def test_merge():
a = pd.TimeSeries(index=pd.date_range('2010-01-01', periods=5),
data=100, name='a')
b = pd.TimeSeries(index=pd.date_range('2010-01-02', periods=5),
data=200, name='b')
actual = ffn.merge(a, b)
assert 'a' in actual
assert 'b' in actual
assert len(actual) == 6
assert len(actual.columns) == 2
assert np.isnan(actual['a'][-1])
assert np.isnan(actual['b'][0])
assert actual['a'][0] == 100
assert actual['a'][1] == 100
assert actual['b'][-1] == 200
assert actual['b'][1] == 200
old = actual
old.columns = ['c', 'd']
actual = ffn.merge(old, a, b)
assert 'a' in actual
assert 'b' in actual
assert 'c' in actual
assert 'd' in actual
assert len(actual) == 6
assert len(actual.columns) == 4
assert np.isnan(actual['a'][-1])
assert np.isnan(actual['b'][0])
assert actual['a'][0] == 100
assert actual['a'][1] == 100
assert actual['b'][-1] == 200
assert actual['b'][1] == 200
def test_merge():
a = pd.Series(index=pd.date_range("2010-01-01", periods=5), data=100, name="a")
b = pd.Series(index=pd.date_range("2010-01-02", periods=5), data=200, name="b")
actual = ffn.merge(a, b)
assert "a" in actual
assert "b" in actual
assert len(actual) == 6
assert len(actual.columns) == 2
assert np.isnan(actual["a"][-1])
assert np.isnan(actual["b"][0])
assert actual["a"][0] == 100
assert actual["a"][1] == 100
assert actual["b"][-1] == 200
assert actual["b"][1] == 200
old = actual
old.columns = ["c", "d"]
actual = ffn.merge(old, a, b)
assert "a" in actual
assert "b" in actual
assert "c" in actual
assert "d" in actual
assert len(actual) == 6
assert len(actual.columns) == 4
assert np.isnan(actual["a"][-1])
assert np.isnan(actual["b"][0])
assert actual["a"][0] == 100
assert actual["a"][1] == 100
assert actual["b"][-1] == 200
assert actual["b"][1] == 200
def test_merge():
a = pd.Series(index=pd.date_range('2010-01-01', periods=5),
data=100, name='a')
b = pd.Series(index=pd.date_range('2010-01-02', periods=5),
data=200, name='b')
actual = ffn.merge(a, b)
assert 'a' in actual
assert 'b' in actual
assert len(actual) == 6
assert len(actual.columns) == 2
assert np.isnan(actual['a'][-1])
assert np.isnan(actual['b'][0])
assert actual['a'][0] == 100
assert actual['a'][1] == 100
assert actual['b'][-1] == 200
assert actual['b'][1] == 200
old = actual
old.columns = ['c', 'd']
actual = ffn.merge(old, a, b)
assert 'a' in actual
assert 'b' in actual
assert 'c' in actual
assert 'd' in actual
assert len(actual) == 6
assert len(actual.columns) == 4
assert np.isnan(actual['a'][-1])
assert np.isnan(actual['b'][0])
assert actual['a'][0] == 100
assert actual['a'][1] == 100
assert actual['b'][-1] == 200
assert actual['b'][1] == 200
def test_merge():
a = pd.Series(index=pd.date_range("2010-01-01", periods=5),
data=100,
name="a")
b = pd.Series(index=pd.date_range("2010-01-02", periods=5),
data=200,
name="b")
actual = ffn.merge(a, b)
assert "a" in actual
assert "b" in actual
assert len(actual) == 6
assert len(actual.columns) == 2
assert np.isnan(actual["a"][-1])
assert np.isnan(actual["b"][0])
assert actual["a"][0] == 100
assert actual["a"][1] == 100
assert actual["b"][-1] == 200
assert actual["b"][1] == 200
old = actual
old.columns = ["c", "d"]
actual = ffn.merge(old, a, b)
assert "a" in actual
assert "b" in actual
assert "c" in actual
assert "d" in actual
assert len(actual) == 6
assert len(actual.columns) == 4
assert np.isnan(actual["a"][-1])
assert np.isnan(actual["b"][0])
assert actual["a"][0] == 100
assert actual["a"][1] == 100
assert actual["b"][-1] == 200
assert actual["b"][1] == 200
def test_drop_duplicate_cols():
a = pd.Series(index=pd.date_range("2010-01-01", periods=5), data=100, name="a")
# second version of a w/ less data
a2 = pd.Series(index=pd.date_range("2010-01-02", periods=4), data=900, name="a")
b = pd.Series(index=pd.date_range("2010-01-02", periods=5), data=200, name="b")
actual = ffn.merge(a, a2, b)
assert actual["a"].shape[1] == 2
assert len(actual.columns) == 3
actual = actual.drop_duplicate_cols()
assert len(actual.columns) == 2
assert "a" in actual
assert "b" in actual
assert len(actual["a"].dropna()) == 5
def test_drop_duplicate_cols():
a = pd.TimeSeries(index=pd.date_range('2010-01-01', periods=5),
data=100, name='a')
# second version of a w/ less data
a2 = pd.TimeSeries(index=pd.date_range('2010-01-02', periods=4),
data=900, name='a')
b = pd.TimeSeries(index=pd.date_range('2010-01-02', periods=5),
data=200, name='b')
actual = ffn.merge(a, a2, b)
assert actual['a'].shape[1] == 2
assert len(actual.columns) == 3
actual = actual.drop_duplicate_cols()
assert len(actual.columns) == 2
assert 'a' in actual
assert 'b' in actual
assert len(actual['a'].dropna()) == 5
def test_drop_duplicate_cols():
a = pd.Series(index=pd.date_range('2010-01-01', periods=5),
data=100, name='a')
# second version of a w/ less data
a2 = pd.Series(index=pd.date_range('2010-01-02', periods=4),
data=900, name='a')
b = pd.Series(index=pd.date_range('2010-01-02', periods=5),
data=200, name='b')
actual = ffn.merge(a, a2, b)
assert actual['a'].shape[1] == 2
assert len(actual.columns) == 3
actual = actual.drop_duplicate_cols()
assert len(actual.columns) == 2
assert 'a' in actual
assert 'b' in actual
assert len(actual['a'].dropna()) == 5
def test_drop_duplicate_cols():
a = pd.Series(index=pd.date_range("2010-01-01", periods=5),
data=100,
name="a")
# second version of a w/ less data
a2 = pd.Series(index=pd.date_range("2010-01-02", periods=4),
data=900,
name="a")
b = pd.Series(index=pd.date_range("2010-01-02", periods=5),
data=200,
name="b")
actual = ffn.merge(a, a2, b)
assert actual["a"].shape[1] == 2
assert len(actual.columns) == 3
actual = actual.drop_duplicate_cols()
assert len(actual.columns) == 2
assert "a" in actual
assert "b" in actual
assert len(actual["a"].dropna()) == 5
def get(tickers, provider=None, common_dates=True, forward_fill=False,
clean_tickers=True, column_names=None, ticker_field_sep=':',
mrefresh=False, existing=None, **kwargs):
"""
Helper function for retrieving data as a DataFrame.
Args:
* tickers (list, string, csv string): Tickers to download.
* provider (function): Provider to use for downloading data.
By default it will be ffn.DEFAULT_PROVIDER if not provided.
* common_dates (bool): Keep common dates only? Drop na's.
* forward_fill (bool): forward fill values if missing. Only works
if common_dates is False, since common_dates will remove
all nan's, so no filling forward necessary.
* clean_tickers (bool): Should the tickers be 'cleaned' using
ffn.utils.clean_tickers? Basically remove non-standard
characters (^VIX -> vix) and standardize to lower case.
* column_names (list): List of column names if clean_tickers
is not satisfactory.
* ticker_field_sep (char): separator used to determine the
ticker and field. This is in case we want to specify
particular, non-default fields. For example, we might
want: AAPL:Low,AAPL:High,AAPL:Close. ':' is the separator.
* mrefresh (bool): Ignore memoization.
* existing (DataFrame): Existing DataFrame to append returns
to - used when we download from multiple sources
* kwargs: passed to provider
"""
if provider is None:
provider = DEFAULT_PROVIDER
tickers = utils.parse_arg(tickers)
data = {}
for ticker in tickers:
t = ticker
f = None
# check for field
bits = ticker.split(ticker_field_sep, 1)
if len(bits) == 2:
t = bits[0]
f = bits[1]
# call provider - check if supports memoization
if hasattr(provider, 'mcache'):
data[ticker] = provider(ticker=t, field=f,
mrefresh=mrefresh, **kwargs)
else:
data[ticker] = provider(ticker=t, field=f, **kwargs)
df = pd.DataFrame(data)
# ensure same order as provided
df = df[tickers]
if existing is not None:
df = ffn.merge(existing, df)
if common_dates:
df = df.dropna()
if forward_fill:
df = df.fillna(method='ffill')
if column_names:
cnames = utils.parse_arg(column_names)
if len(cnames) != len(df.columns):
raise ValueError(
'column_names must be of same length as tickers')
df.columns = cnames
elif clean_tickers:
df.columns = map(utils.clean_ticker, df.columns)
return df
def get(tickers, provider=None, common_dates=True, forward_fill=False,
clean_tickers=True, column_names=None, ticker_field_sep=':',
mrefresh=False, existing=None, **kwargs):
"""
Helper function for retrieving data as a DataFrame.
Args:
* tickers (list, string, csv string): Tickers to download.
* provider (function): Provider to use for downloading data.
By default it will be ffn.DEFAULT_PROVIDER if not provided.
* common_dates (bool): Keep common dates only? Drop na's.
* forward_fill (bool): forward fill values if missing. Only works
if common_dates is False, since common_dates will remove
all nan's, so no filling forward necessary.
* clean_tickers (bool): Should the tickers be 'cleaned' using
ffn.utils.clean_tickers? Basically remove non-standard
characters (^VIX -> vix) and standardize to lower case.
* column_names (list): List of column names if clean_tickers
is not satisfactory.
* ticker_field_sep (char): separator used to determine the
ticker and field. This is in case we want to specify
particular, non-default fields. For example, we might
want: AAPL:Low,AAPL:High,AAPL:Close. ':' is the separator.
* mrefresh (bool): Ignore memoization.
* existing (DataFrame): Existing DataFrame to append returns
to - used when we download from multiple sources
* kwargs: passed to provider
"""
if provider is None:
provider = DEFAULT_PROVIDER
tickers = utils.parse_arg(tickers)
data = {}
for ticker in tickers:
t = ticker
f = None
# check for field
bits = ticker.split(ticker_field_sep, 1)
if len(bits) == 2:
t = bits[0]
f = bits[1]
# call provider - check if supports memoization
if hasattr(provider, 'mcache'):
data[ticker] = provider(ticker=t, field=f,
mrefresh=mrefresh, **kwargs)
else:
data[ticker] = provider(ticker=t, field=f, **kwargs)
df = pd.DataFrame(data)
# ensure same order as provided
df = df[tickers]
if existing is not None:
df = ffn.merge(existing, df)
if common_dates:
df = df.dropna()
if forward_fill:
df = df.fillna(method='ffill')
if column_names:
cnames = utils.parse_arg(column_names)
if len(cnames) != len(df.columns):
raise ValueError(
'column_names must be of same length as tickers')
df.columns = cnames
elif clean_tickers:
df.columns = map(utils.clean_ticker, df.columns)
return df
