def test_data_as_first_argument():
def equals(df1, df2):
return df1.equals(df2)
df = pd.DataFrame({'x': [0, 1, 2, 3, 4, 5], 'y': [0, 0, 1, 1, 2, 3]})
assert equals(define(df.copy(), 'x*2'), df.copy() >> define('x*2'))
assert equals(create(df, 'x*2'), df >> create('x*2'))
assert len(sample_n(df, 5)) == len(df >> sample_n(5))
assert len(sample_frac(df, .3)) == len(df >> sample_frac(.3))
assert equals(select(df, 'x'), df >> select('x'))
assert equals(rename(df.copy(), z='x'), df.copy() >> rename(z='x'))
assert equals(distinct(df), df >> distinct())
assert equals(arrange(df, 'np.sin(x)'), df >> arrange('np.sin(x)'))
assert equals(group_by(df, 'x'), df >> group_by('x'))
assert equals(ungroup(group_by(df, 'x')), df >> group_by('x') >> ungroup())
assert equals(summarize(df, 'sum(x)'), df >> summarize('sum(x)'))
assert equals(query(df, 'x % 2'), df >> query('x % 2'))
assert equals(tally(df, 'x'), df >> tally('x'))
def xsum(gdf):
return [gdf['x'].sum()]
assert equals(do(group_by(df, 'y'), xsum=xsum),
df >> group_by('y') >> do(xsum=xsum))
assert len(head(df, 4) == 4)
assert len(tail(df, 4) == 4)
def test_rename():
x = np.array([1, 2, 3])
df = pd.DataFrame({'bell': x, 'whistle': x, 'nail': x, 'tail': x})
result = df >> rename(gong='bell', pin='nail')
assert len(result.columns) == 4
assert 'gong' in result.columns
assert 'pin' in result.columns
result = df >> rename({'flap': 'tail'}, pin='nail')
assert len(result.columns) == 4
assert 'flap' in result.columns
assert 'pin' in result.columns
def test_data_mutability():
# These tests affirm that we know the consequences of the verbs.
# A test in the Mutable section should not fail without a change
# in implementation. That change should be triggered when Pandas
# implements a consistent copy-on-write policy.
#
# When a test in the mutable section fails, it is bad news. The
# should be no memory usage gains by reusing the original data,
# except for the case of `rename`.
df = pd.DataFrame({'x': [0, 1, 2, 3, 4, 5], 'y': [0, 0, 1, 1, 2, 3]})
# Default to not mutable
df >> define(z='x**2')
assert 'z' not in df
df >> group_by(z='x**2')
assert 'z' not in df
arr = df >> pull('x')
arr[0] = 99
assert df.loc[0, 'x'] != 99
df2 = df >> slice_rows(3)
df2.loc[0, 'x'] = 999
assert df.loc[0, 'x'] != 999
set_option('modify_input_data', True)
df2 = df.copy()
df2 >> define(z='x**2')
assert 'z' in df2
df2 = df.copy()
df2 >> group_by(z='x**2')
assert 'z' in df2
df2 = df.copy()
arr = df2 >> pull('x')
arr[0] = 99
assert df2.loc[0, 'x'] == 99
# Not mutable
df2 = df.copy()
df2 >> create(z='x**2')
assert 'z' not in df2
df2 >> sample_n(3) >> define(z='x**2')
assert 'z' not in df2
df2 >> sample_frac(.5) >> define(z='x**2')
assert 'z' not in df2
df2 >> select('x') >> define(z='x**2')
assert 'z' not in df2
df2 >> select('x', 'y') >> define(z='x**2')
assert 'z' not in df2
# dataframe.rename has copy-on-write (if copy=False) that affects
# only the new frame. This creates possibility for "action at a
# distance" effects on the new frame when the original is modified
result = df2 >> rename(x='z')
df2['y'] = 3
result['x'] = 4
assert 'z' not in df2
assert df2.loc[0, 'y'] != 4
assert result.loc[0, 'x'] != 3
assert result is df2
df2 >> arrange('x') >> define(z='x**2')
assert 'z' not in df2
df2 >> query('x%2') >> define(z='x**2')
assert 'z' not in df2
df2 >> group_indices(z='x%2')
assert 'z' not in df2
set_option('modify_input_data', False)
def test_rename(self):
result = self.df >> rename(z='y')
assert 'x' in result
assert 'z' in result
assert 'y' not in result
assert isinstance(result, GroupedDataFrame)