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)
result = merge_asof(self.trades, q,
on='time',
by='ticker',
check_duplicates=False)
expected = self.read_data('asof.csv')
expected = pd.concat([expected, expected]).sort_values(
['time', 'ticker']).reset_index(drop=True)
# the results are not ordered in a meaningful way
# nor are the exact matches duplicated, so comparisons
# are pretty tricky here, however the uniques are the same
def aligner(x, ticker):
return (x[x.ticker == ticker]
.sort_values(['time', 'ticker', 'quantity', 'price',
'marketCenter', 'bid', 'ask'])
.drop_duplicates(keep='last')
.reset_index(drop=True)
)
for ticker in expected.ticker.unique():
r = aligner(result, ticker)
e = aligner(expected, ticker)
assert_frame_equal(r, e)
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 self.assertRaises(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_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_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 self.assertRaises(MergeError):
pd.merge_asof(left, right, left_index=True, right_index=True,
left_by=['k1', 'k2'], right_by=['k1'])
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]})
pd.merge_asof(left, right, on='a')
def test_valid_allow_exact_matches(self):
trades = self.trades
quotes = self.quotes
with self.assertRaises(MergeError):
merge_asof(trades, quotes,
on='time',
by='ticker',
allow_exact_matches='foo')
def test_merge_datatype_error(self):
""" Tests merge datatype mismatch error """
msg = r'merge keys \[0\] object and int64, must be the same type'
left = pd.DataFrame({'left_val': [1, 5, 10],
'a': ['a', 'b', 'c']})
right = pd.DataFrame({'right_val': [1, 2, 3, 6, 7],
'a': [1, 2, 3, 6, 7]})
with pytest.raises(MergeError, match=msg):
merge_asof(left, right, on='a')
def test_merge_on_nans(self, func, side):
# GH 23189
msg = "Merge keys contain null values on {} side".format(side)
nulls = func([1.0, 5.0, np.nan])
non_nulls = func([1.0, 5.0, 10.])
df_null = pd.DataFrame({'a': nulls, 'left_val': ['a', 'b', 'c']})
df = pd.DataFrame({'a': non_nulls, 'right_val': [1, 6, 11]})
with pytest.raises(ValueError, match=msg):
if side == 'left':
merge_asof(df_null, df, on='a')
else:
merge_asof(df, df_null, on='a')
def test_asof_join_predicate(time_left, time_right, time_df1, time_df2):
expr = time_left.asof_join(time_right, time_left.time == time_right.time)[
time_left, time_right.other_value
]
result = expr.execute()
expected = pd.merge_asof(time_df1, time_df2, on='time')
tm.assert_frame_equal(result[expected.columns], expected)
def test_on_specialized_type_by_int(self, any_real_dtype):
# see gh-13936
dtype = np.dtype(any_real_dtype).type
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)
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)
def test_multi_index(self):
# MultiIndex is prohibited
trades = self.trades.set_index(['time', 'price'])
quotes = self.quotes.set_index('time')
with self.assertRaises(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 self.assertRaises(MergeError):
merge_asof(trades, quotes,
left_index=True,
right_index=True)
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_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_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_on_and_index(self):
# 'on' parameter and index together is prohibited
trades = self.trades.set_index('time')
quotes = self.quotes.set_index('time')
with self.assertRaises(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 self.assertRaises(MergeError):
merge_asof(trades, quotes,
right_on='bid',
left_index=True,
right_index=True)
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_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_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_keyed_asof_join(
time_keyed_left, time_keyed_right, time_keyed_df1, time_keyed_df2
):
expr = time_keyed_left.asof_join(time_keyed_right, 'time', by='key')[
time_keyed_left, time_keyed_right.other_value
]
result = expr.execute()
expected = pd.merge_asof(
time_keyed_df1, time_keyed_df2, on='time', by='key'
)
tm.assert_frame_equal(result[expected.columns], expected)
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_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_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_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')
result = merge_asof(trades, quotes,
on='time',
by='ticker')
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_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_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_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_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_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
self.assertFalse(trades.time.is_monotonic)
self.assertFalse(quotes.time.is_monotonic)
with self.assertRaises(ValueError):
merge_asof(trades, quotes,
on='time',
by='ticker')
trades = self.trades.sort_values('time')
self.assertTrue(trades.time.is_monotonic)
self.assertFalse(quotes.time.is_monotonic)
with self.assertRaises(ValueError):
merge_asof(trades, quotes,
on='time',
by='ticker')
quotes = self.quotes.sort_values('time')
self.assertTrue(trades.time.is_monotonic)
self.assertTrue(quotes.time.is_monotonic)
# ok, though has dupes
merge_asof(trades, self.quotes,
on='time',
by='ticker')
def time_on_uint64(self, direction, tolerance):
merge_asof(self.df1f,
self.df2f,
on="timeu64",
direction=direction,
tolerance=tolerance)
def parse_gpsapilog_to_df_v2(filename):
"""Parse GPS API log to Pandas dataframes, by using merge_asof.
Args:
filename: full log file name.
Type, String.
Returns:
timestamp_df: Timestamp Data Frame.
Type, Pandas DataFrame.
sv_info_df: GNSS SV info Data Frame.
Type, Pandas DataFrame.
sv_stat_df: GNSS SV statistic Data Frame.
Type, Pandas DataFrame
loc_info_df: Location Information Data Frame.
Type, Pandas DataFrame.
include Provider, Latitude, Longitude, Altitude, GNSSTime, Speed, Bearing
"""
# Get parsed dataframe list
parsed_data = parse_log_to_df(
filename=filename,
configs=CONFIG_GPSAPILOG,
)
# get DUT Timestamp
timestamp_df = parsed_data['phone_time']
timestamp_df['phone_time'] = timestamp_df.apply(
lambda row: datetime.datetime.strptime(row.date + '-' + row.time,
'%Y/%m/%d-%H:%M:%S'),
axis=1)
# drop logsize, date, time
parsed_data['phone_time'] = timestamp_df.drop(['logsize', 'date', 'time'],
axis=1)
# Add phone_time from timestamp dataframe by row number
for key in parsed_data:
if (key != 'phone_time') and (not parsed_data[key].empty):
parsed_data[key] = pds.merge_asof(parsed_data[key],
parsed_data['phone_time'],
left_index=True,
right_index=True)
# Get space vehicle info dataframe
# Handle the pre GPSTool 2.12.24 case
if not parsed_data['SpaceVehicle'].empty:
sv_info_df = parsed_data['SpaceVehicle']
# Handle the post GPSTool 2.12.24 case with baseband CNo
elif not parsed_data['SpaceVehicle_wBB'].empty:
sv_info_df = parsed_data['SpaceVehicle_wBB']
# Get space vehicle statistics dataframe
# Handle the pre GPSTool 2.12.24 case
if not parsed_data['HistoryAvgTop4CNo'].empty:
# First merge all dataframe from LIST_SVSTAT[1:],
sv_stat_df = fts.reduce(
lambda item1, item2: pds.merge(item1, item2, on='phone_time'),
[parsed_data[key] for key in LIST_SVSTAT[1:]])
# Then merge with LIST_SVSTAT[0]
sv_stat_df = pds.merge(sv_stat_df,
parsed_data[LIST_SVSTAT[0]],
on='phone_time')
# Handle the post GPSTool 2.12.24 case with baseband CNo
elif not parsed_data['AntennaHistoryAvgTop4CNo'].empty:
# First merge all dataframe from LIST_SVSTAT[1:],
sv_stat_df = fts.reduce(
lambda item1, item2: pds.merge(item1, item2, on='phone_time'),
[parsed_data[key] for key in LIST_SVSTAT_WBB[1:]])
# Then merge with LIST_SVSTAT[0]
sv_stat_df = pds.merge(sv_stat_df,
parsed_data[LIST_SVSTAT_WBB[0]],
on='phone_time')
# Get location fix information dataframe
# First merge all dataframe from LIST_LOCINFO[1:],
loc_info_df = fts.reduce(
lambda item1, item2: pds.merge(item1, item2, on='phone_time'),
[parsed_data[key] for key in LIST_LOCINFO[1:]])
# Then merge with LIST_LOCINFO[8]
loc_info_df = pds.merge(loc_info_df,
parsed_data[LIST_LOCINFO[0]],
on='phone_time')
# Convert GNSS Time
loc_info_df['gnsstime'] = loc_info_df.apply(
lambda row: datetime.datetime.strptime(row.Date + '-' + row.Time,
'%Y/%m/%d-%H:%M:%S'),
axis=1)
# Data Conversion
timestamp_df['logsize'] = timestamp_df['logsize'].astype(int)
sv_info_df['SV'] = sv_info_df['SV'].astype(int)
sv_info_df['Elevation'] = sv_info_df['Elevation'].astype(float)
sv_info_df['Azimuth'] = sv_info_df['Azimuth'].astype(float)
sv_info_df['Frequency'] = sv_info_df['Frequency'].astype(float)
if 'CNo' in list(sv_info_df.columns):
sv_info_df['CNo'] = sv_info_df['CNo'].astype(float)
sv_info_df['AntCNo'] = sv_info_df['CNo']
elif 'AntCNo' in list(sv_info_df.columns):
sv_info_df['AntCNo'] = sv_info_df['AntCNo'].astype(float)
sv_info_df['BbCNo'] = sv_info_df['BbCNo'].astype(float)
if 'CurrentAvgTop4CNo' in list(sv_stat_df.columns):
sv_stat_df['CurrentAvgTop4CNo'] = sv_stat_df[
'CurrentAvgTop4CNo'].astype(float)
sv_stat_df['CurrentAvgCNo'] = sv_stat_df['CurrentAvgCNo'].astype(float)
sv_stat_df['HistoryAvgTop4CNo'] = sv_stat_df[
'HistoryAvgTop4CNo'].astype(float)
sv_stat_df['HistoryAvgCNo'] = sv_stat_df['HistoryAvgCNo'].astype(float)
sv_stat_df['AntennaCurrentAvgTop4CNo'] = sv_stat_df[
'CurrentAvgTop4CNo']
sv_stat_df['AntennaCurrentAvgCNo'] = sv_stat_df['CurrentAvgCNo']
sv_stat_df['AntennaHistoryAvgTop4CNo'] = sv_stat_df[
'HistoryAvgTop4CNo']
sv_stat_df['AntennaHistoryAvgCNo'] = sv_stat_df['HistoryAvgCNo']
sv_stat_df['BasebandCurrentAvgTop4CNo'] = npy.nan
sv_stat_df['BasebandCurrentAvgCNo'] = npy.nan
sv_stat_df['BasebandHistoryAvgTop4CNo'] = npy.nan
sv_stat_df['BasebandHistoryAvgCNo'] = npy.nan
elif 'AntennaCurrentAvgTop4CNo' in list(sv_stat_df.columns):
sv_stat_df['AntennaCurrentAvgTop4CNo'] = sv_stat_df[
'AntennaCurrentAvgTop4CNo'].astype(float)
sv_stat_df['AntennaCurrentAvgCNo'] = sv_stat_df[
'AntennaCurrentAvgCNo'].astype(float)
sv_stat_df['AntennaHistoryAvgTop4CNo'] = sv_stat_df[
'AntennaHistoryAvgTop4CNo'].astype(float)
sv_stat_df['AntennaHistoryAvgCNo'] = sv_stat_df[
'AntennaHistoryAvgCNo'].astype(float)
sv_stat_df['BasebandCurrentAvgTop4CNo'] = sv_stat_df[
'BasebandCurrentAvgTop4CNo'].astype(float)
sv_stat_df['BasebandCurrentAvgCNo'] = sv_stat_df[
'BasebandCurrentAvgCNo'].astype(float)
sv_stat_df['BasebandHistoryAvgTop4CNo'] = sv_stat_df[
'BasebandHistoryAvgTop4CNo'].astype(float)
sv_stat_df['BasebandHistoryAvgCNo'] = sv_stat_df[
'BasebandHistoryAvgCNo'].astype(float)
sv_stat_df['L5EngagingRate'] = sv_stat_df['L5EngagingRate'].astype(float)
loc_info_df['Latitude'] = loc_info_df['Latitude'].astype(float)
loc_info_df['Longitude'] = loc_info_df['Longitude'].astype(float)
loc_info_df['Altitude'] = loc_info_df['Altitude'].astype(float)
loc_info_df['Speed'] = loc_info_df['Speed'].astype(float)
loc_info_df['Bearing'] = loc_info_df['Bearing'].astype(float)
return timestamp_df, sv_info_df, sv_stat_df, loc_info_df
def calc_estimates():
#calculate signals from earnings estimate date (IBES), including earnings surprises, fwd P/E, change in analyst coverage, fwd EPS growth
ibes = get_ibes_summary()
#keep estimates for all fwd financial periods which will be used later to calculate fwd P/E ratios
all_fpi = ibes.loc[ibes.fpi.isin(
['0', '1', '2', '3', '4', '6', '7', '8', '9'])]
#lag by a month to align with t+1 return
all_fpi['date'] = pd.to_datetime(
all_fpi['statpers']) + DateOffset(months=1)
#reshape to create columns for each fwd financial period, indexed by company/date
all_fpi = all_fpi[['ticker', 'date', 'fpi',
'meanest']].pivot_table(index=['ticker', 'date'],
columns=['fpi'],
values='meanest',
aggfunc=np.sum)
all_fpi = all_fpi.sort_values(by=['date', 'ticker'])
#rename columns
replace_dict = dict(
zip([str(i) for i in range(5)] + [str(i) for i in range(6, 10)],
['ltg'] + ['ann' + str(i) for i in range(1, 5)] +
['qtr' + str(i) for i in range(1, 5)]))
all_fpi.columns = [replace_dict[x] for x in all_fpi.columns]
#subset ibes data to 1 qtr fwd EPS estimate and select the most recent estimate before the earnings date
ibes = ibes.loc[(ibes.measure == 'EPS') & (ibes.fpi == '6')]
ibes['fpedats'] = pd.to_datetime(ibes.fpedats)
ibes = ibes.sort_values(by=['statpers', 'ticker'])
ibes = ibes.groupby(['fpedats', 'ticker'])[[
'statpers', 'meanest', 'medest', 'stdev', 'numest'
]].last().reset_index()
#merge on the actual earnings release figures
actuals = get_ibes_actual()
actuals = actuals.rename(columns={'value': 'actual'})
actuals = actuals.loc[(actuals.measure == 'EPS')
& (actuals.pdicity == 'QTR')]
actuals['fpedats'] = pd.to_datetime(actuals.pends)
ibes = ibes.merge(actuals[['fpedats', 'ticker', 'actual', 'anndats']],
how='left',
on=['fpedats', 'ticker'])
#set the date index to the earnings releast date and lag by 2 business days + 1 month to align with t+1 return
ibes['anndats'] = pd.to_datetime(ibes['anndats'])
ibes['date'] = ibes['anndats'] + BDay(2) + MonthBegin(1)
ibes = ibes.loc[pd.notnull(ibes.anndats)]
ibes = ibes.sort_values(by=['date', 'ticker']).drop_duplicates(
subset=['ticker', 'date'])
#calculate standardized unexpected earnings (SUE) and earnings surprise z-score
ibes['sue'] = (ibes['actual'] - ibes['meanest']) / ibes['stdev']
ibes['sue'] = ibes['sue'].where(ibes.stdev != 0, np.nan)
ibes['surprise'] = ibes['actual'] - ibes['meanest']
ibes['surprise_z'] = ibes.groupby('ticker')['surprise'].rolling(
8, min_periods=6).apply(lambda x: (x[-1] - x.mean()) / x.std(),
raw=True).reset_index(0, drop=True)
#calculate change in analyst coverage
ibes['numest_lag'] = ibes.groupby('ticker')['numest'].shift(1)
ibes['chanalyst'] = ibes['numest'] - ibes['numest_lag']
ibes['pchanalyst'] = ibes['numest'] / ibes['numest_lag'] - 1
#characteristics to output
cols = ['sue', 'surprise_z', 'chanalyst', 'pchanalyst']
#get crsp file, merge with IBES linking table, and merge characteristics
crsp = get_crsp_m()
crsp = crsp.sort_values(by=['date', 'permno'])
link = get_crspibes_link()
crsp = crsp.merge(link[['permno', 'ticker', 'sdate', 'edate']],
how='left',
on='permno')
crsp['date'] = pd.to_datetime(crsp.date)
crsp['ticker'] = crsp.ticker.where(
~((crsp.date < crsp.sdate) | (crsp.date > crsp.edate)), np.nan)
crsp['date'] = pd.to_datetime(crsp.date) - MonthBegin(1)
crsp = crsp.dropna(subset=['ticker']).drop_duplicates(
subset=['permno', 'date'])
crsp = crsp.merge(ibes[['ticker', 'date'] + cols],
how='left',
on=['ticker', 'date'])
#merge all fwd earning period estimates onto crsp data
crsp = pd.merge_asof(crsp, all_fpi, on='date', by=['ticker'])
#fwd earnings yield at various horizons
crsp['prc_lag'] = crsp.groupby('permno')['prc'].shift(1)
crsp['prc_lag'] = crsp.prc_lag.abs()
crsp['pe0'] = crsp['ann1'] / crsp['prc_lag']
crsp['pe1'] = crsp['ann2'] / crsp['prc_lag']
crsp['pe2'] = crsp['ann3'] / crsp['prc_lag']
crsp['pe3'] = crsp['ann4'] / crsp['prc_lag']
crsp['pe4'] = crsp['qtr1'] / crsp['prc_lag']
crsp['pe5'] = crsp[['qtr1', 'qtr2', 'qtr3', 'qtr4'
]].sum(axis=1) / crsp['prc_lag']
#add to list of characteristics to output
cols += ['pe' + str(x) for x in range(6)] + ['ltg']
output_characteristics(crsp, cols)
with open(data_output_path + 'characteristics_output_log.txt', 'a') as f:
f.write('estimates\n')
