def add():
def arg_other(v_self: pd.DataFrame):
(nr, nc) = v_self.shape
v_nc = random.choice([nc, nc - 1, nc + 1])
val = next(
defaultRandDf(num_rows=nr,
num_columns=v_nc,
column_levels=v_self.columns.nlevels,
col_prefix='i1_',
value_bags=[*ints_bags, *floats_bags]))
val.index = v_self.index
if (coin_flip() == 0) and (len(val.columns) == nc):
val.columns = v_self.columns
elif v_self.columns.nlevels == 1:
val.columns = pd.Index(
random.sample(
set((list(v_self.columns) + list(val.columns))),
len(val.columns)))
else:
val.columns = pd.MultiIndex.from_tuples(
random.sample(
set((list(v_self.columns) + list(val.columns))),
len(val.columns)))
yield val
def arg_fill_value():
yield random.uniform((-100), 100)
_self = RExt(DType(pd.DataFrame),
defaultRandDf(value_bags=[*ints_bags, *floats_bags]))
_other = RExt(DType(pd.DataFrame), arg_other(_self))
_fill_value = Chain(Default(None), RExt(DType(float),
arg_fill_value()))
def fillna():
def arg_value():
if _spec.depth == _spec.max_depth:
output: pd.DataFrame = _spec.output
all_values = set([
i for col in output for i in output[col]
if (not pd.isnull(i))
])
yield from map(lambda x: AnnotatedVal(x, cost=2),
Select(all_values))
def arg_limit(v_self: pd.DataFrame):
yield from map(lambda x: AnnotatedVal(x, cost=5),
Select(range(1, (max(v_self.shape) + 1))))
def rarg_limit(v_self: pd.DataFrame):
(nr, nc) = v_self.shape
if coin_flip() == 0:
yield random.choice(range(1, max(nr, 2)))
else:
yield random.choice(range(1, max(nc, 2)))
def rarg_value():
yield random.uniform((-1000), 1000)
_self = RExt(DType(pd.DataFrame), defaultRandDf(nan_prob=0.5))
_limit = Chain(Default(None), RExt(DType(int), rarg_limit(_self)),
arg_limit(_self))
_value = Chain(Default(None), RExt(FType(np.isscalar), rarg_value()),
Ext(DType([dict, pd.Series, pd.DataFrame])),
arg_value())
def corrwith():
def arg_other(v_self: pd.DataFrame):
(nr, nc) = v_self.shape
val = next(
defaultRandDf(num_rows=nr,
column_levels=v_self.columns.nlevels,
col_prefix='i1_',
value_bags=[*ints_bags, *floats_bags]))
val.index = v_self.index
if (coin_flip() == 0) and (len(val.columns) == nc):
val.columns = v_self.columns
elif v_self.columns.nlevels == 1:
val.columns = pd.Index(
random.sample(
set((list(v_self.columns) + list(val.columns))),
len(val.columns)))
else:
val.columns = pd.MultiIndex.from_tuples(
random.sample(
set((list(v_self.columns) + list(val.columns))),
len(val.columns)))
yield val
_self = RExt(DType(pd.DataFrame),
defaultRandDf(value_bags=[*ints_bags, *floats_bags]))
_other = RExt(DType(pd.DataFrame), arg_other(_self))
def merge():
def arg_right(v_self: pd.DataFrame):
new_df: pd.DataFrame = next(defaultRandDf(col_prefix='i1_'))
dg1 = collections.defaultdict(list)
dg2 = collections.defaultdict(list)
for (k, v) in dict(v_self.dtypes).items():
dg1[v].append(k)
for (k, v) in dict(new_df.dtypes).items():
dg2[v].append(k)
c = (set(dg1.keys()) & set(dg2.keys()))
for dt in c:
cols1 = list(dg1[dt])
cols2 = list(dg2[dt])
random.shuffle(cols1)
random.shuffle(cols2)
pairs = list(zip(cols1, cols2))
for pair in pairs:
if coin_flip() == 0:
new_df[pair[1]] = random.sample(
(list(new_df[pair[1]]) + list(v_self[pair[0]])),
new_df.shape[0])
if (coin_flip() == 0) and (pair[0]
not in new_df.columns):
new_df = new_df.rename({
pair[1]: pair[0],
},
axis=1)
yield new_df
_self = RExt(DType(pd.DataFrame), defaultRandDf())
_right = RExt(DType(pd.DataFrame), arg_right(_self))
def diff():
def arg_periods(v_self: pd.DataFrame):
(nr, _) = v_self.shape
yield random.choice(range((-(nr - 1)), nr))
_self = RExt(DType(pd.DataFrame), defaultRandDf(nan_prob=0.1))
_periods = Chain(Default(1), RExt(DType(int), arg_periods(_self)))
def round():
def arg_decimals():
yield random.choice([1, 2, 3, 4, 5])
_self = RExt(DType(pd.DataFrame), defaultRandDf(nan_prob=0.1))
_decimals = Chain(Default(0), RExt(DType(int), arg_decimals()))
def align():
def arg_other(v_self: pd.DataFrame):
(nr, nc) = v_self.shape
val = next(defaultRandDf(num_rows=random.choice([max((nr - 1), 1), nr, (nr + 1)]),
num_columns=random.choice([max((nc - 1), 1), nc, (nc + 1)]), col_prefix='i1_',
index_levels=v_self.index.nlevels, column_levels=v_self.columns.nlevels,
value_bags=[*ints_bags, *floats_bags]))
if (coin_flip() == 0) and (len(val.index) == nr):
val.index = v_self.index
elif v_self.index.nlevels == 1:
val.index = pd.Index(random.sample(set((list(v_self.index) + list(val.index))), len(val.index)))
else:
val.index = pd.MultiIndex.from_tuples(
random.sample(set((list(v_self.index) + list(val.index))), len(val.index)))
if (coin_flip() == 0) and (len(val.columns) == nc):
val.columns = v_self.columns
elif v_self.columns.nlevels == 1:
val.columns = pd.Index(random.sample(set((list(v_self.columns) + list(val.columns))), len(val.columns)))
else:
val.columns = pd.MultiIndex.from_tuples(
random.sample(set((list(v_self.columns) + list(val.columns))), len(val.columns)))
yield val
_self = RExt(DType(pd.DataFrame), defaultRandDf())
_other = RExt(DType([pd.DataFrame, pd.Series]), arg_other(_self))
def astype():
def arg_astype_partial(v_self):
if _spec.depth == _spec.max_depth:
v_self: pd.DataFrame = v_self
output: pd.DataFrame = _spec.output
try:
if set(output.columns).issubset(set(v_self.columns)):
yield dict(output.dtypes)
except:
pass
def arg_dtype(v_self: pd.DataFrame):
pool = ['int32', 'uint32', 'float64', 'float32', 'int64', 'uint64']
mapping = {
pool[i]: (([None] + pool[:i]) + pool[(i + 1):])
for i in range(len(pool))
}
mapping['object'] = [None]
res = {}
for col in v_self.columns:
chosen = random.choice(mapping[str(v_self.dtypes[col])])
if chosen is not None:
res[col] = chosen
yield res
_self = RExt(DType(pd.DataFrame), defaultRandDf())
_dtype = Chain(RExt(DType(dict), arg_dtype(_self)),
arg_astype_partial(_self))
def combine_first():
def arg_other(v_self: pd.DataFrame):
(nr, nc) = v_self.shape
if coin_flip() == 0:
val = next(defaultRandDf(num_columns=nc, num_rows=nr, value_bags=(
[*string_bags, *ints_bags] + [*floats_bags, moar_nans_floats_bag])))
val.columns = v_self.columns
val.index = v_self.index
else:
val = next(defaultRandDf(index_levels=v_self.index.nlevels, column_levels=v_self.columns.nlevels,
col_prefix='i1_', value_bags=(
[*string_bags, *ints_bags] + [*floats_bags, moar_nans_floats_bag])))
if v_self.index.nlevels == 1:
val.index = pd.Index(random.sample(set((list(v_self.index) + list(val.index))), len(val.index)))
else:
val.index = pd.MultiIndex.from_tuples(
random.sample(set((list(v_self.index) + list(val.index))), len(val.index)))
if v_self.columns.nlevels == 1:
val.columns = pd.Index(
random.sample(set((list(v_self.columns) + list(val.columns))), len(val.columns)))
else:
val.columns = pd.MultiIndex.from_tuples(
random.sample(set((list(v_self.columns) + list(val.columns))), len(val.columns)))
yield val
_self = RExt(DType(pd.DataFrame), defaultRandDf(value_bags=[*string_bags, *ints_bags] + [*floats_bags, moar_nans_floats_bag]))
_other = RExt(DType(pd.DataFrame), arg_other(_self))
def query():
def arg_expr(v_self: pd.DataFrame):
pool = []
dtypes = v_self.dtypes
for col in v_self:
dtype = dtypes[col]
vals = list(v_self[col])
if ('int' in str(dtype)) or ('float' in str(dtype)):
pool.append('{} > {}'.format(col, random.choice(vals)))
pool.append('{} < {}'.format(col, random.choice(vals)))
pool.append('{} == {}'.format(col, random.choice(vals)))
pool.append('{} != {}'.format(col, random.choice(vals)))
elif 'object' in str(dtype):
pool.append('{} == {}'.format(col, random.choice(vals)))
pool.append('{} != {}'.format(col, random.choice(vals)))
sample_size = random.randint(1, min(5, len(pool)))
sample = random.sample(pool, sample_size)
expr = sample[0]
for i in range(1, len(sample)):
expr += ' {} '.format(random.choice(['and', 'or']))
expr += sample[i]
yield expr
_self = RExt(DType(pd.DataFrame), defaultRandDf())
_expr = RExt(DType(str), arg_expr(_self))
def isin():
def arg_values(v_self: pd.DataFrame):
vals = list(v_self.values.flatten())
sample_size = random.randint(1, max((len(vals) - 1), 1))
yield list(random.sample(vals, sample_size))
_self = RExt(DType(pd.DataFrame), defaultRandDf())
_values = RExt(DType(dict), arg_values(_self))
def clip_lower():
def arg_threshold(v_self: pd.DataFrame):
vals = list(filter((lambda x: (not isinstance(x, str))), list(v_self.values.flatten())))
yield random.uniform(min(vals), max(vals))
_self = RExt(DType(pd.DataFrame), defaultRandDf(value_bags=[*ints_bags, *floats_bags]))
_threshold = RExt(DType(float), arg_threshold(_self))
def apply():
def arg_func(v_self: pd.DataFrame):
numeric_cols = v_self.select_dtypes(include=np.number).columns
if len(numeric_cols) == 0:
return
choice = random.choice(list(numeric_cols))
yield Lambda('lambda x: x["{}"] > 1'.format(choice))
yield Lambda('lambda x: x["{}"] + 1'.format(choice))
_self = RExt(DType(pd.DataFrame), defaultRandDf())
_func = RExt(DType(Callable), arg_func(_self))
def reindex_like():
def arg_other(v_self: pd.DataFrame):
val = next(defaultRandDf(index_levels=v_self.index.nlevels, col_prefix=random.choice(['', 'i1_']),
value_bags=[*ints_bags, *floats_bags]))
if v_self.index.nlevels == 1:
val.index = pd.Index(random.sample(set((list(v_self.index) + list(val.index))), len(val.index)))
else:
val.index = pd.MultiIndex.from_tuples(
random.sample(set((list(v_self.index) + list(val.index))), len(val.index)))
yield val
_self = RExt(DType(pd.DataFrame), defaultRandDf())
_other = RExt(DType(pd.DataFrame), arg_other(_self))
def head():
def arg_head_partial(v_self: pd.DataFrame):
if _spec.depth == _spec.max_depth:
output: pd.DataFrame = _spec.output
yield AnnotatedVal(output.shape[0], cost=0)
yield from Select(list(range(1, v_self.shape[0] + 1)))
def arg_n(v_self: pd.DataFrame):
pool = list(set(([5] + list(range(1, len(v_self))))))
yield random.choice(pool)
_self = RExt(DType(pd.DataFrame), defaultRandDf())
_n = Chain(Default(5), RExt(DType(int), arg_n(_self)), arg_head_partial(_self))
def ne():
def arg_other(v_self: pd.DataFrame):
(nr, nc) = v_self.shape
cond: pd.DataFrame = next(defaultRandDf(num_rows=nr, num_columns=nc, value_bags=bool_bags))
cond.columns = v_self.columns
cond.index = v_self.index
val: pd.DataFrame = next(defaultRandDf(num_rows=nr, num_columns=nc))
val.columns = v_self.columns
val.index = v_self.index
yield v_self.where(cond, val)
_self = RExt(DType(pd.DataFrame), defaultRandDf())
_other = RExt(DType(pd.DataFrame), arg_other(_self))
def take():
def arg_indices(v_self: pd.DataFrame):
(nr, nc) = v_self.shape
if coin_flip() == 0:
val = random.sample(range(nr), random.choice(range(1, (nr + 1))))
random.shuffle(val)
yield val
else:
val = random.sample(range(nc), random.choice(range(1, (nc + 1))))
random.shuffle(val)
yield val
_self = RExt(DType(pd.DataFrame), defaultRandDf())
_indices = RExt(DType(Sequence), arg_indices(_self))
def combine():
def arg_func():
pool = [Lambda('lambda s1, s2: s1.mask(s1 < s2, s2)'), Lambda('lambda s1, s2: s1.mask(s1 > s2, s2)')]
yield random.choice(pool)
def arg_other(v_self: pd.DataFrame):
(nr, nc) = v_self.shape
val: pd.DataFrame = next(
defaultRandDf(num_rows=nr, num_columns=nc, value_bags=[*ints_bags, *floats_bags]))
val.columns = v_self.columns
val.index = v_self.index
yield val
_self = RExt(DType(pd.DataFrame), defaultRandDf(value_bags=[*ints_bags, *floats_bags]))
_func = RExt(DType(Callable), arg_func())
_other = RExt(DType(pd.DataFrame), arg_other(_self))
def clip():
def arg_lower(v_self: pd.DataFrame):
vals = list(filter((lambda x: (is_int(x) or is_float(x))), list(v_self.values.flatten())))
if len(vals) == 0:
return
yield random.uniform(min(vals), max(vals))
def arg_upper(v_self: pd.DataFrame, v_lower):
vals = list(filter((lambda x: (is_int(x) or is_float(x))), list(v_self.values.flatten())))
if len(vals) == 0:
return
if v_lower is None:
v_lower = min(vals)
yield random.uniform(v_lower, max(vals))
_self = RExt(DType(pd.DataFrame), defaultRandDf(value_bags=[*ints_bags, *floats_bags]))
_lower = Chain(Default(None), RExt(DType(float), arg_lower(_self)))
_upper = Chain(Default(None), RExt(DType(float), arg_upper(_self, _lower)))
def reindex():
def arg_labels(v_self: pd.DataFrame):
(nr, nc) = v_self.shape
if coin_flip() == 0:
vals = list(v_self.index)
new_vals = list(StrColGen(all_distinct=True).generate((nr // 2))[1].values())
yield list(random.sample((vals + new_vals), nr))
else:
vals = list(v_self.columns)
new_vals = list(StrColGen(all_distinct=True).generate((nc // 2))[1].values())
yield list(random.sample((vals + new_vals), nc))
def arg_fill_value():
yield random.uniform((- 100), 100)
_self = RExt(DType(pd.DataFrame), defaultRandDf())
_labels = RExt(DType([list, dict]), arg_labels(_self))
_fill_value = Chain(Default(np.NaN), RExt(DType(float), arg_fill_value()))
def mask():
def arg_cond(v_self: pd.DataFrame):
(nr, nc) = v_self.shape
val: pd.DataFrame = next(defaultRandDf(num_rows=nr, num_columns=nc, value_bags=bool_bags))
val.columns = v_self.columns
val.index = v_self.index
yield val
def arg_other(v_self: pd.DataFrame):
(nr, nc) = v_self.shape
val: pd.DataFrame = next(defaultRandDf(num_rows=nr, num_columns=nc))
val.columns = v_self.columns
val.index = v_self.index
yield val
_self = RExt(DType(pd.DataFrame), defaultRandDf())
_cond = RExt(DType([Sequence, pd.DataFrame, Callable]), arg_cond(_self))
_other = RExt(DType([Sequence, pd.DataFrame, Callable]), arg_other(_self))
def dropna():
_self = RExt(DType(pd.DataFrame), defaultRandDf(nan_prob=0.5))
def set_index():
_self = RExt(DType(pd.DataFrame), defaultRandDf())
def select_dtypes():
_self = RExt(DType(pd.DataFrame), defaultRandDf())
def idxmax():
_self = RExt(DType(pd.DataFrame), defaultRandDf(nan_prob=0.2))
def filter():
_self = RExt(DType(pd.DataFrame), defaultRandDf())
def equals():
_self = RExt(DType(pd.DataFrame), defaultRandDf())
_other = RExt(DType(pd.DataFrame),
defaultRandDf(col_prefix=random.choice(['', 'i1_'])))
def get_ftype_counts():
_self = RExt(DType(pd.DataFrame), defaultRandDf())
def duplicated():
_self = RExt(DType(pd.DataFrame), defaultRandDf(min_height=3))
def drop():
_self = RExt(DType(pd.DataFrame), defaultRandDf())
