join_merge.py

import string

import numpy as np

from pandas import DataFrame, MultiIndex, Series, concat, date_range, merge, merge_asof
import pandas.util.testing as tm

try:
    from pandas import merge_ordered
except ImportError:
    from pandas import ordered_merge as merge_ordered


class Append:
    def setup(self):
        self.df1 = DataFrame(np.random.randn(10000, 4), columns=["A", "B", "C", "D"])
        self.df2 = self.df1.copy()
        self.df2.index = np.arange(10000, 20000)
        self.mdf1 = self.df1.copy()
        self.mdf1["obj1"] = "bar"
        self.mdf1["obj2"] = "bar"
        self.mdf1["int1"] = 5
        self.mdf1 = self.mdf1._consolidate()
        self.mdf2 = self.mdf1.copy()
        self.mdf2.index = self.df2.index

    def time_append_homogenous(self):
        self.df1.append(self.df2)

    def time_append_mixed(self):
        self.mdf1.append(self.mdf2)


class Concat:

    params = [0, 1]
    param_names = ["axis"]

    def setup(self, axis):
        N = 1000
        s = Series(N, index=tm.makeStringIndex(N))
        self.series = [s[i:-i] for i in range(1, 10)] * 50
        self.small_frames = [DataFrame(np.random.randn(5, 4))] * 1000
        df = DataFrame(
            {"A": range(N)}, index=date_range("20130101", periods=N, freq="s")
        )
        self.empty_left = [DataFrame(), df]
        self.empty_right = [df, DataFrame()]
        self.mixed_ndims = [df, df.head(N // 2)]

    def time_concat_series(self, axis):
        concat(self.series, axis=axis, sort=False)

    def time_concat_small_frames(self, axis):
        concat(self.small_frames, axis=axis)

    def time_concat_empty_right(self, axis):
        concat(self.empty_right, axis=axis)

    def time_concat_empty_left(self, axis):
        concat(self.empty_left, axis=axis)

    def time_concat_mixed_ndims(self, axis):
        concat(self.mixed_ndims, axis=axis)


class ConcatDataFrames:

    params = ([0, 1], [True, False])
    param_names = ["axis", "ignore_index"]

    def setup(self, axis, ignore_index):
        frame_c = DataFrame(np.zeros((10000, 200), dtype=np.float32, order="C"))
        self.frame_c = [frame_c] * 20
        frame_f = DataFrame(np.zeros((10000, 200), dtype=np.float32, order="F"))
        self.frame_f = [frame_f] * 20

    def time_c_ordered(self, axis, ignore_index):
        concat(self.frame_c, axis=axis, ignore_index=ignore_index)

    def time_f_ordered(self, axis, ignore_index):
        concat(self.frame_f, axis=axis, ignore_index=ignore_index)


class Join:

    params = [True, False]
    param_names = ["sort"]

    def setup(self, sort):
        level1 = tm.makeStringIndex(10).values
        level2 = tm.makeStringIndex(1000).values
        codes1 = np.arange(10).repeat(1000)
        codes2 = np.tile(np.arange(1000), 10)
        index2 = MultiIndex(levels=[level1, level2], codes=[codes1, codes2])
        self.df_multi = DataFrame(
            np.random.randn(len(index2), 4), index=index2, columns=["A", "B", "C", "D"]
        )

        self.key1 = np.tile(level1.take(codes1), 10)
        self.key2 = np.tile(level2.take(codes2), 10)
        self.df = DataFrame(
            {
                "data1": np.random.randn(100000),
                "data2": np.random.randn(100000),
                "key1": self.key1,
                "key2": self.key2,
            }
        )

        self.df_key1 = DataFrame(
            np.random.randn(len(level1), 4), index=level1, columns=["A", "B", "C", "D"]
        )
        self.df_key2 = DataFrame(
            np.random.randn(len(level2), 4), index=level2, columns=["A", "B", "C", "D"]
        )

        shuf = np.arange(100000)
        np.random.shuffle(shuf)
        self.df_shuf = self.df.reindex(self.df.index[shuf])

    def time_join_dataframe_index_multi(self, sort):
        self.df.join(self.df_multi, on=["key1", "key2"], sort=sort)

    def time_join_dataframe_index_single_key_bigger(self, sort):
        self.df.join(self.df_key2, on="key2", sort=sort)

    def time_join_dataframe_index_single_key_small(self, sort):
        self.df.join(self.df_key1, on="key1", sort=sort)

    def time_join_dataframe_index_shuffle_key_bigger_sort(self, sort):
        self.df_shuf.join(self.df_key2, on="key2", sort=sort)


class JoinIndex:
    def setup(self):
        N = 50000
        self.left = DataFrame(
            np.random.randint(1, N / 500, (N, 2)), columns=["jim", "joe"]
        )
        self.right = DataFrame(
            np.random.randint(1, N / 500, (N, 2)), columns=["jolie", "jolia"]
        ).set_index("jolie")

    def time_left_outer_join_index(self):
        self.left.join(self.right, on="jim")


class JoinNonUnique:
    # outer join of non-unique
    # GH 6329
    def setup(self):
        date_index = date_range("01-Jan-2013", "23-Jan-2013", freq="T")
        daily_dates = date_index.to_period("D").to_timestamp("S", "S")
        self.fracofday = date_index.values - daily_dates.values
        self.fracofday = self.fracofday.astype("timedelta64[ns]")
        self.fracofday = self.fracofday.astype(np.float64) / 86400000000000.0
        self.fracofday = Series(self.fracofday, daily_dates)
        index = date_range(date_index.min(), date_index.max(), freq="D")
        self.temp = Series(1.0, index)[self.fracofday.index]

    def time_join_non_unique_equal(self):
        self.fracofday * self.temp


class Merge:

    params = [True, False]
    param_names = ["sort"]

    def setup(self, sort):
        N = 10000
        indices = tm.makeStringIndex(N).values
        indices2 = tm.makeStringIndex(N).values
        key = np.tile(indices[:8000], 10)
        key2 = np.tile(indices2[:8000], 10)
        self.left = DataFrame(
            {"key": key, "key2": key2, "value": np.random.randn(80000)}
        )
        self.right = DataFrame(
            {
                "key": indices[2000:],
                "key2": indices2[2000:],
                "value2": np.random.randn(8000),
            }
        )

        self.df = DataFrame(
            {
                "key1": np.tile(np.arange(500).repeat(10), 2),
                "key2": np.tile(np.arange(250).repeat(10), 4),
                "value": np.random.randn(10000),
            }
        )
        self.df2 = DataFrame({"key1": np.arange(500), "value2": np.random.randn(500)})
        self.df3 = self.df[:5000]

    def time_merge_2intkey(self, sort):
        merge(self.left, self.right, sort=sort)

    def time_merge_dataframe_integer_2key(self, sort):
        merge(self.df, self.df3, sort=sort)

    def time_merge_dataframe_integer_key(self, sort):
        merge(self.df, self.df2, on="key1", sort=sort)


class I8Merge:

    params = ["inner", "outer", "left", "right"]
    param_names = ["how"]

    def setup(self, how):
        low, high, n = -1000, 1000, 10 ** 6
        self.left = DataFrame(
            np.random.randint(low, high, (n, 7)), columns=list("ABCDEFG")
        )
        self.left["left"] = self.left.sum(axis=1)
        self.right = self.left.sample(frac=1).rename({"left": "right"}, axis=1)
        self.right = self.right.reset_index(drop=True)
        self.right["right"] *= -1

    def time_i8merge(self, how):
        merge(self.left, self.right, how=how)


class MergeCategoricals:
    def setup(self):
        self.left_object = DataFrame(
            {
                "X": np.random.choice(range(0, 10), size=(10000,)),
                "Y": np.random.choice(["one", "two", "three"], size=(10000,)),
            }
        )

        self.right_object = DataFrame(
            {
                "X": np.random.choice(range(0, 10), size=(10000,)),
                "Z": np.random.choice(["jjj", "kkk", "sss"], size=(10000,)),
            }
        )

        self.left_cat = self.left_object.assign(
            Y=self.left_object["Y"].astype("category")
        )
        self.right_cat = self.right_object.assign(
            Z=self.right_object["Z"].astype("category")
        )

    def time_merge_object(self):
        merge(self.left_object, self.right_object, on="X")

    def time_merge_cat(self):
        merge(self.left_cat, self.right_cat, on="X")


class MergeOrdered:
    def setup(self):
        groups = tm.makeStringIndex(10).values
        self.left = DataFrame(
            {
                "group": groups.repeat(5000),
                "key": np.tile(np.arange(0, 10000, 2), 10),
                "lvalue": np.random.randn(50000),
            }
        )
        self.right = DataFrame(
            {"key": np.arange(10000), "rvalue": np.random.randn(10000)}
        )

    def time_merge_ordered(self):
        merge_ordered(self.left, self.right, on="key", left_by="group")


class MergeAsof:
    params = [["backward", "forward", "nearest"], [None, 5]]
    param_names = ["direction", "tolerance"]

    def setup(self, direction, tolerance):
        one_count = 200000
        two_count = 1000000

        df1 = DataFrame(
            {
                "time": np.random.randint(0, one_count / 20, one_count),
                "key": np.random.choice(list(string.ascii_uppercase), one_count),
                "key2": np.random.randint(0, 25, one_count),
                "value1": np.random.randn(one_count),
            }
        )
        df2 = DataFrame(
            {
                "time": np.random.randint(0, two_count / 20, two_count),
                "key": np.random.choice(list(string.ascii_uppercase), two_count),
                "key2": np.random.randint(0, 25, two_count),
                "value2": np.random.randn(two_count),
            }
        )

        df1 = df1.sort_values("time")
        df2 = df2.sort_values("time")

        df1["time32"] = np.int32(df1.time)
        df2["time32"] = np.int32(df2.time)

        df1["timeu64"] = np.uint64(df1.time)
        df2["timeu64"] = np.uint64(df2.time)

        self.df1a = df1[["time", "value1"]]
        self.df2a = df2[["time", "value2"]]
        self.df1b = df1[["time", "key", "value1"]]
        self.df2b = df2[["time", "key", "value2"]]
        self.df1c = df1[["time", "key2", "value1"]]
        self.df2c = df2[["time", "key2", "value2"]]
        self.df1d = df1[["time32", "value1"]]
        self.df2d = df2[["time32", "value2"]]
        self.df1e = df1[["time", "key", "key2", "value1"]]
        self.df2e = df2[["time", "key", "key2", "value2"]]
        self.df1f = df1[["timeu64", "value1"]]
        self.df2f = df2[["timeu64", "value2"]]

    def time_on_int(self, direction, tolerance):
        merge_asof(
            self.df1a, self.df2a, on="time", direction=direction, tolerance=tolerance
        )

    def time_on_int32(self, direction, tolerance):
        merge_asof(
            self.df1d, self.df2d, on="time32", direction=direction, tolerance=tolerance
        )

    def time_on_uint64(self, direction, tolerance):
        merge_asof(
            self.df1f, self.df2f, on="timeu64", direction=direction, tolerance=tolerance
        )

    def time_by_object(self, direction, tolerance):
        merge_asof(
            self.df1b,
            self.df2b,
            on="time",
            by="key",
            direction=direction,
            tolerance=tolerance,
        )

    def time_by_int(self, direction, tolerance):
        merge_asof(
            self.df1c,
            self.df2c,
            on="time",
            by="key2",
            direction=direction,
            tolerance=tolerance,
        )

    def time_multiby(self, direction, tolerance):
        merge_asof(
            self.df1e,
            self.df2e,
            on="time",
            by=["key", "key2"],
            direction=direction,
            tolerance=tolerance,
        )


class Align:
    def setup(self):
        size = 5 * 10 ** 5
        rng = np.arange(0, 10 ** 13, 10 ** 7)
        stamps = np.datetime64("now").view("i8") + rng
        idx1 = np.sort(np.random.choice(stamps, size, replace=False))
        idx2 = np.sort(np.random.choice(stamps, size, replace=False))
        self.ts1 = Series(np.random.randn(size), idx1)
        self.ts2 = Series(np.random.randn(size), idx2)

    def time_series_align_int64_index(self):
        self.ts1 + self.ts2

    def time_series_align_left_monotonic(self):
        self.ts1.align(self.ts2, join="left")


from .pandas_vb_common import setup