From 52da1e3fc6c741b963103490dd9897abe7865a5d Mon Sep 17 00:00:00 2001 From: Srikanth K S Date: Thu, 29 Feb 2024 00:14:12 +0530 Subject: [PATCH] intermediary work --- CHANGELOG.md | 7 + poetry.lock | 3364 ++++++++++++++++++++++------- pyproject.toml | 10 +- src/tidypandas/tidy_accessor.py | 32 + src/tidypandas/tidy_utils.py | 11 +- src/tidypandas/tidyframe_class.py | 385 +++- tests/test_tidypandas.py | 20 +- 7 files changed, 2921 insertions(+), 908 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index bbdbcb6..5a4d748 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,12 @@ # Changelog +## v0.3.1 (24th Jan 2024) +- Handled deprecation [warnings](https://github.com/tidypyverse/tidypandas/issues/51) created by pandas >= 2.0.0. Use of `group_modify` still produces warning. This is doe to the fact that `include_groups` argument needs to be set to False by default. +- `unnest` method allows a list of numpy ndarrays (as column to be unnested) +- `unnest_wider` method is introduced +- `fill_na` reimplemented to be performant (using Series.groupby.xfill), no user facing change + + ## v0.3.0 (16th Aug 2023) - `tidyselect` is introduced in a few verbs (methods) which support `start_with`, `ends_with` and `contains`. - `summarise` now supports returning an iterable within a list as a output, no longer restricts the output to a scalar. diff --git a/poetry.lock b/poetry.lock index 6324819..d3353f9 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,217 +1,454 @@ +# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand. + [[package]] name = "alabaster" -version = "0.7.13" -description = "A configurable sidebar-enabled Sphinx theme" -category = "dev" +version = "0.7.16" +description = "A light, configurable Sphinx theme" optional = 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b/pyproject.toml @@ -1,18 +1,18 @@ [tool.poetry] name = "tidypandas" -version = "0.3.0" +version = "0.3.1" description = "A grammar of data manipulation for pandas inspired by tidyverse" authors = ["Srikanth Komala Sheshachala ", "Ashish Raj "] maintainers = ["Srikanth Komala Sheshachala ", "Ashish Raj "] license = "MIT" readme = "README.md" -homepage = "https://talegari.github.io/tidypandas/" -repository = "https://github.com/talegari/tidypandas" -documentation = "https://talegari.github.io/tidypandas/_build/html/autoapi/index.html" +homepage = "https://tidypyverse.github.io/tidypandas/" +repository = "https://github.com/tidypyverse/tidypandas" +documentation = "https://tidypyverse.github.io/tidypandas/_build/html/autoapi/index.html" classifiers = ["Development Status :: 4 - Beta"] [tool.poetry.dependencies] -python = "^3.8" +python = "^3.9" pandas = ">=1.0.0" collections-extended = ">=2.0.2" skimpy = {version = ">=0.0.5", optional = true} diff --git a/src/tidypandas/tidy_accessor.py b/src/tidypandas/tidy_accessor.py index 9b6fcbc..61e1863 100644 --- a/src/tidypandas/tidy_accessor.py +++ b/src/tidypandas/tidy_accessor.py @@ -555,6 +555,38 @@ def unnest(self, nest_column_name = 'data'): tf = tidyframe(self._obj, copy = False, check = False) return tf.unnest(nest_column_name = nest_column_name).to_pandas(copy = False) + def unnest_wider(self, nest_column_name, names_sep = None): + ''' + Unnest a column of dicts into multiple columns + + Parameters + ---------- + nest_column_name: str + Name of the column to be unnested + + Returns + ------- + tidyframe + + Notes + ----- + 1. unnest_wider is helpful when nested input typically parsed from a + a json requires to be unnested. + + Examples + -------- + >>> from tidypandas.tidy_accessor import tp + >>> import pandas as pd + >>> df = pd.DataFrame({'x': [1,2]}) + >>> df['y'] = pd.Series([{"a": 1, 'b': 2}, {'a': 3, 'b': 4}]) + >>> df.tp.unnest_wider('y') + ''' + + tf = tidyframe(self._obj, copy = False, check = True) + res = tf.unnest_wider(nest_column_name = nest_column_name, + names_sep = names_sep + ) + return res.to_pandas(copy = False) def split(self, by): tf = tidyframe(self._obj, copy = False, check = False) diff --git a/src/tidypandas/tidy_utils.py b/src/tidypandas/tidy_utils.py index dcf40af..2ee73e2 100644 --- a/src/tidypandas/tidy_utils.py +++ b/src/tidypandas/tidy_utils.py @@ -15,9 +15,9 @@ # simplify # ----------------------------------------------------------------------------- -def simplify(pdf - , sep = "__" - , verbose = False +def simplify(pdf, + sep = "__", + verbose = False ): ''' simplify(pdf) @@ -184,7 +184,7 @@ def is_simple(pdf, verbose = False): 1. Column names (x.columns) are an unnamed pd.Index object of unique strings. Column names do not start from "_". - 2. Row names (x.index) is a numeric index (x.indx.is_numeric() is True). + 2. Row names (x.index) is a numeric index (x.index.is_numeric() is True). Returns ------- @@ -207,8 +207,9 @@ def is_simple(pdf, verbose = False): col_flag = not isinstance(pdf.columns, pd.MultiIndex) # check if row index is numeric + from pandas.api.types import is_numeric_dtype flag_numeric_index = False - if pdf.index.is_numeric(): + if is_numeric_dtype(pdf.index): flag_numeric_index = True # check if all column names are strings diff --git a/src/tidypandas/tidyframe_class.py b/src/tidypandas/tidyframe_class.py index 80361d2..6de6045 100644 --- a/src/tidypandas/tidyframe_class.py +++ b/src/tidypandas/tidyframe_class.py @@ -879,6 +879,7 @@ def add_row_number(self, by = None, name = 'row_number'): ########################################################################## # group_modify ########################################################################## + def group_modify(self , func , by @@ -1068,6 +1069,183 @@ def wrapper_func(chunk, **kwargs): , check = False , copy = False ) + + # def group_modify(self + # , func + # , by + # , preserve_row_order = False + # , row_order_column_name = "rowid_temp" + # , is_pandas_udf = False + # , **kwargs + # ): + # ''' + # Split by some columns, apply a function per chunk which returns a + # dataframe and combine it back into a single dataframe + # + # Parameters + # ---------- + # func: callable + # Type 1. A function: TidyDataFrame --> TidyDataFrame + # Type 2. A function: simple pandas df --> simple pandas df + # In latter case, set 'is_pandas_udf' to True + # + # by: string or list of strings + # Column names to group by + # + # preserve_row_order: bool (default is False) + # Whether to preserve the row order of the input dataframe + # + # row_order_column_name: string + # Temporary column name to be created to maintain row order + # + # is_pandas_udf: bool (default is False) + # Whether the 'func' argument is of type 2 + # + # **kwargs: arguments to 'func' + # + # Returns + # ------- + # tidyframe + # + # Notes + # ----- + # 1. Chunks will always include the grouping columns. + # 2. If grouping columns are found in output of 'func', then they are + # removed and replaced with value in input chunk. + # 3. When 'preserve_row_order' is True, a temporary column is added + # to each chunk. udf should pass it through without modification. + # + # Examples + # -------- + # >>> from palmerpenguins import load_penguins + # >>> penguins_tidy = tidyframe(load_penguins()) + # >>> + # >>> # pick a sample of rows per chunk defined by 'species' + # >>> penguins_tidy.group_modify(lambda x: x.sample(n = 3), by = 'species') + # >>> + # >>> # apply a pandas udf per chunk defined by 'species' + # >>> # groupby columns are always added to the left + # >>> penguins_tidy.group_modify(lambda x: x.select('year'), by = ['species', 'sex']) + # >>> + # >>> # preserve row order + # >>> # a temporary column (default: 'rowid_temp') is added to each chunk + # >>> # udf should not meddle with the temporary column + # >>> (penguins_tidy + # >>> .select('sex') + # >>> # add 'row_number' column to illustrate row preservation + # >>> .add_row_number() + # >>> # print the output of each chunk + # >>> # sample 2 rows + # >>> .group_modify(lambda x: x.pipe_tee(print).sample(2) + # >>> , by = 'sex' + # >>> , preserve_row_order = True + # >>> ) + # >>> ) + # >>> + # >>> # use kwargs + # >>> penguins_tidy.group_modify(lambda x, **kwargs: x.sample(n = kwargs['size']) + # >>> , by = 'species' + # >>> , size = 3 + # >>> ) + # ''' + # self._validate_by(by) + # by = _enlist(by) + # cn = self.colnames + # nr = self.nrow + # + # assert callable(func),\ + # "'func' should be a function: tidy df --> tidy df" + # assert isinstance(preserve_row_order, bool),\ + # "arg 'preserve_row_order' should be a bool" + # assert isinstance(row_order_column_name, str),\ + # "arg 'row_order_column_name' should be a string" + # assert row_order_column_name not in self.colnames,\ + # "arg 'row_order_column_name' should not be a existing column name" + # + # # create a group column as an alias to grouping columns ---------------- + # # This will handle NA in groups + # group_cn = _generate_new_string(cn) + # group_id_frame = self.__data.loc[:, by].drop_duplicates() + # group_id_frame[group_cn] = np.arange(group_id_frame.shape[0]) + # + # # create wrapper function ---------------------------------------------- + # # grouping columns are present in each chunk + # if is_pandas_udf: + # def wrapper_func(chunk, **kwargs): + # res = (chunk.drop(columns = group_cn, errors = "ignore") + # .reset_index(drop = True) + # .pipe(func, **kwargs) + # .drop(columns = by, errors = "ignore") + # ) + # + # print(chunk) + # # put/edit the grouping columns back + # print(by) + # for acolname in by: + # print(chunk[acolname].loc[0]) + # res[acolname] = chunk[acolname].loc[0] + # + # return res + # else: + # def wrapper_func(chunk, **kwargs): + # # i/o are pdfs + # res = (chunk.drop(columns = group_cn, errors = "ignore") + # .reset_index(drop = True) + # .pipe(lambda x: tidyframe(x, copy = False, check = False)) + # .pipe(func, **kwargs) + # .to_pandas(copy = False) + # .drop(columns = by, errors = "ignore") + # ) + # print(chunk) + # print(by) + # # put/edit the grouping columns back + # for acolname in by: + # print(chunk[acolname].loc[0]) + # res[acolname] = chunk[acolname].loc[0] + # + # return res + # + # # core apply operation ------------------------------------------------- + # if preserve_row_order: + # assert row_order_column_name not in cn,\ + # "arg 'row_order_column_name' should not be an existing column name" + # + # res = (self.__data + # # add a column to track row order + # .assign(**{row_order_column_name: np.arange(nr)}) + # # bring in 'group_cn' column + # .merge(group_id_frame, on = by) + # .groupby(group_cn, sort = False, dropna = False) + # .apply(wrapper_func, include_groups = False, **kwargs) + # .drop(columns = group_cn) + # ) + # else: + # res = (self.__data + # .merge(group_id_frame, on = by) + # .groupby(group_cn, sort = False, dropna = False) + # .apply(wrapper_func, include_groups = False, **kwargs) + # .drop(columns = group_cn) + # ) + # # simplify brings back group columns ----------------------------------- + # try: + # res = simplify(res) + # except: + # exc_msg = "Result of apply is too complex to be simplified" + # raise Exception(exc_msg) + # + # # preserve row order if required --------------------------------------- + # if preserve_row_order: + # if row_order_column_name in list(res.columns): + # res.sort_values(row_order_column_name, inplace = True, ignore_index = True) + # res.drop(columns = row_order_column_name, inplace = True) + # else: + # exc_msg = ("'row_order_column_name' in each chunk should " + # "be retained, when 'preserve_row_order' is True" + # ) + # raise Exception(exc_msg) + # + # # return --------------------------------------------------------------- + # return tidyframe(res, check = False, copy = False) ########################################################################## # basic verbs @@ -1405,11 +1583,11 @@ def slice(self, row_numbers, by = None): "not exceed the number of rows of the group" )) - res = (self.group_modify(lambda chunk: chunk.iloc[row_numbers, :] - , by = by - , is_pandas_udf = True - , preserve_row_order = True - , row_order_column_name = _generate_new_string(self.colnames) + res = (self.group_modify(lambda chunk: chunk.iloc[row_numbers, :], + by = by, + is_pandas_udf = True, + preserve_row_order = True, + row_order_column_name = _generate_new_string(self.colnames) ) .relocate(self.colnames) ) @@ -4125,10 +4303,10 @@ def slice_head(self "arg 'n' should not exceed the size of any chunk after grouping" ro_name = _generate_new_string(cn) - res = (self.group_modify(lambda x: x.slice(np.arange(n)) - , by = by - , preserve_row_order = True - , row_order_column_name = ro_name + res = (self.group_modify(lambda x: x.slice(np.arange(n)), + by = by, + preserve_row_order = True, + row_order_column_name = ro_name ) ) else: @@ -4148,10 +4326,10 @@ def slice_head(self ro_name = _generate_new_string(cn) res = self.group_modify( - lambda x: x.slice(range(int(roundf(x.shape[0] * prop)))) - , by = by - , preserve_row_order = True - , row_order_column_name = ro_name + lambda x: x.slice(range(int(roundf(x.shape[0] * prop)))), + by = by, + preserve_row_order = True, + row_order_column_name = ro_name ) return res.relocate(cn) @@ -4243,12 +4421,12 @@ def slice_tail(self "arg 'n' should not exceed the size of any chunk after grouping" ro_name = _generate_new_string(cn) - res = (self.group_modify(lambda x: x.tail(n).reset_index(drop = True) - , by = by - , is_pandas_udf = True - , preserve_row_order = True - , row_order_column_name = ro_name - ) + res = (self.group_modify(lambda x: x.tail(n).reset_index(drop = True), + by = by, + is_pandas_udf = True, + preserve_row_order = True, + row_order_column_name = ro_name + ) ) else: assert isinstance(rounding_type, str),\ @@ -4954,12 +5132,12 @@ def drop_na(self, column_names = None): # fill_na (fill) ############################################################################ - def fill_na(self - , column_direction_dict - , order_by = None - , ascending = True - , na_position = "last" - , by = None + def fill_na(self, + column_direction_dict, + order_by = None, + ascending = True, + na_position = "last", + by = None ): ''' Fill missing values from neighboring values per column @@ -5011,6 +5189,8 @@ def fill_na(self >>> df.fill_na({'B': 'updown'}, order_by = "C") >>> df.fill_na({'B': 'updown'}, order_by = "C", by = "A") ''' + + # assertions ----------------------------------------------------------- cn = self.colnames assert isinstance(column_direction_dict, dict),\ "arg 'column_direction_dict' should be a dict" @@ -5042,55 +5222,61 @@ def fill_na(self if order_by is not None: assert len(set(by).intersection([x[0] for x in order_by])) == 0,\ "'by' columns should not intersect with 'order_by' columns" + + # generic function to fill the series ---------------------------------- + def fill_series(x, method): - def fill_chunk(x, cdd): - - chunk = x.copy() - - for akey in cdd: - method = cdd[akey] - if method == "up": - chunk[akey] = chunk[akey].bfill() - elif method == "down": - chunk[akey] = chunk[akey].ffill() - elif method == "updown": - chunk[akey] = chunk[akey].bfill() - chunk[akey] = chunk[akey].ffill() - else: - chunk[akey] = chunk[akey].ffill() - chunk[akey] = chunk[akey].bfill() + if method == "up": + res = x.bfill() + elif method == "down": + res = x.ffill() + elif method == "updown": + res = x.bfill().ffill() + else: + res = x.ffill().bfill() - return chunk + return res + # arrange before fill -------------------------------------------------- + # row order of the original dataframe is maintained if order_by is not None: ro_name = _generate_new_string(self.colnames) - res = self.add_row_number(name = ro_name).arrange(order_by) + res_pd = (self.add_row_number(name = ro_name) + .arrange(order_by) + .to_pandas() + ) else: - res = self + res_pd = self.to_pandas() + + # fill each series ----------------------------------------------------- + if by is None: + out_pd = res_pd + else: + out_pd = res_pd.groupby(by, dropna = False) + for acolname, method in column_direction_dict.items(): + res_pd[acolname] = fill_series(out_pd[acolname], method) - if by is not None: - res = res.group_modify( - lambda x: fill_chunk(x, column_direction_dict) - , by = by - , preserve_row_order = True - , is_pandas_udf = True - ) + # reorder the dataframe if necessary ----------------------------------- + if order_by is not None: + res = (tidyframe(res_pd, check = False, copy = False) + .arrange(ro_name) + .select(ro_name, include = False) + ) else: - res = (res.to_pandas(copy = False) - .pipe(lambda x: fill_chunk(x, column_direction_dict)) - .pipe(lambda x: tidyframe(x, copy = False, check = False)) - ) + res = tidyframe(res_pd, check = False, copy = False) - if order_by is not None: - res = res.arrange(ro_name).select(ro_name, include = False) - - return res + # return --------------------------------------------------------------- + return res + # alias for fill_na fill = fill_na + ############################################################################ # string utilities + ############################################################################ + def separate(self , column_name , into @@ -5473,13 +5659,13 @@ def unnest(self, nest_column_name = 'data'): assert nest_column_name in cn,\ "arg 'nest_column_name' should be a exisiting column name" - all_are_tidy = all(map(lambda x: isinstance(x, tidyframe) - , list(self.__data[nest_column_name]) + all_are_tidy = all(map(lambda x: isinstance(x, tidyframe), + list(self.__data[nest_column_name]) ) ) def is_list_scalar_na(x): - if isinstance(x, (list, pd.core.arrays.floating.FloatingArray)): + if isinstance(x, (list, pd.core.arrays.floating.FloatingArray, np.ndarray)): res = True elif np.isscalar(x): res = True @@ -5489,10 +5675,7 @@ def is_list_scalar_na(x): res = False return res - all_are_list = all(map(is_list_scalar_na - , list(self.__data[nest_column_name]) - ) - ) + all_are_list = all(map(is_list_scalar_na, list(self.__data[nest_column_name]))) assert all_are_tidy or all_are_list,\ ("arg 'nest_column_name' column is neither a column of " @@ -5521,6 +5704,76 @@ def is_list_scalar_na(x): return tidyframe(res) + ########################################################################## + # unnest_wider + ########################################################################## + + def unnest_wider(self, nest_column_name, names_sep = None): + ''' + Unnest a column of dicts into multiple columns + + Parameters + ---------- + nest_column_name: str + Name of the column to be unnested + + Returns + ------- + tidyframe + + Notes + ----- + 1. unnest_wider is helpful when nested input typically parsed from a + a json requires to be unnested. + + Examples + -------- + >>> import pandas as pd + >>> df = pd.DataFrame({'x': [1,2]}) + >>> df['y'] = pd.Series([{"a": 1, 'b': 2}, {'a': 3, 'b': 4}]) + >>> df_tidy = tidyframe(df) + >>> + >>> # unnest nested lists + >>> df_tidy.unnest_wider('y') + ''' + + # assertions ----------------------------------------------------------- + assert isinstance(nest_column_name, str),\ + "'nest_column_name' arg should be a string" + assert nest_column_name in self.colnames,\ + f"{nest_column_name} is not a valid column name" + assert all([isinstance(x, dict) for x in self.pull(nest_column_name)]),\ + "Every element of the column should be a dict" + + if names_sep is not None: + assert isinstance(names_sep, str),\ + "When not None, 'names_sep' arg should be a string" + + # core unnest operation ------------------------------------------------ + # max_level at 0 exactly unnests one level + col_wide_df = pd.json_normalize(self.pull(nest_column_name), + max_level = 0 + ) + col_wide_df.reset_index(inplace = True, drop = True) + + # prefix by original column name with separator 'sep' ------------------ + if names_sep is not None: + col_wide_df.columns = map(lambda x: nest_column_name + names_sep + x, + list(col_wide_df.columns) + ) + + # concat untouched columns and unnested df ----------------------------- + res = self.to_pandas() + res.drop(columns = [nest_column_name], inplace = True) + res.reset_index(inplace = True, drop = True) + res = pd.concat([res, col_wide_df], axis = 1) + res.columns = _get_unique_names(list(res.columns)) + + # return --------------------------------------------------------------- + return tidyframe(res, check = False, copy = False) + + + ########################################################################## # split (group_split) ########################################################################## diff --git a/tests/test_tidypandas.py b/tests/test_tidypandas.py index 5070029..5b46811 100644 --- a/tests/test_tidypandas.py +++ b/tests/test_tidypandas.py @@ -76,12 +76,16 @@ def test_mutate(penguins_data): def test_mutate_groupby(penguins_data): penguins_tidy = tidyframe(penguins_data, copy=False) - - expected = penguins_data.groupby("sex", dropna=False)\ - .apply(lambda x: x.assign(year_mod=lambda y: y["year"] + np.mean(y["year"]) - 4015)) + expected = (penguins_data + .groupby("sex", dropna=False) + .apply(lambda x: x.assign(year_mod=lambda y: y["year"] + np.mean(y["year"]) - 4015)) + .reset_index(drop = True) + .sort_values(list(penguins_data.columns), ignore_index = True) + ) result = penguins_tidy.mutate({'year_mod' : "x['year'] + np.mean(x['year']) - 4015"} , by = 'sex' - ).to_pandas() + ).to_pandas().sort_values(list(penguins_data.columns), ignore_index = True) + assert_frame_equal_v2(expected, result) def test_mutate_orderby(penguins_data): @@ -103,8 +107,6 @@ def test_mutate_orderby(penguins_data): def test_mutate_across(penguins_data): penguins_tidy = tidyframe(penguins_data, copy=False) - - # across mode with column names expected = penguins_data[['bill_length_mm', 'body_mass_g']]\ .assign(demean_bill_length_mm=lambda x: x["bill_length_mm"] - np.mean(x["bill_length_mm"]))\ @@ -121,7 +123,10 @@ def test_mutate_across(penguins_data): expected = penguins_data[['bill_length_mm', 'body_mass_g', 'species']] expected = expected.groupby("species")\ .apply(lambda x: x.assign(demean_bill_length_mm=lambda y: y["bill_length_mm"] - np.mean(y["bill_length_mm"]))\ - .assign(demean_body_mass_g=lambda y: y["body_mass_g"] - np.mean(y["body_mass_g"]))) + .assign(demean_body_mass_g=lambda y: y["body_mass_g"] - np.mean(y["body_mass_g"])))\ + .reset_index(drop = True) + + expected = expected.sort_values(list(expected.columns), ignore_index = True) result = (penguins_tidy.select(['bill_length_mm', 'body_mass_g', 'species']) .mutate(column_names = ['bill_length_mm', 'body_mass_g'], @@ -130,6 +135,7 @@ def test_mutate_across(penguins_data): by = 'species' ) ).to_pandas() + result = result.sort_values(list(result.columns), ignore_index = True) assert_frame_equal_v2(expected, result)