframe2'] = dataframe2[k] if 'use_df2_totals' in kwargs.keys(): if kwargs['use_df2_totals'] is True: saved_args['dataframe2'] = dataframe2[k].totals outdict[k] = editor(v.results, **saved_args) if print_info: from time import localtime, strftime 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 outdict the_time_started = strftime("%Y-%m-%d %H:%M:%S") pd.options.mode.chained_assignment = None 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""" 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: from time import localtime, strftime 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: from time import localtime, strftime 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: from time import localtime, strftime 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: from time import localtime, strftime 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: from time import localtime, strftime thetime = strftime("%H:%M:%S", localtime()) print '%s: cannot combine DataFrame 1 and 2: different shapes' % thetime 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 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]).sort() # 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 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() 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""" # FIX MERGE ERROR HERE parsed_input = False import re if the_input == 'all': the_input = r'.*' if type(the_input) == int: try: the_input = str(the_input) except: pass the_input = [the_input] elif type(the_input) == str or type(the_input) == unicode: try: regex = re.compile(the_input) parsed_input = [w for w in list(df) if re.search(regex, w)] return parsed_input except: the_input = [the_input] if type(the_input) == list: if type(the_input[0]) == int: parsed_input = [word for index, word in enumerate(list(df)) if index in the_input] elif type(the_input[0]) == str or type(the_input[0]) == unicode: 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 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): """select only a span of numerical suborpora (first, last)""" 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): """project abs values""" if type(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_of_tuples.items(): 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""" try: from scipy.stats import linregress except ImportError: from time import localtime, strftime thetime = strftime("%H:%M:%S", localtime()) print '%s: sort type not available in this verion of corpkit.' % thetime return False #from stats.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, potentially using scipy's linregress""" # 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 type(df) == bool: if df is False: return False 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) if type(df) == bool: if df is False: return False 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 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 # remove totals and tkinter order for name, ax in zip(['Total'] * 2 + ['tkintertable-order'] * 2, [0, 1, 0, 1]): 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]): 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 type(merge_entries) != list: if type(merge_entries) == str or type(merge_entries) == unicode: merge_entries = {newname: merge_entries} # for newname, criteria for name, the_input in sorted(merge_entries.items()): the_newname = newname_getter(df, parse_input(df, the_input), newname = name, prinf = print_info) df = merge_these_entries(df, parse_input(df, the_input), the_newname, prinf = print_info) if not single_totals: df2 = merge_these_entries(df2, parse_input(df2, the_input), the_newname, prinf = False) else: for i in 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: 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, the_input in sorted(merge_subcorpora.items()): the_newname = newname_getter(df.T, parse_input(df.T, the_input), newname = name, merging_subcorpora = True, prinf = print_info) df = merge_these_entries(df.T, parse_input(df.T, the_input), the_newname, merging = 'subcorpora', prinf = print_info).T if using_totals: df2 = merge_these_entries(df2.T, parse_input(df2.T, the_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() 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 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 type(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 = 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: # fix me if df1_istotals or operation.startswith('k'): if not just_totals: try: total = pd.Series(df['Total'], name = 'Total') except: pass total = 'none' #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 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 def add_tkt_index(df): if type(df) != pandas.core.series.Series: df = df.T df = df.drop('tkintertable-order', errors = 'ignore', axis = 0) df = df.drop('tkintertable-order', errors = 'ignore', axis = 1) df['tkintertable-order'] = pd.Series([index for index, data in enumerate(list(df.index))], index = list(df.index)) df = df.T return df # while tkintertable can't sort rows from corpkit.tests import check_t_kinter tk = check_t_kinter() if tk: df = add_tkt_index(df) if 'df1_always_df' in kwargs.keys(): if kwargs['df1_always_df'] is True: if type(df) == pandas.core.series.Series: df = pandas.DataFrame(df) #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, 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, do keywording, sort, etc. ``just/skip_entries`` and ``just/skip_subcorpora`` can take a few different kinds of input: * str: treated as regular expression to match * list: * of integers: indices to match * of strings: entries/subcorpora to match ``merge_entries`` and ``merge_subcorpora``, however, are best entered as dicts: ``{newname: criteria, newname2: criteria2}``` where criteria is a string, list, etc. :param interrogation: Results to edit :type interrogation: pandas.core.frame.DataFrame :param 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) :type operation: str :param denominator: 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 if 'self', do all merging/keeping operations, then use edited interrogation as denominator :type denominator: pandas.core.series.Series/pandas.core.frame.DataFrame/dict/'self' :param 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 :type sort_by: str :param keep_stats: Keep/drop stats values from dataframe after sorting :type keep_stats: bool :param keep_top: After sorting, remove all but the top *keep_top* results :type keep_top: int :param just_totals: Sum each column and work with sums :type just_totals: bool :param threshold: When using results list as denominator, drop values occurring fewer than n times. If not keywording, you can use: ``'high'``: denominator total / 2500 ``'medium'``: denominator total / 5000 ``'low'``: denominator total / 10000 Note: if keywording, there are smaller default thresholds :type threshold: int/bool :param just_entries: Keep matching entries :type just_entries: see above :param skip_entries: Skip matching entries :type skip_entries: see above :param merge_entries: Merge matching entries :type merge_entries: see above :param newname: New name for merged entries :type newname: str/'combine' :param just_subcorpora: Keep matching subcorpora :type just_subcorpora: see above :param skip_subcorpora: Skip matching subcorpora :type skip_subcorpora: see above :param span_subcorpora: If subcorpora are numerically named, span all from *int* to *int2*, inclusive :type span_subcorpora: tuple -- ``(int, int2)`` :param merge_subcorpora: Merge matching subcorpora :type merge_subcorpora: see above :param new_subcorpus_name: Name for merged subcorpora :type new_subcorpus_name: str/``'combine'`` :param replace_names: Edit result names and then merge duplicate names. :type replace_names: dict -- ``{criteria: replacement_text}``; str -- a regex to delete from names :param projection: a to multiply results in subcorpus by n :type projection: tuple -- ``(subcorpus_name, n)`` :param remove_above_p: Delete any result over p :type remove_above_p: bool :param p: set the p value :type p: float :param revert_year: when doing linear regression on years, turn annual subcorpora into 1, 2 ... :type revert_year: bool :param print_info: Print stuff to console showing what's being edited :type print_info: bool :param spelling: Convert/normalise spelling: :type spelling: str -- ``'US'``/``'UK'`` :param selfdrop: When keywording, try to remove target corpus from reference corpus :type selfdrop: bool :param calc_all: When keywording, calculate words that appear in either corpus :type calc_all: bool :returns: corpkit.interrogation.Interrogation """ # grab arguments, in case we get dict input and have to iterate locs = locals() import corpkit import pandas import signal 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 return_conc = False from interrogation import Interrodict, Interrogation, Concordance if interrogation.__class__ == Interrodict: locs.pop('interrogation', None) from collections import OrderedDict outdict = OrderedDict() from editor import editor for i, (k, v) in enumerate(interrogation.items()): # only print the first time around if i != 0: locs['print_info'] = False # if df2 is also a dict, get the relevant entry if type(denominator) == dict or denominator.__class__ == 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] if kwargs.get('denominator_totals'): locs['denominator'] = denominator[k].totals else: locs['denominator'] = denominator[k].results outdict[k] = editor(v.results, **locs) if print_info: from time import localtime, strftime 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 type(interrogation) in [pandas.core.frame.DataFrame, pandas.core.series.Series]: dataframe1 = interrogation elif interrogation.__class__ == 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 interrogation.__class__ == Concordance or \ all(x in list(dataframe1.columns) for x in ['l', 'm', 'r']): return_conc = True 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 pd.set_option('display.float_format', lambda x: '%.2f' % x) try: from tests import check_pytex except ImportError: 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""" 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: from time import localtime, strftime 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: from time import localtime, strftime 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: from time import localtime, strftime 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: from time import localtime, strftime 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: from time import localtime, strftime thetime = strftime("%H:%M:%S", localtime()) print('%s: cannot combine DataFrame 1 and 2: different shapes' % thetime) 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 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 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() 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 type(the_input) == int: try: the_input = str(the_input) except: pass the_input = [the_input] elif type(the_input) == str or type(the_input) == str: try: regex = re.compile(the_input) parsed_input = [w for w in list(df) if re.search(regex, w)] return parsed_input except: the_input = [the_input] if type(the_input) == list: if type(the_input[0]) == int: parsed_input = [word for index, word in enumerate(list(df)) if index in the_input] elif type(the_input[0]) == str or type(the_input[0]) == unicode: 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 # 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 list(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(iter(sumdict.items()), key=operator.itemgetter(1))[0] if type(the_newname) != str: 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 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): """select only a span of numerical suborpora (first, last)""" 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): """project abs values""" if type(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 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""" try: from scipy.stats import linregress except ImportError: from time import localtime, strftime thetime = strftime("%H:%M:%S", localtime()) print('%s: sort type not available in this verion of corpkit.' % thetime) return False #from stats.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 = list(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, potentially using scipy's linregress""" # translate options and make sure they are parseable options = ['total', 'name', 'infreq', 'increase', 'turbulent', 'decrease', 'static', 'most', 'least', 'none', 'p'] 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 and sort_by: 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_values(by = 'Combined total', ascending = True, axis = 1) elif sort_by == 'total': df = df.sort_values(by = 'Combined total', ascending = False, axis = 1) 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 type(df) == bool: if df is False: return False 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) elif sort_by == 'p': df = df.T.sort_values(by='p').T else: statfields = ['slope', 'intercept', 'r', 'p', 'stderr'] if not keep_stats: df = do_stats(df) if type(df) == bool: if df is False: return False 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 # 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' # do concordance work if return_conc: 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 all(type(e) == str 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 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 all(type(e) == str 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 type(just_subcorpora) == int: just_subcorpora = [just_subcorpora] if type(just_subcorpora) == str: df = df[df['c'].str.contains(just_subcorpora)] if type(just_subcorpora) == list: if all(type(e) == str 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 type(skip_subcorpora) == int: skip_subcorpora = [skip_subcorpora] if type(skip_subcorpora) == str: df = df[~df['c'].str.contains(skip_subcorpora)] if type(skip_subcorpora) == list: if all(type(e) == str 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 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 if denominator.__class__ == Interrogation: try: denominator = denominator.results except AttributeError: denominator = denominator.totals if denominator is not False and type(denominator) != str: df2 = denominator.copy() using_totals = True if type(df2) == pandas.core.frame.DataFrame: if len(df2.columns) > 1: single_totals = False else: df2 = pandas.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 denominator. Use "self"?') else: raise ValueError('Denominator not recognised.') else: if operation in ['k', 'd', '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, 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 # 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 type(merge_entries) != list: if type(merge_entries) == str or type(merge_entries) == str: merge_entries = {newname: merge_entries} # for newname, criteria for name, the_input in sorted(merge_entries.items()): the_newname = newname_getter(df, parse_input(df, the_input), newname = name, prinf = print_info) df = merge_these_entries(df, parse_input(df, the_input), the_newname, prinf = print_info) if not single_totals: df2 = merge_these_entries(df2, parse_input(df2, the_input), the_newname, prinf = False) else: for i in 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: 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]) == str: 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, the_input in sorted(merge_subcorpora.items()): the_newname = newname_getter(df.T, parse_input(df.T, the_input), newname = name, merging_subcorpora = True, prinf = print_info) df = merge_these_entries(df.T, parse_input(df.T, the_input), the_newname, merging = 'subcorpora', prinf = print_info).T if using_totals: df2 = merge_these_entries(df2.T, parse_input(df2.T, the_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() 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 keys import keywords # allow saved dicts to be df2, etc try: if denominator == 'self': df2 = df.copy() except TypeError: pass if type(denominator) == str: if denominator != 'self': df2 = denominator 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 or keep_stats: df = resort(df, keep_stats = keep_stats, sort_by = sort_by) if type(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 = 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: # fix me if df1_istotals or operation.startswith('k'): if not just_totals: try: total = pd.Series(df['Total'], name = 'Total') except: pass total = 'none' #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 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 def add_tkt_index(df): if type(df) != pandas.core.series.Series: df = df.T df = df.drop('tkintertable-order', errors = 'ignore', axis = 0) df = df.drop('tkintertable-order', errors = 'ignore', axis = 1) df['tkintertable-order'] = pd.Series([index for index, data in enumerate(list(df.index))], index = list(df.index)) df = df.T return df # while tkintertable can't sort rows try: from tests import check_t_kinter except ImportError: from corpkit.tests import check_t_kinter tk = check_t_kinter() if tk: df = add_tkt_index(df) if kwargs.get('df1_always_df'): if type(df) == pandas.core.series.Series: df = pandas.DataFrame(df) #outputnames = collections.namedtuple('edited_interrogation', ['query', 'results', 'totals']) #output = outputnames(the_options, df, total) # delete non-appearing conc lines if interrogation.__dict__.get('concordance', None) 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) #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