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stringlengths 2
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⌀ | name
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| docstring
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stringlengths 2
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20,528 | pyobjectify.pyobjectify | __init__ | null | def __init__(self, url, connectivity):
url = url.replace("file://", "")
self.url = url
self.connectivity = connectivity
if connectivity == Connectivity.ONLINE_STATIC:
response = get(url)
self.response = response
self.plaintext = response.text
elif connectivity == Connectivity.LOCAL:
url = url.replace("file://", "")
file_obj = open(url, "r")
self.response = file_obj
try:
self.plaintext = file_obj.read()
self.response.seek(0, 0)
except Exception: # XLSX data does not like to be read
self.plaintext = None
| (self, url, connectivity) |
20,530 | pyobjectify.pyobjectify | convert |
Attempts to convert the resource data through possible conversions.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
resource (:obj:`Resource`): he Resource object for the resource.
conversions (list): The list of all possible conversions, filtered if user specified output data type.
Returns:
object: The first successful conversion from the probable resource type to an output data type.
Raises:
TypeError: None of the possible conversions were successful.
| def convert(resource, conversions):
"""
Attempts to convert the resource data through possible conversions.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
resource (:obj:`Resource`): he Resource object for the resource.
conversions (list): The list of all possible conversions, filtered if user specified output data type.
Returns:
object: The first successful conversion from the probable resource type to an output data type.
Raises:
TypeError: None of the possible conversions were successful.
"""
for conversion in conversions:
try:
i_type, o_type = conversion
# Handle each case. Currently, only JSON has multiple options.
# Return the first conversion that works.
if i_type is InputType.JSON:
if o_type is dict:
return json_to_dict(resource)
elif o_type is list:
return json_to_list(resource)
elif o_type is DataFrame:
return json_to_dataframe(resource)
elif i_type is InputType.CSV:
return csv_to_list(resource)
elif i_type is InputType.TSV:
return tsv_to_list(resource)
elif i_type is InputType.XML:
return xml_to_dict(resource)
elif i_type is InputType.XLSX:
return xlsx_to_dict(resource)
except Exception:
continue # Try the next conversion
# Reach here means none of the conversions worked!
raise TypeError("The type of the resource is not supported.")
| (resource, conversions) |
20,531 | pyobjectify.pyobjectify | from_url |
This is the main interface that the end-user interacts with.
Given a URL, converts the resource data to a parsable Python object.
Args:
url (str): A URL to a resource.
out_type (:obj:`class`, optional): The user-specified data type of the output.
Returns:
object: A parsable Python object representation of the resource.
Raises:
TypeError: The user-specified data type of the output is not supported.
| def from_url(url, out_type=None):
"""
This is the main interface that the end-user interacts with.
Given a URL, converts the resource data to a parsable Python object.
Args:
url (str): A URL to a resource.
out_type (:obj:`class`, optional): The user-specified data type of the output.
Returns:
object: A parsable Python object representation of the resource.
Raises:
TypeError: The user-specified data type of the output is not supported.
"""
if out_type is not None and out_type not in OUTPUT_TYPES:
raise TypeError(f"The specified output type {out_type} is not supported.")
# (1) Get resource connectivity type
connectivity = url_to_connectivity(url)
# (2) Retrieve resource
resource = retrieve_resource(url, connectivity)
# (3) Determine input type
in_types = get_resource_types(resource)
# (4) Determine possible conversions
conversions = get_conversions(in_types, out_type)
# (5) Convert to output type
output = convert(resource, conversions)
# (6) Close the file object if open()-ed
if connectivity is Connectivity.LOCAL:
resource.response.close()
return output
| (url, out_type=None) |
20,532 | pyobjectify.pyobjectify | get_conversions |
Get possible conversions for the probable resource types.
If the user specified a preferred output type, filter out any undesirable conversions to consider.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
in_types (:obj:`list`): A list of calculated possible resource types.
out_type (:obj:`class`, optional): The user-specified data type of the output.
Returns:
list: A list of (in, out) conversion tuples as described above.
Raises:
TypeError: There are no possible conversions.
| def get_conversions(in_types, out_type=None):
"""
Get possible conversions for the probable resource types.
If the user specified a preferred output type, filter out any undesirable conversions to consider.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
in_types (:obj:`list`): A list of calculated possible resource types.
out_type (:obj:`class`, optional): The user-specified data type of the output.
Returns:
list: A list of (in, out) conversion tuples as described above.
Raises:
TypeError: There are no possible conversions.
"""
# There is guaranteed at least one probable in_type
# Go through each probable resource data type.
# Use lists to preserve order.
conversions = [] # To make a list of possible conversions.
poss_out_types = [] # To make list of all output types based on probable input types.
for in_type in in_types:
for poss_out_type in CONVERSIONS[in_type]:
if out_type is None:
if (in_type, poss_out_type) not in conversions:
conversions.append((in_type, poss_out_type))
if poss_out_type not in poss_out_types:
poss_out_types.append(poss_out_type)
elif poss_out_type is out_type: # (and user specified a preferred output type)
if (in_type, poss_out_type) not in conversions:
conversions.append((in_type, poss_out_type))
if poss_out_type not in poss_out_types:
poss_out_types.append(poss_out_type)
if out_type is not None and out_type not in poss_out_types:
raise TypeError(f"The resource cannot be converted into the requested data type {out_type}.")
return conversions
| (in_types, out_type=None) |
20,533 | pyobjectify.pyobjectify | get_resource_types |
Get possible resource types of the resource.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
resource (:obj:`Resource`): The Resource object for the resource.
Returns:
list: A list of possible resource types of the resource.
Raises:
TypeError: The resource is of a type that is not supported.
| def get_resource_types(resource):
"""
Get possible resource types of the resource.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
resource (:obj:`Resource`): The Resource object for the resource.
Returns:
list: A list of possible resource types of the resource.
Raises:
TypeError: The resource is of a type that is not supported.
"""
possible = list(InputType)
try:
_ = loads(resource.plaintext)
except Exception:
possible.remove(InputType.JSON)
pass
try:
dicts = DictReader(resource.response)
if resource.connectivity is Connectivity.LOCAL:
resource.response.seek(0, 0)
# Ensure that each row has the same number of fields
nums_fields = set([len(d.items()) for d in list(dicts)])
if resource.connectivity is Connectivity.LOCAL:
resource.response.seek(0, 0)
assert len(nums_fields) == 1
# Ensure that the number of fields is greater than 1
(num_fields,) = nums_fields
assert num_fields > 1 # Data that have only one column will not be interpreted as CSVs
except Exception:
possible.remove(InputType.CSV)
try:
dicts = DictReader(resource.response, delimiter="\t")
if resource.connectivity is Connectivity.LOCAL:
resource.response.seek(0, 0)
# Ensure that each row has the same number of fields
nums_fields = set([len(d.items()) for d in list(dicts)])
if resource.connectivity is Connectivity.LOCAL:
resource.response.seek(0, 0)
assert len(nums_fields) == 1
# Ensure that the number of fields is greater than 1
(num_fields,) = nums_fields
assert num_fields > 1 # Data that have only one column will not be interpreted as TSVs
except Exception:
possible.remove(InputType.TSV)
try:
_ = parse(resource.plaintext)
assert resource.plaintext[0] == "<"
except Exception:
possible.remove(InputType.XML)
try:
df = read_excel(resource.url, sheet_name=None)
sheets_dict = {}
for sheet_name, df in df.items():
sheets_dict[sheet_name] = df.to_dict()
except Exception:
possible.remove(InputType.XLSX)
if len(possible) == 0:
raise TypeError("The type of the resource is not supported.")
return possible
| (resource) |
20,535 | pyobjectify.pyobjectify | retrieve_resource |
Retrieves the resource at the URL using the connectivity type and stores it in a Resource object.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
url (str): The URL to a resource.
connectivity (:obj:`Connectivity`): An attribute in the enumeration Connectivity.
The calculated connectivity type of the resource.
Returns:
Resource: The Resource object for the resource at the URL specified.
Raises:
TypeError: The connectivity type is not supported.
| def retrieve_resource(url, connectivity):
"""
Retrieves the resource at the URL using the connectivity type and stores it in a Resource object.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
url (str): The URL to a resource.
connectivity (:obj:`Connectivity`): An attribute in the enumeration Connectivity.
The calculated connectivity type of the resource.
Returns:
Resource: The Resource object for the resource at the URL specified.
Raises:
TypeError: The connectivity type is not supported.
"""
if not isinstance(connectivity, Connectivity):
raise TypeError(f"The connectivity type {connectivity} is not supported.")
return Resource(url, connectivity)
| (url, connectivity) |
20,536 | pyobjectify.pyobjectify | url_to_connectivity |
Get the connectivity type of the resource.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
url (str): The URL to a resource.
Returns:
Connectivity: An attribute in the enumeration `Connectivity`. The calculated connectivity of the resource type.
| def url_to_connectivity(url):
"""
Get the connectivity type of the resource.
The end-user does not have to interface with this, but it is provided for more granular operations.
Args:
url (str): The URL to a resource.
Returns:
Connectivity: An attribute in the enumeration `Connectivity`. The calculated connectivity of the resource type.
"""
local_conditions = [url.startswith("file:///"), url.startswith("/"), url.startswith(".")]
if any(local_conditions):
return Connectivity.LOCAL
else:
return Connectivity.ONLINE_STATIC
| (url) |
20,537 | pyratings.utils | _extract_rating_provider | Extract valid rating providers.
It is meant to extract rating providers from the column headings of a
``pd.DataFrame``. For example, let's assume some rating column headers are
["rating_fitch", "S&P rating", "BLOOMBERG composite rating"]. The function would
then return a list of valid rating providers, namely ["Fitch", "SP", "Bloomberg"].
Parameters
----------
rating_provider
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
valid_rtg_provider
List of strings containing the names of valid rating providers. Supported
rating providers are {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
'rating_provider' must be in that list.
Returns
-------
Union[str, List[str]]
str or List[str] with valid rating providers.
Raises
------
AssertionError
If ``rating_provider`` is not a subset of `valid_rtg_provider`.
Examples
--------
>>> _extract_rating_provider(
... rating_provider="S&P",
... valid_rtg_provider=["fitch", "s&p", "moody"],
... )
'SP'
>>> _extract_rating_provider(
... rating_provider="rtg_DBRS",
... valid_rtg_provider=["Fitch", "SP", "DBRS"]
... )
'DBRS'
You can also provide a list of strings.
>>> _extract_rating_provider(
... rating_provider=["Fitch ratings", "rating_SP", "DBRS"],
... valid_rtg_provider=["fitch", "moody", "sp", "bloomberg", "dbrs"]
... )
['Fitch', 'SP', 'DBRS']
| def _extract_rating_provider(
rating_provider: str | list[str] | Hashable,
valid_rtg_provider: list[str],
) -> str | list[str]:
"""Extract valid rating providers.
It is meant to extract rating providers from the column headings of a
``pd.DataFrame``. For example, let's assume some rating column headers are
["rating_fitch", "S&P rating", "BLOOMBERG composite rating"]. The function would
then return a list of valid rating providers, namely ["Fitch", "SP", "Bloomberg"].
Parameters
----------
rating_provider
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
valid_rtg_provider
List of strings containing the names of valid rating providers. Supported
rating providers are {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
'rating_provider' must be in that list.
Returns
-------
Union[str, List[str]]
str or List[str] with valid rating providers.
Raises
------
AssertionError
If ``rating_provider`` is not a subset of `valid_rtg_provider`.
Examples
--------
>>> _extract_rating_provider(
... rating_provider="S&P",
... valid_rtg_provider=["fitch", "s&p", "moody"],
... )
'SP'
>>> _extract_rating_provider(
... rating_provider="rtg_DBRS",
... valid_rtg_provider=["Fitch", "SP", "DBRS"]
... )
'DBRS'
You can also provide a list of strings.
>>> _extract_rating_provider(
... rating_provider=["Fitch ratings", "rating_SP", "DBRS"],
... valid_rtg_provider=["fitch", "moody", "sp", "bloomberg", "dbrs"]
... )
['Fitch', 'SP', 'DBRS']
"""
provider_map = {
"fitch": "Fitch",
"moody": "Moody",
"moody's": "Moody",
"sp": "SP",
"s&p": "SP",
"bloomberg": "Bloomberg",
"dbrs": "DBRS",
}
if isinstance(rating_provider, str):
rating_provider = [rating_provider]
valid_rtg_provider_lowercase = [x.lower() for x in valid_rtg_provider]
for i, provider in enumerate(rating_provider):
if not any(x in provider.lower() for x in valid_rtg_provider_lowercase):
raise AssertionError(
f"{provider!r} is not a valid rating provider. 'rating_provider' must "
f"be in {valid_rtg_provider}."
)
for valid_provider in valid_rtg_provider:
if valid_provider.lower() in provider.lower():
rating_provider[i] = provider_map[valid_provider.lower()]
if len(rating_provider) > 1:
return rating_provider
else:
return rating_provider[0]
| (rating_provider: str | list[str] | collections.abc.Hashable, valid_rtg_provider: list[str]) -> str | list[str] |
20,538 | pyratings.utils | _get_translation_dict | Load translation dictionaries from SQLite database. | def _get_translation_dict(
translation_table: str,
rating_provider: str = None,
tenor: str = "long-term",
st_rtg_strategy: str = "base",
) -> dict | pd.DataFrame:
"""Load translation dictionaries from SQLite database."""
def _rtg_to_scores(tenor: str) -> dict[str, int]:
"""Create translation dictionary to translate from ratings to scores."""
if tenor == "long-term":
sql_query = """
SELECT Rating, RatingScore FROM v_ltRatings
WHERE RatingProvider=?
"""
cursor.execute(sql_query, (rating_provider,))
else:
sql_query = """
SELECT Rating, AvgEquivLTScore FROM v_stRatings
WHERE RatingProvider=? and Strategy=?
"""
cursor.execute(sql_query, (rating_provider, st_rtg_strategy))
return dict(cursor.fetchall())
def _scores_to_rtg(tenor: str, strat: str) -> dict[int, str] | pd.DataFrame:
"""Create translation dictionary to translate from scores to ratings."""
if tenor == "long-term":
sql_query = """
SELECT RatingScore, Rating FROM v_ltRatings
WHERE Rating != 'SD' and RatingProvider=?
"""
cursor.execute(sql_query, (rating_provider,))
translation_dict = dict(cursor.fetchall())
else:
sql_query = """
SELECT MinEquivLTScore, MaxEquivLTScore, Rating FROM v_stRatings
WHERE RatingProvider=? and Strategy=?
ORDER BY MaxEquivLTScore
"""
cursor.execute(sql_query, (rating_provider, strat))
translation_dict = pd.DataFrame.from_records(
cursor.fetchall(), columns=["MinScore", "MaxScore", "Rating"]
)
return translation_dict
def _scores_to_warf() -> dict[int, int]:
"""Create translation dictionary to translate from scores to WARFs."""
sql_query = "SELECT RatingScore, WARF FROM WARFs"
cursor.execute(sql_query)
return dict(cursor.fetchall())
# connect to database
connection = sqlite3.connect(str(RATINGS_DB))
cursor = connection.cursor()
if translation_table == "rtg_to_scores":
translation_dict = _rtg_to_scores(tenor=tenor)
elif translation_table == "scores_to_rtg":
translation_dict = _scores_to_rtg(tenor=tenor, strat=st_rtg_strategy)
else:
translation_dict = _scores_to_warf()
# close database connection
connection.close()
return translation_dict
| (translation_table: str, rating_provider: Optional[str] = None, tenor: str = 'long-term', st_rtg_strategy: str = 'base') -> dict | pandas.core.frame.DataFrame |
20,542 | pyratings.consolidate | consolidate_ratings | Consolidate ratings on a security level basis across rating agencies .
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
method
Defines the method that will be used in order to consolidate the ratings on a
security level basis across rating agencies.
Valid methods are {"best", "second_best", "worst"}.
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
rating_provider_output
Indicates which rating scale will be used for output results.
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Consolidated ratings on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
Identify the best ratings:
>>> consolidate_ratings(
... ratings=ratings_df,
... method="best",
... rating_provider_input=["S&P", "Moody", "Fitch"],
... rating_provider_output="Moody",
... )
0 Aaa
1 Aa3
2 Aa1
3 Ba3
4 Ca
Name: best_rtg, dtype: object
Identify the second-best ratings:
>>> consolidate_ratings(
... ratings=ratings_df,
... method="second_best",
... rating_provider_input=["S&P", "Moody", "Fitch"],
... rating_provider_output="DBRS",
... )
0 AAH
1 AAL
2 AA
3 BBL
4 C
Name: second_best_rtg, dtype: object
Identify the worst ratings:
>>> consolidate_ratings(
... ratings=ratings_df,
... method="worst",
... rating_provider_input=["S&P", "Moody", "Fitch"]
... )
0 AA-
1 AA-
2 AA-
3 B+
4 C
Name: worst_rtg, dtype: object
| def consolidate_ratings(
ratings: pd.DataFrame,
method: Literal["best", "second_best", "worst"] = "worst",
rating_provider_input: list[str] = None,
rating_provider_output: Literal[
"Fitch", "Moody", "S&P", "Bloomberg", "DBRS"
] = "S&P",
tenor: Literal["long-term", "short-term"] = "long-term",
) -> pd.Series:
"""Consolidate ratings on a security level basis across rating agencies .
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
method
Defines the method that will be used in order to consolidate the ratings on a
security level basis across rating agencies.
Valid methods are {"best", "second_best", "worst"}.
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
rating_provider_output
Indicates which rating scale will be used for output results.
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Consolidated ratings on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
Identify the best ratings:
>>> consolidate_ratings(
... ratings=ratings_df,
... method="best",
... rating_provider_input=["S&P", "Moody", "Fitch"],
... rating_provider_output="Moody",
... )
0 Aaa
1 Aa3
2 Aa1
3 Ba3
4 Ca
Name: best_rtg, dtype: object
Identify the second-best ratings:
>>> consolidate_ratings(
... ratings=ratings_df,
... method="second_best",
... rating_provider_input=["S&P", "Moody", "Fitch"],
... rating_provider_output="DBRS",
... )
0 AAH
1 AAL
2 AA
3 BBL
4 C
Name: second_best_rtg, dtype: object
Identify the worst ratings:
>>> consolidate_ratings(
... ratings=ratings_df,
... method="worst",
... rating_provider_input=["S&P", "Moody", "Fitch"]
... )
0 AA-
1 AA-
2 AA-
3 B+
4 C
Name: worst_rtg, dtype: object
"""
func = {
"best": get_best_scores,
"second_best": get_second_best_scores,
"worst": get_worst_scores,
}
# translate ratings -> scores
rating_scores_series = func[method](
ratings, rating_provider_input=rating_provider_input, tenor=tenor
)
# convert back to ratings
ratings_series = get_ratings_from_scores(
rating_scores=rating_scores_series,
rating_provider=rating_provider_output,
tenor=tenor,
)
ratings_series.name = f"{method}_rtg"
return ratings_series
| (ratings: pandas.core.frame.DataFrame, method: Literal['best', 'second_best', 'worst'] = 'worst', rating_provider_input: Optional[list[str]] = None, rating_provider_output: Literal['Fitch', 'Moody', 'S&P', 'Bloomberg', 'DBRS'] = 'S&P', tenor: Literal['long-term', 'short-term'] = 'long-term') -> pandas.core.series.Series |
20,543 | pyratings.consolidate | get_best_ratings | Compute the best rating on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
rating_provider_output
Indicates which rating scale will be used for output results.
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Best ratings on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_best_ratings(ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"])
0 AAA
1 AA-
2 AA+
3 BB-
4 CC
Name: best_rtg, dtype: object
| def get_best_ratings(
ratings: pd.DataFrame,
rating_provider_input: list[str] = None,
rating_provider_output: Literal[
"Fitch", "Moody", "S&P", "Bloomberg", "DBRS"
] = "S&P",
tenor: Literal["long-term", "short-term"] = "long-term",
) -> pd.Series:
"""Compute the best rating on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
rating_provider_output
Indicates which rating scale will be used for output results.
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Best ratings on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_best_ratings(ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"])
0 AAA
1 AA-
2 AA+
3 BB-
4 CC
Name: best_rtg, dtype: object
"""
ratings_series = consolidate_ratings(
method="best",
ratings=ratings,
rating_provider_input=rating_provider_input,
rating_provider_output=rating_provider_output,
tenor=tenor,
)
return ratings_series
| (ratings: pandas.core.frame.DataFrame, rating_provider_input: Optional[list[str]] = None, rating_provider_output: Literal['Fitch', 'Moody', 'S&P', 'Bloomberg', 'DBRS'] = 'S&P', tenor: Literal['long-term', 'short-term'] = 'long-term') -> pandas.core.series.Series |
20,544 | pyratings.consolidate | get_best_scores | Compute the best rating scores on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Best rating scores on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_best_scores(
... ratings=ratings_df,
... rating_provider_input=["S&P", "Moody", "Fitch"]
... )
0 1
1 4
2 2
3 13
4 20
Name: best_scores, dtype: int64
| def get_best_scores(
ratings: pd.DataFrame,
rating_provider_input: list[str] = None,
tenor: Literal["long-term", "short-term"] = "long-term",
) -> pd.Series:
"""Compute the best rating scores on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Best rating scores on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_best_scores(
... ratings=ratings_df,
... rating_provider_input=["S&P", "Moody", "Fitch"]
... )
0 1
1 4
2 2
3 13
4 20
Name: best_scores, dtype: int64
"""
rating_scores_df = get_scores_from_ratings(
ratings=ratings, rating_provider=rating_provider_input, tenor=tenor
)
rating_scores_series = rating_scores_df.min(axis=1)
rating_scores_series.name = "best_scores"
return rating_scores_series
| (ratings: pandas.core.frame.DataFrame, rating_provider_input: Optional[list[str]] = None, tenor: Literal['long-term', 'short-term'] = 'long-term') -> pandas.core.series.Series |
20,545 | pyratings.clean | get_pure_ratings | Remove rating watches/outlooks and other non-actual-rating related information.
Ratings may contain watch, such as 'AA- *+', 'BBB+ (CwNegative)'.
Outlook/watch should be seperated by a blank from the actual rating.
Also, ratings may also contain the letter 'u' (unsolicited) or be prefixed by
'(P)' (public information only).
This kind of information will be removed to retrieve the actual rating(s).
Parameters
----------
ratings
Uncleaned rating(s).
Returns
-------
Union[str, pd.Series, pd.DataFrame]
Regular ratings stripped off of watches. The name of the resulting Series or
the columns of the returning DataFrame will be suffixed with `_clean`.
Examples
--------
Cleaning a single rating:
>>> get_pure_ratings("AA- *+")
'AA-'
>>> get_pure_ratings("Au")
'A'
>>> get_pure_ratings("(P)P-2")
'P-2'
Cleaning a `pd.Series`:
>>> import numpy as np
>>> import pandas as pd
>>> rating_series=pd.Series(
... data=[
... "BB+ *-",
... "(P)BBB *+",
... np.nan,
... "AA- (Developing)",
... np.nan,
... "CCC+ (CwPositive)",
... "BB+u",
... ],
... name="rtg_SP",
... )
>>> get_pure_ratings(rating_series)
0 BB+
1 BBB
2 NaN
3 AA-
4 NaN
5 CCC+
6 BB+
Name: rtg_SP_clean, dtype: object
Cleaning a `pd.DataFrame`:
>>> rtg_df = pd.DataFrame(
... data={
... "rtg_SP": [
... "BB+ *-",
... "BBB *+",
... np.nan,
... "AA- (Developing)",
... np.nan,
... "CCC+ (CwPositive)",
... "BB+u",
... ],
... "rtg_Fitch": [
... "BB+ *-",
... "BBB *+",
... pd.NA,
... "AA- (Developing)",
... np.nan,
... "CCC+ (CwPositive)",
... "BB+u",
... ],
... },
... )
>>> get_pure_ratings(rtg_df)
rtg_SP_clean rtg_Fitch_clean
0 BB+ BB+
1 BBB BBB
2 NaN <NA>
3 AA- AA-
4 NaN NaN
5 CCC+ CCC+
6 BB+ BB+
| def get_pure_ratings(
ratings: str | pd.Series | pd.DataFrame,
) -> str | pd.Series | pd.DataFrame:
"""Remove rating watches/outlooks and other non-actual-rating related information.
Ratings may contain watch, such as 'AA- *+', 'BBB+ (CwNegative)'.
Outlook/watch should be seperated by a blank from the actual rating.
Also, ratings may also contain the letter 'u' (unsolicited) or be prefixed by
'(P)' (public information only).
This kind of information will be removed to retrieve the actual rating(s).
Parameters
----------
ratings
Uncleaned rating(s).
Returns
-------
Union[str, pd.Series, pd.DataFrame]
Regular ratings stripped off of watches. The name of the resulting Series or
the columns of the returning DataFrame will be suffixed with `_clean`.
Examples
--------
Cleaning a single rating:
>>> get_pure_ratings("AA- *+")
'AA-'
>>> get_pure_ratings("Au")
'A'
>>> get_pure_ratings("(P)P-2")
'P-2'
Cleaning a `pd.Series`:
>>> import numpy as np
>>> import pandas as pd
>>> rating_series=pd.Series(
... data=[
... "BB+ *-",
... "(P)BBB *+",
... np.nan,
... "AA- (Developing)",
... np.nan,
... "CCC+ (CwPositive)",
... "BB+u",
... ],
... name="rtg_SP",
... )
>>> get_pure_ratings(rating_series)
0 BB+
1 BBB
2 NaN
3 AA-
4 NaN
5 CCC+
6 BB+
Name: rtg_SP_clean, dtype: object
Cleaning a `pd.DataFrame`:
>>> rtg_df = pd.DataFrame(
... data={
... "rtg_SP": [
... "BB+ *-",
... "BBB *+",
... np.nan,
... "AA- (Developing)",
... np.nan,
... "CCC+ (CwPositive)",
... "BB+u",
... ],
... "rtg_Fitch": [
... "BB+ *-",
... "BBB *+",
... pd.NA,
... "AA- (Developing)",
... np.nan,
... "CCC+ (CwPositive)",
... "BB+u",
... ],
... },
... )
>>> get_pure_ratings(rtg_df)
rtg_SP_clean rtg_Fitch_clean
0 BB+ BB+
1 BBB BBB
2 NaN <NA>
3 AA- AA-
4 NaN NaN
5 CCC+ CCC+
6 BB+ BB+
"""
if isinstance(ratings, str):
ratings = (
ratings.split()[0].rstrip("uU").removeprefix("(p)").removeprefix("(P)")
)
return ratings
elif isinstance(ratings, pd.Series):
# identify string occurrences
isstring = ratings.apply(type).eq(str)
# strip string after occurrence of very first blank and strip character 'u',
# which has usually been added without a blank;
# also remove suffix '(p)' and '(P)'
ratings[isstring] = ratings[isstring].str.split().str[0]
ratings[isstring] = ratings[isstring].str.rstrip("uU")
ratings[isstring] = ratings[isstring].str.removeprefix("(p)")
ratings[isstring] = ratings[isstring].str.removeprefix("(P)")
ratings.name = f"{ratings.name}_clean"
return ratings
elif isinstance(ratings, pd.DataFrame):
# Recursive call of `get_pure_ratings`
return pd.concat(
[get_pure_ratings(ratings=ratings[col]) for col in ratings.columns], axis=1
)
| (ratings: str | pandas.core.series.Series | pandas.core.frame.DataFrame) -> str | pandas.core.series.Series | pandas.core.frame.DataFrame |
20,547 | pyratings.get_ratings | get_ratings_from_scores | Convert numerical rating scores into regular ratings.
Parameters
----------
rating_scores
Numerical rating score(s).
rating_provider
Should contain any valid rating provider out of {"Fitch", "Moody's", "S&P",
"Bloomberg", "DBRS"}.
If None, `rating_provider` will be inferred from the series name or dataframe
column names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}.
short_term_strategy
Will only be used, if `tenor` is "short-term". Choose between three distinct
strategies in order to translate a long-term rating score into a short-term
rating. Must be in {"best", "base", "worst"}.
Compare
https://hsbc.github.io/pyratings/short-term-rating/#there's-one-more-catch...
- Strategy 1 (best):
Always choose the best possible short-term rating. That's the optimistic
approach.
- Strategy 2 (base-case):
Always choose the short-term rating that a rating agency would usually assign
if there aren't any special liquidity issues (positive or negative). That's
the base-case approach.
- Strategy 3 (worst):
Always choose the worst possible short-term rating. That's the conservative
approach.
Returns
-------
Union[str, pd.Series, pd.DataFrame]
Regular ratings according to `rating_provider`'s rating scale.
Raises
------
ValueError
If providing a single rating score and `rating_provider` is None.
Examples
--------
Converting a single long-term rating score:
>>> get_ratings_from_scores(rating_scores=9, rating_provider="Fitch")
'BBB'
Converting a single short-term rating score with different `short_term_strategy`
arguments:
>>> get_ratings_from_scores(
... rating_scores=10,
... rating_provider="DBRS",
... tenor="short-term",
... short_term_strategy="best",
... )
'R-2M'
>>> get_ratings_from_scores(
... rating_scores=10,
... rating_provider="DBRS",
... tenor="short-term",
... short_term_strategy="base",
... )
'R-3'
>>> get_ratings_from_scores(
... rating_scores=10,
... rating_provider="DBRS",
... tenor="short-term",
... short_term_strategy="worst",
... )
'R-3'
Converting a ``pd.Series`` with scores:
>>> import pandas as pd
>>> rating_scores_series = pd.Series(data=[5, 7, 1, np.nan, 22, pd.NA])
>>> get_ratings_from_scores(
... rating_scores=rating_scores_series,
... rating_provider="Moody's",
... tenor="long-term",
... )
0 A1
1 A3
2 Aaa
3 NaN
4 D
5 NaN
Name: rtg_Moody, dtype: object
Providing a ``pd.Series`` without specifying a `rating_provider`:
>>> rating_scores_series = pd.Series(
... data=[5, 7, 1, np.nan, 22, pd.NA],
... name="Moody",
... )
>>> get_ratings_from_scores(rating_scores=rating_scores_series)
0 A1
1 A3
2 Aaa
3 NaN
4 D
5 NaN
Name: rtg_Moody, dtype: object
Converting a ``pd.DataFrame`` with scores:
>>> rating_scores_df = pd.DataFrame(
... data=[[11, 16, "foo"], [4, 2, 1], [22, "bar", 22]]
... )
>>> get_ratings_from_scores(
... rating_scores=rating_scores_df,
... rating_provider=["Fitch", "Bloomberg", "DBRS"],
... tenor="long-term",
... )
rtg_Fitch rtg_Bloomberg rtg_DBRS
0 BB+ B- NaN
1 AA- AA+ AAA
2 D NaN D
When providing a ``pd.DataFrame`` without explicitly providing the
`rating_provider`, they will be inferred by the dataframe's columns.
>>> rating_scores_df = pd.DataFrame(
... data={
... "rtg_fitch": [11, 4, 22],
... "rtg_Bloomberg": [16, 2, "foo"],
... "DBRS Ratings": ["bar", 1, 22],
... }
... )
>>> get_ratings_from_scores(rating_scores=rating_scores_df)
rtg_Fitch rtg_Bloomberg rtg_DBRS
0 BB+ B- NaN
1 AA- AA+ AAA
2 D NaN D
| def get_ratings_from_scores(
rating_scores: int | float | pd.Series | pd.DataFrame,
rating_provider: str | list[str] | None = None,
tenor: str = "long-term",
short_term_strategy: str | None = None,
) -> str | pd.Series | pd.DataFrame:
"""Convert numerical rating scores into regular ratings.
Parameters
----------
rating_scores
Numerical rating score(s).
rating_provider
Should contain any valid rating provider out of {"Fitch", "Moody's", "S&P",
"Bloomberg", "DBRS"}.
If None, `rating_provider` will be inferred from the series name or dataframe
column names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}.
short_term_strategy
Will only be used, if `tenor` is "short-term". Choose between three distinct
strategies in order to translate a long-term rating score into a short-term
rating. Must be in {"best", "base", "worst"}.
Compare
https://hsbc.github.io/pyratings/short-term-rating/#there's-one-more-catch...
- Strategy 1 (best):
Always choose the best possible short-term rating. That's the optimistic
approach.
- Strategy 2 (base-case):
Always choose the short-term rating that a rating agency would usually assign
if there aren't any special liquidity issues (positive or negative). That's
the base-case approach.
- Strategy 3 (worst):
Always choose the worst possible short-term rating. That's the conservative
approach.
Returns
-------
Union[str, pd.Series, pd.DataFrame]
Regular ratings according to `rating_provider`'s rating scale.
Raises
------
ValueError
If providing a single rating score and `rating_provider` is None.
Examples
--------
Converting a single long-term rating score:
>>> get_ratings_from_scores(rating_scores=9, rating_provider="Fitch")
'BBB'
Converting a single short-term rating score with different `short_term_strategy`
arguments:
>>> get_ratings_from_scores(
... rating_scores=10,
... rating_provider="DBRS",
... tenor="short-term",
... short_term_strategy="best",
... )
'R-2M'
>>> get_ratings_from_scores(
... rating_scores=10,
... rating_provider="DBRS",
... tenor="short-term",
... short_term_strategy="base",
... )
'R-3'
>>> get_ratings_from_scores(
... rating_scores=10,
... rating_provider="DBRS",
... tenor="short-term",
... short_term_strategy="worst",
... )
'R-3'
Converting a ``pd.Series`` with scores:
>>> import pandas as pd
>>> rating_scores_series = pd.Series(data=[5, 7, 1, np.nan, 22, pd.NA])
>>> get_ratings_from_scores(
... rating_scores=rating_scores_series,
... rating_provider="Moody's",
... tenor="long-term",
... )
0 A1
1 A3
2 Aaa
3 NaN
4 D
5 NaN
Name: rtg_Moody, dtype: object
Providing a ``pd.Series`` without specifying a `rating_provider`:
>>> rating_scores_series = pd.Series(
... data=[5, 7, 1, np.nan, 22, pd.NA],
... name="Moody",
... )
>>> get_ratings_from_scores(rating_scores=rating_scores_series)
0 A1
1 A3
2 Aaa
3 NaN
4 D
5 NaN
Name: rtg_Moody, dtype: object
Converting a ``pd.DataFrame`` with scores:
>>> rating_scores_df = pd.DataFrame(
... data=[[11, 16, "foo"], [4, 2, 1], [22, "bar", 22]]
... )
>>> get_ratings_from_scores(
... rating_scores=rating_scores_df,
... rating_provider=["Fitch", "Bloomberg", "DBRS"],
... tenor="long-term",
... )
rtg_Fitch rtg_Bloomberg rtg_DBRS
0 BB+ B- NaN
1 AA- AA+ AAA
2 D NaN D
When providing a ``pd.DataFrame`` without explicitly providing the
`rating_provider`, they will be inferred by the dataframe's columns.
>>> rating_scores_df = pd.DataFrame(
... data={
... "rtg_fitch": [11, 4, 22],
... "rtg_Bloomberg": [16, 2, "foo"],
... "DBRS Ratings": ["bar", 1, 22],
... }
... )
>>> get_ratings_from_scores(rating_scores=rating_scores_df)
rtg_Fitch rtg_Bloomberg rtg_DBRS
0 BB+ B- NaN
1 AA- AA+ AAA
2 D NaN D
"""
if tenor == "short-term" and short_term_strategy is None:
short_term_strategy = "base"
if tenor == "short-term" and short_term_strategy not in ["best", "base", "worst"]:
raise ValueError(
"Invalid short_term_strategy. Must be in ['best', 'base', 'worst']."
)
if isinstance(rating_scores, (int, float, np.number)):
if rating_provider is None:
raise ValueError(VALUE_ERROR_PROVIDER_MANDATORY)
rating_provider = _extract_rating_provider(
rating_provider=rating_provider,
valid_rtg_provider=valid_rtg_agncy[tenor],
)
rtg_dict = _get_translation_dict(
"scores_to_rtg",
rating_provider=rating_provider,
tenor=tenor,
st_rtg_strategy=short_term_strategy,
)
if not np.isnan(rating_scores):
rating_scores = int(Decimal(f"{rating_scores}").quantize(0, ROUND_HALF_UP))
if tenor == "long-term":
return rtg_dict.get(rating_scores, pd.NA)
else:
try:
return rtg_dict.loc[
(rating_scores >= rtg_dict["MinScore"])
& (rating_scores <= rtg_dict["MaxScore"]),
"Rating",
].iloc[0]
except IndexError:
return np.nan
elif isinstance(rating_scores, pd.Series):
if rating_provider is None:
rating_provider = _extract_rating_provider(
rating_provider=rating_scores.name,
valid_rtg_provider=valid_rtg_agncy[tenor],
)
else:
rating_provider = _extract_rating_provider(
rating_provider=rating_provider,
valid_rtg_provider=valid_rtg_agncy[tenor],
)
rtg_dict = _get_translation_dict(
"scores_to_rtg",
rating_provider,
tenor=tenor,
st_rtg_strategy=short_term_strategy,
)
# round element to full integer, if element is number
rating_scores = rating_scores.apply(
lambda x: np.round(x, 0) if isinstance(x, (int, float, np.number)) else x
)
if tenor == "long-term":
return pd.Series(
data=rating_scores.map(rtg_dict), name=f"rtg_{rating_provider}"
)
else:
out = []
for score in rating_scores:
try:
out.append(
rtg_dict.loc[
(score >= rtg_dict["MinScore"])
& (score <= rtg_dict["MaxScore"]),
"Rating",
].iloc[0]
)
except (IndexError, TypeError):
out.append(pd.NA)
return pd.Series(data=out, name=f"rtg_{rating_provider}")
elif isinstance(rating_scores, pd.DataFrame):
if rating_provider is None:
rating_provider = _extract_rating_provider(
rating_provider=rating_scores.columns.to_list(),
valid_rtg_provider=valid_rtg_agncy[tenor],
)
else:
rating_provider = _extract_rating_provider(
rating_provider=rating_provider,
valid_rtg_provider=valid_rtg_agncy[tenor],
)
# Recursive call of 'get_ratings_from_score' for every column in dataframe
return pd.concat(
[
get_ratings_from_scores(
rating_scores=rating_scores[col],
rating_provider=provider,
tenor=tenor,
short_term_strategy=short_term_strategy,
)
for col, provider in zip( # noqa: B905
rating_scores.columns, rating_provider
)
],
axis=1,
)
| (rating_scores: int | float | pandas.core.series.Series | pandas.core.frame.DataFrame, rating_provider: Union[str, list[str], NoneType] = None, tenor: str = 'long-term', short_term_strategy: Optional[str] = None) -> str | pandas.core.series.Series | pandas.core.frame.DataFrame |
20,548 | pyratings.get_ratings | get_ratings_from_warf | Convert WARFs into regular ratings.
Parameters
----------
warf
Numerical WARF(s).
rating_provider
Should contain any valid rating provider out of {"Fitch", "Moody's", "S&P",
"Bloomberg", "DBRS"}.
Returns
-------
Union[str, pd.Series, pd.DataFrame]
Regular rating(s) according to `rating_provider`'s rating scale.
Examples
--------
Converting a single WARF:
>>> get_ratings_from_warf(warf=610, rating_provider="DBRS")
'BBBL'
>>> get_ratings_from_warf(warf=1234.5678, rating_provider="SP")
'BB'
Converting a ``pd.Series`` with WARFs:
>>> import pandas as pd
>>> warf_series = pd.Series(data=[90, 218.999, 1, np.nan, 10000, pd.NA])
>>> get_ratings_from_warf(
... warf=warf_series,
... rating_provider="Moody's",
... )
0 A1
1 A3
2 Aaa
3 NaN
4 D
5 NaN
Name: rtg_Moody, dtype: object
Converting a ``pd.DataFrame`` with WARFs:
>>> warf_df = pd.DataFrame(
... data=[[940, 4000, "foo"], [54, 13.5, 1], [10000, "bar", 9999]]
... )
>>> get_ratings_from_warf(
... warf=warf_df,
... rating_provider=["Fitch", "Bloomberg", "DBRS"],
... )
rtg_Fitch rtg_Bloomberg rtg_DBRS
0 BB+ B- NaN
1 AA- AA+ AAA
2 D NaN C
| def get_ratings_from_warf(
warf: int | float | pd.Series | pd.DataFrame,
rating_provider: str | list[str] | None = None,
) -> str | pd.Series | pd.DataFrame:
"""Convert WARFs into regular ratings.
Parameters
----------
warf
Numerical WARF(s).
rating_provider
Should contain any valid rating provider out of {"Fitch", "Moody's", "S&P",
"Bloomberg", "DBRS"}.
Returns
-------
Union[str, pd.Series, pd.DataFrame]
Regular rating(s) according to `rating_provider`'s rating scale.
Examples
--------
Converting a single WARF:
>>> get_ratings_from_warf(warf=610, rating_provider="DBRS")
'BBBL'
>>> get_ratings_from_warf(warf=1234.5678, rating_provider="SP")
'BB'
Converting a ``pd.Series`` with WARFs:
>>> import pandas as pd
>>> warf_series = pd.Series(data=[90, 218.999, 1, np.nan, 10000, pd.NA])
>>> get_ratings_from_warf(
... warf=warf_series,
... rating_provider="Moody's",
... )
0 A1
1 A3
2 Aaa
3 NaN
4 D
5 NaN
Name: rtg_Moody, dtype: object
Converting a ``pd.DataFrame`` with WARFs:
>>> warf_df = pd.DataFrame(
... data=[[940, 4000, "foo"], [54, 13.5, 1], [10000, "bar", 9999]]
... )
>>> get_ratings_from_warf(
... warf=warf_df,
... rating_provider=["Fitch", "Bloomberg", "DBRS"],
... )
rtg_Fitch rtg_Bloomberg rtg_DBRS
0 BB+ B- NaN
1 AA- AA+ AAA
2 D NaN C
"""
if isinstance(warf, (int, float, np.number)):
if rating_provider is None:
raise ValueError(VALUE_ERROR_PROVIDER_MANDATORY)
rating_provider = _extract_rating_provider(
rating_provider=rating_provider,
valid_rtg_provider=valid_rtg_agncy["long-term"],
)
rating_scores = get_scores_from_warf(warf=warf)
return get_ratings_from_scores(
rating_scores=rating_scores,
rating_provider=rating_provider,
tenor="long-term",
)
elif isinstance(warf, (pd.Series, pd.DataFrame)):
rating_scores = get_scores_from_warf(warf=warf)
return get_ratings_from_scores(
rating_scores=rating_scores,
rating_provider=rating_provider,
tenor="long-term",
)
| (warf: int | float | pandas.core.series.Series | pandas.core.frame.DataFrame, rating_provider: Union[str, list[str], NoneType] = None) -> str | pandas.core.series.Series | pandas.core.frame.DataFrame |
20,550 | pyratings.get_scores | get_scores_from_ratings | Convert regular ratings into numerical rating scores.
Parameters
----------
ratings
Rating(s) to be translated into rating score(s).
rating_provider
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider` will be inferred from the series name or dataframe
column names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
Union[int, pd.Series, pd.DataFrame]
Numerical rating score(s)
If returns a ``pd.Series``, the series name will be `rtg_score`
suffixed by `ratings.name`.
If return a ``pd.DataFrame``, the column names will be `rtg_score` suffixed
by the respective `ratings.columns`.
Raises
------
ValueError
If providing a single rating and `rating_provider` is None.
Examples
--------
Converting a single long-term rating:
>>> get_scores_from_ratings(
... ratings="BBB-", rating_provider="S&P", tenor="long-term"
... )
10
Converting a single short-term rating score:
>>> get_scores_from_ratings(
... ratings="P-1", rating_provider="Moody", tenor="short-term"
... )
3.5
Converting a ``pd.Series`` of ratings:
>>> import pandas as pd
>>> ratings_series = pd.Series(
... data=["Baa1", "C", "NR", "WD", "D", "B1", "SD"], name='Moody'
... )
>>> get_scores_from_ratings(
... ratings=ratings_series, rating_provider="Moody's", tenor="long-term"
... )
0 8.0
1 21.0
2 NaN
3 NaN
4 22.0
5 14.0
6 22.0
Name: rtg_score_Moody, dtype: float64
Providing a ``pd.Series`` without specifying a `rating_provider`:
>>> ratings_series = pd.Series(
... data=["Baa1", "C", "NR", "WD", "D", "B1", "SD"], name="Moody"
... )
>>> get_scores_from_ratings(ratings=ratings_series)
0 8.0
1 21.0
2 NaN
3 NaN
4 22.0
5 14.0
6 22.0
Name: rtg_score_Moody, dtype: float64
Converting a ``pd.DataFrame`` with ratings:
>>> ratings_df = pd.DataFrame(
... data=[["BB+", "B3", "BBB-"], ["AA-", "Aa1", "AAA"], ["D", "NR", "D"]],
... columns=["SP", "Moody", "DBRS"],
... )
>>> get_scores_from_ratings(
... ratings=ratings_df,
... rating_provider=["S&P", "Moody's", "DBRS"],
... tenor="long-term",
... )
rtg_score_SP rtg_score_Moody rtg_score_DBRS
0 11 16.0 NaN
1 4 2.0 1.0
2 22 NaN 22.0
When providing a ``pd.DataFrame`` without explicitly providing the
`rating_provider`, they will be inferred from the dataframe's columns.
>>> ratings_df = pd.DataFrame(
... data={
... "rtg_fitch": ["BB+", "AA-", "D"],
... "rtg_Bloomberg": ["B-", "AA+", "NR"],
... "DBRS Ratings": ["BBB-", "AAA", "D"],
... }
... )
>>> get_scores_from_ratings(ratings=ratings_df)
rtg_score_rtg_fitch rtg_score_rtg_Bloomberg rtg_score_DBRS Ratings
0 11 16.0 NaN
1 4 2.0 1.0
2 22 NaN 22.0
| def get_scores_from_ratings(
ratings: str | pd.Series | pd.DataFrame,
rating_provider: str | list[str] | None = None,
tenor: str = "long-term",
) -> int | pd.Series | pd.DataFrame:
"""Convert regular ratings into numerical rating scores.
Parameters
----------
ratings
Rating(s) to be translated into rating score(s).
rating_provider
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider` will be inferred from the series name or dataframe
column names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
Union[int, pd.Series, pd.DataFrame]
Numerical rating score(s)
If returns a ``pd.Series``, the series name will be `rtg_score`
suffixed by `ratings.name`.
If return a ``pd.DataFrame``, the column names will be `rtg_score` suffixed
by the respective `ratings.columns`.
Raises
------
ValueError
If providing a single rating and `rating_provider` is None.
Examples
--------
Converting a single long-term rating:
>>> get_scores_from_ratings(
... ratings="BBB-", rating_provider="S&P", tenor="long-term"
... )
10
Converting a single short-term rating score:
>>> get_scores_from_ratings(
... ratings="P-1", rating_provider="Moody", tenor="short-term"
... )
3.5
Converting a ``pd.Series`` of ratings:
>>> import pandas as pd
>>> ratings_series = pd.Series(
... data=["Baa1", "C", "NR", "WD", "D", "B1", "SD"], name='Moody'
... )
>>> get_scores_from_ratings(
... ratings=ratings_series, rating_provider="Moody's", tenor="long-term"
... )
0 8.0
1 21.0
2 NaN
3 NaN
4 22.0
5 14.0
6 22.0
Name: rtg_score_Moody, dtype: float64
Providing a ``pd.Series`` without specifying a `rating_provider`:
>>> ratings_series = pd.Series(
... data=["Baa1", "C", "NR", "WD", "D", "B1", "SD"], name="Moody"
... )
>>> get_scores_from_ratings(ratings=ratings_series)
0 8.0
1 21.0
2 NaN
3 NaN
4 22.0
5 14.0
6 22.0
Name: rtg_score_Moody, dtype: float64
Converting a ``pd.DataFrame`` with ratings:
>>> ratings_df = pd.DataFrame(
... data=[["BB+", "B3", "BBB-"], ["AA-", "Aa1", "AAA"], ["D", "NR", "D"]],
... columns=["SP", "Moody", "DBRS"],
... )
>>> get_scores_from_ratings(
... ratings=ratings_df,
... rating_provider=["S&P", "Moody's", "DBRS"],
... tenor="long-term",
... )
rtg_score_SP rtg_score_Moody rtg_score_DBRS
0 11 16.0 NaN
1 4 2.0 1.0
2 22 NaN 22.0
When providing a ``pd.DataFrame`` without explicitly providing the
`rating_provider`, they will be inferred from the dataframe's columns.
>>> ratings_df = pd.DataFrame(
... data={
... "rtg_fitch": ["BB+", "AA-", "D"],
... "rtg_Bloomberg": ["B-", "AA+", "NR"],
... "DBRS Ratings": ["BBB-", "AAA", "D"],
... }
... )
>>> get_scores_from_ratings(ratings=ratings_df)
rtg_score_rtg_fitch rtg_score_rtg_Bloomberg rtg_score_DBRS Ratings
0 11 16.0 NaN
1 4 2.0 1.0
2 22 NaN 22.0
"""
if isinstance(ratings, str):
if rating_provider is None:
raise ValueError(VALUE_ERROR_PROVIDER_MANDATORY)
rating_provider = _extract_rating_provider(
rating_provider=rating_provider,
valid_rtg_provider=valid_rtg_agncy[tenor],
)
rtg_dict = _get_translation_dict(
"rtg_to_scores",
rating_provider,
tenor=tenor,
st_rtg_strategy="base",
)
return rtg_dict.get(ratings, pd.NA)
elif isinstance(ratings, pd.Series):
if rating_provider is None:
rating_provider = _extract_rating_provider(
rating_provider=ratings.name,
valid_rtg_provider=valid_rtg_agncy[tenor],
)
else:
rating_provider = _extract_rating_provider(
rating_provider=rating_provider,
valid_rtg_provider=valid_rtg_agncy[tenor],
)
rtg_dict = _get_translation_dict(
"rtg_to_scores",
rating_provider,
tenor=tenor,
st_rtg_strategy="base",
)
return pd.Series(data=ratings.map(rtg_dict), name=f"rtg_score_{ratings.name}")
elif isinstance(ratings, pd.DataFrame):
if rating_provider is None:
rating_provider = _extract_rating_provider(
rating_provider=ratings.columns.to_list(),
valid_rtg_provider=valid_rtg_agncy[tenor],
)
else:
rating_provider = _extract_rating_provider(
rating_provider=rating_provider,
valid_rtg_provider=valid_rtg_agncy[tenor],
)
# Recursive call of `get_scores_from_ratings`
return pd.concat(
[
get_scores_from_ratings(
ratings=ratings[col],
rating_provider=provider,
tenor=tenor,
)
for col, provider in zip(ratings.columns, rating_provider) # noqa: B905
],
axis=1,
)
| (ratings: str | pandas.core.series.Series | pandas.core.frame.DataFrame, rating_provider: Union[str, list[str], NoneType] = None, tenor: str = 'long-term') -> int | pandas.core.series.Series | pandas.core.frame.DataFrame |
20,551 | pyratings.get_scores | get_scores_from_warf | Convert weighted average rating factors (WARFs) into numerical rating scores.
Parameters
----------
warf
Weighted average rating factor (WARF).
Returns
-------
Union[int, float, pd.Series, pd.DataFrame]
Numerical rating score(s).
Examples
--------
Converting a single WARF:
>>> get_scores_from_warf(500)
10
>>> get_scores_from_warf(1992.9999)
13
Converting a ``pd.Series`` of WARFs:
>>> import numpy as np
>>> import pandas as pd
>>> warf_series = pd.Series(data=[260, 9999.49, np.nan, 10000, 2469.99, 2470])
>>> get_scores_from_warf(warf=warf_series)
0 8.0
1 21.0
2 NaN
3 22.0
4 14.0
5 15.0
Name: rtg_score, dtype: float64
Converting a ``pd.DataFrame`` of WARFs:
>>> warf_df = pd.DataFrame(
... data={
... "provider1": [900, 40, 10000],
... "provider2": [3000, 10, np.nan],
... "provider3": [610, 1, 9999.49],
... }
... )
>>> get_scores_from_warf(warf=warf_df)
rtg_score_provider1 rtg_score_provider2 rtg_score_provider3
0 11 15.0 10
1 4 2.0 1
2 22 NaN 21
| def get_scores_from_warf(
warf: int | float | pd.Series | pd.DataFrame,
) -> int | float | pd.Series | pd.DataFrame:
"""Convert weighted average rating factors (WARFs) into numerical rating scores.
Parameters
----------
warf
Weighted average rating factor (WARF).
Returns
-------
Union[int, float, pd.Series, pd.DataFrame]
Numerical rating score(s).
Examples
--------
Converting a single WARF:
>>> get_scores_from_warf(500)
10
>>> get_scores_from_warf(1992.9999)
13
Converting a ``pd.Series`` of WARFs:
>>> import numpy as np
>>> import pandas as pd
>>> warf_series = pd.Series(data=[260, 9999.49, np.nan, 10000, 2469.99, 2470])
>>> get_scores_from_warf(warf=warf_series)
0 8.0
1 21.0
2 NaN
3 22.0
4 14.0
5 15.0
Name: rtg_score, dtype: float64
Converting a ``pd.DataFrame`` of WARFs:
>>> warf_df = pd.DataFrame(
... data={
... "provider1": [900, 40, 10000],
... "provider2": [3000, 10, np.nan],
... "provider3": [610, 1, 9999.49],
... }
... )
>>> get_scores_from_warf(warf=warf_df)
rtg_score_provider1 rtg_score_provider2 rtg_score_provider3
0 11 15.0 10
1 4 2.0 1
2 22 NaN 21
"""
def _get_scores_from_warf_db(
wrf: int | float | pd.Series | pd.DataFrame,
) -> int | float:
if not isinstance(wrf, (int, float, np.number) or np.isnan(wrf)) or not (
1 <= wrf <= 10_000
):
return np.nan
else:
if wrf == 10_000:
return 22
else:
# connect to database
connection = sqlite3.connect(RATINGS_DB)
cursor = connection.cursor()
# create SQL query
sql_query = (
"SELECT RatingScore FROM WARFs WHERE ? >= MinWARF and ? < MaxWARF"
)
# execute SQL query
cursor.execute(sql_query, (wrf, wrf))
rtg_score = cursor.fetchall()
# close database connection
connection.close()
return rtg_score[0][0]
if isinstance(warf, (int, float, np.number)):
return _get_scores_from_warf_db(warf)
elif isinstance(warf, pd.Series):
rating_scores = warf.apply(_get_scores_from_warf_db)
rating_scores.name = "rtg_score"
return rating_scores
elif isinstance(warf, pd.DataFrame):
return warf.applymap(_get_scores_from_warf_db).add_prefix("rtg_score_")
| (warf: int | float | pandas.core.series.Series | pandas.core.frame.DataFrame) -> int | float | pandas.core.series.Series | pandas.core.frame.DataFrame |
20,552 | pyratings.consolidate | get_second_best_ratings | Compute the second-best rating on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
rating_provider_output
Indicates which rating scale will be used for output results.
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Second-best ratings on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_second_best_ratings(
... ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"]
... )
0 AA+
1 AA-
2 AA
3 BB-
4 C
Name: second_best_rtg, dtype: object
| def get_second_best_ratings(
ratings: pd.DataFrame,
rating_provider_input: list[str] = None,
rating_provider_output: Literal[
"Fitch", "Moody", "S&P", "Bloomberg", "DBRS"
] = "S&P",
tenor: Literal["long-term", "short-term"] = "long-term",
) -> pd.Series:
"""Compute the second-best rating on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
rating_provider_output
Indicates which rating scale will be used for output results.
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Second-best ratings on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_second_best_ratings(
... ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"]
... )
0 AA+
1 AA-
2 AA
3 BB-
4 C
Name: second_best_rtg, dtype: object
"""
ratings_series = consolidate_ratings(
method="second_best",
ratings=ratings,
rating_provider_input=rating_provider_input,
rating_provider_output=rating_provider_output,
tenor=tenor,
)
return ratings_series
| (ratings: pandas.core.frame.DataFrame, rating_provider_input: Optional[list[str]] = None, rating_provider_output: Literal['Fitch', 'Moody', 'S&P', 'Bloomberg', 'DBRS'] = 'S&P', tenor: Literal['long-term', 'short-term'] = 'long-term') -> pandas.core.series.Series |
20,553 | pyratings.consolidate | get_second_best_scores | Compute the second-best scores on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Second-best scores on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_second_best_scores(
... ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"]
... )
0 2.0
1 4.0
2 3.0
3 13.0
4 21.0
Name: second_best_scores, dtype: float64
| def get_second_best_scores(
ratings: pd.DataFrame,
rating_provider_input: list[str] = None,
tenor: Literal["long-term", "short-term"] = "long-term",
) -> pd.Series:
"""Compute the second-best scores on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_input` will be inferred from the dataframe column
names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Second-best scores on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_second_best_scores(
... ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"]
... )
0 2.0
1 4.0
2 3.0
3 13.0
4 21.0
Name: second_best_scores, dtype: float64
"""
rating_scores_df = get_scores_from_ratings(
ratings=ratings, rating_provider=rating_provider_input, tenor=tenor
)
# rank scores per security (axis=1)
scores_ranked_df = rating_scores_df.rank(axis=1, method="first", numeric_only=False)
# get column with rank of 2, if available, otherwise get column with rank 1
rating_scores_ranked_series = rating_scores_df[scores_ranked_df <= 2].max(axis=1)
rating_scores_ranked_series.name = "second_best_scores"
return rating_scores_ranked_series
| (ratings: pandas.core.frame.DataFrame, rating_provider_input: Optional[list[str]] = None, tenor: Literal['long-term', 'short-term'] = 'long-term') -> pandas.core.series.Series |
20,555 | pyratings.warf | get_warf_buffer | Compute WARF buffer.
The WARF buffer is the distance from current WARF to the next maxWARF level. It
determines the room until a further rating downgrade.
Parameters
----------
warf
Numerical WARF.
Returns
-------
Union[float, int]
WARF buffer.
Examples
--------
>>> get_warf_buffer(warf=480)
5.0
>>> get_warf_buffer(warf=54)
1.0
| def get_warf_buffer(warf: float | int) -> float | int:
"""Compute WARF buffer.
The WARF buffer is the distance from current WARF to the next maxWARF level. It
determines the room until a further rating downgrade.
Parameters
----------
warf
Numerical WARF.
Returns
-------
Union[float, int]
WARF buffer.
Examples
--------
>>> get_warf_buffer(warf=480)
5.0
>>> get_warf_buffer(warf=54)
1.0
"""
# connect to database
connection = sqlite3.connect(RATINGS_DB)
cursor = connection.cursor()
# create SQL query
sql_query = "SELECT MaxWARF FROM WARFs WHERE ? >= MinWARF and ? < MaxWARF"
# execute SQL query
cursor.execute(sql_query, (warf, warf))
max_warf = cursor.fetchall()
# close database connection
connection.close()
return max_warf[0][0] - warf
| (warf: float | int) -> float | int |
20,556 | pyratings.get_warf | get_warf_from_ratings | Convert regular rating(s) to numerical WARF(s).
Parameters
----------
ratings
Regular rating(s) to be translated into WARF(s).
rating_provider
Should contain any valid rating provider out of {"Fitch", "Moody's", "S&P",
"Bloomberg", "DBRS"}.
If None, `rating_provider` will be inferred from the series name or dataframe
column names.
Returns
-------
Union[int, pd.Series, pd.DataFrame]
Numerical WARF.
If returns a ``pd.Series``, the series name will be `warf` suffixed by
`ratings.name`.
If return a ``pd.DataFrame``, the column names will be `warf` suffixed
by the respective `ratings.columns`.
Examples
--------
Converting a single rating:
>>> get_warf_from_ratings(ratings="BB-", rating_provider="Fitch")
1766
Converting a ``pd.Series`` with ratings:
>>> import numpy as np
>>> import pandas as pd
>>> ratings_series = pd.Series(data=["A1", "A3", "Aaa", np.nan, "D", pd.NA])
>>> get_warf_from_ratings(
... ratings=ratings_series, rating_provider="Moody's"
... )
0 70.0
1 180.0
2 1.0
3 NaN
4 10000.0
5 NaN
Name: warf, dtype: float64
Providing a ``pd.Series`` without specifying a `rating_provider`:
>>> ratings_series = pd.Series(
... data=["A1", "A3", "Aaa", np.nan, "D", pd.NA],
... name="Moody's"
... )
>>> get_warf_from_ratings(ratings=ratings_series)
0 70.0
1 180.0
2 1.0
3 NaN
4 10000.0
5 NaN
Name: warf_Moody's, dtype: float64
Converting a ``pd.DataFrame`` with ratings:
>>> ratings_df = pd.DataFrame(
... data=[["BB+", "B-", "foo"], ["AA-", "AA+", "AAA"], ["D", "bar", "C"]],
... columns=["Fitch", "Bloomberg", "DBRS"],
... )
>>> get_warf_from_ratings(
... ratings= ratings_df, rating_provider=["Fitch", "Bloomberg", "DBRS"]
... )
warf_Fitch warf_Bloomberg warf_DBRS
0 940 3490.0 NaN
1 40 10.0 1.0
2 10000 NaN 9999.0
When providing a ``pd.DataFrame`` without explicitly providing the
`rating_provider`, they will be inferred by the dataframe's columns.
>>> ratings_df = pd.DataFrame(
... data={
... "rtg_fitch": ["BB+", "AA-", "D"],
... "rtg_Bloomberg": ["B-", "AA+", "bar"],
... "DBRS Ratings": ["foo", "AAA", "C"]
... }
... )
>>> get_warf_from_ratings(ratings=ratings_df)
warf_rtg_fitch warf_rtg_Bloomberg warf_DBRS Ratings
0 940 3490.0 NaN
1 40 10.0 1.0
2 10000 NaN 9999.0
| def get_warf_from_ratings(
ratings: str | pd.Series | pd.DataFrame,
rating_provider: str | list[str] | None = None,
) -> int | pd.Series | pd.DataFrame:
"""Convert regular rating(s) to numerical WARF(s).
Parameters
----------
ratings
Regular rating(s) to be translated into WARF(s).
rating_provider
Should contain any valid rating provider out of {"Fitch", "Moody's", "S&P",
"Bloomberg", "DBRS"}.
If None, `rating_provider` will be inferred from the series name or dataframe
column names.
Returns
-------
Union[int, pd.Series, pd.DataFrame]
Numerical WARF.
If returns a ``pd.Series``, the series name will be `warf` suffixed by
`ratings.name`.
If return a ``pd.DataFrame``, the column names will be `warf` suffixed
by the respective `ratings.columns`.
Examples
--------
Converting a single rating:
>>> get_warf_from_ratings(ratings="BB-", rating_provider="Fitch")
1766
Converting a ``pd.Series`` with ratings:
>>> import numpy as np
>>> import pandas as pd
>>> ratings_series = pd.Series(data=["A1", "A3", "Aaa", np.nan, "D", pd.NA])
>>> get_warf_from_ratings(
... ratings=ratings_series, rating_provider="Moody's"
... )
0 70.0
1 180.0
2 1.0
3 NaN
4 10000.0
5 NaN
Name: warf, dtype: float64
Providing a ``pd.Series`` without specifying a `rating_provider`:
>>> ratings_series = pd.Series(
... data=["A1", "A3", "Aaa", np.nan, "D", pd.NA],
... name="Moody's"
... )
>>> get_warf_from_ratings(ratings=ratings_series)
0 70.0
1 180.0
2 1.0
3 NaN
4 10000.0
5 NaN
Name: warf_Moody's, dtype: float64
Converting a ``pd.DataFrame`` with ratings:
>>> ratings_df = pd.DataFrame(
... data=[["BB+", "B-", "foo"], ["AA-", "AA+", "AAA"], ["D", "bar", "C"]],
... columns=["Fitch", "Bloomberg", "DBRS"],
... )
>>> get_warf_from_ratings(
... ratings= ratings_df, rating_provider=["Fitch", "Bloomberg", "DBRS"]
... )
warf_Fitch warf_Bloomberg warf_DBRS
0 940 3490.0 NaN
1 40 10.0 1.0
2 10000 NaN 9999.0
When providing a ``pd.DataFrame`` without explicitly providing the
`rating_provider`, they will be inferred by the dataframe's columns.
>>> ratings_df = pd.DataFrame(
... data={
... "rtg_fitch": ["BB+", "AA-", "D"],
... "rtg_Bloomberg": ["B-", "AA+", "bar"],
... "DBRS Ratings": ["foo", "AAA", "C"]
... }
... )
>>> get_warf_from_ratings(ratings=ratings_df)
warf_rtg_fitch warf_rtg_Bloomberg warf_DBRS Ratings
0 940 3490.0 NaN
1 40 10.0 1.0
2 10000 NaN 9999.0
"""
if rating_provider is not None:
rating_provider = _extract_rating_provider(
rating_provider=rating_provider,
valid_rtg_provider=valid_rtg_agncy["long-term"],
)
warf_dict = _get_translation_dict("scores_to_warf")
if isinstance(ratings, str):
rating_scores = get_scores_from_ratings(
ratings=ratings, rating_provider=rating_provider, tenor="long-term"
)
return warf_dict.get(rating_scores, np.nan)
elif isinstance(ratings, (pd.Series, pd.DataFrame)):
rating_scores = get_scores_from_ratings(
ratings=ratings, rating_provider=rating_provider, tenor="long-term"
)
if isinstance(ratings, pd.Series):
rating_scores.name = ratings.name
elif isinstance(ratings, pd.DataFrame):
rating_scores.columns = ratings.columns
return get_warf_from_scores(rating_scores=rating_scores)
| (ratings: str | pandas.core.series.Series | pandas.core.frame.DataFrame, rating_provider: Union[str, list[str], NoneType] = None) -> int | pandas.core.series.Series | pandas.core.frame.DataFrame |
20,557 | pyratings.get_warf | get_warf_from_scores | Convert numerical rating score(s) to numerical WARF(s).
Parameters
----------
rating_scores
Numerical rating score(s).
Returns
-------
Union[int, pd.Series, pd.DataFrame
Numerical WARF(s).
If returns a ``pd.Series``, the series name will be `warf` suffixed by
`rating_scores.name`.
If return a ``pd.DataFrame``, the column names will be `warf` suffixed
by the respective `rating_scores.columns`.
Examples
--------
Converting a single rating score:
>>> get_warf_from_scores(10)
610
Converting a ``pd.Series`` with rating scores:
>>> import pandas as pd
>>> rating_scores_series = pd.Series(data=[5, 7, 1, np.nan, 22, pd.NA])
>>> get_warf_from_scores(rating_scores=rating_scores_series)
0 70.0
1 180.0
2 1.0
3 NaN
4 10000.0
5 NaN
Name: warf, dtype: float64
Converting a ``pd.DataFrame`` with rating scores:
>>> rating_scores_df = pd.DataFrame(
... data=[[11, 16, "foo"], [4, 2, 1], [22, "bar", 22]],
... columns=["provider1", "provider2", "provider3"],
... )
>>> get_warf_from_scores(rating_scores=rating_scores_df)
warf_provider1 warf_provider2 warf_provider3
0 940 3490.0 NaN
1 40 10.0 1.0
2 10000 NaN 10000.0
| def get_warf_from_scores(
rating_scores: int | float | pd.Series | pd.DataFrame,
) -> int | pd.Series | pd.DataFrame:
"""Convert numerical rating score(s) to numerical WARF(s).
Parameters
----------
rating_scores
Numerical rating score(s).
Returns
-------
Union[int, pd.Series, pd.DataFrame
Numerical WARF(s).
If returns a ``pd.Series``, the series name will be `warf` suffixed by
`rating_scores.name`.
If return a ``pd.DataFrame``, the column names will be `warf` suffixed
by the respective `rating_scores.columns`.
Examples
--------
Converting a single rating score:
>>> get_warf_from_scores(10)
610
Converting a ``pd.Series`` with rating scores:
>>> import pandas as pd
>>> rating_scores_series = pd.Series(data=[5, 7, 1, np.nan, 22, pd.NA])
>>> get_warf_from_scores(rating_scores=rating_scores_series)
0 70.0
1 180.0
2 1.0
3 NaN
4 10000.0
5 NaN
Name: warf, dtype: float64
Converting a ``pd.DataFrame`` with rating scores:
>>> rating_scores_df = pd.DataFrame(
... data=[[11, 16, "foo"], [4, 2, 1], [22, "bar", 22]],
... columns=["provider1", "provider2", "provider3"],
... )
>>> get_warf_from_scores(rating_scores=rating_scores_df)
warf_provider1 warf_provider2 warf_provider3
0 940 3490.0 NaN
1 40 10.0 1.0
2 10000 NaN 10000.0
"""
warf_dict = _get_translation_dict("scores_to_warf")
if isinstance(rating_scores, (int, float, np.number)):
return warf_dict.get(rating_scores, np.nan)
elif isinstance(rating_scores, pd.Series):
warf = pd.Series(data=rating_scores.map(warf_dict))
if rating_scores.name is not None:
warf.name = "warf_" + str(rating_scores.name)
else:
warf.name = "warf"
return warf
elif isinstance(rating_scores, pd.DataFrame):
return rating_scores.apply(lambda x: x.map(warf_dict)).add_prefix("warf_")
| (rating_scores: int | float | pandas.core.series.Series | pandas.core.frame.DataFrame) -> int | pandas.core.series.Series | pandas.core.frame.DataFrame |
20,558 | pyratings.aggregate | get_weighted_average | Compute weighted average.
Parameters
----------
data
Contains numerical values.
weights
Contains weights (between 0 and 1) with respect to `data`.
Returns
-------
float
Weighted average data.
Notes
-----
Computing the weighted average is simply the sumproduct of `data` and `weights`.
``nan`` in `data` will be excluded from calculating the weighted average. All
corresponding weights will be ignored. As a matter of fact, the remaining
weights will be upscaled so that the weights of all ``non-nan`` rows in `data` will
sum up to 1 (100%).
Examples
--------
>>> import numpy as np
>>> import pandas as pd
>>> rtg_scores = pd.Series(data=[5, 7, 9])
>>> wgt = pd.Series(data=[0.5, 0.3, 0.2])
>>> get_weighted_average(data=rtg_scores, weights=wgt)
6.4
>>> warf = pd.Series(data=[500, 735, np.nan, 93, np.nan])
>>> wgt = pd.Series(data=[0.4, 0.1, 0.1, 0.2, 0.2])
>>> get_weighted_average(data=warf, weights=wgt)
417.29
| def get_weighted_average(data: pd.Series, weights: pd.Series) -> float:
"""Compute weighted average.
Parameters
----------
data
Contains numerical values.
weights
Contains weights (between 0 and 1) with respect to `data`.
Returns
-------
float
Weighted average data.
Notes
-----
Computing the weighted average is simply the sumproduct of `data` and `weights`.
``nan`` in `data` will be excluded from calculating the weighted average. All
corresponding weights will be ignored. As a matter of fact, the remaining
weights will be upscaled so that the weights of all ``non-nan`` rows in `data` will
sum up to 1 (100%).
Examples
--------
>>> import numpy as np
>>> import pandas as pd
>>> rtg_scores = pd.Series(data=[5, 7, 9])
>>> wgt = pd.Series(data=[0.5, 0.3, 0.2])
>>> get_weighted_average(data=rtg_scores, weights=wgt)
6.4
>>> warf = pd.Series(data=[500, 735, np.nan, 93, np.nan])
>>> wgt = pd.Series(data=[0.4, 0.1, 0.1, 0.2, 0.2])
>>> get_weighted_average(data=warf, weights=wgt)
417.29
"""
# find indices in warf that correspond to np.nan
idx_nan = data[pd.isna(data)].index
# sum weights of securities with an actual rating, i.e. rating is not NaN
weights_non_nan = 1 - sum(weights.loc[idx_nan])
# upscale to 100%
weights_upscaled = weights / weights_non_nan
return data.fillna(0).dot(weights_upscaled)
| (data: pandas.core.series.Series, weights: pandas.core.series.Series) -> float |
20,559 | pyratings.consolidate | get_worst_ratings | Compute the worst rating on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_innput` will be inferred from the dataframe column
names.
rating_provider_output
Indicates which rating scale will be used for output results.
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Worst ratings on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_worst_ratings(ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"])
0 AA-
1 AA-
2 AA-
3 B+
4 C
Name: worst_rtg, dtype: object
| def get_worst_ratings(
ratings: pd.DataFrame,
rating_provider_input: list[str] = None,
rating_provider_output: Literal[
"Fitch", "Moody", "S&P", "Bloomberg", "DBRS"
] = "S&P",
tenor: Literal["long-term", "short-term"] = "long-term",
) -> pd.Series:
"""Compute the worst rating on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_innput` will be inferred from the dataframe column
names.
rating_provider_output
Indicates which rating scale will be used for output results.
Should contain any valid rating provider out of
{"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Worst ratings on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_worst_ratings(ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"])
0 AA-
1 AA-
2 AA-
3 B+
4 C
Name: worst_rtg, dtype: object
"""
ratings_series = consolidate_ratings(
method="worst",
ratings=ratings,
rating_provider_input=rating_provider_input,
rating_provider_output=rating_provider_output,
tenor=tenor,
)
return ratings_series
| (ratings: pandas.core.frame.DataFrame, rating_provider_input: Optional[list[str]] = None, rating_provider_output: Literal['Fitch', 'Moody', 'S&P', 'Bloomberg', 'DBRS'] = 'S&P', tenor: Literal['long-term', 'short-term'] = 'long-term') -> pandas.core.series.Series |
20,560 | pyratings.consolidate | get_worst_scores | Compute the worst scores on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_innput` will be inferred from the dataframe column
names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Worst scores on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_worst_scores(ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"])
0 4
1 4
2 4
3 14
4 21
Name: worst_scores, dtype: int64
| def get_worst_scores(
ratings: pd.DataFrame,
rating_provider_input: list[str] = None,
tenor: Literal["long-term", "short-term"] = "long-term",
) -> pd.Series:
"""Compute the worst scores on a security level basis across rating agencies.
Parameters
----------
ratings
Dataframe consisting of clean ratings (i.e. stripped off of watches/outlooks)
rating_provider_input
Indicates rating providers within `ratings`. Should contain any valid rating
provider out of {"Fitch", "Moody's", "S&P", "Bloomberg", "DBRS"}.
If None, `rating_provider_innput` will be inferred from the dataframe column
names.
tenor
Should contain any valid tenor out of {"long-term", "short-term"}
Returns
-------
pd.Series
Worst scores on a security level basis.
Examples
--------
>>> import pandas as pd
>>> ratings_df = pd.DataFrame(
... data=(
... {
... "rating_S&P": ['AAA', 'AA-', 'AA+', 'BB-', 'C'],
... "rating_Moody's": ['Aa1', 'Aa3', 'Aa2', 'Ba3', 'Ca'],
... "rating_Fitch": ['AA-', 'AA-', 'AA-', 'B+', 'C'],
... }
... )
... )
>>> get_worst_scores(ratings_df, rating_provider_input=["S&P", "Moody", "Fitch"])
0 4
1 4
2 4
3 14
4 21
Name: worst_scores, dtype: int64
"""
rating_scores_df = get_scores_from_ratings(
ratings=ratings, rating_provider=rating_provider_input, tenor=tenor
)
rating_scores_series = rating_scores_df.max(axis=1)
rating_scores_series.name = "worst_scores"
return rating_scores_series
| (ratings: pandas.core.frame.DataFrame, rating_provider_input: Optional[list[str]] = None, tenor: Literal['long-term', 'short-term'] = 'long-term') -> pandas.core.series.Series |
20,564 | tabledata.error | DataError |
Exception raised when data is invalid as tabular data.
| class DataError(ValueError):
"""
Exception raised when data is invalid as tabular data.
"""
| null |
20,565 | tabledata.error | InvalidHeaderNameError |
Exception raised when a table header name is invalid.
| class InvalidHeaderNameError(NameValidationError):
"""
Exception raised when a table header name is invalid.
"""
| null |
20,566 | tabledata.error | InvalidTableNameError |
Exception raised when a table name is invalid.
| class InvalidTableNameError(NameValidationError):
"""
Exception raised when a table name is invalid.
"""
| null |
20,567 | tabledata.error | NameValidationError |
Exception raised when a name is invalid.
| class NameValidationError(ValueError):
"""
Exception raised when a name is invalid.
"""
| null |
20,568 | tabledata._constant | PatternMatch | An enumeration. | class PatternMatch(enum.Enum):
OR = 0
AND = 1
| (value, names=None, *, module=None, qualname=None, type=None, start=1) |
20,569 | tabledata._core | TableData |
Class to represent a table data structure.
:param table_name: Name of the table.
:param headers: Table header names.
:param rows: Data of the table.
| class TableData:
"""
Class to represent a table data structure.
:param table_name: Name of the table.
:param headers: Table header names.
:param rows: Data of the table.
"""
def __init__(
self,
table_name: Optional[str],
headers: Sequence[str],
rows: Sequence,
dp_extractor: Optional[dp.DataPropertyExtractor] = None,
type_hints: Optional[Sequence[Union[str, TypeHint]]] = None,
max_workers: Optional[int] = None,
max_precision: Optional[int] = None,
) -> None:
self.__table_name = table_name
self.__value_matrix: List[List[Any]] = []
self.__value_dp_matrix: Optional[DataPropertyMatrix] = None
if rows:
self.__rows = rows
else:
self.__rows = []
if dp_extractor:
self.__dp_extractor = copy.deepcopy(dp_extractor)
else:
self.__dp_extractor = dp.DataPropertyExtractor(max_precision=max_precision)
if type_hints:
self.__dp_extractor.column_type_hints = type_hints
self.__dp_extractor.strip_str_header = '"'
if max_workers:
self.__dp_extractor.max_workers = max_workers
if not headers:
self.__dp_extractor.headers = []
else:
self.__dp_extractor.headers = headers
def __repr__(self) -> str:
element_list = [f"table_name={self.table_name}"]
try:
element_list.append("headers=[{}]".format(", ".join(self.headers)))
except TypeError:
element_list.append("headers=None")
element_list.extend([f"cols={self.num_columns}", f"rows={self.num_rows}"])
return ", ".join(element_list)
def __eq__(self, other: Any) -> bool:
if not isinstance(other, TableData):
return False
return self.equals(other, cmp_by_dp=False)
def __ne__(self, other: Any) -> bool:
if not isinstance(other, TableData):
return True
return not self.equals(other, cmp_by_dp=False)
@property
def table_name(self) -> Optional[str]:
"""str: Name of the table."""
return self.__table_name
@table_name.setter
def table_name(self, value: Optional[str]) -> None:
self.__table_name = value
@property
def headers(self) -> Sequence[str]:
"""Sequence[str]: Table header names."""
return self.__dp_extractor.headers
@property
def rows(self) -> Sequence:
"""Sequence: Original rows of tabular data."""
return self.__rows
@property
def value_matrix(self) -> DataPropertyMatrix:
"""DataPropertyMatrix: Converted rows of tabular data."""
if self.__value_matrix:
return self.__value_matrix
self.__value_matrix = [
[value_dp.data for value_dp in value_dp_list] for value_dp_list in self.value_dp_matrix
]
return self.__value_matrix
@property
def has_value_dp_matrix(self) -> bool:
return self.__value_dp_matrix is not None
@property
def max_workers(self) -> int:
return self.__dp_extractor.max_workers
@max_workers.setter
def max_workers(self, value: Optional[int]) -> None:
self.__dp_extractor.max_workers = value
@property
def num_rows(self) -> Optional[int]:
"""Optional[int]:
Number of rows in the tabular data.
|None| if the ``rows`` is neither list nor tuple.
"""
try:
return len(self.rows)
except TypeError:
return None
@property
def num_columns(self) -> Optional[int]:
if typepy.is_not_empty_sequence(self.headers):
return len(self.headers)
try:
return len(self.rows[0])
except TypeError:
return None
except IndexError:
return 0
@property
def value_dp_matrix(self) -> DataPropertyMatrix:
"""DataPropertyMatrix: DataProperty for table data."""
if self.__value_dp_matrix is None:
self.__value_dp_matrix = self.__dp_extractor.to_dp_matrix(
to_value_matrix(self.headers, self.rows)
)
return self.__value_dp_matrix
@property
def header_dp_list(self) -> List[dp.DataProperty]:
return self.__dp_extractor.to_header_dp_list()
@property
def column_dp_list(self) -> List[dp.ColumnDataProperty]:
return self.__dp_extractor.to_column_dp_list(self.value_dp_matrix)
@property
def dp_extractor(self) -> dp.DataPropertyExtractor:
return self.__dp_extractor
def is_empty_header(self) -> bool:
"""bool: |True| if the data :py:attr:`.headers` is empty."""
return typepy.is_empty_sequence(self.headers)
def is_empty_rows(self) -> bool:
"""
:return: |True| if the tabular data has no rows.
:rtype: bool
"""
return self.num_rows == 0
def is_empty(self) -> bool:
"""
:return:
|True| if the data :py:attr:`.headers` or
:py:attr:`.value_matrix` is empty.
:rtype: bool
"""
return any([self.is_empty_header(), self.is_empty_rows()])
def equals(self, other: "TableData", cmp_by_dp: bool = True) -> bool:
if cmp_by_dp:
return self.__equals_dp(other)
return self.__equals_raw(other)
def __equals_base(self, other: "TableData") -> bool:
compare_item_list = [self.table_name == other.table_name]
if self.num_rows is not None:
compare_item_list.append(self.num_rows == other.num_rows)
return all(compare_item_list)
def __equals_raw(self, other: "TableData") -> bool:
if not self.__equals_base(other):
return False
if self.headers != other.headers:
return False
for lhs_row, rhs_row in zip(self.rows, other.rows):
if len(lhs_row) != len(rhs_row):
return False
if not all(
[
lhs == rhs
for lhs, rhs in zip(lhs_row, rhs_row)
if not Nan(lhs).is_type() and not Nan(rhs).is_type()
]
):
return False
return True
def __equals_dp(self, other: "TableData") -> bool:
if not self.__equals_base(other):
return False
if self.header_dp_list != other.header_dp_list:
return False
if self.value_dp_matrix is None or other.value_dp_matrix is None:
return False
for lhs_list, rhs_list in zip(self.value_dp_matrix, other.value_dp_matrix):
if len(lhs_list) != len(rhs_list):
return False
if any([lhs != rhs for lhs, rhs in zip(lhs_list, rhs_list)]):
return False
return True
def in_tabledata_list(self, other: Sequence["TableData"], cmp_by_dp: bool = True) -> bool:
for table_data in other:
if self.equals(table_data, cmp_by_dp=cmp_by_dp):
return True
return False
def validate_rows(self) -> None:
"""
:raises ValueError:
"""
invalid_row_idx_list = []
for row_idx, row in enumerate(self.rows):
if isinstance(row, (list, tuple)) and len(self.headers) != len(row):
invalid_row_idx_list.append(row_idx)
if isinstance(row, dict):
if not all([header in row for header in self.headers]):
invalid_row_idx_list.append(row_idx)
if not invalid_row_idx_list:
return
for invalid_row_idx in invalid_row_idx_list:
logger.debug(f"invalid row (line={invalid_row_idx}): {self.rows[invalid_row_idx]}")
raise ValueError(
"table header length and row length are mismatch:\n"
+ f" header(len={len(self.headers)}): {self.headers}\n"
+ " # of miss match rows: {} ouf of {}\n".format(
len(invalid_row_idx_list), self.num_rows
)
)
def as_dict(self, default_key: str = "table") -> Dict[str, List["OrderedDict[str, Any]"]]:
"""
Args:
default_key:
Key of a returning dictionary when the ``table_name`` is empty.
Returns:
dict: Table data as a |dict| instance.
Sample Code:
.. code:: python
from tabledata import TableData
TableData(
"sample",
["a", "b"],
[[1, 2], [3.3, 4.4]]
).as_dict()
Output:
.. code:: json
{'sample': [OrderedDict([('a', 1), ('b', 2)]), OrderedDict([('a', 3.3), ('b', 4.4)])]}
""" # noqa
dict_body = []
for row in self.value_matrix:
if not row:
continue
values = [
(header, value) for header, value in zip(self.headers, row) if value is not None
]
if not values:
continue
dict_body.append(OrderedDict(values))
table_name = self.table_name
if not table_name:
table_name = default_key
return {table_name: dict_body}
def as_tuple(self) -> Iterator[Tuple]:
"""
:return: Rows of the tuple.
:rtype: list of |namedtuple|
:Sample Code:
.. code:: python
from tabledata import TableData
records = TableData(
"sample",
["a", "b"],
[[1, 2], [3.3, 4.4]]
).as_tuple()
for record in records:
print(record)
:Output:
.. code-block:: none
Row(a=1, b=2)
Row(a=Decimal('3.3'), b=Decimal('4.4'))
"""
Row = namedtuple("Row", self.headers) # type: ignore
for value_dp_list in self.value_dp_matrix:
if typepy.is_empty_sequence(value_dp_list):
continue
row = Row(*(value_dp.data for value_dp in value_dp_list))
yield row
def as_dataframe(self) -> "pandas.DataFrame":
"""
:return: Table data as a ``pandas.DataFrame`` instance.
:rtype: pandas.DataFrame
:Sample Code:
.. code-block:: python
from tabledata import TableData
TableData(
"sample",
["a", "b"],
[[1, 2], [3.3, 4.4]]
).as_dataframe()
:Output:
.. code-block:: none
a b
0 1 2
1 3.3 4.4
:Dependency Packages:
- `pandas <https://pandas.pydata.org/>`__
"""
try:
from pandas import DataFrame
except ImportError:
raise RuntimeError("required 'pandas' package to execute as_dataframe method")
dataframe = DataFrame(self.value_matrix)
if not self.is_empty_header():
dataframe.columns = self.headers
return dataframe
def transpose(self) -> "TableData":
return TableData(
self.table_name,
self.headers,
[row for row in zip(*self.rows)],
max_workers=self.max_workers,
)
def filter_column(
self,
patterns: Optional[str] = None,
is_invert_match: bool = False,
is_re_match: bool = False,
pattern_match: PatternMatch = PatternMatch.OR,
) -> "TableData":
logger.debug(
"filter_column: patterns={}, is_invert_match={}, "
"is_re_match={}, pattern_match={}".format(
patterns, is_invert_match, is_re_match, pattern_match
)
)
if not patterns:
return self
match_header_list = []
match_column_matrix = []
if pattern_match == PatternMatch.OR:
match_method = any
elif pattern_match == PatternMatch.AND:
match_method = all
else:
raise ValueError(f"unknown matching: {pattern_match}")
for header, column in zip(self.headers, zip(*self.rows)):
is_match_list = []
for pattern in patterns:
is_match = self.__is_match(header, pattern, is_re_match)
is_match_list.append(
any([is_match and not is_invert_match, not is_match and is_invert_match])
)
if match_method(is_match_list):
match_header_list.append(header)
match_column_matrix.append(column)
logger.debug(
"filter_column: table={}, match_header_list={}".format(
self.table_name, match_header_list
)
)
return TableData(
self.table_name,
match_header_list,
list(zip(*match_column_matrix)),
max_workers=self.max_workers,
)
@staticmethod
def from_dataframe(
dataframe: "pandas.DataFrame",
table_name: str = "",
type_hints: Optional[Sequence[TypeHint]] = None,
max_workers: Optional[int] = None,
) -> "TableData":
"""
Initialize TableData instance from a pandas.DataFrame instance.
:param pandas.DataFrame dataframe:
:param str table_name: Table name to create.
"""
return TableData(
table_name,
list(dataframe.columns.values),
dataframe.values.tolist(),
type_hints=type_hints,
max_workers=max_workers,
)
@staticmethod
def __is_match(header: str, pattern: str, is_re_match: bool) -> bool:
if is_re_match:
return re.search(pattern, header) is not None
return header == pattern
| (table_name: Optional[str], headers: Sequence[str], rows: Sequence, dp_extractor: Optional[dataproperty._extractor.DataPropertyExtractor] = None, type_hints: Optional[Sequence[Union[str, Type[typepy.type._base.AbstractType], NoneType]]] = None, max_workers: Optional[int] = None, max_precision: Optional[int] = None) -> None |
20,570 | tabledata._core | __equals_base | null | def __equals_base(self, other: "TableData") -> bool:
compare_item_list = [self.table_name == other.table_name]
if self.num_rows is not None:
compare_item_list.append(self.num_rows == other.num_rows)
return all(compare_item_list)
| (self, other: tabledata._core.TableData) -> bool |
20,571 | tabledata._core | __equals_dp | null | def __equals_dp(self, other: "TableData") -> bool:
if not self.__equals_base(other):
return False
if self.header_dp_list != other.header_dp_list:
return False
if self.value_dp_matrix is None or other.value_dp_matrix is None:
return False
for lhs_list, rhs_list in zip(self.value_dp_matrix, other.value_dp_matrix):
if len(lhs_list) != len(rhs_list):
return False
if any([lhs != rhs for lhs, rhs in zip(lhs_list, rhs_list)]):
return False
return True
| (self, other: tabledata._core.TableData) -> bool |
20,572 | tabledata._core | __equals_raw | null | def __equals_raw(self, other: "TableData") -> bool:
if not self.__equals_base(other):
return False
if self.headers != other.headers:
return False
for lhs_row, rhs_row in zip(self.rows, other.rows):
if len(lhs_row) != len(rhs_row):
return False
if not all(
[
lhs == rhs
for lhs, rhs in zip(lhs_row, rhs_row)
if not Nan(lhs).is_type() and not Nan(rhs).is_type()
]
):
return False
return True
| (self, other: tabledata._core.TableData) -> bool |
20,573 | tabledata._core | __is_match | null | @staticmethod
def __is_match(header: str, pattern: str, is_re_match: bool) -> bool:
if is_re_match:
return re.search(pattern, header) is not None
return header == pattern
| (header: str, pattern: str, is_re_match: bool) -> bool |
20,574 | tabledata._core | __eq__ | null | def __eq__(self, other: Any) -> bool:
if not isinstance(other, TableData):
return False
return self.equals(other, cmp_by_dp=False)
| (self, other: Any) -> bool |
20,575 | tabledata._core | __init__ | null | def __init__(
self,
table_name: Optional[str],
headers: Sequence[str],
rows: Sequence,
dp_extractor: Optional[dp.DataPropertyExtractor] = None,
type_hints: Optional[Sequence[Union[str, TypeHint]]] = None,
max_workers: Optional[int] = None,
max_precision: Optional[int] = None,
) -> None:
self.__table_name = table_name
self.__value_matrix: List[List[Any]] = []
self.__value_dp_matrix: Optional[DataPropertyMatrix] = None
if rows:
self.__rows = rows
else:
self.__rows = []
if dp_extractor:
self.__dp_extractor = copy.deepcopy(dp_extractor)
else:
self.__dp_extractor = dp.DataPropertyExtractor(max_precision=max_precision)
if type_hints:
self.__dp_extractor.column_type_hints = type_hints
self.__dp_extractor.strip_str_header = '"'
if max_workers:
self.__dp_extractor.max_workers = max_workers
if not headers:
self.__dp_extractor.headers = []
else:
self.__dp_extractor.headers = headers
| (self, table_name: Optional[str], headers: Sequence[str], rows: Sequence, dp_extractor: Optional[dataproperty._extractor.DataPropertyExtractor] = None, type_hints: Optional[Sequence[Union[str, Type[typepy.type._base.AbstractType], NoneType]]] = None, max_workers: Optional[int] = None, max_precision: Optional[int] = None) -> NoneType |
20,576 | tabledata._core | __ne__ | null | def __ne__(self, other: Any) -> bool:
if not isinstance(other, TableData):
return True
return not self.equals(other, cmp_by_dp=False)
| (self, other: Any) -> bool |
20,577 | tabledata._core | __repr__ | null | def __repr__(self) -> str:
element_list = [f"table_name={self.table_name}"]
try:
element_list.append("headers=[{}]".format(", ".join(self.headers)))
except TypeError:
element_list.append("headers=None")
element_list.extend([f"cols={self.num_columns}", f"rows={self.num_rows}"])
return ", ".join(element_list)
| (self) -> str |
20,578 | tabledata._core | as_dataframe |
:return: Table data as a ``pandas.DataFrame`` instance.
:rtype: pandas.DataFrame
:Sample Code:
.. code-block:: python
from tabledata import TableData
TableData(
"sample",
["a", "b"],
[[1, 2], [3.3, 4.4]]
).as_dataframe()
:Output:
.. code-block:: none
a b
0 1 2
1 3.3 4.4
:Dependency Packages:
- `pandas <https://pandas.pydata.org/>`__
| def as_dataframe(self) -> "pandas.DataFrame":
"""
:return: Table data as a ``pandas.DataFrame`` instance.
:rtype: pandas.DataFrame
:Sample Code:
.. code-block:: python
from tabledata import TableData
TableData(
"sample",
["a", "b"],
[[1, 2], [3.3, 4.4]]
).as_dataframe()
:Output:
.. code-block:: none
a b
0 1 2
1 3.3 4.4
:Dependency Packages:
- `pandas <https://pandas.pydata.org/>`__
"""
try:
from pandas import DataFrame
except ImportError:
raise RuntimeError("required 'pandas' package to execute as_dataframe method")
dataframe = DataFrame(self.value_matrix)
if not self.is_empty_header():
dataframe.columns = self.headers
return dataframe
| (self) -> 'pandas.DataFrame' |
20,579 | tabledata._core | as_dict |
Args:
default_key:
Key of a returning dictionary when the ``table_name`` is empty.
Returns:
dict: Table data as a |dict| instance.
Sample Code:
.. code:: python
from tabledata import TableData
TableData(
"sample",
["a", "b"],
[[1, 2], [3.3, 4.4]]
).as_dict()
Output:
.. code:: json
{'sample': [OrderedDict([('a', 1), ('b', 2)]), OrderedDict([('a', 3.3), ('b', 4.4)])]}
| def as_dict(self, default_key: str = "table") -> Dict[str, List["OrderedDict[str, Any]"]]:
"""
Args:
default_key:
Key of a returning dictionary when the ``table_name`` is empty.
Returns:
dict: Table data as a |dict| instance.
Sample Code:
.. code:: python
from tabledata import TableData
TableData(
"sample",
["a", "b"],
[[1, 2], [3.3, 4.4]]
).as_dict()
Output:
.. code:: json
{'sample': [OrderedDict([('a', 1), ('b', 2)]), OrderedDict([('a', 3.3), ('b', 4.4)])]}
""" # noqa
dict_body = []
for row in self.value_matrix:
if not row:
continue
values = [
(header, value) for header, value in zip(self.headers, row) if value is not None
]
if not values:
continue
dict_body.append(OrderedDict(values))
table_name = self.table_name
if not table_name:
table_name = default_key
return {table_name: dict_body}
| (self, default_key: str = 'table') -> Dict[str, List[collections.OrderedDict[str, Any]]] |
20,580 | tabledata._core | as_tuple |
:return: Rows of the tuple.
:rtype: list of |namedtuple|
:Sample Code:
.. code:: python
from tabledata import TableData
records = TableData(
"sample",
["a", "b"],
[[1, 2], [3.3, 4.4]]
).as_tuple()
for record in records:
print(record)
:Output:
.. code-block:: none
Row(a=1, b=2)
Row(a=Decimal('3.3'), b=Decimal('4.4'))
| def as_tuple(self) -> Iterator[Tuple]:
"""
:return: Rows of the tuple.
:rtype: list of |namedtuple|
:Sample Code:
.. code:: python
from tabledata import TableData
records = TableData(
"sample",
["a", "b"],
[[1, 2], [3.3, 4.4]]
).as_tuple()
for record in records:
print(record)
:Output:
.. code-block:: none
Row(a=1, b=2)
Row(a=Decimal('3.3'), b=Decimal('4.4'))
"""
Row = namedtuple("Row", self.headers) # type: ignore
for value_dp_list in self.value_dp_matrix:
if typepy.is_empty_sequence(value_dp_list):
continue
row = Row(*(value_dp.data for value_dp in value_dp_list))
yield row
| (self) -> Iterator[Tuple] |
20,581 | tabledata._core | equals | null | def equals(self, other: "TableData", cmp_by_dp: bool = True) -> bool:
if cmp_by_dp:
return self.__equals_dp(other)
return self.__equals_raw(other)
| (self, other: tabledata._core.TableData, cmp_by_dp: bool = True) -> bool |
20,582 | tabledata._core | filter_column | null | def filter_column(
self,
patterns: Optional[str] = None,
is_invert_match: bool = False,
is_re_match: bool = False,
pattern_match: PatternMatch = PatternMatch.OR,
) -> "TableData":
logger.debug(
"filter_column: patterns={}, is_invert_match={}, "
"is_re_match={}, pattern_match={}".format(
patterns, is_invert_match, is_re_match, pattern_match
)
)
if not patterns:
return self
match_header_list = []
match_column_matrix = []
if pattern_match == PatternMatch.OR:
match_method = any
elif pattern_match == PatternMatch.AND:
match_method = all
else:
raise ValueError(f"unknown matching: {pattern_match}")
for header, column in zip(self.headers, zip(*self.rows)):
is_match_list = []
for pattern in patterns:
is_match = self.__is_match(header, pattern, is_re_match)
is_match_list.append(
any([is_match and not is_invert_match, not is_match and is_invert_match])
)
if match_method(is_match_list):
match_header_list.append(header)
match_column_matrix.append(column)
logger.debug(
"filter_column: table={}, match_header_list={}".format(
self.table_name, match_header_list
)
)
return TableData(
self.table_name,
match_header_list,
list(zip(*match_column_matrix)),
max_workers=self.max_workers,
)
| (self, patterns: Optional[str] = None, is_invert_match: bool = False, is_re_match: bool = False, pattern_match: tabledata._constant.PatternMatch = <PatternMatch.OR: 0>) -> tabledata._core.TableData |
20,583 | tabledata._core | from_dataframe |
Initialize TableData instance from a pandas.DataFrame instance.
:param pandas.DataFrame dataframe:
:param str table_name: Table name to create.
| @staticmethod
def from_dataframe(
dataframe: "pandas.DataFrame",
table_name: str = "",
type_hints: Optional[Sequence[TypeHint]] = None,
max_workers: Optional[int] = None,
) -> "TableData":
"""
Initialize TableData instance from a pandas.DataFrame instance.
:param pandas.DataFrame dataframe:
:param str table_name: Table name to create.
"""
return TableData(
table_name,
list(dataframe.columns.values),
dataframe.values.tolist(),
type_hints=type_hints,
max_workers=max_workers,
)
| (dataframe: 'pandas.DataFrame', table_name: str = '', type_hints: Optional[Sequence[Optional[Type[typepy.type._base.AbstractType]]]] = None, max_workers: Optional[int] = None) -> 'TableData' |
20,584 | tabledata._core | in_tabledata_list | null | def in_tabledata_list(self, other: Sequence["TableData"], cmp_by_dp: bool = True) -> bool:
for table_data in other:
if self.equals(table_data, cmp_by_dp=cmp_by_dp):
return True
return False
| (self, other: Sequence[tabledata._core.TableData], cmp_by_dp: bool = True) -> bool |
20,585 | tabledata._core | is_empty |
:return:
|True| if the data :py:attr:`.headers` or
:py:attr:`.value_matrix` is empty.
:rtype: bool
| def is_empty(self) -> bool:
"""
:return:
|True| if the data :py:attr:`.headers` or
:py:attr:`.value_matrix` is empty.
:rtype: bool
"""
return any([self.is_empty_header(), self.is_empty_rows()])
| (self) -> bool |
20,586 | tabledata._core | is_empty_header | bool: |True| if the data :py:attr:`.headers` is empty. | def is_empty_header(self) -> bool:
"""bool: |True| if the data :py:attr:`.headers` is empty."""
return typepy.is_empty_sequence(self.headers)
| (self) -> bool |
20,587 | tabledata._core | is_empty_rows |
:return: |True| if the tabular data has no rows.
:rtype: bool
| def is_empty_rows(self) -> bool:
"""
:return: |True| if the tabular data has no rows.
:rtype: bool
"""
return self.num_rows == 0
| (self) -> bool |
20,588 | tabledata._core | transpose | null | def transpose(self) -> "TableData":
return TableData(
self.table_name,
self.headers,
[row for row in zip(*self.rows)],
max_workers=self.max_workers,
)
| (self) -> tabledata._core.TableData |
20,589 | tabledata._core | validate_rows |
:raises ValueError:
| def validate_rows(self) -> None:
"""
:raises ValueError:
"""
invalid_row_idx_list = []
for row_idx, row in enumerate(self.rows):
if isinstance(row, (list, tuple)) and len(self.headers) != len(row):
invalid_row_idx_list.append(row_idx)
if isinstance(row, dict):
if not all([header in row for header in self.headers]):
invalid_row_idx_list.append(row_idx)
if not invalid_row_idx_list:
return
for invalid_row_idx in invalid_row_idx_list:
logger.debug(f"invalid row (line={invalid_row_idx}): {self.rows[invalid_row_idx]}")
raise ValueError(
"table header length and row length are mismatch:\n"
+ f" header(len={len(self.headers)}): {self.headers}\n"
+ " # of miss match rows: {} ouf of {}\n".format(
len(invalid_row_idx_list), self.num_rows
)
)
| (self) -> NoneType |
20,595 | tabledata._common | convert_idx_to_alphabet | null | def convert_idx_to_alphabet(idx: int) -> str:
if idx < 26:
return chr(65 + idx)
div, mod = divmod(idx, 26)
return convert_idx_to_alphabet(div - 1) + convert_idx_to_alphabet(mod)
| (idx: int) -> str |
20,597 | tabledata._logger._logger | set_log_level | null | def set_log_level(log_level): # type: ignore
warnings.warn(
"'set_log_level' method is deprecated and will be removed in the future. ",
DeprecationWarning,
)
return
| (log_level) |
20,598 | tabledata._logger._logger | set_logger | null | def set_logger(is_enable: bool, propagation_depth: int = 1) -> None:
if is_enable:
logger.enable(MODULE_NAME)
else:
logger.disable(MODULE_NAME)
if propagation_depth <= 0:
return
dataproperty.set_logger(is_enable, propagation_depth - 1)
| (is_enable: bool, propagation_depth: int = 1) -> NoneType |
20,599 | tabledata._converter | to_value_matrix | null | def to_value_matrix(headers: Sequence[str], value_matrix: Sequence[Any]) -> List[Row]:
if not value_matrix:
return []
return [_to_row(headers, values, row_idx)[1] for row_idx, values in enumerate(value_matrix)]
| (headers: Sequence[str], value_matrix: Sequence[Any]) -> List[Tuple[int, Any]] |
20,602 | amaze_dict.amaze_dict | wrap_value | null | def wrap_value(value):
return LeafBase(value)
| (value) |
20,603 | http.client | HTTPConnection | null | class HTTPConnection:
_http_vsn = 11
_http_vsn_str = 'HTTP/1.1'
response_class = HTTPResponse
default_port = HTTP_PORT
auto_open = 1
debuglevel = 0
@staticmethod
def _is_textIO(stream):
"""Test whether a file-like object is a text or a binary stream.
"""
return isinstance(stream, io.TextIOBase)
@staticmethod
def _get_content_length(body, method):
"""Get the content-length based on the body.
If the body is None, we set Content-Length: 0 for methods that expect
a body (RFC 7230, Section 3.3.2). We also set the Content-Length for
any method if the body is a str or bytes-like object and not a file.
"""
if body is None:
# do an explicit check for not None here to distinguish
# between unset and set but empty
if method.upper() in _METHODS_EXPECTING_BODY:
return 0
else:
return None
if hasattr(body, 'read'):
# file-like object.
return None
try:
# does it implement the buffer protocol (bytes, bytearray, array)?
mv = memoryview(body)
return mv.nbytes
except TypeError:
pass
if isinstance(body, str):
return len(body)
return None
def __init__(self, host, port=None, timeout=socket._GLOBAL_DEFAULT_TIMEOUT,
source_address=None, blocksize=8192):
self.timeout = timeout
self.source_address = source_address
self.blocksize = blocksize
self.sock = None
self._buffer = []
self.__response = None
self.__state = _CS_IDLE
self._method = None
self._tunnel_host = None
self._tunnel_port = None
self._tunnel_headers = {}
(self.host, self.port) = self._get_hostport(host, port)
self._validate_host(self.host)
# This is stored as an instance variable to allow unit
# tests to replace it with a suitable mockup
self._create_connection = socket.create_connection
def set_tunnel(self, host, port=None, headers=None):
"""Set up host and port for HTTP CONNECT tunnelling.
In a connection that uses HTTP CONNECT tunneling, the host passed to the
constructor is used as a proxy server that relays all communication to
the endpoint passed to `set_tunnel`. This done by sending an HTTP
CONNECT request to the proxy server when the connection is established.
This method must be called before the HTTP connection has been
established.
The headers argument should be a mapping of extra HTTP headers to send
with the CONNECT request.
"""
if self.sock:
raise RuntimeError("Can't set up tunnel for established connection")
self._tunnel_host, self._tunnel_port = self._get_hostport(host, port)
if headers:
self._tunnel_headers = headers
else:
self._tunnel_headers.clear()
def _get_hostport(self, host, port):
if port is None:
i = host.rfind(':')
j = host.rfind(']') # ipv6 addresses have [...]
if i > j:
try:
port = int(host[i+1:])
except ValueError:
if host[i+1:] == "": # http://foo.com:/ == http://foo.com/
port = self.default_port
else:
raise InvalidURL("nonnumeric port: '%s'" % host[i+1:])
host = host[:i]
else:
port = self.default_port
if host and host[0] == '[' and host[-1] == ']':
host = host[1:-1]
return (host, port)
def set_debuglevel(self, level):
self.debuglevel = level
def _tunnel(self):
connect = b"CONNECT %s:%d HTTP/1.0\r\n" % (
self._tunnel_host.encode("ascii"), self._tunnel_port)
headers = [connect]
for header, value in self._tunnel_headers.items():
headers.append(f"{header}: {value}\r\n".encode("latin-1"))
headers.append(b"\r\n")
# Making a single send() call instead of one per line encourages
# the host OS to use a more optimal packet size instead of
# potentially emitting a series of small packets.
self.send(b"".join(headers))
del headers
response = self.response_class(self.sock, method=self._method)
(version, code, message) = response._read_status()
if code != http.HTTPStatus.OK:
self.close()
raise OSError(f"Tunnel connection failed: {code} {message.strip()}")
while True:
line = response.fp.readline(_MAXLINE + 1)
if len(line) > _MAXLINE:
raise LineTooLong("header line")
if not line:
# for sites which EOF without sending a trailer
break
if line in (b'\r\n', b'\n', b''):
break
if self.debuglevel > 0:
print('header:', line.decode())
def connect(self):
"""Connect to the host and port specified in __init__."""
sys.audit("http.client.connect", self, self.host, self.port)
self.sock = self._create_connection(
(self.host,self.port), self.timeout, self.source_address)
# Might fail in OSs that don't implement TCP_NODELAY
try:
self.sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
except OSError as e:
if e.errno != errno.ENOPROTOOPT:
raise
if self._tunnel_host:
self._tunnel()
def close(self):
"""Close the connection to the HTTP server."""
self.__state = _CS_IDLE
try:
sock = self.sock
if sock:
self.sock = None
sock.close() # close it manually... there may be other refs
finally:
response = self.__response
if response:
self.__response = None
response.close()
def send(self, data):
"""Send `data' to the server.
``data`` can be a string object, a bytes object, an array object, a
file-like object that supports a .read() method, or an iterable object.
"""
if self.sock is None:
if self.auto_open:
self.connect()
else:
raise NotConnected()
if self.debuglevel > 0:
print("send:", repr(data))
if hasattr(data, "read") :
if self.debuglevel > 0:
print("sendIng a read()able")
encode = self._is_textIO(data)
if encode and self.debuglevel > 0:
print("encoding file using iso-8859-1")
while 1:
datablock = data.read(self.blocksize)
if not datablock:
break
if encode:
datablock = datablock.encode("iso-8859-1")
sys.audit("http.client.send", self, datablock)
self.sock.sendall(datablock)
return
sys.audit("http.client.send", self, data)
try:
self.sock.sendall(data)
except TypeError:
if isinstance(data, collections.abc.Iterable):
for d in data:
self.sock.sendall(d)
else:
raise TypeError("data should be a bytes-like object "
"or an iterable, got %r" % type(data))
def _output(self, s):
"""Add a line of output to the current request buffer.
Assumes that the line does *not* end with \\r\\n.
"""
self._buffer.append(s)
def _read_readable(self, readable):
if self.debuglevel > 0:
print("sendIng a read()able")
encode = self._is_textIO(readable)
if encode and self.debuglevel > 0:
print("encoding file using iso-8859-1")
while True:
datablock = readable.read(self.blocksize)
if not datablock:
break
if encode:
datablock = datablock.encode("iso-8859-1")
yield datablock
def _send_output(self, message_body=None, encode_chunked=False):
"""Send the currently buffered request and clear the buffer.
Appends an extra \\r\\n to the buffer.
A message_body may be specified, to be appended to the request.
"""
self._buffer.extend((b"", b""))
msg = b"\r\n".join(self._buffer)
del self._buffer[:]
self.send(msg)
if message_body is not None:
# create a consistent interface to message_body
if hasattr(message_body, 'read'):
# Let file-like take precedence over byte-like. This
# is needed to allow the current position of mmap'ed
# files to be taken into account.
chunks = self._read_readable(message_body)
else:
try:
# this is solely to check to see if message_body
# implements the buffer API. it /would/ be easier
# to capture if PyObject_CheckBuffer was exposed
# to Python.
memoryview(message_body)
except TypeError:
try:
chunks = iter(message_body)
except TypeError:
raise TypeError("message_body should be a bytes-like "
"object or an iterable, got %r"
% type(message_body))
else:
# the object implements the buffer interface and
# can be passed directly into socket methods
chunks = (message_body,)
for chunk in chunks:
if not chunk:
if self.debuglevel > 0:
print('Zero length chunk ignored')
continue
if encode_chunked and self._http_vsn == 11:
# chunked encoding
chunk = f'{len(chunk):X}\r\n'.encode('ascii') + chunk \
+ b'\r\n'
self.send(chunk)
if encode_chunked and self._http_vsn == 11:
# end chunked transfer
self.send(b'0\r\n\r\n')
def putrequest(self, method, url, skip_host=False,
skip_accept_encoding=False):
"""Send a request to the server.
`method' specifies an HTTP request method, e.g. 'GET'.
`url' specifies the object being requested, e.g. '/index.html'.
`skip_host' if True does not add automatically a 'Host:' header
`skip_accept_encoding' if True does not add automatically an
'Accept-Encoding:' header
"""
# if a prior response has been completed, then forget about it.
if self.__response and self.__response.isclosed():
self.__response = None
# in certain cases, we cannot issue another request on this connection.
# this occurs when:
# 1) we are in the process of sending a request. (_CS_REQ_STARTED)
# 2) a response to a previous request has signalled that it is going
# to close the connection upon completion.
# 3) the headers for the previous response have not been read, thus
# we cannot determine whether point (2) is true. (_CS_REQ_SENT)
#
# if there is no prior response, then we can request at will.
#
# if point (2) is true, then we will have passed the socket to the
# response (effectively meaning, "there is no prior response"), and
# will open a new one when a new request is made.
#
# Note: if a prior response exists, then we *can* start a new request.
# We are not allowed to begin fetching the response to this new
# request, however, until that prior response is complete.
#
if self.__state == _CS_IDLE:
self.__state = _CS_REQ_STARTED
else:
raise CannotSendRequest(self.__state)
self._validate_method(method)
# Save the method for use later in the response phase
self._method = method
url = url or '/'
self._validate_path(url)
request = '%s %s %s' % (method, url, self._http_vsn_str)
self._output(self._encode_request(request))
if self._http_vsn == 11:
# Issue some standard headers for better HTTP/1.1 compliance
if not skip_host:
# this header is issued *only* for HTTP/1.1
# connections. more specifically, this means it is
# only issued when the client uses the new
# HTTPConnection() class. backwards-compat clients
# will be using HTTP/1.0 and those clients may be
# issuing this header themselves. we should NOT issue
# it twice; some web servers (such as Apache) barf
# when they see two Host: headers
# If we need a non-standard port,include it in the
# header. If the request is going through a proxy,
# but the host of the actual URL, not the host of the
# proxy.
netloc = ''
if url.startswith('http'):
nil, netloc, nil, nil, nil = urlsplit(url)
if netloc:
try:
netloc_enc = netloc.encode("ascii")
except UnicodeEncodeError:
netloc_enc = netloc.encode("idna")
self.putheader('Host', netloc_enc)
else:
if self._tunnel_host:
host = self._tunnel_host
port = self._tunnel_port
else:
host = self.host
port = self.port
try:
host_enc = host.encode("ascii")
except UnicodeEncodeError:
host_enc = host.encode("idna")
# As per RFC 273, IPv6 address should be wrapped with []
# when used as Host header
if host.find(':') >= 0:
host_enc = b'[' + host_enc + b']'
if port == self.default_port:
self.putheader('Host', host_enc)
else:
host_enc = host_enc.decode("ascii")
self.putheader('Host', "%s:%s" % (host_enc, port))
# note: we are assuming that clients will not attempt to set these
# headers since *this* library must deal with the
# consequences. this also means that when the supporting
# libraries are updated to recognize other forms, then this
# code should be changed (removed or updated).
# we only want a Content-Encoding of "identity" since we don't
# support encodings such as x-gzip or x-deflate.
if not skip_accept_encoding:
self.putheader('Accept-Encoding', 'identity')
# we can accept "chunked" Transfer-Encodings, but no others
# NOTE: no TE header implies *only* "chunked"
#self.putheader('TE', 'chunked')
# if TE is supplied in the header, then it must appear in a
# Connection header.
#self.putheader('Connection', 'TE')
else:
# For HTTP/1.0, the server will assume "not chunked"
pass
def _encode_request(self, request):
# ASCII also helps prevent CVE-2019-9740.
return request.encode('ascii')
def _validate_method(self, method):
"""Validate a method name for putrequest."""
# prevent http header injection
match = _contains_disallowed_method_pchar_re.search(method)
if match:
raise ValueError(
f"method can't contain control characters. {method!r} "
f"(found at least {match.group()!r})")
def _validate_path(self, url):
"""Validate a url for putrequest."""
# Prevent CVE-2019-9740.
match = _contains_disallowed_url_pchar_re.search(url)
if match:
raise InvalidURL(f"URL can't contain control characters. {url!r} "
f"(found at least {match.group()!r})")
def _validate_host(self, host):
"""Validate a host so it doesn't contain control characters."""
# Prevent CVE-2019-18348.
match = _contains_disallowed_url_pchar_re.search(host)
if match:
raise InvalidURL(f"URL can't contain control characters. {host!r} "
f"(found at least {match.group()!r})")
def putheader(self, header, *values):
"""Send a request header line to the server.
For example: h.putheader('Accept', 'text/html')
"""
if self.__state != _CS_REQ_STARTED:
raise CannotSendHeader()
if hasattr(header, 'encode'):
header = header.encode('ascii')
if not _is_legal_header_name(header):
raise ValueError('Invalid header name %r' % (header,))
values = list(values)
for i, one_value in enumerate(values):
if hasattr(one_value, 'encode'):
values[i] = one_value.encode('latin-1')
elif isinstance(one_value, int):
values[i] = str(one_value).encode('ascii')
if _is_illegal_header_value(values[i]):
raise ValueError('Invalid header value %r' % (values[i],))
value = b'\r\n\t'.join(values)
header = header + b': ' + value
self._output(header)
def endheaders(self, message_body=None, *, encode_chunked=False):
"""Indicate that the last header line has been sent to the server.
This method sends the request to the server. The optional message_body
argument can be used to pass a message body associated with the
request.
"""
if self.__state == _CS_REQ_STARTED:
self.__state = _CS_REQ_SENT
else:
raise CannotSendHeader()
self._send_output(message_body, encode_chunked=encode_chunked)
def request(self, method, url, body=None, headers={}, *,
encode_chunked=False):
"""Send a complete request to the server."""
self._send_request(method, url, body, headers, encode_chunked)
def _send_request(self, method, url, body, headers, encode_chunked):
# Honor explicitly requested Host: and Accept-Encoding: headers.
header_names = frozenset(k.lower() for k in headers)
skips = {}
if 'host' in header_names:
skips['skip_host'] = 1
if 'accept-encoding' in header_names:
skips['skip_accept_encoding'] = 1
self.putrequest(method, url, **skips)
# chunked encoding will happen if HTTP/1.1 is used and either
# the caller passes encode_chunked=True or the following
# conditions hold:
# 1. content-length has not been explicitly set
# 2. the body is a file or iterable, but not a str or bytes-like
# 3. Transfer-Encoding has NOT been explicitly set by the caller
if 'content-length' not in header_names:
# only chunk body if not explicitly set for backwards
# compatibility, assuming the client code is already handling the
# chunking
if 'transfer-encoding' not in header_names:
# if content-length cannot be automatically determined, fall
# back to chunked encoding
encode_chunked = False
content_length = self._get_content_length(body, method)
if content_length is None:
if body is not None:
if self.debuglevel > 0:
print('Unable to determine size of %r' % body)
encode_chunked = True
self.putheader('Transfer-Encoding', 'chunked')
else:
self.putheader('Content-Length', str(content_length))
else:
encode_chunked = False
for hdr, value in headers.items():
self.putheader(hdr, value)
if isinstance(body, str):
# RFC 2616 Section 3.7.1 says that text default has a
# default charset of iso-8859-1.
body = _encode(body, 'body')
self.endheaders(body, encode_chunked=encode_chunked)
def getresponse(self):
"""Get the response from the server.
If the HTTPConnection is in the correct state, returns an
instance of HTTPResponse or of whatever object is returned by
the response_class variable.
If a request has not been sent or if a previous response has
not be handled, ResponseNotReady is raised. If the HTTP
response indicates that the connection should be closed, then
it will be closed before the response is returned. When the
connection is closed, the underlying socket is closed.
"""
# if a prior response has been completed, then forget about it.
if self.__response and self.__response.isclosed():
self.__response = None
# if a prior response exists, then it must be completed (otherwise, we
# cannot read this response's header to determine the connection-close
# behavior)
#
# note: if a prior response existed, but was connection-close, then the
# socket and response were made independent of this HTTPConnection
# object since a new request requires that we open a whole new
# connection
#
# this means the prior response had one of two states:
# 1) will_close: this connection was reset and the prior socket and
# response operate independently
# 2) persistent: the response was retained and we await its
# isclosed() status to become true.
#
if self.__state != _CS_REQ_SENT or self.__response:
raise ResponseNotReady(self.__state)
if self.debuglevel > 0:
response = self.response_class(self.sock, self.debuglevel,
method=self._method)
else:
response = self.response_class(self.sock, method=self._method)
try:
try:
response.begin()
except ConnectionError:
self.close()
raise
assert response.will_close != _UNKNOWN
self.__state = _CS_IDLE
if response.will_close:
# this effectively passes the connection to the response
self.close()
else:
# remember this, so we can tell when it is complete
self.__response = response
return response
except:
response.close()
raise
| (host, port=None, timeout=<object object at 0x7fbc93ea0e50>, source_address=None, blocksize=8192) |
20,604 | http.client | __init__ | null | def __init__(self, host, port=None, timeout=socket._GLOBAL_DEFAULT_TIMEOUT,
source_address=None, blocksize=8192):
self.timeout = timeout
self.source_address = source_address
self.blocksize = blocksize
self.sock = None
self._buffer = []
self.__response = None
self.__state = _CS_IDLE
self._method = None
self._tunnel_host = None
self._tunnel_port = None
self._tunnel_headers = {}
(self.host, self.port) = self._get_hostport(host, port)
self._validate_host(self.host)
# This is stored as an instance variable to allow unit
# tests to replace it with a suitable mockup
self._create_connection = socket.create_connection
| (self, host, port=None, timeout=<object object at 0x7fbc93ea0e50>, source_address=None, blocksize=8192) |
20,605 | http.client | _encode_request | null | def _encode_request(self, request):
# ASCII also helps prevent CVE-2019-9740.
return request.encode('ascii')
| (self, request) |
20,606 | http.client | _get_content_length | Get the content-length based on the body.
If the body is None, we set Content-Length: 0 for methods that expect
a body (RFC 7230, Section 3.3.2). We also set the Content-Length for
any method if the body is a str or bytes-like object and not a file.
| @staticmethod
def _get_content_length(body, method):
"""Get the content-length based on the body.
If the body is None, we set Content-Length: 0 for methods that expect
a body (RFC 7230, Section 3.3.2). We also set the Content-Length for
any method if the body is a str or bytes-like object and not a file.
"""
if body is None:
# do an explicit check for not None here to distinguish
# between unset and set but empty
if method.upper() in _METHODS_EXPECTING_BODY:
return 0
else:
return None
if hasattr(body, 'read'):
# file-like object.
return None
try:
# does it implement the buffer protocol (bytes, bytearray, array)?
mv = memoryview(body)
return mv.nbytes
except TypeError:
pass
if isinstance(body, str):
return len(body)
return None
| (body, method) |
20,607 | http.client | _get_hostport | null | def _get_hostport(self, host, port):
if port is None:
i = host.rfind(':')
j = host.rfind(']') # ipv6 addresses have [...]
if i > j:
try:
port = int(host[i+1:])
except ValueError:
if host[i+1:] == "": # http://foo.com:/ == http://foo.com/
port = self.default_port
else:
raise InvalidURL("nonnumeric port: '%s'" % host[i+1:])
host = host[:i]
else:
port = self.default_port
if host and host[0] == '[' and host[-1] == ']':
host = host[1:-1]
return (host, port)
| (self, host, port) |
20,608 | http.client | _is_textIO | Test whether a file-like object is a text or a binary stream.
| @staticmethod
def _is_textIO(stream):
"""Test whether a file-like object is a text or a binary stream.
"""
return isinstance(stream, io.TextIOBase)
| (stream) |
20,609 | http.client | _output | Add a line of output to the current request buffer.
Assumes that the line does *not* end with \r\n.
| def _output(self, s):
"""Add a line of output to the current request buffer.
Assumes that the line does *not* end with \\r\\n.
"""
self._buffer.append(s)
| (self, s) |
20,610 | http.client | _read_readable | null | def _read_readable(self, readable):
if self.debuglevel > 0:
print("sendIng a read()able")
encode = self._is_textIO(readable)
if encode and self.debuglevel > 0:
print("encoding file using iso-8859-1")
while True:
datablock = readable.read(self.blocksize)
if not datablock:
break
if encode:
datablock = datablock.encode("iso-8859-1")
yield datablock
| (self, readable) |
20,611 | http.client | _send_output | Send the currently buffered request and clear the buffer.
Appends an extra \r\n to the buffer.
A message_body may be specified, to be appended to the request.
| def _send_output(self, message_body=None, encode_chunked=False):
"""Send the currently buffered request and clear the buffer.
Appends an extra \\r\\n to the buffer.
A message_body may be specified, to be appended to the request.
"""
self._buffer.extend((b"", b""))
msg = b"\r\n".join(self._buffer)
del self._buffer[:]
self.send(msg)
if message_body is not None:
# create a consistent interface to message_body
if hasattr(message_body, 'read'):
# Let file-like take precedence over byte-like. This
# is needed to allow the current position of mmap'ed
# files to be taken into account.
chunks = self._read_readable(message_body)
else:
try:
# this is solely to check to see if message_body
# implements the buffer API. it /would/ be easier
# to capture if PyObject_CheckBuffer was exposed
# to Python.
memoryview(message_body)
except TypeError:
try:
chunks = iter(message_body)
except TypeError:
raise TypeError("message_body should be a bytes-like "
"object or an iterable, got %r"
% type(message_body))
else:
# the object implements the buffer interface and
# can be passed directly into socket methods
chunks = (message_body,)
for chunk in chunks:
if not chunk:
if self.debuglevel > 0:
print('Zero length chunk ignored')
continue
if encode_chunked and self._http_vsn == 11:
# chunked encoding
chunk = f'{len(chunk):X}\r\n'.encode('ascii') + chunk \
+ b'\r\n'
self.send(chunk)
if encode_chunked and self._http_vsn == 11:
# end chunked transfer
self.send(b'0\r\n\r\n')
| (self, message_body=None, encode_chunked=False) |
20,612 | http.client | _send_request | null | def _send_request(self, method, url, body, headers, encode_chunked):
# Honor explicitly requested Host: and Accept-Encoding: headers.
header_names = frozenset(k.lower() for k in headers)
skips = {}
if 'host' in header_names:
skips['skip_host'] = 1
if 'accept-encoding' in header_names:
skips['skip_accept_encoding'] = 1
self.putrequest(method, url, **skips)
# chunked encoding will happen if HTTP/1.1 is used and either
# the caller passes encode_chunked=True or the following
# conditions hold:
# 1. content-length has not been explicitly set
# 2. the body is a file or iterable, but not a str or bytes-like
# 3. Transfer-Encoding has NOT been explicitly set by the caller
if 'content-length' not in header_names:
# only chunk body if not explicitly set for backwards
# compatibility, assuming the client code is already handling the
# chunking
if 'transfer-encoding' not in header_names:
# if content-length cannot be automatically determined, fall
# back to chunked encoding
encode_chunked = False
content_length = self._get_content_length(body, method)
if content_length is None:
if body is not None:
if self.debuglevel > 0:
print('Unable to determine size of %r' % body)
encode_chunked = True
self.putheader('Transfer-Encoding', 'chunked')
else:
self.putheader('Content-Length', str(content_length))
else:
encode_chunked = False
for hdr, value in headers.items():
self.putheader(hdr, value)
if isinstance(body, str):
# RFC 2616 Section 3.7.1 says that text default has a
# default charset of iso-8859-1.
body = _encode(body, 'body')
self.endheaders(body, encode_chunked=encode_chunked)
| (self, method, url, body, headers, encode_chunked) |
20,613 | http.client | _tunnel | null | def _tunnel(self):
connect = b"CONNECT %s:%d HTTP/1.0\r\n" % (
self._tunnel_host.encode("ascii"), self._tunnel_port)
headers = [connect]
for header, value in self._tunnel_headers.items():
headers.append(f"{header}: {value}\r\n".encode("latin-1"))
headers.append(b"\r\n")
# Making a single send() call instead of one per line encourages
# the host OS to use a more optimal packet size instead of
# potentially emitting a series of small packets.
self.send(b"".join(headers))
del headers
response = self.response_class(self.sock, method=self._method)
(version, code, message) = response._read_status()
if code != http.HTTPStatus.OK:
self.close()
raise OSError(f"Tunnel connection failed: {code} {message.strip()}")
while True:
line = response.fp.readline(_MAXLINE + 1)
if len(line) > _MAXLINE:
raise LineTooLong("header line")
if not line:
# for sites which EOF without sending a trailer
break
if line in (b'\r\n', b'\n', b''):
break
if self.debuglevel > 0:
print('header:', line.decode())
| (self) |
20,614 | http.client | _validate_host | Validate a host so it doesn't contain control characters. | def _validate_host(self, host):
"""Validate a host so it doesn't contain control characters."""
# Prevent CVE-2019-18348.
match = _contains_disallowed_url_pchar_re.search(host)
if match:
raise InvalidURL(f"URL can't contain control characters. {host!r} "
f"(found at least {match.group()!r})")
| (self, host) |
20,615 | http.client | _validate_method | Validate a method name for putrequest. | def _validate_method(self, method):
"""Validate a method name for putrequest."""
# prevent http header injection
match = _contains_disallowed_method_pchar_re.search(method)
if match:
raise ValueError(
f"method can't contain control characters. {method!r} "
f"(found at least {match.group()!r})")
| (self, method) |
20,616 | http.client | _validate_path | Validate a url for putrequest. | def _validate_path(self, url):
"""Validate a url for putrequest."""
# Prevent CVE-2019-9740.
match = _contains_disallowed_url_pchar_re.search(url)
if match:
raise InvalidURL(f"URL can't contain control characters. {url!r} "
f"(found at least {match.group()!r})")
| (self, url) |
20,617 | http.client | close | Close the connection to the HTTP server. | def close(self):
"""Close the connection to the HTTP server."""
self.__state = _CS_IDLE
try:
sock = self.sock
if sock:
self.sock = None
sock.close() # close it manually... there may be other refs
finally:
response = self.__response
if response:
self.__response = None
response.close()
| (self) |
20,618 | http.client | connect | Connect to the host and port specified in __init__. | def connect(self):
"""Connect to the host and port specified in __init__."""
sys.audit("http.client.connect", self, self.host, self.port)
self.sock = self._create_connection(
(self.host,self.port), self.timeout, self.source_address)
# Might fail in OSs that don't implement TCP_NODELAY
try:
self.sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
except OSError as e:
if e.errno != errno.ENOPROTOOPT:
raise
if self._tunnel_host:
self._tunnel()
| (self) |
20,619 | http.client | endheaders | Indicate that the last header line has been sent to the server.
This method sends the request to the server. The optional message_body
argument can be used to pass a message body associated with the
request.
| def endheaders(self, message_body=None, *, encode_chunked=False):
"""Indicate that the last header line has been sent to the server.
This method sends the request to the server. The optional message_body
argument can be used to pass a message body associated with the
request.
"""
if self.__state == _CS_REQ_STARTED:
self.__state = _CS_REQ_SENT
else:
raise CannotSendHeader()
self._send_output(message_body, encode_chunked=encode_chunked)
| (self, message_body=None, *, encode_chunked=False) |
20,620 | http.client | getresponse | Get the response from the server.
If the HTTPConnection is in the correct state, returns an
instance of HTTPResponse or of whatever object is returned by
the response_class variable.
If a request has not been sent or if a previous response has
not be handled, ResponseNotReady is raised. If the HTTP
response indicates that the connection should be closed, then
it will be closed before the response is returned. When the
connection is closed, the underlying socket is closed.
| def getresponse(self):
"""Get the response from the server.
If the HTTPConnection is in the correct state, returns an
instance of HTTPResponse or of whatever object is returned by
the response_class variable.
If a request has not been sent or if a previous response has
not be handled, ResponseNotReady is raised. If the HTTP
response indicates that the connection should be closed, then
it will be closed before the response is returned. When the
connection is closed, the underlying socket is closed.
"""
# if a prior response has been completed, then forget about it.
if self.__response and self.__response.isclosed():
self.__response = None
# if a prior response exists, then it must be completed (otherwise, we
# cannot read this response's header to determine the connection-close
# behavior)
#
# note: if a prior response existed, but was connection-close, then the
# socket and response were made independent of this HTTPConnection
# object since a new request requires that we open a whole new
# connection
#
# this means the prior response had one of two states:
# 1) will_close: this connection was reset and the prior socket and
# response operate independently
# 2) persistent: the response was retained and we await its
# isclosed() status to become true.
#
if self.__state != _CS_REQ_SENT or self.__response:
raise ResponseNotReady(self.__state)
if self.debuglevel > 0:
response = self.response_class(self.sock, self.debuglevel,
method=self._method)
else:
response = self.response_class(self.sock, method=self._method)
try:
try:
response.begin()
except ConnectionError:
self.close()
raise
assert response.will_close != _UNKNOWN
self.__state = _CS_IDLE
if response.will_close:
# this effectively passes the connection to the response
self.close()
else:
# remember this, so we can tell when it is complete
self.__response = response
return response
except:
response.close()
raise
| (self) |
20,621 | http.client | putheader | Send a request header line to the server.
For example: h.putheader('Accept', 'text/html')
| def putheader(self, header, *values):
"""Send a request header line to the server.
For example: h.putheader('Accept', 'text/html')
"""
if self.__state != _CS_REQ_STARTED:
raise CannotSendHeader()
if hasattr(header, 'encode'):
header = header.encode('ascii')
if not _is_legal_header_name(header):
raise ValueError('Invalid header name %r' % (header,))
values = list(values)
for i, one_value in enumerate(values):
if hasattr(one_value, 'encode'):
values[i] = one_value.encode('latin-1')
elif isinstance(one_value, int):
values[i] = str(one_value).encode('ascii')
if _is_illegal_header_value(values[i]):
raise ValueError('Invalid header value %r' % (values[i],))
value = b'\r\n\t'.join(values)
header = header + b': ' + value
self._output(header)
| (self, header, *values) |
20,622 | http.client | putrequest | Send a request to the server.
`method' specifies an HTTP request method, e.g. 'GET'.
`url' specifies the object being requested, e.g. '/index.html'.
`skip_host' if True does not add automatically a 'Host:' header
`skip_accept_encoding' if True does not add automatically an
'Accept-Encoding:' header
| def putrequest(self, method, url, skip_host=False,
skip_accept_encoding=False):
"""Send a request to the server.
`method' specifies an HTTP request method, e.g. 'GET'.
`url' specifies the object being requested, e.g. '/index.html'.
`skip_host' if True does not add automatically a 'Host:' header
`skip_accept_encoding' if True does not add automatically an
'Accept-Encoding:' header
"""
# if a prior response has been completed, then forget about it.
if self.__response and self.__response.isclosed():
self.__response = None
# in certain cases, we cannot issue another request on this connection.
# this occurs when:
# 1) we are in the process of sending a request. (_CS_REQ_STARTED)
# 2) a response to a previous request has signalled that it is going
# to close the connection upon completion.
# 3) the headers for the previous response have not been read, thus
# we cannot determine whether point (2) is true. (_CS_REQ_SENT)
#
# if there is no prior response, then we can request at will.
#
# if point (2) is true, then we will have passed the socket to the
# response (effectively meaning, "there is no prior response"), and
# will open a new one when a new request is made.
#
# Note: if a prior response exists, then we *can* start a new request.
# We are not allowed to begin fetching the response to this new
# request, however, until that prior response is complete.
#
if self.__state == _CS_IDLE:
self.__state = _CS_REQ_STARTED
else:
raise CannotSendRequest(self.__state)
self._validate_method(method)
# Save the method for use later in the response phase
self._method = method
url = url or '/'
self._validate_path(url)
request = '%s %s %s' % (method, url, self._http_vsn_str)
self._output(self._encode_request(request))
if self._http_vsn == 11:
# Issue some standard headers for better HTTP/1.1 compliance
if not skip_host:
# this header is issued *only* for HTTP/1.1
# connections. more specifically, this means it is
# only issued when the client uses the new
# HTTPConnection() class. backwards-compat clients
# will be using HTTP/1.0 and those clients may be
# issuing this header themselves. we should NOT issue
# it twice; some web servers (such as Apache) barf
# when they see two Host: headers
# If we need a non-standard port,include it in the
# header. If the request is going through a proxy,
# but the host of the actual URL, not the host of the
# proxy.
netloc = ''
if url.startswith('http'):
nil, netloc, nil, nil, nil = urlsplit(url)
if netloc:
try:
netloc_enc = netloc.encode("ascii")
except UnicodeEncodeError:
netloc_enc = netloc.encode("idna")
self.putheader('Host', netloc_enc)
else:
if self._tunnel_host:
host = self._tunnel_host
port = self._tunnel_port
else:
host = self.host
port = self.port
try:
host_enc = host.encode("ascii")
except UnicodeEncodeError:
host_enc = host.encode("idna")
# As per RFC 273, IPv6 address should be wrapped with []
# when used as Host header
if host.find(':') >= 0:
host_enc = b'[' + host_enc + b']'
if port == self.default_port:
self.putheader('Host', host_enc)
else:
host_enc = host_enc.decode("ascii")
self.putheader('Host', "%s:%s" % (host_enc, port))
# note: we are assuming that clients will not attempt to set these
# headers since *this* library must deal with the
# consequences. this also means that when the supporting
# libraries are updated to recognize other forms, then this
# code should be changed (removed or updated).
# we only want a Content-Encoding of "identity" since we don't
# support encodings such as x-gzip or x-deflate.
if not skip_accept_encoding:
self.putheader('Accept-Encoding', 'identity')
# we can accept "chunked" Transfer-Encodings, but no others
# NOTE: no TE header implies *only* "chunked"
#self.putheader('TE', 'chunked')
# if TE is supplied in the header, then it must appear in a
# Connection header.
#self.putheader('Connection', 'TE')
else:
# For HTTP/1.0, the server will assume "not chunked"
pass
| (self, method, url, skip_host=False, skip_accept_encoding=False) |
20,623 | http.client | request | Send a complete request to the server. | def request(self, method, url, body=None, headers={}, *,
encode_chunked=False):
"""Send a complete request to the server."""
self._send_request(method, url, body, headers, encode_chunked)
| (self, method, url, body=None, headers={}, *, encode_chunked=False) |
20,624 | http.client | send | Send `data' to the server.
``data`` can be a string object, a bytes object, an array object, a
file-like object that supports a .read() method, or an iterable object.
| def send(self, data):
"""Send `data' to the server.
``data`` can be a string object, a bytes object, an array object, a
file-like object that supports a .read() method, or an iterable object.
"""
if self.sock is None:
if self.auto_open:
self.connect()
else:
raise NotConnected()
if self.debuglevel > 0:
print("send:", repr(data))
if hasattr(data, "read") :
if self.debuglevel > 0:
print("sendIng a read()able")
encode = self._is_textIO(data)
if encode and self.debuglevel > 0:
print("encoding file using iso-8859-1")
while 1:
datablock = data.read(self.blocksize)
if not datablock:
break
if encode:
datablock = datablock.encode("iso-8859-1")
sys.audit("http.client.send", self, datablock)
self.sock.sendall(datablock)
return
sys.audit("http.client.send", self, data)
try:
self.sock.sendall(data)
except TypeError:
if isinstance(data, collections.abc.Iterable):
for d in data:
self.sock.sendall(d)
else:
raise TypeError("data should be a bytes-like object "
"or an iterable, got %r" % type(data))
| (self, data) |
20,625 | http.client | set_debuglevel | null | def set_debuglevel(self, level):
self.debuglevel = level
| (self, level) |
20,626 | http.client | set_tunnel | Set up host and port for HTTP CONNECT tunnelling.
In a connection that uses HTTP CONNECT tunneling, the host passed to the
constructor is used as a proxy server that relays all communication to
the endpoint passed to `set_tunnel`. This done by sending an HTTP
CONNECT request to the proxy server when the connection is established.
This method must be called before the HTTP connection has been
established.
The headers argument should be a mapping of extra HTTP headers to send
with the CONNECT request.
| def set_tunnel(self, host, port=None, headers=None):
"""Set up host and port for HTTP CONNECT tunnelling.
In a connection that uses HTTP CONNECT tunneling, the host passed to the
constructor is used as a proxy server that relays all communication to
the endpoint passed to `set_tunnel`. This done by sending an HTTP
CONNECT request to the proxy server when the connection is established.
This method must be called before the HTTP connection has been
established.
The headers argument should be a mapping of extra HTTP headers to send
with the CONNECT request.
"""
if self.sock:
raise RuntimeError("Can't set up tunnel for established connection")
self._tunnel_host, self._tunnel_port = self._get_hostport(host, port)
if headers:
self._tunnel_headers = headers
else:
self._tunnel_headers.clear()
| (self, host, port=None, headers=None) |
20,627 | mureq | HTTPErrorStatus | HTTPErrorStatus is raised by Response.raise_for_status() to indicate an
HTTP error code (a 40x or a 50x). Note that a well-formed response with an
error code does not result in an exception unless raise_for_status() is
called explicitly.
| class HTTPErrorStatus(HTTPException):
"""HTTPErrorStatus is raised by Response.raise_for_status() to indicate an
HTTP error code (a 40x or a 50x). Note that a well-formed response with an
error code does not result in an exception unless raise_for_status() is
called explicitly.
"""
def __init__(self, status_code):
self.status_code = status_code
def __str__(self):
return f"HTTP response returned error code {self.status_code:d}"
| (status_code) |
20,628 | mureq | __init__ | null | def __init__(self, status_code):
self.status_code = status_code
| (self, status_code) |
20,629 | mureq | __str__ | null | def __str__(self):
return f"HTTP response returned error code {self.status_code:d}"
| (self) |
20,630 | http.client | HTTPException | null | class HTTPException(Exception):
# Subclasses that define an __init__ must call Exception.__init__
# or define self.args. Otherwise, str() will fail.
pass
| null |
20,631 | http.client | HTTPMessage | null | class HTTPMessage(email.message.Message):
# XXX The only usage of this method is in
# http.server.CGIHTTPRequestHandler. Maybe move the code there so
# that it doesn't need to be part of the public API. The API has
# never been defined so this could cause backwards compatibility
# issues.
def getallmatchingheaders(self, name):
"""Find all header lines matching a given header name.
Look through the list of headers and find all lines matching a given
header name (and their continuation lines). A list of the lines is
returned, without interpretation. If the header does not occur, an
empty list is returned. If the header occurs multiple times, all
occurrences are returned. Case is not important in the header name.
"""
name = name.lower() + ':'
n = len(name)
lst = []
hit = 0
for line in self.keys():
if line[:n].lower() == name:
hit = 1
elif not line[:1].isspace():
hit = 0
if hit:
lst.append(line)
return lst
| (policy=Compat32()) |
20,632 | email.message | __bytes__ | Return the entire formatted message as a bytes object.
| def __bytes__(self):
"""Return the entire formatted message as a bytes object.
"""
return self.as_bytes()
| (self) |
20,633 | email.message | __contains__ | null | def __contains__(self, name):
return name.lower() in [k.lower() for k, v in self._headers]
| (self, name) |
20,634 | email.message | __delitem__ | Delete all occurrences of a header, if present.
Does not raise an exception if the header is missing.
| def __delitem__(self, name):
"""Delete all occurrences of a header, if present.
Does not raise an exception if the header is missing.
"""
name = name.lower()
newheaders = []
for k, v in self._headers:
if k.lower() != name:
newheaders.append((k, v))
self._headers = newheaders
| (self, name) |
20,635 | email.message | __getitem__ | Get a header value.
Return None if the header is missing instead of raising an exception.
Note that if the header appeared multiple times, exactly which
occurrence gets returned is undefined. Use get_all() to get all
the values matching a header field name.
| def __getitem__(self, name):
"""Get a header value.
Return None if the header is missing instead of raising an exception.
Note that if the header appeared multiple times, exactly which
occurrence gets returned is undefined. Use get_all() to get all
the values matching a header field name.
"""
return self.get(name)
| (self, name) |
20,636 | email.message | __init__ | null | def __init__(self, policy=compat32):
self.policy = policy
self._headers = []
self._unixfrom = None
self._payload = None
self._charset = None
# Defaults for multipart messages
self.preamble = self.epilogue = None
self.defects = []
# Default content type
self._default_type = 'text/plain'
| (self, policy=Compat32()) |
20,637 | email.message | __iter__ | null | def __iter__(self):
for field, value in self._headers:
yield field
| (self) |
20,638 | email.message | __len__ | Return the total number of headers, including duplicates. | def __len__(self):
"""Return the total number of headers, including duplicates."""
return len(self._headers)
| (self) |
20,639 | email.message | __setitem__ | Set the value of a header.
Note: this does not overwrite an existing header with the same field
name. Use __delitem__() first to delete any existing headers.
| def __setitem__(self, name, val):
"""Set the value of a header.
Note: this does not overwrite an existing header with the same field
name. Use __delitem__() first to delete any existing headers.
"""
max_count = self.policy.header_max_count(name)
if max_count:
lname = name.lower()
found = 0
for k, v in self._headers:
if k.lower() == lname:
found += 1
if found >= max_count:
raise ValueError("There may be at most {} {} headers "
"in a message".format(max_count, name))
self._headers.append(self.policy.header_store_parse(name, val))
| (self, name, val) |
20,640 | email.message | __str__ | Return the entire formatted message as a string.
| def __str__(self):
"""Return the entire formatted message as a string.
"""
return self.as_string()
| (self) |
20,641 | email.message | _get_params_preserve | null | def _get_params_preserve(self, failobj, header):
# Like get_params() but preserves the quoting of values. BAW:
# should this be part of the public interface?
missing = object()
value = self.get(header, missing)
if value is missing:
return failobj
params = []
for p in _parseparam(value):
try:
name, val = p.split('=', 1)
name = name.strip()
val = val.strip()
except ValueError:
# Must have been a bare attribute
name = p.strip()
val = ''
params.append((name, val))
params = utils.decode_params(params)
return params
| (self, failobj, header) |
20,642 | email.message | add_header | Extended header setting.
name is the header field to add. keyword arguments can be used to set
additional parameters for the header field, with underscores converted
to dashes. Normally the parameter will be added as key="value" unless
value is None, in which case only the key will be added. If a
parameter value contains non-ASCII characters it can be specified as a
three-tuple of (charset, language, value), in which case it will be
encoded according to RFC2231 rules. Otherwise it will be encoded using
the utf-8 charset and a language of ''.
Examples:
msg.add_header('content-disposition', 'attachment', filename='bud.gif')
msg.add_header('content-disposition', 'attachment',
filename=('utf-8', '', Fußballer.ppt'))
msg.add_header('content-disposition', 'attachment',
filename='Fußballer.ppt'))
| def add_header(self, _name, _value, **_params):
"""Extended header setting.
name is the header field to add. keyword arguments can be used to set
additional parameters for the header field, with underscores converted
to dashes. Normally the parameter will be added as key="value" unless
value is None, in which case only the key will be added. If a
parameter value contains non-ASCII characters it can be specified as a
three-tuple of (charset, language, value), in which case it will be
encoded according to RFC2231 rules. Otherwise it will be encoded using
the utf-8 charset and a language of ''.
Examples:
msg.add_header('content-disposition', 'attachment', filename='bud.gif')
msg.add_header('content-disposition', 'attachment',
filename=('utf-8', '', Fußballer.ppt'))
msg.add_header('content-disposition', 'attachment',
filename='Fußballer.ppt'))
"""
parts = []
for k, v in _params.items():
if v is None:
parts.append(k.replace('_', '-'))
else:
parts.append(_formatparam(k.replace('_', '-'), v))
if _value is not None:
parts.insert(0, _value)
self[_name] = SEMISPACE.join(parts)
| (self, _name, _value, **_params) |
20,643 | email.message | as_bytes | Return the entire formatted message as a bytes object.
Optional 'unixfrom', when true, means include the Unix From_ envelope
header. 'policy' is passed to the BytesGenerator instance used to
serialize the message; if not specified the policy associated with
the message instance is used.
| def as_bytes(self, unixfrom=False, policy=None):
"""Return the entire formatted message as a bytes object.
Optional 'unixfrom', when true, means include the Unix From_ envelope
header. 'policy' is passed to the BytesGenerator instance used to
serialize the message; if not specified the policy associated with
the message instance is used.
"""
from email.generator import BytesGenerator
policy = self.policy if policy is None else policy
fp = BytesIO()
g = BytesGenerator(fp, mangle_from_=False, policy=policy)
g.flatten(self, unixfrom=unixfrom)
return fp.getvalue()
| (self, unixfrom=False, policy=None) |
20,644 | email.message | as_string | Return the entire formatted message as a string.
Optional 'unixfrom', when true, means include the Unix From_ envelope
header. For backward compatibility reasons, if maxheaderlen is
not specified it defaults to 0, so you must override it explicitly
if you want a different maxheaderlen. 'policy' is passed to the
Generator instance used to serialize the message; if it is not
specified the policy associated with the message instance is used.
If the message object contains binary data that is not encoded
according to RFC standards, the non-compliant data will be replaced by
unicode "unknown character" code points.
| def as_string(self, unixfrom=False, maxheaderlen=0, policy=None):
"""Return the entire formatted message as a string.
Optional 'unixfrom', when true, means include the Unix From_ envelope
header. For backward compatibility reasons, if maxheaderlen is
not specified it defaults to 0, so you must override it explicitly
if you want a different maxheaderlen. 'policy' is passed to the
Generator instance used to serialize the message; if it is not
specified the policy associated with the message instance is used.
If the message object contains binary data that is not encoded
according to RFC standards, the non-compliant data will be replaced by
unicode "unknown character" code points.
"""
from email.generator import Generator
policy = self.policy if policy is None else policy
fp = StringIO()
g = Generator(fp,
mangle_from_=False,
maxheaderlen=maxheaderlen,
policy=policy)
g.flatten(self, unixfrom=unixfrom)
return fp.getvalue()
| (self, unixfrom=False, maxheaderlen=0, policy=None) |
20,645 | email.message | attach | Add the given payload to the current payload.
The current payload will always be a list of objects after this method
is called. If you want to set the payload to a scalar object, use
set_payload() instead.
| def attach(self, payload):
"""Add the given payload to the current payload.
The current payload will always be a list of objects after this method
is called. If you want to set the payload to a scalar object, use
set_payload() instead.
"""
if self._payload is None:
self._payload = [payload]
else:
try:
self._payload.append(payload)
except AttributeError:
raise TypeError("Attach is not valid on a message with a"
" non-multipart payload")
| (self, payload) |
20,646 | email.message | del_param | Remove the given parameter completely from the Content-Type header.
The header will be re-written in place without the parameter or its
value. All values will be quoted as necessary unless requote is
False. Optional header specifies an alternative to the Content-Type
header.
| def del_param(self, param, header='content-type', requote=True):
"""Remove the given parameter completely from the Content-Type header.
The header will be re-written in place without the parameter or its
value. All values will be quoted as necessary unless requote is
False. Optional header specifies an alternative to the Content-Type
header.
"""
if header not in self:
return
new_ctype = ''
for p, v in self.get_params(header=header, unquote=requote):
if p.lower() != param.lower():
if not new_ctype:
new_ctype = _formatparam(p, v, requote)
else:
new_ctype = SEMISPACE.join([new_ctype,
_formatparam(p, v, requote)])
if new_ctype != self.get(header):
del self[header]
self[header] = new_ctype
| (self, param, header='content-type', requote=True) |
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