reshape.py

from .pandas_vb_common import *
from pandas.core.reshape import melt, wide_to_long


class melt_dataframe(object):
    goal_time = 0.2

    def setup(self):
        self.index = MultiIndex.from_arrays([np.arange(100).repeat(100), np.roll(np.tile(np.arange(100), 100), 25)])
        self.df = DataFrame(np.random.randn(10000, 4), index=self.index)
        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 reshape_pivot_time_series(object):
    goal_time = 0.2

    def setup(self):
        self.index = MultiIndex.from_arrays([np.arange(100).repeat(100), np.roll(np.tile(np.arange(100), 100), 25)])
        self.df = DataFrame(np.random.randn(10000, 4), index=self.index)
        self.index = date_range('1/1/2000', periods=10000, freq='h')
        self.df = DataFrame(randn(10000, 50), index=self.index, columns=range(50))
        self.pdf = self.unpivot(self.df)
        self.f = (lambda : self.pdf.pivot('date', 'variable', 'value'))

    def time_reshape_pivot_time_series(self):
        self.f()

    def unpivot(self, frame):
        (N, K) = frame.shape
        self.data = {'value': frame.values.ravel('F'), 'variable': np.asarray(frame.columns).repeat(N), 'date': np.tile(np.asarray(frame.index), K), }
        return DataFrame(self.data, columns=['date', 'variable', 'value'])


class reshape_stack_simple(object):
    goal_time = 0.2

    def setup(self):
        self.index = MultiIndex.from_arrays([np.arange(100).repeat(100), np.roll(np.tile(np.arange(100), 100), 25)])
        self.df = DataFrame(np.random.randn(10000, 4), index=self.index)
        self.udf = self.df.unstack(1)

    def time_reshape_stack_simple(self):
        self.udf.stack()


class reshape_unstack_simple(object):
    goal_time = 0.2

    def setup(self):
        self.index = MultiIndex.from_arrays([np.arange(100).repeat(100), np.roll(np.tile(np.arange(100), 100), 25)])
        self.df = DataFrame(np.random.randn(10000, 4), index=self.index)

    def time_reshape_unstack_simple(self):
        self.df.unstack(1)


class reshape_unstack_large_single_dtype(object):
    goal_time = 0.2

    def setup(self):
        m = 100
        n = 1000

        levels = np.arange(m)
        index = pd.MultiIndex.from_product([levels]*2)
        columns = np.arange(n)
        values = np.arange(m*m*n).reshape(m*m, n)
        self.df = pd.DataFrame(values, index, columns)
        self.df2 = self.df.iloc[:-1]

    def time_unstack_full_product(self):
        self.df.unstack()

    def time_unstack_with_mask(self):
        self.df2.unstack()


class unstack_sparse_keyspace(object):
    goal_time = 0.2

    def setup(self):
        self.index = MultiIndex.from_arrays([np.arange(100).repeat(100), np.roll(np.tile(np.arange(100), 100), 25)])
        self.df = DataFrame(np.random.randn(10000, 4), index=self.index)
        self.NUM_ROWS = 1000
        for iter in range(10):
            self.df = DataFrame({'A': np.random.randint(50, size=self.NUM_ROWS), 'B': np.random.randint(50, size=self.NUM_ROWS), 'C': np.random.randint((-10), 10, size=self.NUM_ROWS), 'D': np.random.randint((-10), 10, size=self.NUM_ROWS), 'E': np.random.randint(10, size=self.NUM_ROWS), 'F': np.random.randn(self.NUM_ROWS), })
            self.idf = self.df.set_index(['A', 'B', 'C', 'D', 'E'])
            if (len(self.idf.index.unique()) == self.NUM_ROWS):
                break

    def time_unstack_sparse_keyspace(self):
        self.idf.unstack()


class wide_to_long_big(object):
    goal_time = 0.2

    def setup(self):
        vars = 'ABCD'
        nyrs = 20
        nidvars = 20
        N = 5000
        yrvars = []
        for var in vars:
            for yr in range(1, nyrs + 1):
                yrvars.append(var + str(yr))

        self.df = pd.DataFrame(np.random.randn(N, nidvars + len(yrvars)),
                               columns=list(range(nidvars)) + yrvars)
        self.vars = vars

    def time_wide_to_long_big(self):
        self.df['id'] = self.df.index
        wide_to_long(self.df, list(self.vars), i='id', j='year')