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import numpy as np
import pandas as pd
import pytest
@pytest.fixture(autouse=True)
def close_figs():
yield
import matplotlib.pyplot as plt
plt.close("all")
@pytest.fixture(autouse=True)
def random_seed():
seed = sum(map(ord, "seaborn random global"))
np.random.seed(seed)
@pytest.fixture()
def rng():
seed = sum(map(ord, "seaborn random object"))
return np.random.RandomState(seed)
@pytest.fixture
def wide_df(rng):
columns = list("abc")
index = pd.RangeIndex(10, 50, 2, name="wide_index")
values = rng.normal(size=(len(index), len(columns)))
return pd.DataFrame(values, index=index, columns=columns)
@pytest.fixture
def wide_array(wide_df):
return wide_df.to_numpy()
# TODO s/flat/thin?
@pytest.fixture
def flat_series(rng):
index = pd.RangeIndex(10, 30, name="t")
return pd.Series(rng.normal(size=20), index, name="s")
@pytest.fixture
def flat_array(flat_series):
return flat_series.to_numpy()
@pytest.fixture
def flat_list(flat_series):
return flat_series.to_list()
@pytest.fixture(params=["series", "array", "list"])
def flat_data(rng, request):
index = pd.RangeIndex(10, 30, name="t")
series = pd.Series(rng.normal(size=20), index, name="s")
if request.param == "series":
data = series
elif request.param == "array":
data = series.to_numpy()
elif request.param == "list":
data = series.to_list()
return data
@pytest.fixture
def wide_list_of_series(rng):
return [pd.Series(rng.normal(size=20), np.arange(20), name="a"),
pd.Series(rng.normal(size=10), np.arange(5, 15), name="b")]
@pytest.fixture
def wide_list_of_arrays(wide_list_of_series):
return [s.to_numpy() for s in wide_list_of_series]
@pytest.fixture
def wide_list_of_lists(wide_list_of_series):
return [s.to_list() for s in wide_list_of_series]
@pytest.fixture
def wide_dict_of_series(wide_list_of_series):
return {s.name: s for s in wide_list_of_series}
@pytest.fixture
def wide_dict_of_arrays(wide_list_of_series):
return {s.name: s.to_numpy() for s in wide_list_of_series}
@pytest.fixture
def wide_dict_of_lists(wide_list_of_series):
return {s.name: s.to_list() for s in wide_list_of_series}
@pytest.fixture
def long_df(rng):
n = 100
df = pd.DataFrame(dict(
x=rng.uniform(0, 20, n).round().astype("int"),
y=rng.normal(size=n),
z=rng.lognormal(size=n),
a=rng.choice(list("abc"), n),
b=rng.choice(list("mnop"), n),
c=rng.choice([0, 1], n, [.3, .7]),
d=rng.choice(np.arange("2004-07-30", "2007-07-30", dtype="datetime64[Y]"), n),
t=rng.choice(np.arange("2004-07-30", "2004-07-31", dtype="datetime64[m]"), n),
s=rng.choice([2, 4, 8], n),
f=rng.choice([0.2, 0.3], n),
))
a_cat = df["a"].astype("category")
new_categories = np.roll(a_cat.cat.categories, 1)
df["a_cat"] = a_cat.cat.reorder_categories(new_categories)
df["s_cat"] = df["s"].astype("category")
df["s_str"] = df["s"].astype(str)
return df
@pytest.fixture
def long_dict(long_df):
return long_df.to_dict()
@pytest.fixture
def repeated_df(rng):
n = 100
return pd.DataFrame(dict(
x=np.tile(np.arange(n // 2), 2),
y=rng.normal(size=n),
a=rng.choice(list("abc"), n),
u=np.repeat(np.arange(2), n // 2),
))
@pytest.fixture
def missing_df(rng, long_df):
df = long_df.copy()
for col in df:
idx = rng.permutation(df.index)[:10]
df.loc[idx, col] = np.nan
return df
@pytest.fixture
def object_df(rng, long_df):
df = long_df.copy()
# objectify numeric columns
for col in ["c", "s", "f"]:
df[col] = df[col].astype(object)
return df
@pytest.fixture
def null_series(flat_series):
return pd.Series(index=flat_series.index, dtype='float64')
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