forked from pandas-dev/pandas
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test_merge_asof.py
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test_merge_asof.py
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import os
import pytest
import pytz
import numpy as np
import pandas as pd
from pandas import (merge_asof, read_csv,
to_datetime, Timedelta)
from pandas.core.reshape.merge import MergeError
from pandas.util import testing as tm
from pandas.util.testing import assert_frame_equal
class TestAsOfMerge(object):
def read_data(self, name, dedupe=False):
path = os.path.join(tm.get_data_path(), name)
x = read_csv(path)
if dedupe:
x = (x.drop_duplicates(['time', 'ticker'], keep='last')
.reset_index(drop=True)
)
x.time = to_datetime(x.time)
return x
def setup_method(self, method):
self.trades = self.read_data('trades.csv')
self.quotes = self.read_data('quotes.csv', dedupe=True)
self.asof = self.read_data('asof.csv')
self.tolerance = self.read_data('tolerance.csv')
self.allow_exact_matches = self.read_data('allow_exact_matches.csv')
self.allow_exact_matches_and_tolerance = self.read_data(
'allow_exact_matches_and_tolerance.csv')
def test_examples1(self):
""" doc-string examples """
left = pd.DataFrame({'a': [1, 5, 10],
'left_val': ['a', 'b', 'c']})
right = pd.DataFrame({'a': [1, 2, 3, 6, 7],
'right_val': [1, 2, 3, 6, 7]})
expected = pd.DataFrame({'a': [1, 5, 10],
'left_val': ['a', 'b', 'c'],
'right_val': [1, 3, 7]})
result = pd.merge_asof(left, right, on='a')
assert_frame_equal(result, expected)
def test_examples2(self):
""" doc-string examples """
trades = pd.DataFrame({
'time': pd.to_datetime(['20160525 13:30:00.023',
'20160525 13:30:00.038',
'20160525 13:30:00.048',
'20160525 13:30:00.048',
'20160525 13:30:00.048']),
'ticker': ['MSFT', 'MSFT',
'GOOG', 'GOOG', 'AAPL'],
'price': [51.95, 51.95,
720.77, 720.92, 98.00],
'quantity': [75, 155,
100, 100, 100]},
columns=['time', 'ticker', 'price', 'quantity'])
quotes = pd.DataFrame({
'time': pd.to_datetime(['20160525 13:30:00.023',
'20160525 13:30:00.023',
'20160525 13:30:00.030',
'20160525 13:30:00.041',
'20160525 13:30:00.048',
'20160525 13:30:00.049',
'20160525 13:30:00.072',
'20160525 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]},
columns=['time', 'ticker', 'bid', 'ask'])
pd.merge_asof(trades, quotes,
on='time',
by='ticker')
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('10ms'),
allow_exact_matches=False)
def test_examples3(self):
""" doc-string examples """
# GH14887
left = pd.DataFrame({'a': [1, 5, 10],
'left_val': ['a', 'b', 'c']})
right = pd.DataFrame({'a': [1, 2, 3, 6, 7],
'right_val': [1, 2, 3, 6, 7]})
expected = pd.DataFrame({'a': [1, 5, 10],
'left_val': ['a', 'b', 'c'],
'right_val': [1, 6, np.nan]})
result = pd.merge_asof(left, right, on='a', direction='forward')
assert_frame_equal(result, expected)
def test_examples4(self):
""" doc-string examples """
# GH14887
left = pd.DataFrame({'a': [1, 5, 10],
'left_val': ['a', 'b', 'c']})
right = pd.DataFrame({'a': [1, 2, 3, 6, 7],
'right_val': [1, 2, 3, 6, 7]})
expected = pd.DataFrame({'a': [1, 5, 10],
'left_val': ['a', 'b', 'c'],
'right_val': [1, 6, 7]})
result = pd.merge_asof(left, right, on='a', direction='nearest')
assert_frame_equal(result, expected)
def test_basic(self):
expected = self.asof
trades = self.trades
quotes = self.quotes
result = merge_asof(trades, quotes,
on='time',
by='ticker')
assert_frame_equal(result, expected)
def test_basic_categorical(self):
expected = self.asof
trades = self.trades.copy()
trades.ticker = trades.ticker.astype('category')
quotes = self.quotes.copy()
quotes.ticker = quotes.ticker.astype('category')
expected.ticker = expected.ticker.astype('category')
result = merge_asof(trades, quotes,
on='time',
by='ticker')
assert_frame_equal(result, expected)
def test_basic_left_index(self):
# GH14253
expected = self.asof
trades = self.trades.set_index('time')
quotes = self.quotes
result = merge_asof(trades, quotes,
left_index=True,
right_on='time',
by='ticker')
# left-only index uses right's index, oddly
expected.index = result.index
# time column appears after left's columns
expected = expected[result.columns]
assert_frame_equal(result, expected)
def test_basic_right_index(self):
expected = self.asof
trades = self.trades
quotes = self.quotes.set_index('time')
result = merge_asof(trades, quotes,
left_on='time',
right_index=True,
by='ticker')
assert_frame_equal(result, expected)
def test_basic_left_index_right_index(self):
expected = self.asof.set_index('time')
trades = self.trades.set_index('time')
quotes = self.quotes.set_index('time')
result = merge_asof(trades, quotes,
left_index=True,
right_index=True,
by='ticker')
assert_frame_equal(result, expected)
def test_multi_index(self):
# MultiIndex is prohibited
trades = self.trades.set_index(['time', 'price'])
quotes = self.quotes.set_index('time')
with pytest.raises(MergeError):
merge_asof(trades, quotes,
left_index=True,
right_index=True)
trades = self.trades.set_index('time')
quotes = self.quotes.set_index(['time', 'bid'])
with pytest.raises(MergeError):
merge_asof(trades, quotes,
left_index=True,
right_index=True)
def test_on_and_index(self):
# 'on' parameter and index together is prohibited
trades = self.trades.set_index('time')
quotes = self.quotes.set_index('time')
with pytest.raises(MergeError):
merge_asof(trades, quotes,
left_on='price',
left_index=True,
right_index=True)
trades = self.trades.set_index('time')
quotes = self.quotes.set_index('time')
with pytest.raises(MergeError):
merge_asof(trades, quotes,
right_on='bid',
left_index=True,
right_index=True)
def test_basic_left_by_right_by(self):
# GH14253
expected = self.asof
trades = self.trades
quotes = self.quotes
result = merge_asof(trades, quotes,
on='time',
left_by='ticker',
right_by='ticker')
assert_frame_equal(result, expected)
def test_missing_right_by(self):
expected = self.asof
trades = self.trades
quotes = self.quotes
q = quotes[quotes.ticker != 'MSFT']
result = merge_asof(trades, q,
on='time',
by='ticker')
expected.loc[expected.ticker == 'MSFT', ['bid', 'ask']] = np.nan
assert_frame_equal(result, expected)
def test_multiby(self):
# GH13936
trades = pd.DataFrame({
'time': pd.to_datetime(['20160525 13:30:00.023',
'20160525 13:30:00.023',
'20160525 13:30:00.046',
'20160525 13:30:00.048',
'20160525 13:30:00.050']),
'ticker': ['MSFT', 'MSFT',
'GOOG', 'GOOG', 'AAPL'],
'exch': ['ARCA', 'NSDQ', 'NSDQ', 'BATS', 'NSDQ'],
'price': [51.95, 51.95,
720.77, 720.92, 98.00],
'quantity': [75, 155,
100, 100, 100]},
columns=['time', 'ticker', 'exch',
'price', 'quantity'])
quotes = pd.DataFrame({
'time': pd.to_datetime(['20160525 13:30:00.023',
'20160525 13:30:00.023',
'20160525 13:30:00.030',
'20160525 13:30:00.041',
'20160525 13:30:00.045',
'20160525 13:30:00.049']),
'ticker': ['GOOG', 'MSFT', 'MSFT',
'MSFT', 'GOOG', 'AAPL'],
'exch': ['BATS', 'NSDQ', 'ARCA', 'ARCA',
'NSDQ', 'ARCA'],
'bid': [720.51, 51.95, 51.97, 51.99,
720.50, 97.99],
'ask': [720.92, 51.96, 51.98, 52.00,
720.93, 98.01]},
columns=['time', 'ticker', 'exch', 'bid', 'ask'])
expected = pd.DataFrame({
'time': pd.to_datetime(['20160525 13:30:00.023',
'20160525 13:30:00.023',
'20160525 13:30:00.046',
'20160525 13:30:00.048',
'20160525 13:30:00.050']),
'ticker': ['MSFT', 'MSFT',
'GOOG', 'GOOG', 'AAPL'],
'exch': ['ARCA', 'NSDQ', 'NSDQ', 'BATS', 'NSDQ'],
'price': [51.95, 51.95,
720.77, 720.92, 98.00],
'quantity': [75, 155,
100, 100, 100],
'bid': [np.nan, 51.95, 720.50, 720.51, np.nan],
'ask': [np.nan, 51.96, 720.93, 720.92, np.nan]},
columns=['time', 'ticker', 'exch',
'price', 'quantity', 'bid', 'ask'])
result = pd.merge_asof(trades, quotes, on='time',
by=['ticker', 'exch'])
assert_frame_equal(result, expected)
def test_multiby_heterogeneous_types(self):
# GH13936
trades = pd.DataFrame({
'time': pd.to_datetime(['20160525 13:30:00.023',
'20160525 13:30:00.023',
'20160525 13:30:00.046',
'20160525 13:30:00.048',
'20160525 13:30:00.050']),
'ticker': [0, 0, 1, 1, 2],
'exch': ['ARCA', 'NSDQ', 'NSDQ', 'BATS', 'NSDQ'],
'price': [51.95, 51.95,
720.77, 720.92, 98.00],
'quantity': [75, 155,
100, 100, 100]},
columns=['time', 'ticker', 'exch',
'price', 'quantity'])
quotes = pd.DataFrame({
'time': pd.to_datetime(['20160525 13:30:00.023',
'20160525 13:30:00.023',
'20160525 13:30:00.030',
'20160525 13:30:00.041',
'20160525 13:30:00.045',
'20160525 13:30:00.049']),
'ticker': [1, 0, 0, 0, 1, 2],
'exch': ['BATS', 'NSDQ', 'ARCA', 'ARCA',
'NSDQ', 'ARCA'],
'bid': [720.51, 51.95, 51.97, 51.99,
720.50, 97.99],
'ask': [720.92, 51.96, 51.98, 52.00,
720.93, 98.01]},
columns=['time', 'ticker', 'exch', 'bid', 'ask'])
expected = pd.DataFrame({
'time': pd.to_datetime(['20160525 13:30:00.023',
'20160525 13:30:00.023',
'20160525 13:30:00.046',
'20160525 13:30:00.048',
'20160525 13:30:00.050']),
'ticker': [0, 0, 1, 1, 2],
'exch': ['ARCA', 'NSDQ', 'NSDQ', 'BATS', 'NSDQ'],
'price': [51.95, 51.95,
720.77, 720.92, 98.00],
'quantity': [75, 155,
100, 100, 100],
'bid': [np.nan, 51.95, 720.50, 720.51, np.nan],
'ask': [np.nan, 51.96, 720.93, 720.92, np.nan]},
columns=['time', 'ticker', 'exch',
'price', 'quantity', 'bid', 'ask'])
result = pd.merge_asof(trades, quotes, on='time',
by=['ticker', 'exch'])
assert_frame_equal(result, expected)
def test_multiby_indexed(self):
# GH15676
left = pd.DataFrame([
[pd.to_datetime('20160602'), 1, 'a'],
[pd.to_datetime('20160602'), 2, 'a'],
[pd.to_datetime('20160603'), 1, 'b'],
[pd.to_datetime('20160603'), 2, 'b']],
columns=['time', 'k1', 'k2']).set_index('time')
right = pd.DataFrame([
[pd.to_datetime('20160502'), 1, 'a', 1.0],
[pd.to_datetime('20160502'), 2, 'a', 2.0],
[pd.to_datetime('20160503'), 1, 'b', 3.0],
[pd.to_datetime('20160503'), 2, 'b', 4.0]],
columns=['time', 'k1', 'k2', 'value']).set_index('time')
expected = pd.DataFrame([
[pd.to_datetime('20160602'), 1, 'a', 1.0],
[pd.to_datetime('20160602'), 2, 'a', 2.0],
[pd.to_datetime('20160603'), 1, 'b', 3.0],
[pd.to_datetime('20160603'), 2, 'b', 4.0]],
columns=['time', 'k1', 'k2', 'value']).set_index('time')
result = pd.merge_asof(left,
right,
left_index=True,
right_index=True,
by=['k1', 'k2'])
assert_frame_equal(expected, result)
with pytest.raises(MergeError):
pd.merge_asof(left, right, left_index=True, right_index=True,
left_by=['k1', 'k2'], right_by=['k1'])
def test_basic2(self):
expected = self.read_data('asof2.csv')
trades = self.read_data('trades2.csv')
quotes = self.read_data('quotes2.csv', dedupe=True)
result = merge_asof(trades, quotes,
on='time',
by='ticker')
assert_frame_equal(result, expected)
def test_basic_no_by(self):
f = lambda x: x[x.ticker == 'MSFT'].drop('ticker', axis=1) \
.reset_index(drop=True)
# just use a single ticker
expected = f(self.asof)
trades = f(self.trades)
quotes = f(self.quotes)
result = merge_asof(trades, quotes,
on='time')
assert_frame_equal(result, expected)
def test_valid_join_keys(self):
trades = self.trades
quotes = self.quotes
with pytest.raises(MergeError):
merge_asof(trades, quotes,
left_on='time',
right_on='bid',
by='ticker')
with pytest.raises(MergeError):
merge_asof(trades, quotes,
on=['time', 'ticker'],
by='ticker')
with pytest.raises(MergeError):
merge_asof(trades, quotes,
by='ticker')
def test_with_duplicates(self):
q = pd.concat([self.quotes, self.quotes]).sort_values(
['time', 'ticker']).reset_index(drop=True)
result = merge_asof(self.trades, q,
on='time',
by='ticker')
expected = self.read_data('asof.csv')
assert_frame_equal(result, expected)
def test_with_duplicates_no_on(self):
df1 = pd.DataFrame({'key': [1, 1, 3],
'left_val': [1, 2, 3]})
df2 = pd.DataFrame({'key': [1, 2, 2],
'right_val': [1, 2, 3]})
result = merge_asof(df1, df2, on='key')
expected = pd.DataFrame({'key': [1, 1, 3],
'left_val': [1, 2, 3],
'right_val': [1, 1, 3]})
assert_frame_equal(result, expected)
def test_valid_allow_exact_matches(self):
trades = self.trades
quotes = self.quotes
with pytest.raises(MergeError):
merge_asof(trades, quotes,
on='time',
by='ticker',
allow_exact_matches='foo')
def test_valid_tolerance(self):
trades = self.trades
quotes = self.quotes
# dti
merge_asof(trades, quotes,
on='time',
by='ticker',
tolerance=Timedelta('1s'))
# integer
merge_asof(trades.reset_index(), quotes.reset_index(),
on='index',
by='ticker',
tolerance=1)
# incompat
with pytest.raises(MergeError):
merge_asof(trades, quotes,
on='time',
by='ticker',
tolerance=1)
# invalid
with pytest.raises(MergeError):
merge_asof(trades.reset_index(), quotes.reset_index(),
on='index',
by='ticker',
tolerance=1.0)
# invalid negative
with pytest.raises(MergeError):
merge_asof(trades, quotes,
on='time',
by='ticker',
tolerance=-Timedelta('1s'))
with pytest.raises(MergeError):
merge_asof(trades.reset_index(), quotes.reset_index(),
on='index',
by='ticker',
tolerance=-1)
def test_non_sorted(self):
trades = self.trades.sort_values('time', ascending=False)
quotes = self.quotes.sort_values('time', ascending=False)
# we require that we are already sorted on time & quotes
assert not trades.time.is_monotonic
assert not quotes.time.is_monotonic
with pytest.raises(ValueError):
merge_asof(trades, quotes,
on='time',
by='ticker')
trades = self.trades.sort_values('time')
assert trades.time.is_monotonic
assert not quotes.time.is_monotonic
with pytest.raises(ValueError):
merge_asof(trades, quotes,
on='time',
by='ticker')
quotes = self.quotes.sort_values('time')
assert trades.time.is_monotonic
assert quotes.time.is_monotonic
# ok, though has dupes
merge_asof(trades, self.quotes,
on='time',
by='ticker')
def test_tolerance(self):
trades = self.trades
quotes = self.quotes
result = merge_asof(trades, quotes,
on='time',
by='ticker',
tolerance=Timedelta('1day'))
expected = self.tolerance
assert_frame_equal(result, expected)
def test_tolerance_forward(self):
# GH14887
left = pd.DataFrame({'a': [1, 5, 10],
'left_val': ['a', 'b', 'c']})
right = pd.DataFrame({'a': [1, 2, 3, 7, 11],
'right_val': [1, 2, 3, 7, 11]})
expected = pd.DataFrame({'a': [1, 5, 10],
'left_val': ['a', 'b', 'c'],
'right_val': [1, np.nan, 11]})
result = pd.merge_asof(left, right, on='a', direction='forward',
tolerance=1)
assert_frame_equal(result, expected)
def test_tolerance_nearest(self):
# GH14887
left = pd.DataFrame({'a': [1, 5, 10],
'left_val': ['a', 'b', 'c']})
right = pd.DataFrame({'a': [1, 2, 3, 7, 11],
'right_val': [1, 2, 3, 7, 11]})
expected = pd.DataFrame({'a': [1, 5, 10],
'left_val': ['a', 'b', 'c'],
'right_val': [1, np.nan, 11]})
result = pd.merge_asof(left, right, on='a', direction='nearest',
tolerance=1)
assert_frame_equal(result, expected)
def test_tolerance_tz(self):
# GH 14844
left = pd.DataFrame(
{'date': pd.DatetimeIndex(start=pd.to_datetime('2016-01-02'),
freq='D', periods=5,
tz=pytz.timezone('UTC')),
'value1': np.arange(5)})
right = pd.DataFrame(
{'date': pd.DatetimeIndex(start=pd.to_datetime('2016-01-01'),
freq='D', periods=5,
tz=pytz.timezone('UTC')),
'value2': list("ABCDE")})
result = pd.merge_asof(left, right, on='date',
tolerance=pd.Timedelta('1 day'))
expected = pd.DataFrame(
{'date': pd.DatetimeIndex(start=pd.to_datetime('2016-01-02'),
freq='D', periods=5,
tz=pytz.timezone('UTC')),
'value1': np.arange(5),
'value2': list("BCDEE")})
assert_frame_equal(result, expected)
def test_index_tolerance(self):
# GH 15135
expected = self.tolerance.set_index('time')
trades = self.trades.set_index('time')
quotes = self.quotes.set_index('time')
result = pd.merge_asof(trades, quotes,
left_index=True,
right_index=True,
by='ticker',
tolerance=pd.Timedelta('1day'))
assert_frame_equal(result, expected)
def test_allow_exact_matches(self):
result = merge_asof(self.trades, self.quotes,
on='time',
by='ticker',
allow_exact_matches=False)
expected = self.allow_exact_matches
assert_frame_equal(result, expected)
def test_allow_exact_matches_forward(self):
# GH14887
left = pd.DataFrame({'a': [1, 5, 10],
'left_val': ['a', 'b', 'c']})
right = pd.DataFrame({'a': [1, 2, 3, 7, 11],
'right_val': [1, 2, 3, 7, 11]})
expected = pd.DataFrame({'a': [1, 5, 10],
'left_val': ['a', 'b', 'c'],
'right_val': [2, 7, 11]})
result = pd.merge_asof(left, right, on='a', direction='forward',
allow_exact_matches=False)
assert_frame_equal(result, expected)
def test_allow_exact_matches_nearest(self):
# GH14887
left = pd.DataFrame({'a': [1, 5, 10],
'left_val': ['a', 'b', 'c']})
right = pd.DataFrame({'a': [1, 2, 3, 7, 11],
'right_val': [1, 2, 3, 7, 11]})
expected = pd.DataFrame({'a': [1, 5, 10],
'left_val': ['a', 'b', 'c'],
'right_val': [2, 3, 11]})
result = pd.merge_asof(left, right, on='a', direction='nearest',
allow_exact_matches=False)
assert_frame_equal(result, expected)
def test_allow_exact_matches_and_tolerance(self):
result = merge_asof(self.trades, self.quotes,
on='time',
by='ticker',
tolerance=Timedelta('100ms'),
allow_exact_matches=False)
expected = self.allow_exact_matches_and_tolerance
assert_frame_equal(result, expected)
def test_allow_exact_matches_and_tolerance2(self):
# GH 13695
df1 = pd.DataFrame({
'time': pd.to_datetime(['2016-07-15 13:30:00.030']),
'username': ['bob']})
df2 = pd.DataFrame({
'time': pd.to_datetime(['2016-07-15 13:30:00.000',
'2016-07-15 13:30:00.030']),
'version': [1, 2]})
result = pd.merge_asof(df1, df2, on='time')
expected = pd.DataFrame({
'time': pd.to_datetime(['2016-07-15 13:30:00.030']),
'username': ['bob'],
'version': [2]})
assert_frame_equal(result, expected)
result = pd.merge_asof(df1, df2, on='time', allow_exact_matches=False)
expected = pd.DataFrame({
'time': pd.to_datetime(['2016-07-15 13:30:00.030']),
'username': ['bob'],
'version': [1]})
assert_frame_equal(result, expected)
result = pd.merge_asof(df1, df2, on='time', allow_exact_matches=False,
tolerance=pd.Timedelta('10ms'))
expected = pd.DataFrame({
'time': pd.to_datetime(['2016-07-15 13:30:00.030']),
'username': ['bob'],
'version': [np.nan]})
assert_frame_equal(result, expected)
def test_allow_exact_matches_and_tolerance3(self):
# GH 13709
df1 = pd.DataFrame({
'time': pd.to_datetime(['2016-07-15 13:30:00.030',
'2016-07-15 13:30:00.030']),
'username': ['bob', 'charlie']})
df2 = pd.DataFrame({
'time': pd.to_datetime(['2016-07-15 13:30:00.000',
'2016-07-15 13:30:00.030']),
'version': [1, 2]})
result = pd.merge_asof(df1, df2, on='time', allow_exact_matches=False,
tolerance=pd.Timedelta('10ms'))
expected = pd.DataFrame({
'time': pd.to_datetime(['2016-07-15 13:30:00.030',
'2016-07-15 13:30:00.030']),
'username': ['bob', 'charlie'],
'version': [np.nan, np.nan]})
assert_frame_equal(result, expected)
def test_allow_exact_matches_and_tolerance_forward(self):
# GH14887
left = pd.DataFrame({'a': [1, 5, 10],
'left_val': ['a', 'b', 'c']})
right = pd.DataFrame({'a': [1, 3, 4, 6, 11],
'right_val': [1, 3, 4, 6, 11]})
expected = pd.DataFrame({'a': [1, 5, 10],
'left_val': ['a', 'b', 'c'],
'right_val': [np.nan, 6, 11]})
result = pd.merge_asof(left, right, on='a', direction='forward',
allow_exact_matches=False, tolerance=1)
assert_frame_equal(result, expected)
def test_allow_exact_matches_and_tolerance_nearest(self):
# GH14887
left = pd.DataFrame({'a': [1, 5, 10],
'left_val': ['a', 'b', 'c']})
right = pd.DataFrame({'a': [1, 3, 4, 6, 11],
'right_val': [1, 3, 4, 7, 11]})
expected = pd.DataFrame({'a': [1, 5, 10],
'left_val': ['a', 'b', 'c'],
'right_val': [np.nan, 4, 11]})
result = pd.merge_asof(left, right, on='a', direction='nearest',
allow_exact_matches=False, tolerance=1)
assert_frame_equal(result, expected)
def test_forward_by(self):
# GH14887
left = pd.DataFrame({'a': [1, 5, 10, 12, 15],
'b': ['X', 'X', 'Y', 'Z', 'Y'],
'left_val': ['a', 'b', 'c', 'd', 'e']})
right = pd.DataFrame({'a': [1, 6, 11, 15, 16],
'b': ['X', 'Z', 'Y', 'Z', 'Y'],
'right_val': [1, 6, 11, 15, 16]})
expected = pd.DataFrame({'a': [1, 5, 10, 12, 15],
'b': ['X', 'X', 'Y', 'Z', 'Y'],
'left_val': ['a', 'b', 'c', 'd', 'e'],
'right_val': [1, np.nan, 11, 15, 16]})
result = pd.merge_asof(left, right, on='a', by='b',
direction='forward')
assert_frame_equal(result, expected)
def test_nearest_by(self):
# GH14887
left = pd.DataFrame({'a': [1, 5, 10, 12, 15],
'b': ['X', 'X', 'Z', 'Z', 'Y'],
'left_val': ['a', 'b', 'c', 'd', 'e']})
right = pd.DataFrame({'a': [1, 6, 11, 15, 16],
'b': ['X', 'Z', 'Z', 'Z', 'Y'],
'right_val': [1, 6, 11, 15, 16]})
expected = pd.DataFrame({'a': [1, 5, 10, 12, 15],
'b': ['X', 'X', 'Z', 'Z', 'Y'],
'left_val': ['a', 'b', 'c', 'd', 'e'],
'right_val': [1, 1, 11, 11, 16]})
result = pd.merge_asof(left, right, on='a', by='b',
direction='nearest')
assert_frame_equal(result, expected)
def test_by_int(self):
# we specialize by type, so test that this is correct
df1 = pd.DataFrame({
'time': pd.to_datetime(['20160525 13:30:00.020',
'20160525 13:30:00.030',
'20160525 13:30:00.040',
'20160525 13:30:00.050',
'20160525 13:30:00.060']),
'key': [1, 2, 1, 3, 2],
'value1': [1.1, 1.2, 1.3, 1.4, 1.5]},
columns=['time', 'key', 'value1'])
df2 = pd.DataFrame({
'time': pd.to_datetime(['20160525 13:30:00.015',
'20160525 13:30:00.020',
'20160525 13:30:00.025',
'20160525 13:30:00.035',
'20160525 13:30:00.040',
'20160525 13:30:00.055',
'20160525 13:30:00.060',
'20160525 13:30:00.065']),
'key': [2, 1, 1, 3, 2, 1, 2, 3],
'value2': [2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8]},
columns=['time', 'key', 'value2'])
result = pd.merge_asof(df1, df2, on='time', by='key')
expected = pd.DataFrame({
'time': pd.to_datetime(['20160525 13:30:00.020',
'20160525 13:30:00.030',
'20160525 13:30:00.040',
'20160525 13:30:00.050',
'20160525 13:30:00.060']),
'key': [1, 2, 1, 3, 2],
'value1': [1.1, 1.2, 1.3, 1.4, 1.5],
'value2': [2.2, 2.1, 2.3, 2.4, 2.7]},
columns=['time', 'key', 'value1', 'value2'])
assert_frame_equal(result, expected)
def test_on_float(self):
# mimics how to determine the minimum-price variation
df1 = pd.DataFrame({
'price': [5.01, 0.0023, 25.13, 340.05, 30.78, 1040.90, 0.0078],
'symbol': list("ABCDEFG")},
columns=['symbol', 'price'])
df2 = pd.DataFrame({
'price': [0.0, 1.0, 100.0],
'mpv': [0.0001, 0.01, 0.05]},
columns=['price', 'mpv'])
df1 = df1.sort_values('price').reset_index(drop=True)
result = pd.merge_asof(df1, df2, on='price')
expected = pd.DataFrame({
'symbol': list("BGACEDF"),
'price': [0.0023, 0.0078, 5.01, 25.13, 30.78, 340.05, 1040.90],
'mpv': [0.0001, 0.0001, 0.01, 0.01, 0.01, 0.05, 0.05]},
columns=['symbol', 'price', 'mpv'])
assert_frame_equal(result, expected)
def test_on_specialized_type(self):
# GH13936
for dtype in [np.uint8, np.uint16, np.uint32, np.uint64,
np.int8, np.int16, np.int32, np.int64,
np.float16, np.float32, np.float64]:
df1 = pd.DataFrame({
'value': [5, 2, 25, 100, 78, 120, 79],
'symbol': list("ABCDEFG")},
columns=['symbol', 'value'])
df1.value = dtype(df1.value)
df2 = pd.DataFrame({
'value': [0, 80, 120, 125],
'result': list('xyzw')},
columns=['value', 'result'])
df2.value = dtype(df2.value)
df1 = df1.sort_values('value').reset_index(drop=True)
if dtype == np.float16:
with pytest.raises(MergeError):
pd.merge_asof(df1, df2, on='value')
continue
result = pd.merge_asof(df1, df2, on='value')
expected = pd.DataFrame(
{'symbol': list("BACEGDF"),
'value': [2, 5, 25, 78, 79, 100, 120],
'result': list('xxxxxyz')
}, columns=['symbol', 'value', 'result'])
expected.value = dtype(expected.value)
assert_frame_equal(result, expected)
def test_on_specialized_type_by_int(self):
# GH13936
for dtype in [np.uint8, np.uint16, np.uint32, np.uint64,
np.int8, np.int16, np.int32, np.int64,
np.float16, np.float32, np.float64]:
df1 = pd.DataFrame({
'value': [5, 2, 25, 100, 78, 120, 79],
'key': [1, 2, 3, 2, 3, 1, 2],
'symbol': list("ABCDEFG")},
columns=['symbol', 'key', 'value'])
df1.value = dtype(df1.value)
df2 = pd.DataFrame({
'value': [0, 80, 120, 125],
'key': [1, 2, 2, 3],
'result': list('xyzw')},
columns=['value', 'key', 'result'])
df2.value = dtype(df2.value)
df1 = df1.sort_values('value').reset_index(drop=True)
if dtype == np.float16:
with pytest.raises(MergeError):
pd.merge_asof(df1, df2, on='value', by='key')
else:
result = pd.merge_asof(df1, df2, on='value', by='key')
expected = pd.DataFrame({
'symbol': list("BACEGDF"),
'key': [2, 1, 3, 3, 2, 2, 1],
'value': [2, 5, 25, 78, 79, 100, 120],
'result': [np.nan, 'x', np.nan, np.nan, np.nan, 'y', 'x']},
columns=['symbol', 'key', 'value', 'result'])
expected.value = dtype(expected.value)
assert_frame_equal(result, expected)
def test_on_float_by_int(self):
# type specialize both "by" and "on" parameters
df1 = pd.DataFrame({
'symbol': list("AAABBBCCC"),
'exch': [1, 2, 3, 1, 2, 3, 1, 2, 3],
'price': [3.26, 3.2599, 3.2598, 12.58, 12.59,
12.5, 378.15, 378.2, 378.25]},
columns=['symbol', 'exch', 'price'])
df2 = pd.DataFrame({
'exch': [1, 1, 1, 2, 2, 2, 3, 3, 3],
'price': [0.0, 1.0, 100.0, 0.0, 5.0, 100.0, 0.0, 5.0, 1000.0],
'mpv': [0.0001, 0.01, 0.05, 0.0001, 0.01, 0.1, 0.0001, 0.25, 1.0]},
columns=['exch', 'price', 'mpv'])
df1 = df1.sort_values('price').reset_index(drop=True)
df2 = df2.sort_values('price').reset_index(drop=True)
result = pd.merge_asof(df1, df2, on='price', by='exch')
expected = pd.DataFrame({
'symbol': list("AAABBBCCC"),
'exch': [3, 2, 1, 3, 1, 2, 1, 2, 3],
'price': [3.2598, 3.2599, 3.26, 12.5, 12.58,
12.59, 378.15, 378.2, 378.25],
'mpv': [0.0001, 0.0001, 0.01, 0.25, 0.01, 0.01, 0.05, 0.1, 0.25]},
columns=['symbol', 'exch', 'price', 'mpv'])
assert_frame_equal(result, expected)