How to use the datatable.by function in datatable

def test_key_after_group():
    n = 1000
    DT = dt.Frame(A=[random.choice("abcd") for _ in range(n)])
    tmp = DT[:, dt.count(), dt.by(0)]
    frame_integrity_check(tmp)
    tmp.key = "A"
    assert tmp.to_list()[0] == ["a", "b", "c", "d"]
    assert sum(tmp.to_list()[1]) == n

def test_groupby_multi():
    DT = dt.Frame(A=[1, 2, 3] * 3, B=[1, 2] * 4 + [1], C=range(9))
    res = DT[:, sum(f.C), by("A", "B")]
    assert res.to_list() == [[1, 1, 2, 2, 3, 3],
                             [1, 2, 1, 2, 1, 2],
                             [6, 3, 4, 8, 10, 5]]

def test_groups_large1():
    n = 251 * 4000
    xs = [(i * 19) % 251 for i in range(n)]
    f0 = dt.Frame({"A": xs})
    f1 = f0[:, count(), by("A")]
    assert f1.to_list() == [list(range(251)), [4000] * 251]

question = "largest two v3 by id6" # q8
gc.collect()
t_start = timeit.default_timer()
ans = x[:2, {"largest2_v3": f.v3}, by(f.id6), sort(-f.v3)]
print(ans.shape, flush=True)
t = timeit.default_timer() - t_start
m = memory_usage()
t_start = timeit.default_timer()
chk = ans[:, sum(f.largest2_v3)]
chkt = timeit.default_timer() - t_start
write_log(task=task, data=data_name, in_rows=x.shape[0], question=question, out_rows=ans.shape[0], out_cols=ans.shape[1], solution=solution, version=ver, git=git, fun=fun, run=1, time_sec=t, mem_gb=m, cache=cache, chk=make_chk(flatten(chk.to_list())), chk_time_sec=chkt, on_disk=on_disk)
del ans
gc.collect()
t_start = timeit.default_timer()
ans = x[:2, {"largest2_v3": f.v3}, by(f.id6), sort(-f.v3)]
print(ans.shape, flush=True)
t = timeit.default_timer() - t_start
m = memory_usage()
t_start = timeit.default_timer()
chk = ans[:, sum(f.largest2_v3)]
chkt = timeit.default_timer() - t_start
write_log(task=task, data=data_name, in_rows=x.shape[0], question=question, out_rows=ans.shape[0], out_cols=ans.shape[1], solution=solution, version=ver, git=git, fun=fun, run=2, time_sec=t, mem_gb=m, cache=cache, chk=make_chk(flatten(chk.to_list())), chk_time_sec=chkt, on_disk=on_disk)
print(ans.head(3), flush=True)
print(ans.tail(3), flush=True)
del ans

question = "regression v1 v2 by id2 id4" # q9 # not yet implemeneted https://github.com/h2oai/datatable/issues/1543
gc.collect()
t_start = timeit.default_timer()
ans = x[:, {"r2": corr(f.v1, f.v2)**2}, by(f.id2, f.id4)]
print(ans.shape, flush=True)

print('=' * 50)
                print("MyIEEEGroupBysTransformers name  {} {}".format(
                    self._output_feature_names, self._feature_desc
                )
                )

                return X[:, dt.f["col_cnt"] / dt.f["daily_cnt"]]

            elif self.group_type == 'hour':

                X = dt.Frame(X[:, self.group_col])
                X[:, 'date'] = dt.Frame(ieee_datetime.dt.strftime('%Y%m%d_%H').values)

                # Compute daily counts
                hourly_cnt = X[:, {"hourly_cnt": dt.count()}, dt.by("date")]
                hourly_cnt.key = ["date"]
                X = X[:, :, dt.join(hourly_cnt)]

                # Compute card count
                col_cnt = X[:, {"col_cnt": dt.count()}, dt.by(*["date", self.group_col])]
                col_cnt.key = ["date", self.group_col]
                X = X[:, :, dt.join(col_cnt)]

                self._output_feature_names = ["IEEEGroupBys_{}_{}".format(self.group_col, self.group_type)]
                self._feature_desc = ["IEEEGroupBys_{}_{}".format(self.group_col, self.group_type)]

                print('=' * 50)
                print("MyIEEEGroupBysTransformers name  {} {}".format(
                    self._output_feature_names, self._feature_desc
                )
                )

# datatable doesn't seem to have per-group transform yet (other than the whole dataframe)
        res = op.sources[0].eval_implementation(
            data_map=data_map, eval_env=eval_env, data_model=self
        )
        if len(op.order_by) > 0:
            ascending = [False if ci in set(op.reverse) else True for ci in op.order_by]
            if not all(ascending):
                raise RuntimeError(
                    "reverse isn't implemented for datatable yet"
                )  # TODO: implement
            syms = [datatable.f[c] for c in op.order_by]
            res = res.sort(*syms)
        if len(op.partition_by) > 0:
            for (col, expr) in op.ops.items():
                dt_expr = expr_to_dt_expr(expr)
                res[col] = res[:, {col: dt_expr}, datatable.by(*op.partition_by)][col]
        else:
            for (col, expr) in op.ops.items():
                dt_expr = expr_to_dt_expr(expr)
                res[col] = res[:, {col: dt_expr}][col]
        return res

question = "median v3 sd v3 by id4 id5" # q6
gc.collect()
t_start = timeit.default_timer()
ans = x[:, {'median_v3': median(f.v3), 'sd_v3': sd(f.v3)}, by(f.id4, f.id5)]
print(ans.shape, flush=True)
t = timeit.default_timer() - t_start
m = memory_usage()
t_start = timeit.default_timer()
chk = ans[:, [sum(f.median_v3), sum(f.sd_v3)]]
chkt = timeit.default_timer() - t_start
write_log(task=task, data=data_name, in_rows=x.shape[0], question=question, out_rows=ans.shape[0], out_cols=ans.shape[1], solution=solution, version=ver, git=git, fun=fun, run=1, time_sec=t, mem_gb=m, cache=cache, chk=make_chk(flatten(chk.to_list())), chk_time_sec=chkt, on_disk=on_disk)
del ans
gc.collect()
t_start = timeit.default_timer()
ans = x[:, {'median_v3': median(f.v3), 'sd_v3': sd(f.v3)}, by(f.id4, f.id5)]
print(ans.shape, flush=True)
t = timeit.default_timer() - t_start
m = memory_usage()
t_start = timeit.default_timer()
chk = ans[:, [sum(f.median_v3), sum(f.sd_v3)]]
chkt = timeit.default_timer() - t_start
write_log(task=task, data=data_name, in_rows=x.shape[0], question=question, out_rows=ans.shape[0], out_cols=ans.shape[1], solution=solution, version=ver, git=git, fun=fun, run=2, time_sec=t, mem_gb=m, cache=cache, chk=make_chk(flatten(chk.to_list())), chk_time_sec=chkt, on_disk=on_disk)
print(ans.head(3), flush=True)
print(ans.tail(3), flush=True)
del ans

question = "max v1 - min v2 by id3" # q7
gc.collect()
t_start = timeit.default_timer()
ans = x[:, {"range_v1_v2": max(f.v1)-min(f.v2)}, by(f.id3)]
print(ans.shape, flush=True)