Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
79 lines
2.0 KiB
79 lines
2.0 KiB
4 years ago
|
import numpy as np
|
||
|
|
|
||
|
|
import pandas as pd
|
||
|
|
from pandas import DataFrame, Index
|
||
|
|
import pandas._testing as tm
|
||
|
|
|
||
|
|
|
||
|
|
def test_pipe():
|
||
|
|
# Test the pipe method of DataFrameGroupBy.
|
||
|
|
# Issue #17871
|
||
|
|
|
||
|
|
random_state = np.random.RandomState(1234567890)
|
||
|
|
|
||
|
|
df = DataFrame(
|
||
|
|
{
|
||
|
|
"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
|
||
|
|
"B": random_state.randn(8),
|
||
|
|
"C": random_state.randn(8),
|
||
|
|
}
|
||
|
|
)
|
||
|
|
|
||
|
|
def f(dfgb):
|
||
|
|
return dfgb.B.max() - dfgb.C.min().min()
|
||
|
|
|
||
|
|
def square(srs):
|
||
|
|
return srs ** 2
|
||
|
|
|
||
|
|
# Note that the transformations are
|
||
|
|
# GroupBy -> Series
|
||
|
|
# Series -> Series
|
||
|
|
# This then chains the GroupBy.pipe and the
|
||
|
|
# NDFrame.pipe methods
|
||
|
|
result = df.groupby("A").pipe(f).pipe(square)
|
||
|
|
|
||
|
|
index = Index(["bar", "foo"], dtype="object", name="A")
|
||
|
|
expected = pd.Series([8.99110003361, 8.17516964785], name="B", index=index)
|
||
|
|
|
||
|
|
tm.assert_series_equal(expected, result)
|
||
|
|
|
||
|
|
|
||
|
|
def test_pipe_args():
|
||
|
|
# Test passing args to the pipe method of DataFrameGroupBy.
|
||
|
|
# Issue #17871
|
||
|
|
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"group": ["A", "A", "B", "B", "C"],
|
||
|
|
"x": [1.0, 2.0, 3.0, 2.0, 5.0],
|
||
|
|
"y": [10.0, 100.0, 1000.0, -100.0, -1000.0],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
|
||
|
|
def f(dfgb, arg1):
|
||
|
|
return dfgb.filter(lambda grp: grp.y.mean() > arg1, dropna=False).groupby(
|
||
|
|
dfgb.grouper
|
||
|
|
)
|
||
|
|
|
||
|
|
def g(dfgb, arg2):
|
||
|
|
return dfgb.sum() / dfgb.sum().sum() + arg2
|
||
|
|
|
||
|
|
def h(df, arg3):
|
||
|
|
return df.x + df.y - arg3
|
||
|
|
|
||
|
|
result = df.groupby("group").pipe(f, 0).pipe(g, 10).pipe(h, 100)
|
||
|
|
|
||
|
|
# Assert the results here
|
||
|
|
index = pd.Index(["A", "B", "C"], name="group")
|
||
|
|
expected = pd.Series([-79.5160891089, -78.4839108911, -80], index=index)
|
||
|
|
|
||
|
|
tm.assert_series_equal(expected, result)
|
||
|
|
|
||
|
|
# test SeriesGroupby.pipe
|
||
|
|
ser = pd.Series([1, 1, 2, 2, 3, 3])
|
||
|
|
result = ser.groupby(ser).pipe(lambda grp: grp.sum() * grp.count())
|
||
|
|
|
||
|
|
expected = pd.Series([4, 8, 12], index=pd.Int64Index([1, 2, 3]))
|
||
|
|
|
||
|
|
tm.assert_series_equal(result, expected)
|