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from itertools import product
import numpy as np
from pandas import DataFrame, MultiIndex, date_range, melt, wide_to_long
from .pandas_vb_common import setup # noqa
class Melt(object):
goal_time = 0.2
def setup(self):
self.df = DataFrame(np.random.randn(10000, 3), columns=['A', 'B', 'C'])
self.df['id1'] = np.random.randint(0, 10, 10000)
self.df['id2'] = np.random.randint(100, 1000, 10000)
def time_melt_dataframe(self):
melt(self.df, id_vars=['id1', 'id2'])
class Pivot(object):
goal_time = 0.2
def setup(self):
N = 10000
index = date_range('1/1/2000', periods=N, freq='h')
data = {'value': np.random.randn(N * 50),
'variable': np.arange(50).repeat(N),
'date': np.tile(index.values, 50)}
self.df = DataFrame(data)
def time_reshape_pivot_time_series(self):
self.df.pivot('date', 'variable', 'value')
class SimpleReshape(object):
goal_time = 0.2
def setup(self):
arrays = [np.arange(100).repeat(100),
np.roll(np.tile(np.arange(100), 100), 25)]
index = MultiIndex.from_arrays(arrays)
self.df = DataFrame(np.random.randn(10000, 4), index=index)
self.udf = self.df.unstack(1)
def time_stack(self):
self.udf.stack()
def time_unstack(self):
self.df.unstack(1)
class Unstack(object):
goal_time = 0.2
def setup(self):
m = 100
n = 1000
levels = np.arange(m)
index = MultiIndex.from_product([levels] * 2)
columns = np.arange(n)
values = np.arange(m * m * n).reshape(m * m, n)
self.df = DataFrame(values, index, columns)
self.df2 = self.df.iloc[:-1]
def time_full_product(self):
self.df.unstack()
def time_without_last_row(self):
self.df2.unstack()
class SparseIndex(object):
goal_time = 0.2
def setup(self):
NUM_ROWS = 1000
self.df = DataFrame({'A': np.random.randint(50, size=NUM_ROWS),
'B': np.random.randint(50, size=NUM_ROWS),
'C': np.random.randint(-10, 10, size=NUM_ROWS),
'D': np.random.randint(-10, 10, size=NUM_ROWS),
'E': np.random.randint(10, size=NUM_ROWS),
'F': np.random.randn(NUM_ROWS)})
self.df = self.df.set_index(['A', 'B', 'C', 'D', 'E'])
def time_unstack(self):
self.df.unstack()
class WideToLong(object):
goal_time = 0.2
def setup(self):
nyrs = 20
nidvars = 20
N = 5000
self.letters = list('ABCD')
yrvars = [l + str(num)
for l, num in product(self.letters, range(1, nyrs + 1))]
columns = [str(i) for i in range(nidvars)] + yrvars
self.df = DataFrame(np.random.randn(N, nidvars + len(yrvars)),
columns=columns)
self.df['id'] = self.df.index
def time_wide_to_long_big(self):
wide_to_long(self.df, self.letters, i='id', j='year')
class PivotTable(object):
goal_time = 0.2
def setup(self):
N = 100000
fac1 = np.array(['A', 'B', 'C'], dtype='O')
fac2 = np.array(['one', 'two'], dtype='O')
ind1 = np.random.randint(0, 3, size=N)
ind2 = np.random.randint(0, 2, size=N)
self.df = DataFrame({'key1': fac1.take(ind1),
'key2': fac2.take(ind2),
'key3': fac2.take(ind2),
'value1': np.random.randn(N),
'value2': np.random.randn(N),
'value3': np.random.randn(N)})
def time_pivot_table(self):
self.df.pivot_table(index='key1', columns=['key2', 'key3'])
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