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import numpy as np
import pandas as pd
from pandas import (
NA,
Categorical,
DataFrame,
Float64Dtype,
MultiIndex,
Series,
Timestamp,
date_range,
)
from .pandas_vb_common import tm
try:
from pandas.tseries.offsets import (
Hour,
Nano,
)
except ImportError:
# For compatibility with older versions
from pandas.core.datetools import (
Hour,
Nano,
)
class FromDicts:
def setup(self):
N, K = 5000, 50
self.index = tm.makeStringIndex(N)
self.columns = tm.makeStringIndex(K)
frame = DataFrame(np.random.randn(N, K), index=self.index, columns=self.columns)
self.data = frame.to_dict()
self.dict_list = frame.to_dict(orient="records")
self.data2 = {i: {j: float(j) for j in range(100)} for i in range(2000)}
# arrays which we won't consolidate
self.dict_of_categoricals = {i: Categorical(np.arange(N)) for i in range(K)}
def time_list_of_dict(self):
DataFrame(self.dict_list)
def time_nested_dict(self):
DataFrame(self.data)
def time_nested_dict_index(self):
DataFrame(self.data, index=self.index)
def time_nested_dict_columns(self):
DataFrame(self.data, columns=self.columns)
def time_nested_dict_index_columns(self):
DataFrame(self.data, index=self.index, columns=self.columns)
def time_nested_dict_int64(self):
# nested dict, integer indexes, regression described in #621
DataFrame(self.data2)
def time_dict_of_categoricals(self):
# dict of arrays that we won't consolidate
DataFrame(self.dict_of_categoricals)
class FromSeries:
def setup(self):
mi = MultiIndex.from_product([range(100), range(100)])
self.s = Series(np.random.randn(10000), index=mi)
def time_mi_series(self):
DataFrame(self.s)
class FromDictwithTimestamp:
params = [Nano(1), Hour(1)]
param_names = ["offset"]
def setup(self, offset):
N = 10**3
idx = date_range(Timestamp("1/1/1900"), freq=offset, periods=N)
df = DataFrame(np.random.randn(N, 10), index=idx)
self.d = df.to_dict()
def time_dict_with_timestamp_offsets(self, offset):
DataFrame(self.d)
class FromRecords:
params = [None, 1000]
param_names = ["nrows"]
# Generators get exhausted on use, so run setup before every call
number = 1
repeat = (3, 250, 10)
def setup(self, nrows):
N = 100000
self.gen = ((x, (x * 20), (x * 100)) for x in range(N))
def time_frame_from_records_generator(self, nrows):
# issue-6700
self.df = DataFrame.from_records(self.gen, nrows=nrows)
class FromNDArray:
def setup(self):
N = 100000
self.data = np.random.randn(N)
def time_frame_from_ndarray(self):
self.df = DataFrame(self.data)
class FromLists:
goal_time = 0.2
def setup(self):
N = 1000
M = 100
self.data = [list(range(M)) for i in range(N)]
def time_frame_from_lists(self):
self.df = DataFrame(self.data)
class FromRange:
goal_time = 0.2
def setup(self):
N = 1_000_000
self.data = range(N)
def time_frame_from_range(self):
self.df = DataFrame(self.data)
class FromScalar:
def setup(self):
self.nrows = 100_000
def time_frame_from_scalar_ea_float64(self):
DataFrame(
1.0,
index=range(self.nrows),
columns=list("abc"),
dtype=Float64Dtype(),
)
def time_frame_from_scalar_ea_float64_na(self):
DataFrame(
NA,
index=range(self.nrows),
columns=list("abc"),
dtype=Float64Dtype(),
)
class FromArrays:
goal_time = 0.2
def setup(self):
N_rows = 1000
N_cols = 1000
self.float_arrays = [np.random.randn(N_rows) for _ in range(N_cols)]
self.sparse_arrays = [
pd.arrays.SparseArray(np.random.randint(0, 2, N_rows), dtype="float64")
for _ in range(N_cols)
]
self.int_arrays = [
pd.array(np.random.randint(1000, size=N_rows), dtype="Int64")
for _ in range(N_cols)
]
self.index = pd.Index(range(N_rows))
self.columns = pd.Index(range(N_cols))
def time_frame_from_arrays_float(self):
self.df = DataFrame._from_arrays(
self.float_arrays,
index=self.index,
columns=self.columns,
verify_integrity=False,
)
def time_frame_from_arrays_int(self):
self.df = DataFrame._from_arrays(
self.int_arrays,
index=self.index,
columns=self.columns,
verify_integrity=False,
)
def time_frame_from_arrays_sparse(self):
self.df = DataFrame._from_arrays(
self.sparse_arrays,
index=self.index,
columns=self.columns,
verify_integrity=False,
)
class From3rdParty:
# GH#44616
def setup(self):
try:
import torch
except ImportError:
raise NotImplementedError
row = 700000
col = 64
self.val_tensor = torch.randn(row, col)
def time_from_torch(self):
DataFrame(self.val_tensor)
from .pandas_vb_common import setup # noqa: F401 isort:skip
|
