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import numpy as np
import scipy.sparse
import pandas as pd
from pandas import (
MultiIndex,
Series,
date_range,
)
from pandas.arrays import SparseArray
def make_array(size, dense_proportion, fill_value, dtype):
dense_size = int(size * dense_proportion)
arr = np.full(size, fill_value, dtype)
indexer = np.random.choice(np.arange(size), dense_size, replace=False)
arr[indexer] = np.random.choice(np.arange(100, dtype=dtype), dense_size)
return arr
class SparseSeriesToFrame:
def setup(self):
K = 50
N = 50001
rng = date_range("1/1/2000", periods=N, freq="min")
self.series = {}
for i in range(1, K):
data = np.random.randn(N)[:-i]
idx = rng[:-i]
data[100:] = np.nan
self.series[i] = Series(SparseArray(data), index=idx)
def time_series_to_frame(self):
pd.DataFrame(self.series)
class SparseArrayConstructor:
params = ([0.1, 0.01], [0, np.nan], [np.int64, np.float64, object])
param_names = ["dense_proportion", "fill_value", "dtype"]
def setup(self, dense_proportion, fill_value, dtype):
N = 10**6
self.array = make_array(N, dense_proportion, fill_value, dtype)
def time_sparse_array(self, dense_proportion, fill_value, dtype):
SparseArray(self.array, fill_value=fill_value, dtype=dtype)
class SparseDataFrameConstructor:
def setup(self):
N = 1000
self.sparse = scipy.sparse.rand(N, N, 0.005)
def time_from_scipy(self):
pd.DataFrame.sparse.from_spmatrix(self.sparse)
class FromCoo:
def setup(self):
self.matrix = scipy.sparse.coo_matrix(
([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(100, 100)
)
def time_sparse_series_from_coo(self):
Series.sparse.from_coo(self.matrix)
class ToCoo:
params = [True, False]
param_names = ["sort_labels"]
def setup(self, sort_labels):
s = Series([np.nan] * 10000)
s[0] = 3.0
s[100] = -1.0
s[999] = 12.1
s_mult_lvl = s.set_axis(MultiIndex.from_product([range(10)] * 4))
self.ss_mult_lvl = s_mult_lvl.astype("Sparse")
s_two_lvl = s.set_axis(MultiIndex.from_product([range(100)] * 2))
self.ss_two_lvl = s_two_lvl.astype("Sparse")
def time_sparse_series_to_coo(self, sort_labels):
self.ss_mult_lvl.sparse.to_coo(
row_levels=[0, 1], column_levels=[2, 3], sort_labels=sort_labels
)
def time_sparse_series_to_coo_single_level(self, sort_labels):
self.ss_two_lvl.sparse.to_coo(sort_labels=sort_labels)
class ToCooFrame:
def setup(self):
N = 10000
k = 10
arr = np.zeros((N, k), dtype=float)
arr[0, 0] = 3.0
arr[12, 7] = -1.0
arr[0, 9] = 11.2
self.df = pd.DataFrame(arr, dtype=pd.SparseDtype("float", fill_value=0.0))
def time_to_coo(self):
self.df.sparse.to_coo()
class Arithmetic:
params = ([0.1, 0.01], [0, np.nan])
param_names = ["dense_proportion", "fill_value"]
def setup(self, dense_proportion, fill_value):
N = 10**6
arr1 = make_array(N, dense_proportion, fill_value, np.int64)
self.array1 = SparseArray(arr1, fill_value=fill_value)
arr2 = make_array(N, dense_proportion, fill_value, np.int64)
self.array2 = SparseArray(arr2, fill_value=fill_value)
def time_make_union(self, dense_proportion, fill_value):
self.array1.sp_index.make_union(self.array2.sp_index)
def time_intersect(self, dense_proportion, fill_value):
self.array1.sp_index.intersect(self.array2.sp_index)
def time_add(self, dense_proportion, fill_value):
self.array1 + self.array2
def time_divide(self, dense_proportion, fill_value):
self.array1 / self.array2
class ArithmeticBlock:
params = [np.nan, 0]
param_names = ["fill_value"]
def setup(self, fill_value):
N = 10**6
self.arr1 = self.make_block_array(
length=N, num_blocks=1000, block_size=10, fill_value=fill_value
)
self.arr2 = self.make_block_array(
length=N, num_blocks=1000, block_size=10, fill_value=fill_value
)
def make_block_array(self, length, num_blocks, block_size, fill_value):
arr = np.full(length, fill_value)
indices = np.random.choice(
np.arange(0, length, block_size), num_blocks, replace=False
)
for ind in indices:
arr[ind : ind + block_size] = np.random.randint(0, 100, block_size)
return SparseArray(arr, fill_value=fill_value)
def time_make_union(self, fill_value):
self.arr1.sp_index.make_union(self.arr2.sp_index)
def time_intersect(self, fill_value):
self.arr2.sp_index.intersect(self.arr2.sp_index)
def time_addition(self, fill_value):
self.arr1 + self.arr2
def time_division(self, fill_value):
self.arr1 / self.arr2
class MinMax:
params = (["min", "max"], [0.0, np.nan])
param_names = ["func", "fill_value"]
def setup(self, func, fill_value):
N = 1_000_000
arr = make_array(N, 1e-5, fill_value, np.float64)
self.sp_arr = SparseArray(arr, fill_value=fill_value)
def time_min_max(self, func, fill_value):
getattr(self.sp_arr, func)()
class Take:
params = ([np.array([0]), np.arange(100_000), np.full(100_000, -1)], [True, False])
param_names = ["indices", "allow_fill"]
def setup(self, indices, allow_fill):
N = 1_000_000
fill_value = 0.0
arr = make_array(N, 1e-5, fill_value, np.float64)
self.sp_arr = SparseArray(arr, fill_value=fill_value)
def time_take(self, indices, allow_fill):
self.sp_arr.take(indices, allow_fill=allow_fill)
class GetItem:
def setup(self):
N = 1_000_000
d = 1e-5
arr = make_array(N, d, np.nan, np.float64)
self.sp_arr = SparseArray(arr)
def time_integer_indexing(self):
self.sp_arr[78]
def time_slice(self):
self.sp_arr[1:]
class GetItemMask:
params = [True, False, np.nan]
param_names = ["fill_value"]
def setup(self, fill_value):
N = 1_000_000
d = 1e-5
arr = make_array(N, d, np.nan, np.float64)
self.sp_arr = SparseArray(arr)
b_arr = np.full(shape=N, fill_value=fill_value, dtype=np.bool_)
fv_inds = np.unique(
np.random.randint(low=0, high=N - 1, size=int(N * d), dtype=np.int32)
)
b_arr[fv_inds] = True if pd.isna(fill_value) else not fill_value
self.sp_b_arr = SparseArray(b_arr, dtype=np.bool_, fill_value=fill_value)
def time_mask(self, fill_value):
self.sp_arr[self.sp_b_arr]
from .pandas_vb_common import setup # noqa: F401 isort:skip
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