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Mar 27, 2020
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DOC: Fix examples in reshape #32980

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d46ffd3
DOC: Fix examples in reshape
Mar 24, 2020
29d61c4
Merge remote-tracking branch 'upstream/master' into DOC-doctest-panda…
Mar 25, 2020
fda91fd
Merge remote-tracking branch 'upstream/master' into DOC-doctest-panda…
Mar 26, 2020
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10 changes: 2 additions & 8 deletions ci/code_checks.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -283,14 +283,8 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
pytest -q --doctest-modules pandas/core/tools/datetimes.py
RET=$(($RET + $?)) ; echo $MSG "DONE"

MSG='Doctests top-level reshaping functions' ; echo $MSG
pytest -q --doctest-modules \
pandas/core/reshape/concat.py \
pandas/core/reshape/pivot.py \
pandas/core/reshape/reshape.py \
pandas/core/reshape/tile.py \
pandas/core/reshape/melt.py \
-k"-crosstab -pivot_table -cut"
MSG='Doctests reshaping functions' ; echo $MSG
pytest -q --doctest-modules pandas/core/reshape/
RET=$(($RET + $?)) ; echo $MSG "DONE"

MSG='Doctests interval classes' ; echo $MSG
Expand Down
128 changes: 87 additions & 41 deletions pandas/core/reshape/merge.py
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Original file line number Diff line number Diff line change
Expand Up @@ -222,7 +222,14 @@ def merge_ordered(

Examples
--------
>>> A
>>> df1 = pd.DataFrame(
... {
... "key": ["a", "c", "e", "a", "c", "e"],
... "lvalue": [1, 2, 3, 1, 2, 3],
... "group": ["a", "a", "a", "b", "b", "b"]
... }
... )
>>> df1
key lvalue group
0 a 1 a
1 c 2 a
Expand All @@ -231,24 +238,25 @@ def merge_ordered(
4 c 2 b
5 e 3 b

>>> B
Key rvalue
0 b 1
1 c 2
2 d 3

>>> merge_ordered(A, B, fill_method='ffill', left_by='group')
group key lvalue rvalue
0 a a 1 NaN
1 a b 1 1.0
2 a c 2 2.0
3 a d 2 3.0
4 a e 3 3.0
5 b a 1 NaN
6 b b 1 1.0
7 b c 2 2.0
8 b d 2 3.0
9 b e 3 3.0
>>> df2 = pd.DataFrame({"key": ["b", "c", "d"], "rvalue": [1, 2, 3]})
>>> df2
key rvalue
0 b 1
1 c 2
2 d 3

>>> merge_ordered(df1, df2, fill_method="ffill", left_by="group")
key lvalue group rvalue
0 a 1 a NaN
1 b 1 a 1.0
2 c 2 a 2.0
3 d 2 a 3.0
4 e 3 a 3.0
5 a 1 b NaN
6 b 1 b 1.0
7 c 2 b 2.0
8 d 2 b 3.0
9 e 3 b 3.0
"""

def _merger(x, y):
Expand Down Expand Up @@ -369,15 +377,14 @@ def merge_asof(

Examples
--------
>>> left = pd.DataFrame({'a': [1, 5, 10], 'left_val': ['a', 'b', 'c']})
>>> left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
>>> left
a left_val
0 1 a
1 5 b
2 10 c

>>> right = pd.DataFrame({'a': [1, 2, 3, 6, 7],
... 'right_val': [1, 2, 3, 6, 7]})
>>> right = pd.DataFrame({"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]})
>>> right
a right_val
0 1 1
Expand All @@ -386,41 +393,40 @@ def merge_asof(
3 6 6
4 7 7

>>> pd.merge_asof(left, right, on='a')
>>> pd.merge_asof(left, right, on="a")
a left_val right_val
0 1 a 1
1 5 b 3
2 10 c 7

>>> pd.merge_asof(left, right, on='a', allow_exact_matches=False)
>>> pd.merge_asof(left, right, on="a", allow_exact_matches=False)
a left_val right_val
0 1 a NaN
1 5 b 3.0
2 10 c 7.0

>>> pd.merge_asof(left, right, on='a', direction='forward')
>>> pd.merge_asof(left, right, on="a", direction="forward")
a left_val right_val
0 1 a 1.0
1 5 b 6.0
2 10 c NaN

>>> pd.merge_asof(left, right, on='a', direction='nearest')
>>> pd.merge_asof(left, right, on="a", direction="nearest")
a left_val right_val
0 1 a 1
1 5 b 6
2 10 c 7

We can use indexed DataFrames as well.

>>> left = pd.DataFrame({'left_val': ['a', 'b', 'c']}, index=[1, 5, 10])
>>> left = pd.DataFrame({"left_val": ["a", "b", "c"]}, index=[1, 5, 10])
>>> left
left_val
1 a
5 b
10 c

>>> right = pd.DataFrame({'right_val': [1, 2, 3, 6, 7]},
... index=[1, 2, 3, 6, 7])
>>> right = pd.DataFrame({"right_val": [1, 2, 3, 6, 7]}, index=[1, 2, 3, 6, 7])
>>> right
right_val
1 1
Expand All @@ -437,6 +443,32 @@ def merge_asof(

Here is a real-world times-series example

>>> quotes = pd.DataFrame(
... {
... "time": [
... pd.Timestamp("2016-05-25 13:30:00.023"),
... pd.Timestamp("2016-05-25 13:30:00.023"),
... pd.Timestamp("2016-05-25 13:30:00.030"),
... pd.Timestamp("2016-05-25 13:30:00.041"),
... pd.Timestamp("2016-05-25 13:30:00.048"),
... pd.Timestamp("2016-05-25 13:30:00.049"),
... pd.Timestamp("2016-05-25 13:30:00.072"),
... pd.Timestamp("2016-05-25 13:30:00.075")
... ],
... "ticker": [
... "GOOG",
... "MSFT",
... "MSFT",
... "MSFT",
... "GOOG",
... "AAPL",
... "GOOG",
... "MSFT"
... ],
... "bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01],
... "ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03]
... }
... )
>>> quotes
time ticker bid ask
0 2016-05-25 13:30:00.023 GOOG 720.50 720.93
Expand All @@ -448,6 +480,20 @@ def merge_asof(
6 2016-05-25 13:30:00.072 GOOG 720.50 720.88
7 2016-05-25 13:30:00.075 MSFT 52.01 52.03

>>> trades = pd.DataFrame(
... {
... "time": [
... pd.Timestamp("2016-05-25 13:30:00.023"),
... pd.Timestamp("2016-05-25 13:30:00.038"),
... pd.Timestamp("2016-05-25 13:30:00.048"),
... pd.Timestamp("2016-05-25 13:30:00.048"),
... pd.Timestamp("2016-05-25 13:30:00.048")
... ],
... "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
... "price": [51.95, 51.95, 720.77, 720.92, 98.0],
... "quantity": [75, 155, 100, 100, 100]
... }
... )
>>> trades
time ticker price quantity
0 2016-05-25 13:30:00.023 MSFT 51.95 75
Expand All @@ -458,9 +504,7 @@ def merge_asof(

By default we are taking the asof of the quotes

>>> pd.merge_asof(trades, quotes,
... on='time',
... by='ticker')
>>> pd.merge_asof(trades, quotes, on="time", by="ticker")
time ticker price quantity bid ask
0 2016-05-25 13:30:00.023 MSFT 51.95 75 51.95 51.96
1 2016-05-25 13:30:00.038 MSFT 51.95 155 51.97 51.98
Expand All @@ -470,10 +514,9 @@ def merge_asof(

We only asof within 2ms between the quote time and the trade time

>>> pd.merge_asof(trades, quotes,
... on='time',
... by='ticker',
... tolerance=pd.Timedelta('2ms'))
>>> pd.merge_asof(
... trades, quotes, on="time", by="ticker", tolerance=pd.Timedelta("2ms")
... )
time ticker price quantity bid ask
0 2016-05-25 13:30:00.023 MSFT 51.95 75 51.95 51.96
1 2016-05-25 13:30:00.038 MSFT 51.95 155 NaN NaN
Expand All @@ -485,11 +528,14 @@ def merge_asof(
and we exclude exact matches on time. However *prior* data will
propagate forward

>>> pd.merge_asof(trades, quotes,
... on='time',
... by='ticker',
... tolerance=pd.Timedelta('10ms'),
... allow_exact_matches=False)
>>> pd.merge_asof(
... trades,
... quotes,
... on="time",
... by="ticker",
... tolerance=pd.Timedelta("10ms"),
... allow_exact_matches=False
... )
time ticker price quantity bid ask
0 2016-05-25 13:30:00.023 MSFT 51.95 75 NaN NaN
1 2016-05-25 13:30:00.038 MSFT 51.95 155 51.97 51.98
Expand Down
26 changes: 14 additions & 12 deletions pandas/core/reshape/tile.py
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Original file line number Diff line number Diff line change
Expand Up @@ -171,24 +171,26 @@ def cut(
... index=['a', 'b', 'c', 'd', 'e'])
>>> pd.cut(s, [0, 2, 4, 6, 8, 10], labels=False, retbins=True, right=False)
... # doctest: +ELLIPSIS
(a 0.0
b 1.0
c 2.0
d 3.0
e 4.0
dtype: float64, array([0, 2, 4, 6, 8]))
(a 1.0
b 2.0
c 3.0
d 4.0
e NaN
dtype: float64,
array([ 0, 2, 4, 6, 8, 10]))

Use `drop` optional when bins is not unique

>>> pd.cut(s, [0, 2, 4, 6, 10, 10], labels=False, retbins=True,
... right=False, duplicates='drop')
... # doctest: +ELLIPSIS
(a 0.0
b 1.0
c 2.0
(a 1.0
b 2.0
c 3.0
d 3.0
e 3.0
dtype: float64, array([0, 2, 4, 6, 8]))
e NaN
dtype: float64,
array([ 0, 2, 4, 6, 10]))

Passing an IntervalIndex for `bins` results in those categories exactly.
Notice that values not covered by the IntervalIndex are set to NaN. 0
Expand All @@ -197,7 +199,7 @@ def cut(

>>> bins = pd.IntervalIndex.from_tuples([(0, 1), (2, 3), (4, 5)])
>>> pd.cut([0, 0.5, 1.5, 2.5, 4.5], bins)
[NaN, (0, 1], NaN, (2, 3], (4, 5]]
[NaN, (0.0, 1.0], NaN, (2.0, 3.0], (4.0, 5.0]]
Categories (3, interval[int64]): [(0, 1] < (2, 3] < (4, 5]]
"""
# NOTE: this binning code is changed a bit from histogram for var(x) == 0
Expand Down
3 changes: 1 addition & 2 deletions pandas/core/reshape/util.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,8 +19,7 @@ def cartesian_product(X):
Examples
--------
>>> cartesian_product([list('ABC'), [1, 2]])
[array(['A', 'A', 'B', 'B', 'C', 'C'], dtype='|S1'),
array([1, 2, 1, 2, 1, 2])]
[array(['A', 'A', 'B', 'B', 'C', 'C'], dtype='<U1'), array([1, 2, 1, 2, 1, 2])]

See Also
--------
Expand Down