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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="T")
self.series = {}
for i in range(1, K):
data = np.random.randn(N)[:-i]
idx = rng[:-i]
data[100:] = np.nan
self.series[i] = pd.Series(pd.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):
pd.Series.sparse.from_coo(self.matrix)
class ToCoo:
def setup(self):
s = Series([np.nan] * 10000)
s[0] = 3.0
s[100] = -1.0
s[999] = 12.1
s.index = MultiIndex.from_product([range(10)] * 4)
self.ss = s.astype("Sparse")
def time_sparse_series_to_coo(self):
self.ss.sparse.to_coo(row_levels=[0, 1], column_levels=[2, 3], sort_labels=True)
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)
indicies = np.random.choice(
np.arange(0, length, block_size), num_blocks, replace=False
)
for ind in indicies:
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
from .pandas_vb_common import setup # noqa: F401 isort:skip
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