code string | signature string | docstring string | loss_without_docstring float64 | loss_with_docstring float64 | factor float64 |
|---|---|---|---|---|---|
stdev = self.semi_stdev(threshold=threshold, ddof=ddof, freq=freq)
return (self.anlzd_ret() - threshold) / stdev | def sortino_ratio(self, threshold=0.0, ddof=0, freq=None) | Return over a threshold per unit of downside deviation.
A performance appraisal ratio that replaces standard deviation
in the Sharpe ratio with downside deviation.
[Source: CFA Institute]
Parameters
----------
threshold : {float, TSeries, pd.Series}, default 0.
... | 7.122885 | 8.918459 | 0.798668 |
er = self.excess_ret(benchmark=benchmark)
return er.anlzd_stdev(ddof=ddof) | def tracking_error(self, benchmark, ddof=0) | Standard deviation of excess returns.
The standard deviation of the differences between
a portfolio's returns and its benchmark's returns.
[Source: CFA Institute]
Also known as: tracking risk; active risk
Parameters
----------
benchmark : {pd.Series, TSeries, 1... | 14.375051 | 18.369011 | 0.782571 |
benchmark = _try_to_squeeze(benchmark)
if benchmark.ndim > 1:
raise ValueError("Treynor ratio requires a single benchmark")
rf = self._validate_rf(rf)
beta = self.beta(benchmark)
return (self.anlzd_ret() - rf) / beta | def treynor_ratio(self, benchmark, rf=0.02) | Return over `rf` per unit of systematic risk.
A measure of risk-adjusted performance that relates a
portfolio's excess returns to the portfolio's beta.
[Source: CFA Institute]
Parameters
----------
benchmark : {pd.Series, TSeries, 1d np.ndarray}
The benchmar... | 6.595362 | 6.791247 | 0.971156 |
slf, bm = self.upmarket_filter(
benchmark=benchmark,
threshold=threshold,
compare_op=compare_op,
include_benchmark=True,
)
return slf.geomean() / bm.geomean() | def up_capture(self, benchmark, threshold=0.0, compare_op="ge") | Upside capture ratio.
Measures the performance of `self` relative to benchmark
conditioned on periods where `benchmark` is gt or ge to
`threshold`.
Upside capture ratios are calculated by taking the fund's
monthly return during the periods of positive benchmark
performa... | 7.392924 | 6.541338 | 1.130185 |
return self._mkt_filter(
benchmark=benchmark,
threshold=threshold,
compare_op=compare_op,
include_benchmark=include_benchmark,
) | def upmarket_filter(
self,
benchmark,
threshold=0.0,
compare_op="ge",
include_benchmark=False,
) | Drop elementwise samples where `benchmark` < `threshold`.
Filters `self` (and optionally, `benchmark`) to periods
where `benchmark` > `threshold`. (Or >= `threshold`.)
Parameters
----------
benchmark : {pd.Series, TSeries, 1d np.ndarray}
The benchmark security to w... | 2.429896 | 3.237752 | 0.750489 |
return ols.OLS(
y=self, x=benchmark, has_const=has_const, use_const=use_const
) | def CAPM(self, benchmark, has_const=False, use_const=True) | Interface to OLS regression against `benchmark`.
`self.alpha()`, `self.beta()` and several other methods
stem from here. For the full method set, see
`pyfinance.ols.OLS`.
Parameters
----------
benchmark : {pd.Series, TSeries, pd.DataFrame, np.ndarray}
The b... | 4.437985 | 4.962368 | 0.894328 |
def load_13f(url):
resp = requests.get(url).text
data = xmltodict.parse(resp)["informationTable"]["infoTable"]
df = pd.DataFrame(data).drop(
["shrsOrPrnAmt", "investmentDiscretion"], axis=1
)
df["votingAuthority"] = df["votingAuthority"].apply(lambda d: d["Sole"])
df.loc[:... | Load and parse an SEC.gov-hosted 13F XML file to Pandas DataFrame.
Provide the URL to the raw .xml form13fInfoTable. (See example below.)
Parameters
----------
url : str
Link to .xml file.
Returns
-------
df : pd.DataFrame
Holdings snapshot.
Example
... | null | null | null | |
def load_industries():
n = [5, 10, 12, 17, 30, 38, 48]
port = ("%s_Industry_Portfolios" % i for i in n)
rets = []
for p in port:
ret = pdr.get_data_famafrench(p, start=DSTART)[0]
rets.append(ret.to_timestamp(how="end", copy=False))
industries = dict(zip(n, rets))
... | Load industry portfolio returns from Ken French's website.
Returns
-------
industries : dictionary of Pandas DataFrames
Each key is a portfolio group.
Example
-------
>>> from pyfinance import datasets
>>> ind = datasets.load_industries()
# Monthly returns to 5 ind... | null | null | null | |
def load_rates(freq="D"):
months = [1, 3, 6]
years = [1, 2, 3, 5, 7, 10, 20, 30]
# Nested dictionaries of symbols from fred.stlouisfed.org
nom = {
"D": ["DGS%sMO" % m for m in months] + ["DGS%s" % y for y in years],
"W": ["WGS%sMO" % m for m in months] + ["WGS%sYR" % y fo... | Load interest rates from https://fred.stlouisfed.org/.
Parameters
----------
reload : bool, default True
If True, download the data from source rather than loading pickled data
freq : str {'D', 'W', 'M'}, default 'D'
Frequency of time series; daily, weekly, or monthly
start ... | null | null | null | |
def load_shiller():
xls = "http://www.econ.yale.edu/~shiller/data/ie_data.xls"
cols = [
"date",
"sp50p",
"sp50d",
"sp50e",
"cpi",
"frac",
"real_rate",
"real_sp50p",
"real_sp50d",
"real_sp50e",
"cape",
... | Load market & macroeconomic data from Robert Shiller's website.
Returns
-------
iedata : pd.DataFrame
Time series of S&P 500 and interest rate variables.
Example
-------
>>> from pyfinance import datasets
>>> shiller = datasets.load_shiller()
>>> shiller.iloc[:7, :5]... | null | null | null | |
def appender(defaultdocs, passed_to=None):
def _doc(func):
params = inspect.signature(func).parameters
params = [param.name for param in params.values()]
msg = "\n**kwargs : passed to `%s`"
params = "".join(
[
textwrap.dedent(defaultdocs.get... | Decorator for appending commonly used parameter definitions.
Useful in cases where functions repeatedly use the same parameters. (But
where a class implementation is not appropriate.)
`defaultdocs` -> dict, format as shown in Example below, with keys being
parameters and values being descriptio... | null | null | null | |
def avail(df):
avail = DataFrame(
{
"start": df.apply(lambda col: col.first_valid_index()),
"end": df.apply(lambda col: col.last_valid_index()),
}
)
return avail[["start", "end"]] | Return start & end availability for each column in a DataFrame. | null | null | null | |
def constrain(*objs):
# TODO: build in the options to first dropna on each index before finding
# intersection, AND to use `dropcol` from this module. Note that this
# would require filtering out Series to which dropcol isn't applicable.
# A little bit of set magic below.
# N... | Constrain group of DataFrames & Series to intersection of indices.
Parameters
----------
objs : iterable
DataFrames and/or Series to constrain
Returns
-------
new_dfs : list of DataFrames, copied rather than inplace | null | null | null | |
def constrain_horizon(
r,
strict=False,
cust=None,
years=0,
quarters=0,
months=0,
days=0,
weeks=0,
year=None,
month=None,
day=None,
):
textnum = {
"zero": 0,
"one": 1,
"two": 2,
"three": 3,
"four": 4,
... | Constrain a Series/DataFrame to a specified lookback period.
See the documentation for dateutil.relativedelta:
dateutil.readthedocs.io/en/stable/relativedelta.html
Parameters
----------
r : DataFrame or Series
The target pandas object to constrain
strict : bool, default False
... | null | null | null | |
def cumargmax(a):
# Thank you @Alex Riley
# https://stackoverflow.com/a/40675969/7954504
m = np.asarray(np.maximum.accumulate(a))
if a.ndim == 1:
x = np.arange(a.shape[0])
else:
x = np.repeat(np.arange(a.shape[0])[:, None], a.shape[1], axis=1)
x[1:] *= m[:-1] ... | Cumulative argmax.
Parameters
----------
a : np.ndarray
Returns
-------
np.ndarray | null | null | null | |
def dropcols(df, start=None, end=None):
if isinstance(df, Series):
raise ValueError("func only applies to `pd.DataFrame`")
if start is None:
start = df.index[0]
if end is None:
end = df.index[-1]
subset = df.index[(df.index >= start) & (df.index <= end)]
retur... | Drop columns that contain NaN within [start, end] inclusive.
A wrapper around DataFrame.dropna() that builds an easier *subset*
syntax for tseries-indexed DataFrames.
Parameters
----------
df : DataFrame
start : str or datetime, default None
start cutoff date, inclusive
e... | null | null | null | |
def dropout(a, p=0.5, inplace=False):
dt = a.dtype
if p == 0.5:
# Can't pass float dtype to `randint` directly.
rand = np.random.randint(0, high=2, size=a.shape).astype(dtype=dt)
else:
rand = np.random.choice([0, 1], p=[p, 1 - p], size=a.shape).astype(dt)
if inplac... | Randomly set elements from `a` equal to zero, with proportion `p`.
Similar in concept to the dropout technique employed within
neural networks.
Parameters
----------
a: numpy.ndarray
Array to be modified.
p: float in [0, 1]
Expected proportion of elements in the resul... | null | null | null | |
def _uniquewords(*args):
words = {}
n = 0
for word in itertools.chain(*args):
if word not in words:
words[word] = n
n += 1
return words | Dictionary of words to their indices. Helper function to `encode.` | null | null | null | |
def encode(*args):
args = [arg.split() for arg in args]
unique = _uniquewords(*args)
feature_vectors = np.zeros((len(args), len(unique)))
for vec, s in zip(feature_vectors, args):
for word in s:
vec[unique[word]] = 1
return feature_vectors | One-hot encode the given input strings. | null | null | null | |
def expanding_stdize(obj, **kwargs):
return (obj - obj.expanding(**kwargs).mean()) / (
obj.expanding(**kwargs).std()
) | Standardize a pandas object column-wise on expanding window.
**kwargs -> passed to `obj.expanding`
Example
-------
df = pd.DataFrame(np.random.randn(10, 3))
print(expanding_stdize(df, min_periods=5))
0 1 2
0 NaN NaN NaN
1 NaN Na... | null | null | null | |
def get_anlz_factor(freq):
# 'Q-NOV' would give us (2001, 1); we just want (2000, 1).
try:
base, mult = get_freq_code(freq)
except ValueError:
# The above will fail for a bunch of irregular frequencies, such
# as 'Q-NOV' or 'BQS-APR'
freq = freq.upper()
... | Find the number of periods per year given a frequency.
Parameters
----------
freq : str
Any frequency str or anchored offset str recognized by Pandas.
Returns
-------
float
Example
-------
>>> get_anlz_factor('D')
252.0
>>> get_anlz_factor('5D') # 5... | null | null | null | |
def public_dir(obj, max_underscores=0, type_=None):
if max_underscores > 0:
cond1 = lambda i: not i.startswith("_" * max_underscores) # noqa
else:
cond1 = lambda i: True # noqa
if type_:
if isinstance(type_, str):
if type_ in ["callable", "func", "functio... | Like `dir()` with additional options for object inspection.
Attributes
----------
obj: object
Any object that could be passed to `dir()`
max_underscores: int, default 0
If > 0, names that begin with underscores repeated n or more
times will be excluded.
type_: None,... | null | null | null | |
def random_tickers(
length, n_tickers, endswith=None, letters=None, slicer=itertools.islice
):
# The trick here is that we need uniqueness. That defeats the
# purpose of using NumPy because we need to generate 1x1.
# (Although the alternative is just to generate a "large
# ... | Generate a length-n_tickers list of unique random ticker symbols.
Parameters
----------
length : int
The length of each ticker string.
n_tickers : int
Number of tickers to generate.
endswith : str, default None
Specify the ending element(s) of each ticker (for examp... | null | null | null | |
def random_weights(size, sumto=1.0):
w = np.random.random(size)
if w.ndim == 2:
if isinstance(sumto, (np.ndarray, list, tuple)):
sumto = np.asarray(sumto)[:, None]
w = sumto * w / w.sum(axis=-1)[:, None]
elif w.ndim == 1:
w = sumto * w / w.sum()
else:
... | Generate an array of random weights that sum to `sumto`.
The result may be of arbitrary dimensions. `size` is passed to
the `size` parameter of `np.random.random`, which acts as a shape
parameter in this case.
Note that `sumto` is subject to typical Python floating point limitations.
This ... | null | null | null | |
def rolling_windows(a, window):
if window > a.shape[0]:
raise ValueError(
"Specified `window` length of {0} exceeds length of"
" `a`, {1}.".format(window, a.shape[0])
)
if isinstance(a, (Series, DataFrame)):
a = a.values
if a.ndim == 1:
... | Creates rolling-window 'blocks' of length `window` from `a`.
Note that the orientation of rows/columns follows that of pandas.
Example
-------
import numpy as np
onedim = np.arange(20)
twodim = onedim.reshape((5,4))
print(twodim)
[[ 0 1 2 3]
[ 4 5 6 7]
[ 8... | null | null | null | |
def unique_everseen(iterable, filterfalse_=itertools.filterfalse):
# Itertools recipes:
# https://docs.python.org/3/library/itertools.html#itertools-recipes
seen = set()
seen_add = seen.add
for element in filterfalse_(seen.__contains__, iterable):
seen_add(element)
yield... | Unique elements, preserving order. | null | null | null | |
def uniqify(seq):
if PY37:
# Credit: Raymond Hettinger
return list(dict.fromkeys(seq))
else:
# Credit: Dave Kirby
# https://www.peterbe.com/plog/uniqifiers-benchmark
seen = set()
# We don't care about truth value of `not seen.add(x)`;
# ju... | `Uniqify` a sequence, preserving order.
A plain-vanilla version of itertools' `unique_everseen`.
Example
-------
>>> s = list('zyxabccabxyz')
>>> uniqify(s)
['z', 'y', 'x', 'a', 'b', 'c'
Returns
-------
list | null | null | null | |
return super(GetAppListJsonView, self).dispatch(*args, **kwargs) | def dispatch(self, *args, **kwargs) | Only staff members can access this view | 9.445714 | 8.375665 | 1.127757 |
self.app_list = site.get_app_list(request)
self.apps_dict = self.create_app_list_dict()
# no menu provided
items = get_config('MENU')
if not items:
voices = self.get_default_voices()
else:
voices = []
for item in items:
... | def get(self, request) | Returns a json representing the menu voices
in a format eaten by the js menu.
Raised ImproperlyConfigured exceptions can be viewed
in the browser console | 5.74608 | 4.849069 | 1.184986 |
voice = None
if item.get('type') == 'title':
voice = self.get_title_voice(item)
elif item.get('type') == 'app':
voice = self.get_app_voice(item)
elif item.get('type') == 'model':
voice = self.get_app_model_voice(item)
elif item.get('ty... | def add_voice(self, voices, item) | Adds a voice to the list | 2.047537 | 2.026294 | 1.010483 |
view = True
if item.get('perms', None):
view = self.check_user_permission(item.get('perms', []))
elif item.get('apps', None):
view = self.check_apps_permission(item.get('apps', []))
if view:
return {
'type': 'title',
... | def get_title_voice(self, item) | Title voice
Returns the js menu compatible voice dict if the user
can see it, None otherwise | 3.11827 | 3.021215 | 1.032124 |
view = True
if item.get('perms', None):
view = self.check_user_permission(item.get('perms', []))
elif item.get('apps', None):
view = self.check_apps_permission(item.get('apps', []))
if view:
return {
'type': 'free',
... | def get_free_voice(self, item) | Free voice
Returns the js menu compatible voice dict if the user
can see it, None otherwise | 2.784549 | 2.764332 | 1.007314 |
if item.get('name', None) is None:
raise ImproperlyConfigured('App menu voices must have a name key')
if self.check_apps_permission([item.get('name', None)]):
children = []
if item.get('models', None) is None:
for name, model in self.apps_dict... | def get_app_voice(self, item) | App voice
Returns the js menu compatible voice dict if the user
can see it, None otherwise | 2.912383 | 2.776037 | 1.049115 |
if app_model_item.get('name', None) is None:
raise ImproperlyConfigured('Model menu voices must have a name key') # noqa
if app_model_item.get('app', None) is None:
raise ImproperlyConfigured('Model menu voices must have an app key') # noqa
return self.get_mode... | def get_app_model_voice(self, app_model_item) | App Model voice
Returns the js menu compatible voice dict if the user
can see it, None otherwise | 3.038272 | 2.99329 | 1.015028 |
if model_item.get('name', None) is None:
raise ImproperlyConfigured('Model menu voices must have a name key') # noqa
if self.check_model_permission(app, model_item.get('name', None)):
return {
'type': 'model',
'label': model_item.get('lab... | def get_model_voice(self, app, model_item) | Model voice
Returns the js menu compatible voice dict if the user
can see it, None otherwise | 3.67502 | 3.393629 | 1.082917 |
d = {}
for app in self.app_list:
models = {}
for model in app.get('models', []):
models[model.get('object_name').lower()] = model
d[app.get('app_label').lower()] = {
'app_url': app.get('app_url', ''),
'app_label... | def create_app_list_dict(self) | Creates a more efficient to check dictionary from
the app_list list obtained from django admin | 2.447576 | 2.179962 | 1.12276 |
for app in apps:
if app in self.apps_dict:
return True
return False | def check_apps_permission(self, apps) | Checks if one of apps is listed in apps_dict
Since apps_dict is derived from the app_list
given by django admin, it lists only the apps
the user can view | 5.049221 | 3.925662 | 1.286209 |
if self.apps_dict.get(app, False) and model in self.apps_dict[app]['models']:
return True
return False | def check_model_permission(self, app, model) | Checks if model is listed in apps_dict
Since apps_dict is derived from the app_list
given by django admin, it lists only the apps
and models the user can view | 4.437167 | 3.83407 | 1.157299 |
voices = []
for app in self.app_list:
children = []
for model in app.get('models', []):
child = {
'type': 'model',
'label': model.get('name', ''),
'url': model.get('admin_url', '')
... | def get_default_voices(self) | When no custom menu is defined in settings
Retrieves a js menu ready dict from the django admin app list | 2.517753 | 2.168968 | 1.160807 |
if not index.isValid():
return
dataFrame = self.model().dataFrame()
# get all infos from dataFrame
dfindex = dataFrame.iloc[[index.row()]].index
columnName = dataFrame.columns[index.column()]
dtype = dataFrame[columnName].dtype
valu... | def startDrag(self, index) | start a drag operation with a PandasCellPayload on defined index.
Args:
index (QModelIndex): model index you want to start the drag operation. | 3.271029 | 3.050652 | 1.072239 |
for button in self.buttons[1:]:
button.setEnabled(enabled)
if button.isChecked():
button.setChecked(False)
model = self.tableView.model()
if model is not None:
model.enableEditing(enabled) | def enableEditing(self, enabled) | Enable the editing buttons to add/remove rows/columns and to edit the data.
This method is also a slot.
In addition, the data of model will be made editable,
if the `enabled` parameter is true.
Args:
enabled (bool): This flag indicates, if the buttons
shall ... | 3.676249 | 4.351478 | 0.844828 |
#for button in self.buttons[1:]:
for button in self.buttons:
# supress editButtons toggled event
button.blockSignals(True)
if button.isChecked():
button.setChecked(False)
button.blockSignals(False) | def uncheckButton(self) | Removes the checked stated of all buttons in this widget.
This method is also a slot. | 5.373014 | 5.435134 | 0.988571 |
model = self.tableView.model()
if model is not None:
model.addDataFrameColumn(columnName, dtype, defaultValue)
self.addColumnButton.setChecked(False) | def addColumn(self, columnName, dtype, defaultValue) | Adds a column with the given parameters to the underlying model
This method is also a slot.
If no model is set, nothing happens.
Args:
columnName (str): The name of the new column.
dtype (numpy.dtype): The datatype of the new column.
defaultValue (object): F... | 5.236551 | 6.77341 | 0.773104 |
if triggered:
dialog = AddAttributesDialog(self)
dialog.accepted.connect(self.addColumn)
dialog.rejected.connect(self.uncheckButton)
dialog.show() | def showAddColumnDialog(self, triggered) | Display the dialog to add a column to the model.
This method is also a slot.
Args:
triggered (bool): If the corresponding button was
activated, the dialog will be created and shown. | 4.507356 | 5.496082 | 0.820103 |
if triggered:
model = self.tableView.model()
model.addDataFrameRows()
self.sender().setChecked(False) | def addRow(self, triggered) | Adds a row to the model.
This method is also a slot.
Args:
triggered (bool): If the corresponding button was
activated, the row will be appended to the end. | 10.053901 | 11.573091 | 0.868731 |
if triggered:
model = self.tableView.model()
selection = self.tableView.selectedIndexes()
rows = [index.row() for index in selection]
model.removeDataFrameRows(set(rows))
self.sender().setChecked(False) | def removeRow(self, triggered) | Removes a row to the model.
This method is also a slot.
Args:
triggered (bool): If the corresponding button was
activated, the selected row will be removed
from the model. | 4.598934 | 5.269763 | 0.872702 |
model = self.tableView.model()
if model is not None:
model.removeDataFrameColumns(columnNames)
self.removeColumnButton.setChecked(False) | def removeColumns(self, columnNames) | Removes one or multiple columns from the model.
This method is also a slot.
Args:
columnNames (list): A list of columns, which shall
be removed from the model. | 5.855736 | 7.779119 | 0.752751 |
if triggered:
model = self.tableView.model()
if model is not None:
columns = model.dataFrameColumns()
dialog = RemoveAttributesDialog(columns, self)
dialog.accepted.connect(self.removeColumns)
dialog.rejected.connec... | def showRemoveColumnDialog(self, triggered) | Display the dialog to remove column(s) from the model.
This method is also a slot.
Args:
triggered (bool): If the corresponding button was
activated, the dialog will be created and shown. | 4.16858 | 4.443394 | 0.938152 |
if isinstance(model, DataFrameModel):
self.enableEditing(False)
self.uncheckButton()
selectionModel = self.tableView.selectionModel()
self.tableView.setModel(model)
model.dtypeChanged.connect(self.updateDelegate)
model... | def setViewModel(self, model) | Sets the model for the enclosed TableView in this widget.
Args:
model (DataFrameModel): The model to be displayed by
the Table View. | 5.912446 | 6.072576 | 0.973631 |
for index, column in enumerate(self.tableView.model().dataFrame().columns):
dtype = self.tableView.model().dataFrame()[column].dtype
self.updateDelegate(index, dtype) | def updateDelegates(self) | reset all delegates | 4.873944 | 4.490877 | 1.085299 |
thread = QtCore.QThread(parent)
thread.started.connect(worker.doWork)
worker.finished.connect(thread.quit)
if deleteWorkerLater:
thread.finished.connect(worker.deleteLater)
worker.moveToThread(thread)
worker.setParent(parent)
return thread | def createThread(parent, worker, deleteWorkerLater=False) | Create a new thread for given worker.
Args:
parent (QObject): parent of thread and worker.
worker (ProgressWorker): worker to use in thread.
deleteWorkerLater (bool, optional): delete the worker if thread finishes.
Returns:
QThread | 2.263613 | 2.88274 | 0.78523 |
# lists, tuples, dicts refer to numpy.object types and
# return a 'text' description - working as intended or bug?
try:
value = np.dtype(value)
except TypeError as e:
return None
for (dtype, string) in self._all:
if dtype == value:
... | def description(self, value) | Fetches the translated description for the given datatype.
The given value will be converted to a `numpy.dtype` object, matched
against the supported datatypes and the description will be translated
into the preferred language. (Usually a settings dialog should be
available to change th... | 14.750491 | 13.184709 | 1.118757 |
for (dtype, string) in self._all:
if string == value:
return dtype
return None | def dtype(self, value) | Gets the datatype for the given `value` (description).
Args:
value (str): A text description for any datatype.
Returns:
numpy.dtype: The matching datatype for the given text.
None: If no match can be found, `None` will be returned. | 9.28178 | 9.096848 | 1.020329 |
if column.dtype == object:
column.fillna('', inplace=True)
return column | def fillNoneValues(column) | Fill all NaN/NaT values of a column with an empty string
Args:
column (pandas.Series): A Series object with all rows.
Returns:
column: Series with filled NaN values. | 4.559227 | 6.353499 | 0.717593 |
tempColumn = column
try:
# Try to convert the first row and a random row instead of the complete
# column, might be faster
# tempValue = np.datetime64(column[0])
tempValue = np.datetime64(column[randint(0, len(column.index) - 1)])
tempColumn = column.apply(to_dateti... | def convertTimestamps(column) | Convert a dtype of a given column to a datetime.
This method tries to do this by brute force.
Args:
column (pandas.Series): A Series object with all rows.
Returns:
column: Converted to datetime if no errors occured, else the
original column will be returned. | 6.149848 | 6.101107 | 1.007989 |
if isinstance(filepath, pd.DataFrame):
return filepath
assert isinstance(first_codec, str), "first_codec must be a string"
codecs = ['UTF_8', 'ISO-8859-1', 'ASCII', 'UTF_16', 'UTF_32']
try:
codecs.remove(first_codec)
except ValueError as not_i... | def superReadCSV(filepath, first_codec='UTF_8', usecols=None,
low_memory=False, dtype=None, parse_dates=True,
sep=',', chunksize=None, verbose=False, **kwargs) | A wrap to pandas read_csv with mods to accept a dataframe or
filepath. returns dataframe untouched, reads filepath and returns
dataframe based on arguments. | 2.991992 | 2.996599 | 0.998462 |
ext = os.path.splitext(filepath)[1].lower()
allowed_exts = ['.csv', '.txt', '.tsv']
assert ext in ['.csv', '.txt'], "Unexpected file extension {}. \
Supported extensions {}\n filename: {}".format(
ext, allowed_exts, os.path.basenam... | def identify_sep(filepath) | Identifies the separator of data in a filepath.
It reads the first line of the file and counts supported separators.
Currently supported separators: ['|', ';', ',','\t',':'] | 3.88712 | 3.61266 | 1.075972 |
if isinstance(filepath, pd.DataFrame):
return filepath
sep = kwargs.get('sep', None)
ext = os.path.splitext(filepath)[1].lower()
if sep is None:
if ext == '.tsv':
kwargs['sep'] = '\t'
elif ext == '.csv':
kwargs['sep'] = ','
else:
... | def superReadText(filepath, **kwargs) | A wrapper to superReadCSV which wraps pandas.read_csv().
The benefit of using this function is that it automatically identifies the
column separator.
.tsv files are assumed to have a \t (tab) separation
.csv files are assumed to have a comma separation.
.txt (or any other type) get the first line of... | 3.073235 | 2.291147 | 1.341352 |
if isinstance(filepath, pd.DataFrame):
return filepath
ext = os.path.splitext(filepath)[1].lower()
if ext in ['.xlsx', '.xls']:
df = pd.read_excel(filepath, **kwargs)
elif ext in ['.pkl', '.p', '.pickle', '.pk']:
df = pd.read_pickle(filepath)
else:
# Assume i... | def superReadFile(filepath, **kwargs) | Uses pandas.read_excel (on excel files) and returns a dataframe of the
first sheet (unless sheet is specified in kwargs)
Uses superReadText (on .txt,.tsv, or .csv files) and returns a dataframe of
the data. One function to read almost all types of data files. | 3.554359 | 3.345788 | 1.062338 |
cols = list(frame.columns)
for i, item in enumerate(frame.columns):
if item in frame.columns[:i]:
cols[i] = "toDROP"
frame.columns = cols
return frame.drop("toDROP", 1, errors='ignore') | def dedupe_cols(frame) | Need to dedupe columns that have the same name. | 4.113492 | 3.855443 | 1.066931 |
counts = {}
positions = {pos: fld for pos, fld in enumerate(cols)}
for c in cols:
if c in counts.keys():
counts[c] += 1
else:
counts[c] = 1
fixed_cols = {}
for pos, col in positions.items():
if counts[col] > 1:
fix_cols = {pos: fld ... | def rename_dupe_cols(cols) | Takes a list of strings and appends 2,3,4 etc to duplicates. Never
appends a 0 or 1. Appended #s are not always in order...but if you wrap
this in a dataframe.to_sql function you're guaranteed to not have dupe
column name errors importing data to SQL...you'll just have to check
yourself to see which fie... | 2.798503 | 2.883444 | 0.970542 |
editor = BigIntSpinbox(parent)
try:
editor.setMinimum(self.minimum)
editor.setMaximum(self.maximum)
editor.setSingleStep(self.singleStep)
except TypeError as err:
# initiate the editor with default values
pass
return ed... | def createEditor(self, parent, option, index) | Returns the widget used to edit the item specified by index for editing. The parent widget and style option are used to control how the editor widget appears.
Args:
parent (QWidget): parent widget.
option (QStyleOptionViewItem): controls how editor widget appears.
index (QMo... | 5.304949 | 7.079028 | 0.74939 |
if index.isValid():
value = index.model().data(index, QtCore.Qt.EditRole)
spinBox.setValue(value) | def setEditorData(self, spinBox, index) | Sets the data to be displayed and edited by the editor from the data model item specified by the model index.
Args:
spinBox (BigIntSpinbox): editor widget.
index (QModelIndex): model data index. | 2.396078 | 2.883996 | 0.830819 |
editor = QtGui.QDoubleSpinBox(parent)
try:
editor.setMinimum(self.minimum)
editor.setMaximum(self.maximum)
editor.setSingleStep(self.singleStep)
editor.setDecimals(self.decimals)
except TypeError as err:
# initiate the spinbox ... | def createEditor(self, parent, option, index) | Returns the widget used to edit the item specified by index for editing. The parent widget and style option are used to control how the editor widget appears.
Args:
parent (QWidget): parent widget.
option (QStyleOptionViewItem): controls how editor widget appears.
index (QMo... | 3.106763 | 3.888836 | 0.798893 |
spinBox.interpretText()
value = spinBox.value()
model.setData(index, value, QtCore.Qt.EditRole) | def setModelData(self, spinBox, model, index) | Gets data from the editor widget and stores it in the specified model at the item index.
Args:
spinBox (QDoubleSpinBox): editor widget.
model (QAbstractItemModel): parent model.
index (QModelIndex): model data index. | 3.872892 | 4.385142 | 0.883185 |
editor = QtGui.QLineEdit(parent)
return editor | def createEditor(self, parent, option, index) | Returns the widget used to edit the item specified by index for editing. The parent widget and style option are used to control how the editor widget appears.
Args:
parent (QWidget): parent widget.
option (QStyleOptionViewItem): controls how editor widget appears.
index (QMo... | 6.734537 | 17.852448 | 0.377233 |
if index.isValid():
value = editor.text()
model.setData(index, value, QtCore.Qt.EditRole) | def setModelData(self, editor, model, index) | Gets data from the editor widget and stores it in the specified model at the item index.
Args:
editor (QtGui.QLineEdit): editor widget.
model (QAbstractItemModel): parent model.
index (QModelIndex): model data index. | 3.14307 | 2.750444 | 1.14275 |
combo = QtGui.QComboBox(parent)
combo.addItems(SupportedDtypes.names())
combo.currentIndexChanged.connect(self.currentIndexChanged)
return combo | def createEditor(self, parent, option, index) | Creates an Editor Widget for the given index.
Enables the user to manipulate the displayed data in place. An editor
is created, which performs the change.
The widget used will be a `QComboBox` with all available datatypes in the
`pandas` project.
Args:
parent (QtCor... | 5.295277 | 5.202297 | 1.017873 |
editor.blockSignals(True)
data = index.data()
dataIndex = editor.findData(data)
# dataIndex = editor.findData(data, role=Qt.EditRole)
editor.setCurrentIndex(dataIndex)
editor.blockSignals(False) | def setEditorData(self, editor, index) | Sets the current data for the editor.
The data displayed has the same value as `index.data(Qt.EditRole)`
(the translated name of the datatype). Therefor a lookup for all items
of the combobox is made and the matching item is set as the currently
displayed item.
Signals emitted ... | 3.290253 | 3.538611 | 0.929815 |
model.setData(index, editor.itemText(editor.currentIndex())) | def setModelData(self, editor, model, index) | Updates the model after changing data in the editor.
Args:
editor (QtGui.QComboBox): The current editor for the item. Should be
a `QtGui.QComboBox` as defined in `createEditor`.
model (ColumnDtypeModel): The model which holds the displayed data.
index (QtCore... | 7.276262 | 7.360343 | 0.988577 |
try:
bytestream = pickle.dumps(data)
super(MimeData, self).setData(self._mimeType, bytestream)
except TypeError:
raise TypeError(self.tr("can not pickle added data"))
except:
raise | def setData(self, data) | Add some data.
Args:
data (object): Object to add as data. This object has to be pickable.
Qt objects don't work!
Raises:
TypeError if data is not pickable | 7.596085 | 6.770501 | 1.121938 |
try:
bytestream = super(MimeData, self).data(self._mimeType).data()
return pickle.loads(bytestream)
except:
raise | def data(self) | return stored data
Returns:
unpickled data | 7.805067 | 7.450575 | 1.047579 |
#return super(DataFrameExportDialog, self).accepted
try:
self._saveModel()
except Exception as err:
self._statusBar.showMessage(str(err))
raise
else:
self._resetWidgets()
self.exported.emit(True)
self.accept... | def accepted(self) | Successfully close the widget and emit an export signal.
This method is also a `SLOT`.
The dialog will be closed, when the `Export Data` button is
pressed. If errors occur during the export, the status bar
will show the error message and the dialog will not be closed. | 7.471069 | 5.620615 | 1.329226 |
self._resetWidgets()
self.exported.emit(False)
self.reject() | def rejected(self) | Close the widget and reset its inital state.
This method is also a `SLOT`.
The dialog will be closed and all changes reverted, when the
`cancel` button is pressed. | 19.90596 | 18.24507 | 1.091032 |
delimiter = self._delimiterBox.currentSelected()
header = self._headerCheckBox.isChecked() # column labels
if self._filename is None:
filename = self._filenameLineEdit.text()
else:
filename = self._filename
ext = os.path.splitext(filename)[1].lowe... | def _saveModel(self) | Reimplements _saveModel to utilize all of the
Pandas export options based on file extension.
:return: None | 3.716372 | 3.597037 | 1.033176 |
if value >= self.minimum() and value <= self.maximum():
self._lineEdit.setText(str(value))
elif value < self.minimum():
self._lineEdit.setText(str(self.minimum()))
elif value > self.maximum():
self._lineEdit.setText(str(self.maximum()))
return... | def setValue(self, value) | setter function to _lineEdit.text. Sets minimum/maximum as new value if value is out of bounds.
Args:
value (int/long): new value to set.
Returns
True if all went fine. | 2.092934 | 1.909061 | 1.096316 |
self.setValue(self.value() + steps*self.singleStep()) | def stepBy(self, steps) | steps value up/down by a single step. Single step is defined in singleStep().
Args:
steps (int): positiv int steps up, negativ steps down | 7.335743 | 5.461839 | 1.34309 |
if self.value() > self.minimum() and self.value() < self.maximum():
return self.StepUpEnabled | self.StepDownEnabled
elif self.value() <= self.minimum():
return self.StepUpEnabled
elif self.value() >= self.maximum():
return self.StepDownEnabled | def stepEnabled(self) | Virtual function that determines whether stepping up and down is legal at any given time.
Returns:
ored combination of StepUpEnabled | StepDownEnabled | 2.617961 | 2.299881 | 1.138303 |
if not isinstance(singleStep, int):
raise TypeError("Argument is not of type int")
# don't use negative values
self._singleStep = abs(singleStep)
return self._singleStep | def setSingleStep(self, singleStep) | setter to _singleStep. converts negativ values to positiv ones.
Args:
singleStep (int): new _singleStep value. converts negativ values to positiv ones.
Raises:
TypeError: If the given argument is not an integer.
Returns:
int or long: the absolute value of t... | 5.141188 | 4.228911 | 1.215724 |
if not isinstance(minimum, int):
raise TypeError("Argument is not of type int or long")
self._minimum = minimum | def setMinimum(self, minimum) | setter to _minimum.
Args:
minimum (int or long): new _minimum value.
Raises:
TypeError: If the given argument is not an integer. | 5.340186 | 5.662768 | 0.943035 |
if not isinstance(maximum, int):
raise TypeError("Argument is not of type int or long")
self._maximum = maximum | def setMaximum(self, maximum) | setter to _maximum.
Args:
maximum (int or long): new _maximum value | 5.244268 | 5.597598 | 0.936878 |
df = self._models[filepath].dataFrame()
kwargs['index'] = kwargs.get('index', False)
if save_as is not None:
to_path = save_as
else:
to_path = filepath
ext = os.path.splitext(to_path)[1].lower()
if ext == ".xlsx":
kwargs.pop... | def save_file(self, filepath, save_as=None, keep_orig=False, **kwargs) | Saves a DataFrameModel to a file.
:param filepath: (str)
The filepath of the DataFrameModel to save.
:param save_as: (str, default None)
The new filepath to save as.
:param keep_orig: (bool, default False)
True keeps the original filepath/DataFrameModel if sa... | 3.818239 | 3.469741 | 1.100439 |
assert isinstance(df_model, DataFrameModel), "df_model argument must be a DataFrameModel!"
df_model._filePath = file_path
try:
self._models[file_path]
except KeyError:
self.signalNewModelRead.emit(file_path)
self._models[file_path] = df_model | def set_model(self, df_model, file_path) | Sets a DataFrameModel and registers it to the given file_path.
:param df_model: (DataFrameModel)
The DataFrameModel to register.
:param file_path:
The file path to associate with the DataFrameModel.
*Overrides the current filePath on the DataFrameModel (if any)
... | 4.214875 | 3.307338 | 1.274401 |
assert isinstance(df, pd.DataFrame), "Cannot update file with type '{}'".format(type(df))
self._models[filepath].setDataFrame(df, copyDataFrame=False)
if notes:
update = dict(date=pd.Timestamp(datetime.datetime.now()),
n... | def update_file(self, filepath, df, notes=None) | Sets a new DataFrame for the DataFrameModel registered to filepath.
:param filepath (str)
The filepath to the DataFrameModel to be updated
:param df (pandas.DataFrame)
The new DataFrame to register to the model.
:param notes (str, default None)
Optional notes... | 6.043829 | 5.932638 | 1.018742 |
self._models.pop(filepath)
self._updates.pop(filepath, default=None)
self.signalModelDestroyed.emit(filepath) | def remove_file(self, filepath) | Removes the DataFrameModel from being registered.
:param filepath: (str)
The filepath to delete from the DataFrameModelManager.
:return: None | 8.896426 | 7.52484 | 1.182274 |
try:
model = self._models[filepath]
except KeyError:
model = read_file(filepath, **kwargs)
self._models[filepath] = model
self.signalNewModelRead.emit(filepath)
finally:
self._paths_read.append(filepath)
return self._m... | def read_file(self, filepath, **kwargs) | Reads a filepath into a DataFrameModel and registers
it.
Example use:
dfmm = DataFrameModelManger()
dfmm.read_file(path_to_file)
dfm = dfmm.get_model(path_to_file)
df = dfm.get_frame(path_to_file)
:param filepath: (str)
The filepath to... | 3.843225 | 3.602902 | 1.066703 |
separator = '-' * 80
logFile = os.path.join(tempfile.gettempdir(), "error.log")
notice = "An unhandled exception occurred. Please report the problem.\n"
notice += .format(logFile)
timeString = time.strftime("%Y-%m-%d, %H:%M:%S")
tbinfofile = io.StringIO()
traceback.print_tb(traceba... | def excepthook(excType, excValue, tracebackobj) | Global function to catch unhandled exceptions.
@param excType exception type
@param excValue exception value
@param tracebackobj traceback object | 3.167089 | 3.240655 | 0.977299 |
return "".join(traceback.format_exception(
sys.exc_info()[0], sys.exc_info()[1], sys.exc_info()[2]
)) | def exception_format() | Convert exception info into a string suitable for display. | 2.501618 | 2.190529 | 1.142015 |
output_list = list()
for i, item in enumerate(input_list):
tempList = input_list[:i] + input_list[i + 1:]
if item not in tempList:
output_list.append(item)
else:
output_list.append('{0}_{1}'.format(item, i))
return output_list | def uniquify_list_of_strings(input_list) | Ensure that every string within input_list is unique.
:param list input_list: List of strings
:return: New list with unique names as needed. | 2.269139 | 2.385495 | 0.951224 |
ret_val = [text, None, None] # Default return values
if text is None:
return ret_val
# Single character, remain visible
res = re.search('(?<=\[).(?=\])', text)
if res:
start = res.start(0)
end = res.end(0)
caption = text[:start - 1] + text[start:end] + text[en... | def parse_hotkey(text) | Extract a desired hotkey from the text. The format to enclose
the hotkey in square braces
as in Button_[1] which would assign the keyboard key 1 to that button.
The one will be included in the
button text. To hide they key, use double square braces as in: Ex[[qq]]
it , which would assign
t... | 2.557001 | 2.392947 | 1.068557 |
if filename is None:
return None
if not os.path.isfile(filename):
raise ValueError('Image file {} does not exist.'.format(filename))
tk_image = None
filename = os.path.normpath(filename)
_, ext = os.path.splitext(filename)
try:
pil_image = PILImage.open(filename... | def load_tk_image(filename) | Load in an image file and return as a tk Image.
:param filename: image filename to load
:return: tk Image object | 2.940039 | 2.880591 | 1.020638 |
if not isinstance(dataFrame, pandas.core.frame.DataFrame):
raise TypeError('Argument is not of type pandas.core.frame.DataFrame')
self.layoutAboutToBeChanged.emit()
self._dataFrame = dataFrame
self.layoutChanged.emit() | def setDataFrame(self, dataFrame) | setter function to _dataFrame. Holds all data.
Note:
It's not implemented with python properties to keep Qt conventions.
Raises:
TypeError: if dataFrame is not of type pandas.core.frame.DataFrame.
Args:
dataFrame (pandas.core.frame.DataFrame): assign dataFr... | 2.764775 | 2.658953 | 1.039798 |
if not isinstance(editable, bool):
raise TypeError('Argument is not of type bool')
self._editable = editable | def setEditable(self, editable) | setter to _editable. apply changes while changing dtype.
Raises:
TypeError: if editable is not of type bool.
Args:
editable (bool): apply changes while changing dtype. | 3.945098 | 4.999915 | 0.789033 |
# an index is invalid, if a row or column does not exist or extends
# the bounds of self.columnCount() or self.rowCount()
# therefor a check for col>1 is unnecessary.
if not index.isValid():
return None
col = index.column()
#row = self._dataFrame.c... | def data(self, index, role=Qt.DisplayRole) | Retrieve the data stored in the model at the given `index`.
Args:
index (QtCore.QModelIndex): The model index, which points at a
data object.
role (Qt.ItemDataRole, optional): Defaults to `Qt.DisplayRole`. You
have to use different roles to retrieve diffe... | 4.782597 | 4.243036 | 1.127164 |
if role != DTYPE_CHANGE_ROLE or not index.isValid():
return False
if not self.editable():
return False
self.layoutAboutToBeChanged.emit()
dtype = SupportedDtypes.dtype(value)
currentDtype = np.dtype(index.data(role=DTYPE_ROLE))
if dtyp... | def setData(self, index, value, role=DTYPE_CHANGE_ROLE) | Updates the datatype of a column.
The model must be initated with a dataframe already, since valid
indexes are necessary. The `value` is a translated description of the
data type. The translations can be found at
`qtpandas.translation.DTypeTranslator`.
If a datatype can not be ... | 3.425548 | 3.434344 | 0.997439 |
if not index.isValid():
return Qt.NoItemFlags
col = index.column()
flags = Qt.ItemIsEnabled | Qt.ItemIsSelectable
if col > 0 and self.editable():
flags = Qt.ItemIsSelectable | Qt.ItemIsEnabled | Qt.ItemIsEditable
return flags | def flags(self, index) | Returns the item flags for the given index as ored value, e.x.: Qt.ItemIsUserCheckable | Qt.ItemIsEditable
Args:
index (QtCore.QModelIndex): Index to define column and row
Returns:
for column 'column': Qt.ItemIsSelectable | Qt.ItemIsEnabled
for column 'data type': Q... | 2.967424 | 2.893338 | 1.025606 |
# there should be a grammar defined and some lexer/parser.
# instead of this quick-and-dirty implementation.
safeEnvDict = {
'freeSearch': self.freeSearch,
'extentSearch': self.extentSearch,
'indexSearch': self.indexSearch
}
for col ... | def search(self) | Applies the filter to the stored dataframe.
A safe environment dictionary will be created, which stores all allowed
functions and attributes, which may be used for the filter.
If any object in the given `filterString` could not be found in the
dictionary, the filter does not apply and r... | 7.688715 | 6.467187 | 1.188881 |
if not self._dataFrame.empty:
# set question to the indexes of data and set everything to false.
question = self._dataFrame.index == -9999
for column in self._dataFrame.columns:
dfColumn = self._dataFrame[column]
dfColumn = dfColumn.a... | def freeSearch(self, searchString) | Execute a free text search for all columns in the dataframe.
Parameters
----------
searchString (str): Any string which may be contained in a column.
Returns
-------
list: A list containing all indexes with filtered data. Matches
will be `True`, ... | 4.490747 | 4.095984 | 1.096378 |
if not self._dataFrame.empty:
try:
questionMin = (self._dataFrame.lat >= xmin) & (
self._dataFrame.lng >= ymin)
questionMax = (self._dataFrame.lat <= xmax) & (
self._dataFrame.lng <= ymax)
return np.logi... | def extentSearch(self, xmin, ymin, xmax, ymax) | Filters the data by a geographical bounding box.
The bounding box is given as lower left point coordinates and upper
right point coordinates.
Note:
It's necessary that the dataframe has a `lat` and `lng` column
in order to apply the filter.
Check if the met... | 3.237372 | 2.79717 | 1.157374 |
if not self._dataFrame.empty:
filter0 = self._dataFrame.index == -9999
for index in indexes:
filter1 = self._dataFrame.index == index
filter0 = np.logical_or(filter0, filter1)
return filter0
else:
return [] | def indexSearch(self, indexes) | Filters the data by a list of indexes.
Args:
indexes (list of int): List of index numbers to return.
Returns:
list: A list containing all indexes with filtered data. Matches
will be `True`, the remaining items will be `False`. If the
dataFrame is empty, ... | 3.790253 | 3.304156 | 1.147117 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.