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ENH: show percentiles in timestamp describe (#30164) #30209

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Merged
merged 3 commits into from
Jan 20, 2020
Merged

ENH: show percentiles in timestamp describe (#30164) #30209

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2f57671
ENH: show percentiles in timestamp describe (#30164)
david-cortes Jan 18, 2020
0082f81
use internal series methods for datetime describe (#30209)
david-cortes Jan 20, 2020
57a77db
more tests for datetimes describe
david-cortes Jan 20, 2020
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43 changes: 22 additions & 21 deletions pandas/core/generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@

from pandas._config import config

from pandas._libs import Timestamp, iNaT, lib, properties
from pandas._libs import NaT, Timestamp, iNaT, lib, properties
from pandas._typing import (
Axis,
Dtype,
Expand Down Expand Up @@ -9626,26 +9626,8 @@ def describe_categorical_1d(data):
dtype = None
if result[1] > 0:
top, freq = objcounts.index[0], objcounts.iloc[0]

if is_datetime64_any_dtype(data):
tz = data.dt.tz
asint = data.dropna().values.view("i8")
top = Timestamp(top)
if top.tzinfo is not None and tz is not None:
# Don't tz_localize(None) if key is already tz-aware
top = top.tz_convert(tz)
else:
top = top.tz_localize(tz)
names += ["top", "freq", "first", "last"]
result += [
top,
freq,
Timestamp(asint.min(), tz=tz),
Timestamp(asint.max(), tz=tz),
]
else:
names += ["top", "freq"]
result += [top, freq]
names += ["top", "freq"]
result += [top, freq]

# If the DataFrame is empty, set 'top' and 'freq' to None
# to maintain output shape consistency
Expand All @@ -9656,11 +9638,30 @@ def describe_categorical_1d(data):

return pd.Series(result, index=names, name=data.name, dtype=dtype)

def describe_timestamp_1d(data):
# GH-30164
tz = data.dt.tz
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@gfyoung gfyoung Dec 12, 2019 •
edited
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Reference issue number above this line.

asint = data.dropna().values.view("i8")
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you don't need the .values.view, just use len(data.dropna()) for the shape check

data.min() and so on all work and return NaT correctly, none of these gymnastics are needed

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I know that's what the current code does, but let's simplify

is_empty = asint.shape[0] == 0
stat_index = ["count", "mean", "min"] + formatted_percentiles + ["max"]
d = (
[
asint.shape[0],
Timestamp(asint.mean(), tz=tz) if not is_empty else NaT,
Timestamp(asint.min(), tz=tz) if not is_empty else NaT,
]
+ data.quantile(percentiles).tolist()
+ [Timestamp(asint.max(), tz=tz) if not is_empty else NaT]
)
return pd.Series(d, index=stat_index, name=data.name)

def describe_1d(data):
if is_bool_dtype(data):
return describe_categorical_1d(data)
elif is_numeric_dtype(data):
return describe_numeric_1d(data)
elif is_datetime64_any_dtype(data):
return describe_timestamp_1d(data)
elif is_timedelta64_dtype(data):
return describe_numeric_1d(data)
else:
Expand Down
45 changes: 6 additions & 39 deletions pandas/tests/frame/methods/test_describe.py
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Expand Up @@ -253,52 +253,19 @@ def test_describe_tz_values(self, tz_naive_fixture):

expected = DataFrame(
{
"s1": [
5,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
2,
1.581139,
0,
1,
2,
3,
4,
],
"s1": [5, 2, 0, 1, 2, 3, 4, 1.581139],
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do we have sufficient tests for timedeltas?

can you create a test for Period (which likely don't work), if you'd just xfail it (alt if you'd create an issue)

"s2": [
5,
5,
s2.value_counts().index[0],
1,
Timestamp(2018, 1, 3).tz_localize(tz),
start.tz_localize(tz),
s2[1],
s2[2],
s2[3],
end.tz_localize(tz),
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
],
},
index=[
"count",
"unique",
"top",
"freq",
"first",
"last",
"mean",
"std",
"min",
"25%",
"50%",
"75%",
"max",
],
index=["count", "mean", "min", "25%", "50%", "75%", "max", "std"],
)
result = df.describe(include="all")
tm.assert_frame_equal(result, expected)
Expand Down
9 changes: 5 additions & 4 deletions pandas/tests/series/methods/test_describe.py
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Expand Up @@ -57,13 +57,14 @@ def test_describe_with_tz(self, tz_naive_fixture):
expected = Series(
[
5,
5,
s.value_counts().index[0],
1,
Timestamp(2018, 1, 3).tz_localize(tz),
start.tz_localize(tz),
s[1],
s[2],
s[3],
end.tz_localize(tz),
],
name=name,
index=["count", "unique", "top", "freq", "first", "last"],
index=["count", "mean", "min", "25%", "50%", "75%", "max"],
)
tm.assert_series_equal(result, expected)