def test_cummin():
df = diamonds.copy() >> head(5) >> select(X.cut, X.x)
df_cm = df >> mutate(cm=cummin(X.x))
df_truth = df
df_truth['cm'] = pd.Series([3.95, 3.89, 3.89, 3.89, 3.89])
assert df_cm.equals(df_truth)
df_cm = df >> groupby(X.cut) >> mutate(cm=cummin(X.x))
df_truth['cm'] = pd.Series([3.95, 3.89, 4.05, 3.89, 4.05])
assert df_cm.equals(df_truth)
def test_cumsum():
df = diamonds.copy() >> head(5) >> select(X.cut, X.x)
df_cs = df >> mutate(cs=cumsum(X.x))
df_truth = df
df_truth['cs'] = pd.Series([3.95, 7.84, 11.89, 16.09, 20.43])
assert df_cs.equals(df_truth)
df_cs = df >> groupby(X.cut) >> mutate(cs=cumsum(X.x))
df_truth['cs'] = pd.Series([3.95, 3.89, 4.05, 8.09, 8.39])
assert df_cs.equals(df_truth)
def test_cummean():
df = diamonds.copy() >> head(5) >> select(X.cut, X.x)
df_cm = df >> mutate(cm=cummean(X.x))
df_truth = df
df_truth['cm'] = pd.Series([3.950000, 3.920000, 3.963333, 4.022500, 4.086000])
assert df_cm.equals(df_truth)
df_cm = df >> groupby(X.cut) >> mutate(cm=cummean(X.x))
df_truth['cm'] = pd.Series([3.950, 3.890, 4.050, 4.045, 4.195])
assert df_cm.equals(df_truth)
def test_mutate():
df = diamonds.copy()
df['testcol'] = 1
assert df.equals(diamonds >> mutate(testcol=1))
df['testcol'] = df['x']
assert df.equals(diamonds >> mutate(testcol=X.x))
df['testcol'] = df['x'] * df['y']
assert df.equals(diamonds >> mutate(testcol=X.x * X.y))
df['testcol'] = df['x'].mean()
assert df.equals(diamonds >> mutate(testcol=np.mean(X.x)))
def test_cumprod():
df = diamonds.copy() >> head(5) >> select(X.cut, X.x)
df_cp = df >> mutate(cp=cumprod(X.x))
df_truth = df
df_truth['cp'] = pd.Series([3.950000, 15.365500, 62.230275, 261.367155, 1134.333453])
assert df_cp.equals(df_truth)
df_cp = df >> groupby(X.cut) >> mutate(cp=cumprod(X.x))
df_truth['cp'] = pd.Series([3.950, 3.890, 4.050, 16.338, 17.577])
# some tricky floating point stuff going on here
diffs = df_cp.cp - df_truth.cp
assert all(diffs < .0000001)
def test_min_rank():
df = diamonds.copy() >> head(5) >> select(X.cut, X.x)
df_mr = df >> mutate(mr=min_rank(X.x))
df_truth = df
df_truth['mr'] = pd.Series([2.0, 1.0, 3.0, 4.0, 5.0])
assert df_mr.equals(df_truth)
df_mr = df >> mutate(mr=min_rank(X.cut))
df_truth['mr'] = pd.Series([3.0, 4.0, 1.0, 4.0, 1.0])
assert df_mr.equals(df_truth)
df_mr = df >> groupby(X.cut) >> mutate(mr=min_rank(X.x))
df_truth['mr'] = pd.Series([1.0, 1.0, 1.0, 2.0, 2.0])
assert df_mr.equals(df_truth)
df_mr = df >> mutate(mr=min_rank(X.x, ascending=False))
df_truth['mr'] = pd.Series([4.0, 5.0, 3.0, 2.0, 1.0])
assert df_mr.equals(df_truth)
def test_group_transmute():
df = diamonds.copy()
df = df.groupby('cut').apply(group_mutate_helper).reset_index(drop=True)
df = df[['cut','testcol']]
d = diamonds >> groupby('cut') >> transmute(testcol=X.x*X.shape[0])
assert df.equals(d)
def test_transmute():
df = diamonds.copy()
df['testcol'] = df['x'] * df['y']
df = df[['testcol']]
assert df.equals(diamonds >> transmute(testcol=X.x * X.y))
def test_group_mutate():
df = diamonds.copy()
df = df.groupby('cut').apply(group_mutate_helper)
d = diamonds >> groupby('cut') >> mutate(testcol=X.x*X.shape[0]) >> ungroup()
assert df.equals(d)
