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import numpy as np
import pandas as pd
ops = ["mean", "sum", "median", "std", "skew", "kurt", "prod", "sem", "var"]
class FrameOps:
params = [ops, ["float", "int", "Int64"], [0, 1, None]]
param_names = ["op", "dtype", "axis"]
def setup(self, op, dtype, axis):
values = np.random.randn(100000, 4)
if dtype == "Int64":
values = values.astype(int)
df = pd.DataFrame(values).astype(dtype)
self.df_func = getattr(df, op)
def time_op(self, op, dtype, axis):
self.df_func(axis=axis)
class FrameMixedDtypesOps:
params = [ops, [0, 1, None]]
param_names = ["op", "axis"]
def setup(self, op, axis):
if op in ("sum", "skew", "kurt", "prod", "sem", "var") or (
(op, axis)
in (
("mean", 1),
("mean", None),
("median", 1),
("median", None),
("std", 1),
)
):
# Skipping cases where datetime aggregations are not implemented
raise NotImplementedError
N = 1_000_000
df = pd.DataFrame(
{
"f": np.random.normal(0.0, 1.0, N),
"i": np.random.randint(0, N, N),
"ts": pd.date_range(start="1/1/2000", periods=N, freq="h"),
}
)
self.df_func = getattr(df, op)
def time_op(self, op, axis):
self.df_func(axis=axis)
class FrameMultiIndexOps:
params = [ops]
param_names = ["op"]
def setup(self, op):
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),
]
index = pd.MultiIndex(levels=levels, codes=codes)
df = pd.DataFrame(np.random.randn(len(index), 4), index=index)
self.df_func = getattr(df, op)
def time_op(self, op):
self.df_func()
class SeriesOps:
params = [ops, ["float", "int"]]
param_names = ["op", "dtype"]
def setup(self, op, dtype):
s = pd.Series(np.random.randn(100000)).astype(dtype)
self.s_func = getattr(s, op)
def time_op(self, op, dtype):
self.s_func()
class SeriesMultiIndexOps:
params = [ops]
param_names = ["op"]
def setup(self, op):
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),
]
index = pd.MultiIndex(levels=levels, codes=codes)
s = pd.Series(np.random.randn(len(index)), index=index)
self.s_func = getattr(s, op)
def time_op(self, op):
self.s_func()
class Rank:
params = [["DataFrame", "Series"], [True, False]]
param_names = ["constructor", "pct"]
def setup(self, constructor, pct):
values = np.random.randn(10**5)
self.data = getattr(pd, constructor)(values)
def time_rank(self, constructor, pct):
self.data.rank(pct=pct)
def time_average_old(self, constructor, pct):
self.data.rank(pct=pct) / len(self.data)
class Correlation:
params = [["spearman", "kendall", "pearson"]]
param_names = ["method"]
def setup(self, method):
self.df = pd.DataFrame(np.random.randn(500, 15))
self.df2 = pd.DataFrame(np.random.randn(500, 15))
self.df_wide = pd.DataFrame(np.random.randn(500, 100))
self.df_wide_nans = self.df_wide.where(np.random.random((500, 100)) < 0.9)
self.s = pd.Series(np.random.randn(500))
self.s2 = pd.Series(np.random.randn(500))
def time_corr(self, method):
self.df.corr(method=method)
def time_corr_wide(self, method):
self.df_wide.corr(method=method)
def time_corr_wide_nans(self, method):
self.df_wide_nans.corr(method=method)
def peakmem_corr_wide(self, method):
self.df_wide.corr(method=method)
def time_corr_series(self, method):
self.s.corr(self.s2, method=method)
def time_corrwith_cols(self, method):
self.df.corrwith(self.df2, method=method)
def time_corrwith_rows(self, method):
self.df.corrwith(self.df2, axis=1, method=method)
class Covariance:
params = []
param_names = []
def setup(self):
self.s = pd.Series(np.random.randn(100000))
self.s2 = pd.Series(np.random.randn(100000))
def time_cov_series(self):
self.s.cov(self.s2)
from .pandas_vb_common import setup # noqa: F401 isort:skip
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