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import numpy as np
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
date_range,
period_range,
)
from .pandas_vb_common import tm
class Reindex:
def setup(self):
rng = date_range(start="1/1/1970", periods=10000, freq="1min")
self.df = DataFrame(np.random.rand(10000, 10), index=rng, columns=range(10))
self.df["foo"] = "bar"
self.rng_subset = Index(rng[::2])
self.df2 = DataFrame(
index=range(10000), data=np.random.rand(10000, 30), columns=range(30)
)
N = 5000
K = 200
level1 = tm.makeStringIndex(N).values.repeat(K)
level2 = np.tile(tm.makeStringIndex(K).values, N)
index = MultiIndex.from_arrays([level1, level2])
self.s = Series(np.random.randn(N * K), index=index)
self.s_subset = self.s[::2]
self.s_subset_no_cache = self.s[::2].copy()
mi = MultiIndex.from_product([rng, range(100)])
self.s2 = Series(np.random.randn(len(mi)), index=mi)
self.s2_subset = self.s2[::2].copy()
def time_reindex_dates(self):
self.df.reindex(self.rng_subset)
def time_reindex_columns(self):
self.df2.reindex(columns=self.df.columns[1:5])
def time_reindex_multiindex_with_cache(self):
# MultiIndex._values gets cached
self.s.reindex(self.s_subset.index)
def time_reindex_multiindex_no_cache(self):
# Copy to avoid MultiIndex._values getting cached
self.s.reindex(self.s_subset_no_cache.index.copy())
def time_reindex_multiindex_no_cache_dates(self):
# Copy to avoid MultiIndex._values getting cached
self.s2_subset.reindex(self.s2.index.copy())
class ReindexMethod:
params = [["pad", "backfill"], [date_range, period_range]]
param_names = ["method", "constructor"]
def setup(self, method, constructor):
N = 100000
self.idx = constructor("1/1/2000", periods=N, freq="1min")
self.ts = Series(np.random.randn(N), index=self.idx)[::2]
def time_reindex_method(self, method, constructor):
self.ts.reindex(self.idx, method=method)
class Fillna:
params = ["pad", "backfill"]
param_names = ["method"]
def setup(self, method):
N = 100000
self.idx = date_range("1/1/2000", periods=N, freq="1min")
ts = Series(np.random.randn(N), index=self.idx)[::2]
self.ts_reindexed = ts.reindex(self.idx)
self.ts_float32 = self.ts_reindexed.astype("float32")
def time_reindexed(self, method):
self.ts_reindexed.fillna(method=method)
def time_float_32(self, method):
self.ts_float32.fillna(method=method)
class LevelAlign:
def setup(self):
self.index = MultiIndex(
levels=[np.arange(10), np.arange(100), np.arange(100)],
codes=[
np.arange(10).repeat(10000),
np.tile(np.arange(100).repeat(100), 10),
np.tile(np.tile(np.arange(100), 100), 10),
],
)
self.df = DataFrame(np.random.randn(len(self.index), 4), index=self.index)
self.df_level = DataFrame(np.random.randn(100, 4), index=self.index.levels[1])
def time_align_level(self):
self.df.align(self.df_level, level=1, copy=False)
def time_reindex_level(self):
self.df_level.reindex(self.index, level=1)
class DropDuplicates:
params = [True, False]
param_names = ["inplace"]
def setup(self, inplace):
N = 10000
K = 10
key1 = tm.makeStringIndex(N).values.repeat(K)
key2 = tm.makeStringIndex(N).values.repeat(K)
self.df = DataFrame(
{"key1": key1, "key2": key2, "value": np.random.randn(N * K)}
)
self.df_nan = self.df.copy()
self.df_nan.iloc[:10000, :] = np.nan
self.s = Series(np.random.randint(0, 1000, size=10000))
self.s_str = Series(np.tile(tm.makeStringIndex(1000).values, 10))
N = 1000000
K = 10000
key1 = np.random.randint(0, K, size=N)
self.df_int = DataFrame({"key1": key1})
self.df_bool = DataFrame(np.random.randint(0, 2, size=(K, 10), dtype=bool))
def time_frame_drop_dups(self, inplace):
self.df.drop_duplicates(["key1", "key2"], inplace=inplace)
def time_frame_drop_dups_na(self, inplace):
self.df_nan.drop_duplicates(["key1", "key2"], inplace=inplace)
def time_series_drop_dups_int(self, inplace):
self.s.drop_duplicates(inplace=inplace)
def time_series_drop_dups_string(self, inplace):
self.s_str.drop_duplicates(inplace=inplace)
def time_frame_drop_dups_int(self, inplace):
self.df_int.drop_duplicates(inplace=inplace)
def time_frame_drop_dups_bool(self, inplace):
self.df_bool.drop_duplicates(inplace=inplace)
class Align:
# blog "pandas escaped the zoo"
def setup(self):
n = 50000
indices = tm.makeStringIndex(n)
subsample_size = 40000
self.x = Series(np.random.randn(n), indices)
self.y = Series(
np.random.randn(subsample_size),
index=np.random.choice(indices, subsample_size, replace=False),
)
def time_align_series_irregular_string(self):
self.x + self.y
from .pandas_vb_common import setup # noqa: F401 isort:skip
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