_id stringlengths 5 9 | text stringlengths 5 385k | title stringclasses 1
value |
|---|---|---|
doc_26900 | Handle requests until an explicit shutdown() request. Poll for shutdown every poll_interval seconds. Ignores the timeout attribute. It also calls service_actions(), which may be used by a subclass or mixin to provide actions specific to a given service. For example, the ForkingMixIn class uses service_actions() to clean up zombie child processes. Changed in version 3.3: Added service_actions call to the serve_forever method. | |
doc_26901 |
Compute group sizes. Returns
DataFrame or Series
Number of rows in each group as a Series if as_index is True or a DataFrame if as_index is False. See also Series.groupby
Apply a function groupby to a Series. DataFrame.groupby
Apply a function groupby to each row or column of a DataFrame. | |
doc_26902 | The type of lock to use when updating. Changelog New in version 1.0. | |
doc_26903 | Fill the shape drawn after the last call to begin_fill(). Whether or not overlap regions for self-intersecting polygons or multiple shapes are filled depends on the operating system graphics, type of overlap, and number of overlaps. For example, the Turtle star above may be either all yellow or have some white regions. >>> turtle.color("black", "red")
>>> turtle.begin_fill()
>>> turtle.circle(80)
>>> turtle.end_fill() | |
doc_26904 | See Migration guide for more details. tf.compat.v1.raw_ops.TensorListPushBackBatch
tf.raw_ops.TensorListPushBackBatch(
input_handles, tensor, name=None
)
Args
input_handles A Tensor of type variant.
tensor A Tensor.
name A name for the operation (optional).
Returns A Tensor of type variant. | |
doc_26905 | Creates a temporary directory in the most secure manner possible. There are no race conditions in the directory’s creation. The directory is readable, writable, and searchable only by the creating user ID. The user of mkdtemp() is responsible for deleting the temporary directory and its contents when done with it. The prefix, suffix, and dir arguments are the same as for mkstemp(). mkdtemp() returns the absolute pathname of the new directory. Raises an auditing event tempfile.mkdtemp with argument fullpath. Changed in version 3.5: suffix, prefix, and dir may now be supplied in bytes in order to obtain a bytes return value. Prior to this, only str was allowed. suffix and prefix now accept and default to None to cause an appropriate default value to be used. Changed in version 3.6: The dir parameter now accepts a path-like object. | |
doc_26906 | tf.compat.v1.scatter_nd_sub(
ref, indices, updates, use_locking=False, name=None
)
ref is a Tensor with rank P and indices is a Tensor of rank Q. indices must be integer tensor, containing indices into ref. It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P. The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the Kth dimension of ref. updates is Tensor of rank Q-1+P-K with shape: [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]
For example, say we want to subtract 4 scattered elements from a rank-1 tensor with 8 elements. In Python, that update would look like this: ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
op = tf.compat.v1.scatter_nd_sub(ref, indices, updates)
with tf.compat.v1.Session() as sess:
print sess.run(op)
The resulting update to ref would look like this: [1, -9, 3, -6, -6, 6, 7, -4]
See tf.scatter_nd for more details about how to make updates to slices.
Args
ref A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. A mutable Tensor. Should be from a Variable node.
indices A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref.
updates A Tensor. Must have the same type as ref. A tensor of updated values to add to ref.
use_locking An optional bool. Defaults to False. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
name A name for the operation (optional).
Returns A mutable Tensor. Has the same type as ref. | |
doc_26907 |
Pops a range off of a stack of nested range spans. Returns the zero-based depth of the range that is ended. | |
doc_26908 |
Alias for set_linewidth. | |
doc_26909 | Run an interactive Python shell in the context of a given Flask application. The application will populate the default namespace of this shell according to its configuration. This is useful for executing small snippets of management code without having to manually configure the application. Parameters
args (Any) –
kwargs (Any) – Return type
Any | |
doc_26910 | The name of the URL pattern that matches the URL. | |
doc_26911 | A string representation of the interface with the mask in prefix notation. >>> interface = IPv4Interface('192.0.2.5/24')
>>> interface.with_prefixlen
'192.0.2.5/24' | |
doc_26912 | A model for storing the domain and name attributes of a website.
domain
The fully qualified domain name associated with the website. For example, www.example.com.
name
A human-readable “verbose” name for the website. | |
doc_26913 |
Bases: skimage.transform._geometric.ProjectiveTransform 2D Euclidean transformation. Has the following form: X = a0 * x - b0 * y + a1 =
= x * cos(rotation) - y * sin(rotation) + a1
Y = b0 * x + a0 * y + b1 =
= x * sin(rotation) + y * cos(rotation) + b1
where the homogeneous transformation matrix is: [[a0 b0 a1]
[b0 a0 b1]
[0 0 1]]
The Euclidean transformation is a rigid transformation with rotation and translation parameters. The similarity transformation extends the Euclidean transformation with a single scaling factor. Parameters
matrix(3, 3) array, optional
Homogeneous transformation matrix.
rotationfloat, optional
Rotation angle in counter-clockwise direction as radians.
translation(tx, ty) as array, list or tuple, optional
x, y translation parameters. Attributes
params(3, 3) array
Homogeneous transformation matrix.
__init__(matrix=None, rotation=None, translation=None) [source]
Initialize self. See help(type(self)) for accurate signature.
estimate(src, dst) [source]
Estimate the transformation from a set of corresponding points. You can determine the over-, well- and under-determined parameters with the total least-squares method. Number of source and destination coordinates must match. Parameters
src(N, 2) array
Source coordinates.
dst(N, 2) array
Destination coordinates. Returns
successbool
True, if model estimation succeeds.
property rotation
property translation | |
doc_26914 | The instance of ModelChoiceField or ModelMultipleChoiceField to iterate and yield choices. | |
doc_26915 | Exception.with_traceback(tb) – set self.__traceback__ to tb and return self. | |
doc_26916 | Return a random integer N such that a <= N <= b. Alias for randrange(a, b+1). | |
doc_26917 |
Draw the Artist (and its children) using the given renderer. This has no effect if the artist is not visible (Artist.get_visible returns False). Parameters
rendererRendererBase subclass.
Notes This method is overridden in the Artist subclasses. | |
doc_26918 | Extract the device minor number from a raw device number (usually the st_dev or st_rdev field from stat). | |
doc_26919 |
Set the hatching pattern hatch can be one of: / - diagonal hatching
\ - back diagonal
| - vertical
- - horizontal
+ - crossed
x - crossed diagonal
o - small circle
O - large circle
. - dots
* - stars
Letters can be combined, in which case all the specified hatchings are done. If same letter repeats, it increases the density of hatching of that pattern. Hatching is supported in the PostScript, PDF, SVG and Agg backends only. Unlike other properties such as linewidth and colors, hatching can only be specified for the collection as a whole, not separately for each member. Parameters
hatch{'/', '\', '|', '-', '+', 'x', 'o', 'O', '.', '*'} | |
doc_26920 | Sets the module in training mode. This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc. Parameters
mode (bool) – whether to set training mode (True) or evaluation mode (False). Default: True. Returns
self Return type
Module | |
doc_26921 | Cause the byte string char to be “pushed back” into the console buffer; it will be the next character read by getch() or getche(). | |
doc_26922 |
Alias for get_ylim3d. | |
doc_26923 | See Migration guide for more details. tf.compat.v1.raw_ops.MultiDeviceIteratorGetNextFromShard
tf.raw_ops.MultiDeviceIteratorGetNextFromShard(
multi_device_iterator, shard_num, incarnation_id, output_types, output_shapes,
name=None
)
Args
multi_device_iterator A Tensor of type resource. A MultiDeviceIterator resource.
shard_num A Tensor of type int32. Integer representing which shard to fetch data for.
incarnation_id A Tensor of type int64. Which incarnation of the MultiDeviceIterator is running.
output_types A list of tf.DTypes that has length >= 1. The type list for the return values.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1. The list of shapes being produced.
name A name for the operation (optional).
Returns A list of Tensor objects of type output_types. | |
doc_26924 | Return True if endwin() has been called (that is, the curses library has been deinitialized). | |
doc_26925 | Clean up indentation from docstrings that are indented to line up with blocks of code. All leading whitespace is removed from the first line. Any leading whitespace that can be uniformly removed from the second line onwards is removed. Empty lines at the beginning and end are subsequently removed. Also, all tabs are expanded to spaces. | |
doc_26926 | See Migration guide for more details. tf.compat.v1.raw_ops.SparseSplit
tf.raw_ops.SparseSplit(
split_dim, indices, values, shape, num_split, name=None
)
If the shape[split_dim] is not an integer multiple of num_split. Slices [0 : shape[split_dim] % num_split] gets one extra dimension. For example, if split_dim = 1 and num_split = 2 and the input is input_tensor = shape = [2, 7]
[ a d e ]
[b c ]
Graphically the output tensors are: output_tensor[0] = shape = [2, 4]
[ a ]
[b c ]
output_tensor[1] = shape = [2, 3]
[ d e ]
[ ]
Args
split_dim A Tensor of type int64. 0-D. The dimension along which to split. Must be in the range [0, rank(shape)).
indices A Tensor of type int64. 2-D tensor represents the indices of the sparse tensor.
values A Tensor. 1-D tensor represents the values of the sparse tensor.
shape A Tensor of type int64. 1-D. tensor represents the shape of the sparse tensor. output indices: A list of 1-D tensors represents the indices of the output sparse tensors.
num_split An int that is >= 1. The number of ways to split.
name A name for the operation (optional).
Returns A tuple of Tensor objects (output_indices, output_values, output_shape). output_indices A list of num_split Tensor objects with type int64.
output_values A list of num_split Tensor objects with the same type as values.
output_shape A list of num_split Tensor objects with type int64. | |
doc_26927 |
Return whether this axes supports the zoom box button functionality. 3D axes objects do not use the zoom box button. | |
doc_26928 |
The month as January=1, December=12. | |
doc_26929 | control where the overlay is displayed set_location(rect) -> None Set the location for the overlay. The overlay will always be shown relative to the main display Surface. This does not actually redraw the overlay, it will be updated on the next call to Overlay.display(). | |
doc_26930 |
Differentiate a Laguerre series. Returns the Laguerre series coefficients c differentiated m times along axis. At each iteration the result is multiplied by scl (the scaling factor is for use in a linear change of variable). The argument c is an array of coefficients from low to high degree along each axis, e.g., [1,2,3] represents the series 1*L_0 + 2*L_1 + 3*L_2 while [[1,2],[1,2]] represents 1*L_0(x)*L_0(y) + 1*L_1(x)*L_0(y) +
2*L_0(x)*L_1(y) + 2*L_1(x)*L_1(y) if axis=0 is x and axis=1 is y. Parameters
carray_like
Array of Laguerre series coefficients. If c is multidimensional the different axis correspond to different variables with the degree in each axis given by the corresponding index.
mint, optional
Number of derivatives taken, must be non-negative. (Default: 1)
sclscalar, optional
Each differentiation is multiplied by scl. The end result is multiplication by scl**m. This is for use in a linear change of variable. (Default: 1)
axisint, optional
Axis over which the derivative is taken. (Default: 0). New in version 1.7.0. Returns
derndarray
Laguerre series of the derivative. See also lagint
Notes In general, the result of differentiating a Laguerre series does not resemble the same operation on a power series. Thus the result of this function may be “unintuitive,” albeit correct; see Examples section below. Examples >>> from numpy.polynomial.laguerre import lagder
>>> lagder([ 1., 1., 1., -3.])
array([1., 2., 3.])
>>> lagder([ 1., 0., 0., -4., 3.], m=2)
array([1., 2., 3.]) | |
doc_26931 | Connect to the given host and port. The default port number is 21, as specified by the FTP protocol specification. It is rarely needed to specify a different port number. This function should be called only once for each instance; it should not be called at all if a host was given when the instance was created. All other methods can only be used after a connection has been made. The optional timeout parameter specifies a timeout in seconds for the connection attempt. If no timeout is passed, the global default timeout setting will be used. source_address is a 2-tuple (host, port) for the socket to bind to as its source address before connecting. Raises an auditing event ftplib.connect with arguments self, host, port. Changed in version 3.3: source_address parameter was added. | |
doc_26932 |
Return the mean absolute deviation of the values over the requested axis. Parameters
axis:{index (0)}
Axis for the function to be applied on.
skipna:bool, default True
Exclude NA/null values when computing the result.
level:int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar. Returns
scalar or Series (if level specified) | |
doc_26933 |
Return the corresponding inverse transformation. It holds x == self.inverted().transform(self.transform(x)). The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy. | |
doc_26934 |
__init__(fps=5, metadata=None, codec=None, bitrate=None)[source]
Methods
__init__([fps, metadata, codec, bitrate])
finish() Finish any processing for writing the movie.
grab_frame(**savefig_kwargs) Grab the image information from the figure and save as a movie frame.
isAvailable()
saving(fig, outfile, dpi, *args, **kwargs) Context manager to facilitate writing the movie file.
setup(fig, outfile[, dpi]) Setup for writing the movie file. Attributes
frame_size A tuple (width, height) in pixels of a movie frame. finish()[source]
Finish any processing for writing the movie.
grab_frame(**savefig_kwargs)[source]
Grab the image information from the figure and save as a movie frame. All keyword arguments in savefig_kwargs are passed on to the savefig call that saves the figure.
classmethodisAvailable()[source]
setup(fig, outfile, dpi=None)[source]
Setup for writing the movie file. Parameters
figFigure
The figure object that contains the information for frames.
outfilestr
The filename of the resulting movie file.
dpifloat, default: fig.dpi
The DPI (or resolution) for the file. This controls the size in pixels of the resulting movie file. | |
doc_26935 | Make the response object ready to be pickled. Does the following: Buffer the response into a list, ignoring implicity_sequence_conversion and direct_passthrough. Set the Content-Length header. Generate an ETag header if one is not already set. Changed in version 2.0: An ETag header is added, the no_etag parameter is deprecated and will be removed in Werkzeug 2.1. Changelog Changed in version 0.6: The Content-Length header is set. Parameters
no_etag (None) – Return type
None | |
doc_26936 |
Synonym for DataFrame.fillna() with method='ffill'. Returns
Series/DataFrame or None
Object with missing values filled or None if inplace=True. | |
doc_26937 |
Prediction voting regressor for unfitted estimators. A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the whole dataset. Then it averages the individual predictions to form a final prediction. Read more in the User Guide. New in version 0.21. Parameters
estimatorslist of (str, estimator) tuples
Invoking the fit method on the VotingRegressor will fit clones of those original estimators that will be stored in the class attribute self.estimators_. An estimator can be set to 'drop' using set_params. Changed in version 0.21: 'drop' is accepted. Using None was deprecated in 0.22 and support was removed in 0.24.
weightsarray-like of shape (n_regressors,), default=None
Sequence of weights (float or int) to weight the occurrences of predicted values before averaging. Uses uniform weights if None.
n_jobsint, default=None
The number of jobs to run in parallel for fit. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
verbosebool, default=False
If True, the time elapsed while fitting will be printed as it is completed. New in version 0.23. Attributes
estimators_list of regressors
The collection of fitted sub-estimators as defined in estimators that are not ‘drop’.
named_estimators_Bunch
Attribute to access any fitted sub-estimators by name. New in version 0.20. See also
VotingClassifier
Soft Voting/Majority Rule classifier. Examples >>> import numpy as np
>>> from sklearn.linear_model import LinearRegression
>>> from sklearn.ensemble import RandomForestRegressor
>>> from sklearn.ensemble import VotingRegressor
>>> r1 = LinearRegression()
>>> r2 = RandomForestRegressor(n_estimators=10, random_state=1)
>>> X = np.array([[1, 1], [2, 4], [3, 9], [4, 16], [5, 25], [6, 36]])
>>> y = np.array([2, 6, 12, 20, 30, 42])
>>> er = VotingRegressor([('lr', r1), ('rf', r2)])
>>> print(er.fit(X, y).predict(X))
[ 3.3 5.7 11.8 19.7 28. 40.3]
Methods
fit(X, y[, sample_weight]) Fit the estimators.
fit_transform(X[, y]) Return class labels or probabilities for each estimator.
get_params([deep]) Get the parameters of an estimator from the ensemble.
predict(X) Predict regression target for X.
score(X, y[, sample_weight]) Return the coefficient of determination \(R^2\) of the prediction.
set_params(**params) Set the parameters of an estimator from the ensemble.
transform(X) Return predictions for X for each estimator.
fit(X, y, sample_weight=None) [source]
Fit the estimators. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)
Target values.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted. Note that this is supported only if all underlying estimators support sample weights. Returns
selfobject
Fitted estimator.
fit_transform(X, y=None, **fit_params) [source]
Return class labels or probabilities for each estimator. Return predictions for X for each estimator. Parameters
X{array-like, sparse matrix, dataframe} of shape (n_samples, n_features)
Input samples
yndarray of shape (n_samples,), default=None
Target values (None for unsupervised transformations).
**fit_paramsdict
Additional fit parameters. Returns
X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
get_params(deep=True) [source]
Get the parameters of an estimator from the ensemble. Returns the parameters given in the constructor as well as the estimators contained within the estimators parameter. Parameters
deepbool, default=True
Setting it to True gets the various estimators and the parameters of the estimators as well.
predict(X) [source]
Predict regression target for X. The predicted regression target of an input sample is computed as the mean predicted regression targets of the estimators in the ensemble. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Returns
yndarray of shape (n_samples,)
The predicted values.
score(X, y, sample_weight=None) [source]
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0. Parameters
Xarray-like of shape (n_samples, n_features)
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights. Returns
scorefloat
\(R^2\) of self.predict(X) wrt. y. Notes The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).
set_params(**params) [source]
Set the parameters of an estimator from the ensemble. Valid parameter keys can be listed with get_params(). Note that you can directly set the parameters of the estimators contained in estimators. Parameters
**paramskeyword arguments
Specific parameters using e.g. set_params(parameter_name=new_value). In addition, to setting the parameters of the estimator, the individual estimator of the estimators can also be set, or can be removed by setting them to ‘drop’.
transform(X) [source]
Return predictions for X for each estimator. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Returns
predictions: ndarray of shape (n_samples, n_classifiers)
Values predicted by each regressor. | |
doc_26938 | Return the path to an executable which would be run if the given cmd was called. If no cmd would be called, return None. mode is a permission mask passed to os.access(), by default determining if the file exists and executable. When no path is specified, the results of os.environ() are used, returning either the “PATH” value or a fallback of os.defpath. On Windows, the current directory is always prepended to the path whether or not you use the default or provide your own, which is the behavior the command shell uses when finding executables. Additionally, when finding the cmd in the path, the PATHEXT environment variable is checked. For example, if you call shutil.which("python"), which() will search PATHEXT to know that it should look for python.exe within the path directories. For example, on Windows: >>> shutil.which("python")
'C:\\Python33\\python.EXE'
New in version 3.3. Changed in version 3.8: The bytes type is now accepted. If cmd type is bytes, the result type is also bytes. | |
doc_26939 |
Set whether the existing bounds of the box should be ignored by subsequent calls to update_from_data_xy(). valuebool
When True, subsequent calls to update_from_data_xy() will ignore the existing bounds of the Bbox. When False, subsequent calls to update_from_data_xy() will include the existing bounds of the Bbox. | |
doc_26940 | streamreader
Stream writer and reader classes or factory functions. These have to provide the interface defined by the base classes StreamWriter and StreamReader, respectively. Stream codecs can maintain state. | |
doc_26941 |
Set the width of the figure in inches. Parameters
valfloat
forwardbool
See set_size_inches. See also matplotlib.figure.Figure.set_figheight
matplotlib.figure.Figure.set_size_inches | |
doc_26942 |
Alias for get_facecolor. | |
doc_26943 |
Return a list of artists and labels suitable for passing through to legend which represent this ContourSet. The labels have the form "0 < x <= 1" stating the data ranges which the artists represent. Parameters
variable_namestr
The string used inside the inequality used on the labels.
str_formatfunction: float -> str
Function used to format the numbers in the labels. Returns
artistslist[Artist]
A list of the artists.
labelslist[str]
A list of the labels. | |
doc_26944 | See Migration guide for more details. tf.compat.v1.raw_ops.IRFFT2D
tf.raw_ops.IRFFT2D(
input, fft_length, Treal=tf.dtypes.float32, name=None
)
Computes the inverse 2-dimensional discrete Fourier transform of a real-valued signal over the inner-most 2 dimensions of input. The inner-most 2 dimensions of input are assumed to be the result of RFFT2D: The inner-most dimension contains the fft_length / 2 + 1 unique components of the DFT of a real-valued signal. If fft_length is not provided, it is computed from the size of the inner-most 2 dimensions of input. If the FFT length used to compute input is odd, it should be provided since it cannot be inferred properly. Along each axis IRFFT2D is computed on, if fft_length (or fft_length / 2 + 1 for the inner-most dimension) is smaller than the corresponding dimension of input, the dimension is cropped. If it is larger, the dimension is padded with zeros.
Args
input A Tensor. Must be one of the following types: complex64, complex128. A complex tensor.
fft_length A Tensor of type int32. An int32 tensor of shape [2]. The FFT length for each dimension.
Treal An optional tf.DType from: tf.float32, tf.float64. Defaults to tf.float32.
name A name for the operation (optional).
Returns A Tensor of type Treal. | |
doc_26945 |
Return whether the Artist has an explicitly set transform. This is True after set_transform has been called. | |
doc_26946 | Call func with arguments args and keyword arguments kwds. It blocks until the result is ready. Given this blocks, apply_async() is better suited for performing work in parallel. Additionally, func is only executed in one of the workers of the pool. | |
doc_26947 | Line number (int) of the filter. If lineno is None, the filter matches any line number. | |
doc_26948 | Test whether or not the selection points to a valid file that is not a directory. Confirmation is required if an already existing file is selected. | |
doc_26949 | 'blogs.blog': lambda o: "/blogs/%s/" % o.slug,
'news.story': lambda o: "/stories/%s/%s/" % (o.pub_year, o.slug),
}
The model name used in this setting should be all lowercase, regardless of the case of the actual model class name. ADMINS Default: [] (Empty list) A list of all the people who get code error notifications. When DEBUG=False and AdminEmailHandler is configured in LOGGING (done by default), Django emails these people the details of exceptions raised in the request/response cycle. Each item in the list should be a tuple of (Full name, email address). Example: [('John', 'john@example.com'), ('Mary', 'mary@example.com')]
ALLOWED_HOSTS Default: [] (Empty list) A list of strings representing the host/domain names that this Django site can serve. This is a security measure to prevent HTTP Host header attacks, which are possible even under many seemingly-safe web server configurations. Values in this list can be fully qualified names (e.g. 'www.example.com'), in which case they will be matched against the request’s Host header exactly (case-insensitive, not including port). A value beginning with a period can be used as a subdomain wildcard: '.example.com' will match example.com, www.example.com, and any other subdomain of example.com. A value of '*' will match anything; in this case you are responsible to provide your own validation of the Host header (perhaps in a middleware; if so this middleware must be listed first in MIDDLEWARE). Django also allows the fully qualified domain name (FQDN) of any entries. Some browsers include a trailing dot in the Host header which Django strips when performing host validation. If the Host header (or X-Forwarded-Host if USE_X_FORWARDED_HOST is enabled) does not match any value in this list, the django.http.HttpRequest.get_host() method will raise SuspiciousOperation. When DEBUG is True and ALLOWED_HOSTS is empty, the host is validated against ['.localhost', '127.0.0.1', '[::1]']. ALLOWED_HOSTS is also checked when running tests. This validation only applies via get_host(); if your code accesses the Host header directly from request.META you are bypassing this security protection. APPEND_SLASH Default: True When set to True, if the request URL does not match any of the patterns in the URLconf and it doesn’t end in a slash, an HTTP redirect is issued to the same URL with a slash appended. Note that the redirect may cause any data submitted in a POST request to be lost. The APPEND_SLASH setting is only used if CommonMiddleware is installed (see Middleware). See also PREPEND_WWW. CACHES Default: {
'default': {
'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',
}
}
A dictionary containing the settings for all caches to be used with Django. It is a nested dictionary whose contents maps cache aliases to a dictionary containing the options for an individual cache. The CACHES setting must configure a default cache; any number of additional caches may also be specified. If you are using a cache backend other than the local memory cache, or you need to define multiple caches, other options will be required. The following cache options are available. BACKEND Default: '' (Empty string) The cache backend to use. The built-in cache backends are: 'django.core.cache.backends.db.DatabaseCache' 'django.core.cache.backends.dummy.DummyCache' 'django.core.cache.backends.filebased.FileBasedCache' 'django.core.cache.backends.locmem.LocMemCache' 'django.core.cache.backends.memcached.PyMemcacheCache' 'django.core.cache.backends.memcached.PyLibMCCache' 'django.core.cache.backends.redis.RedisCache' You can use a cache backend that doesn’t ship with Django by setting BACKEND to a fully-qualified path of a cache backend class (i.e. mypackage.backends.whatever.WhateverCache). Changed in Django 3.2: The PyMemcacheCache backend was added. Changed in Django 4.0: The RedisCache backend was added. KEY_FUNCTION A string containing a dotted path to a function (or any callable) that defines how to compose a prefix, version and key into a final cache key. The default implementation is equivalent to the function: def make_key(key, key_prefix, version):
return ':'.join([key_prefix, str(version), key])
You may use any key function you want, as long as it has the same argument signature. See the cache documentation for more information. KEY_PREFIX Default: '' (Empty string) A string that will be automatically included (prepended by default) to all cache keys used by the Django server. See the cache documentation for more information. LOCATION Default: '' (Empty string) The location of the cache to use. This might be the directory for a file system cache, a host and port for a memcache server, or an identifying name for a local memory cache. e.g.: CACHES = {
'default': {
'BACKEND': 'django.core.cache.backends.filebased.FileBasedCache',
'LOCATION': '/var/tmp/django_cache',
}
}
OPTIONS Default: None Extra parameters to pass to the cache backend. Available parameters vary depending on your cache backend. Some information on available parameters can be found in the cache arguments documentation. For more information, consult your backend module’s own documentation. TIMEOUT Default: 300 The number of seconds before a cache entry is considered stale. If the value of this setting is None, cache entries will not expire. A value of 0 causes keys to immediately expire (effectively “don’t cache”). VERSION Default: 1 The default version number for cache keys generated by the Django server. See the cache documentation for more information. CACHE_MIDDLEWARE_ALIAS Default: 'default' The cache connection to use for the cache middleware. CACHE_MIDDLEWARE_KEY_PREFIX Default: '' (Empty string) A string which will be prefixed to the cache keys generated by the cache middleware. This prefix is combined with the KEY_PREFIX setting; it does not replace it. See Django’s cache framework. CACHE_MIDDLEWARE_SECONDS Default: 600 The default number of seconds to cache a page for the cache middleware. See Django’s cache framework. CSRF_COOKIE_AGE Default: 31449600 (approximately 1 year, in seconds) The age of CSRF cookies, in seconds. The reason for setting a long-lived expiration time is to avoid problems in the case of a user closing a browser or bookmarking a page and then loading that page from a browser cache. Without persistent cookies, the form submission would fail in this case. Some browsers (specifically Internet Explorer) can disallow the use of persistent cookies or can have the indexes to the cookie jar corrupted on disk, thereby causing CSRF protection checks to (sometimes intermittently) fail. Change this setting to None to use session-based CSRF cookies, which keep the cookies in-memory instead of on persistent storage. CSRF_COOKIE_DOMAIN Default: None The domain to be used when setting the CSRF cookie. This can be useful for easily allowing cross-subdomain requests to be excluded from the normal cross site request forgery protection. It should be set to a string such as ".example.com" to allow a POST request from a form on one subdomain to be accepted by a view served from another subdomain. Please note that the presence of this setting does not imply that Django’s CSRF protection is safe from cross-subdomain attacks by default - please see the CSRF limitations section. CSRF_COOKIE_HTTPONLY Default: False Whether to use HttpOnly flag on the CSRF cookie. If this is set to True, client-side JavaScript will not be able to access the CSRF cookie. Designating the CSRF cookie as HttpOnly doesn’t offer any practical protection because CSRF is only to protect against cross-domain attacks. If an attacker can read the cookie via JavaScript, they’re already on the same domain as far as the browser knows, so they can do anything they like anyway. (XSS is a much bigger hole than CSRF.) Although the setting offers little practical benefit, it’s sometimes required by security auditors. If you enable this and need to send the value of the CSRF token with an AJAX request, your JavaScript must pull the value from a hidden CSRF token form input instead of from the cookie. See SESSION_COOKIE_HTTPONLY for details on HttpOnly. CSRF_COOKIE_NAME Default: 'csrftoken' The name of the cookie to use for the CSRF authentication token. This can be whatever you want (as long as it’s different from the other cookie names in your application). See Cross Site Request Forgery protection. CSRF_COOKIE_PATH Default: '/' The path set on the CSRF cookie. This should either match the URL path of your Django installation or be a parent of that path. This is useful if you have multiple Django instances running under the same hostname. They can use different cookie paths, and each instance will only see its own CSRF cookie. CSRF_COOKIE_SAMESITE Default: 'Lax' The value of the SameSite flag on the CSRF cookie. This flag prevents the cookie from being sent in cross-site requests. See SESSION_COOKIE_SAMESITE for details about SameSite. CSRF_COOKIE_SECURE Default: False Whether to use a secure cookie for the CSRF cookie. If this is set to True, the cookie will be marked as “secure”, which means browsers may ensure that the cookie is only sent with an HTTPS connection. CSRF_USE_SESSIONS Default: False Whether to store the CSRF token in the user’s session instead of in a cookie. It requires the use of django.contrib.sessions. Storing the CSRF token in a cookie (Django’s default) is safe, but storing it in the session is common practice in other web frameworks and therefore sometimes demanded by security auditors. Since the default error views require the CSRF token, SessionMiddleware must appear in MIDDLEWARE before any middleware that may raise an exception to trigger an error view (such as PermissionDenied) if you’re using CSRF_USE_SESSIONS. See Middleware ordering. CSRF_FAILURE_VIEW Default: 'django.views.csrf.csrf_failure' A dotted path to the view function to be used when an incoming request is rejected by the CSRF protection. The function should have this signature: def csrf_failure(request, reason=""):
...
where reason is a short message (intended for developers or logging, not for end users) indicating the reason the request was rejected. It should return an HttpResponseForbidden. django.views.csrf.csrf_failure() accepts an additional template_name parameter that defaults to '403_csrf.html'. If a template with that name exists, it will be used to render the page. CSRF_HEADER_NAME Default: 'HTTP_X_CSRFTOKEN' The name of the request header used for CSRF authentication. As with other HTTP headers in request.META, the header name received from the server is normalized by converting all characters to uppercase, replacing any hyphens with underscores, and adding an 'HTTP_' prefix to the name. For example, if your client sends a 'X-XSRF-TOKEN' header, the setting should be 'HTTP_X_XSRF_TOKEN'. CSRF_TRUSTED_ORIGINS Default: [] (Empty list) A list of trusted origins for unsafe requests (e.g. POST). For requests that include the Origin header, Django’s CSRF protection requires that header match the origin present in the Host header. For a secure unsafe request that doesn’t include the Origin header, the request must have a Referer header that matches the origin present in the Host header. These checks prevent, for example, a POST request from subdomain.example.com from succeeding against api.example.com. If you need cross-origin unsafe requests, continuing the example, add 'https://subdomain.example.com' to this list (and/or http://... if requests originate from an insecure page). The setting also supports subdomains, so you could add 'https://*.example.com', for example, to allow access from all subdomains of example.com. Changed in Django 4.0: The values in older versions must only include the hostname (possibly with a leading dot) and not the scheme or an asterisk. Also, Origin header checking isn’t performed in older versions. DATABASES Default: {} (Empty dictionary) A dictionary containing the settings for all databases to be used with Django. It is a nested dictionary whose contents map a database alias to a dictionary containing the options for an individual database. The DATABASES setting must configure a default database; any number of additional databases may also be specified. The simplest possible settings file is for a single-database setup using SQLite. This can be configured using the following: DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': 'mydatabase',
}
}
When connecting to other database backends, such as MariaDB, MySQL, Oracle, or PostgreSQL, additional connection parameters will be required. See the ENGINE setting below on how to specify other database types. This example is for PostgreSQL: DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'NAME': 'mydatabase',
'USER': 'mydatabaseuser',
'PASSWORD': 'mypassword',
'HOST': '127.0.0.1',
'PORT': '5432',
}
}
The following inner options that may be required for more complex configurations are available: ATOMIC_REQUESTS Default: False Set this to True to wrap each view in a transaction on this database. See Tying transactions to HTTP requests. AUTOCOMMIT Default: True Set this to False if you want to disable Django’s transaction management and implement your own. ENGINE Default: '' (Empty string) The database backend to use. The built-in database backends are: 'django.db.backends.postgresql' 'django.db.backends.mysql' 'django.db.backends.sqlite3' 'django.db.backends.oracle' You can use a database backend that doesn’t ship with Django by setting ENGINE to a fully-qualified path (i.e. mypackage.backends.whatever). HOST Default: '' (Empty string) Which host to use when connecting to the database. An empty string means localhost. Not used with SQLite. If this value starts with a forward slash ('/') and you’re using MySQL, MySQL will connect via a Unix socket to the specified socket. For example: "HOST": '/var/run/mysql'
If you’re using MySQL and this value doesn’t start with a forward slash, then this value is assumed to be the host. If you’re using PostgreSQL, by default (empty HOST), the connection to the database is done through UNIX domain sockets (‘local’ lines in pg_hba.conf). If your UNIX domain socket is not in the standard location, use the same value of unix_socket_directory from postgresql.conf. If you want to connect through TCP sockets, set HOST to ‘localhost’ or ‘127.0.0.1’ (‘host’ lines in pg_hba.conf). On Windows, you should always define HOST, as UNIX domain sockets are not available. NAME Default: '' (Empty string) The name of the database to use. For SQLite, it’s the full path to the database file. When specifying the path, always use forward slashes, even on Windows (e.g. C:/homes/user/mysite/sqlite3.db). CONN_MAX_AGE Default: 0 The lifetime of a database connection, as an integer of seconds. Use 0 to close database connections at the end of each request — Django’s historical behavior — and None for unlimited persistent connections. OPTIONS Default: {} (Empty dictionary) Extra parameters to use when connecting to the database. Available parameters vary depending on your database backend. Some information on available parameters can be found in the Database Backends documentation. For more information, consult your backend module’s own documentation. PASSWORD Default: '' (Empty string) The password to use when connecting to the database. Not used with SQLite. PORT Default: '' (Empty string) The port to use when connecting to the database. An empty string means the default port. Not used with SQLite. TIME_ZONE Default: None A string representing the time zone for this database connection or None. This inner option of the DATABASES setting accepts the same values as the general TIME_ZONE setting. When USE_TZ is True and this option is set, reading datetimes from the database returns aware datetimes in this time zone instead of UTC. When USE_TZ is False, it is an error to set this option.
If the database backend doesn’t support time zones (e.g. SQLite, MySQL, Oracle), Django reads and writes datetimes in local time according to this option if it is set and in UTC if it isn’t. Changing the connection time zone changes how datetimes are read from and written to the database. If Django manages the database and you don’t have a strong reason to do otherwise, you should leave this option unset. It’s best to store datetimes in UTC because it avoids ambiguous or nonexistent datetimes during daylight saving time changes. Also, receiving datetimes in UTC keeps datetime arithmetic simple — there’s no need to consider potential offset changes over a DST transition. If you’re connecting to a third-party database that stores datetimes in a local time rather than UTC, then you must set this option to the appropriate time zone. Likewise, if Django manages the database but third-party systems connect to the same database and expect to find datetimes in local time, then you must set this option.
If the database backend supports time zones (e.g. PostgreSQL), the TIME_ZONE option is very rarely needed. It can be changed at any time; the database takes care of converting datetimes to the desired time zone. Setting the time zone of the database connection may be useful for running raw SQL queries involving date/time functions provided by the database, such as date_trunc, because their results depend on the time zone. However, this has a downside: receiving all datetimes in local time makes datetime arithmetic more tricky — you must account for possible offset changes over DST transitions. Consider converting to local time explicitly with AT TIME ZONE in raw SQL queries instead of setting the TIME_ZONE option. DISABLE_SERVER_SIDE_CURSORS Default: False Set this to True if you want to disable the use of server-side cursors with QuerySet.iterator(). Transaction pooling and server-side cursors describes the use case. This is a PostgreSQL-specific setting. USER Default: '' (Empty string) The username to use when connecting to the database. Not used with SQLite. TEST Default: {} (Empty dictionary) A dictionary of settings for test databases; for more details about the creation and use of test databases, see The test database. Here’s an example with a test database configuration: DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'USER': 'mydatabaseuser',
'NAME': 'mydatabase',
'TEST': {
'NAME': 'mytestdatabase',
},
},
}
The following keys in the TEST dictionary are available: CHARSET Default: None The character set encoding used to create the test database. The value of this string is passed directly through to the database, so its format is backend-specific. Supported by the PostgreSQL (postgresql) and MySQL (mysql) backends. COLLATION Default: None The collation order to use when creating the test database. This value is passed directly to the backend, so its format is backend-specific. Only supported for the mysql backend (see the MySQL manual for details). DEPENDENCIES Default: ['default'], for all databases other than default, which has no dependencies. The creation-order dependencies of the database. See the documentation on controlling the creation order of test databases for details. MIGRATE Default: True When set to False, migrations won’t run when creating the test database. This is similar to setting None as a value in MIGRATION_MODULES, but for all apps. MIRROR Default: None The alias of the database that this database should mirror during testing. This setting exists to allow for testing of primary/replica (referred to as master/slave by some databases) configurations of multiple databases. See the documentation on testing primary/replica configurations for details. NAME Default: None The name of database to use when running the test suite. If the default value (None) is used with the SQLite database engine, the tests will use a memory resident database. For all other database engines the test database will use the name 'test_' + DATABASE_NAME. See The test database. SERIALIZE Boolean value to control whether or not the default test runner serializes the database into an in-memory JSON string before running tests (used to restore the database state between tests if you don’t have transactions). You can set this to False to speed up creation time if you don’t have any test classes with serialized_rollback=True. Deprecated since version 4.0: This setting is deprecated as it can be inferred from the databases with the serialized_rollback option enabled. TEMPLATE This is a PostgreSQL-specific setting. The name of a template (e.g. 'template0') from which to create the test database. CREATE_DB Default: True This is an Oracle-specific setting. If it is set to False, the test tablespaces won’t be automatically created at the beginning of the tests or dropped at the end. CREATE_USER Default: True This is an Oracle-specific setting. If it is set to False, the test user won’t be automatically created at the beginning of the tests and dropped at the end. USER Default: None This is an Oracle-specific setting. The username to use when connecting to the Oracle database that will be used when running tests. If not provided, Django will use 'test_' + USER. PASSWORD Default: None This is an Oracle-specific setting. The password to use when connecting to the Oracle database that will be used when running tests. If not provided, Django will generate a random password. ORACLE_MANAGED_FILES Default: False This is an Oracle-specific setting. If set to True, Oracle Managed Files (OMF) tablespaces will be used. DATAFILE and DATAFILE_TMP will be ignored. TBLSPACE Default: None This is an Oracle-specific setting. The name of the tablespace that will be used when running tests. If not provided, Django will use 'test_' + USER. TBLSPACE_TMP Default: None This is an Oracle-specific setting. The name of the temporary tablespace that will be used when running tests. If not provided, Django will use 'test_' + USER + '_temp'. DATAFILE Default: None This is an Oracle-specific setting. The name of the datafile to use for the TBLSPACE. If not provided, Django will use TBLSPACE + '.dbf'. DATAFILE_TMP Default: None This is an Oracle-specific setting. The name of the datafile to use for the TBLSPACE_TMP. If not provided, Django will use TBLSPACE_TMP + '.dbf'. DATAFILE_MAXSIZE Default: '500M' This is an Oracle-specific setting. The maximum size that the DATAFILE is allowed to grow to. DATAFILE_TMP_MAXSIZE Default: '500M' This is an Oracle-specific setting. The maximum size that the DATAFILE_TMP is allowed to grow to. DATAFILE_SIZE Default: '50M' This is an Oracle-specific setting. The initial size of the DATAFILE. DATAFILE_TMP_SIZE Default: '50M' This is an Oracle-specific setting. The initial size of the DATAFILE_TMP. DATAFILE_EXTSIZE Default: '25M' This is an Oracle-specific setting. The amount by which the DATAFILE is extended when more space is required. DATAFILE_TMP_EXTSIZE Default: '25M' This is an Oracle-specific setting. The amount by which the DATAFILE_TMP is extended when more space is required. DATA_UPLOAD_MAX_MEMORY_SIZE Default: 2621440 (i.e. 2.5 MB). The maximum size in bytes that a request body may be before a SuspiciousOperation (RequestDataTooBig) is raised. The check is done when accessing request.body or request.POST and is calculated against the total request size excluding any file upload data. You can set this to None to disable the check. Applications that are expected to receive unusually large form posts should tune this setting. The amount of request data is correlated to the amount of memory needed to process the request and populate the GET and POST dictionaries. Large requests could be used as a denial-of-service attack vector if left unchecked. Since web servers don’t typically perform deep request inspection, it’s not possible to perform a similar check at that level. See also FILE_UPLOAD_MAX_MEMORY_SIZE. DATA_UPLOAD_MAX_NUMBER_FIELDS Default: 1000 The maximum number of parameters that may be received via GET or POST before a SuspiciousOperation (TooManyFields) is raised. You can set this to None to disable the check. Applications that are expected to receive an unusually large number of form fields should tune this setting. The number of request parameters is correlated to the amount of time needed to process the request and populate the GET and POST dictionaries. Large requests could be used as a denial-of-service attack vector if left unchecked. Since web servers don’t typically perform deep request inspection, it’s not possible to perform a similar check at that level. DATABASE_ROUTERS Default: [] (Empty list) The list of routers that will be used to determine which database to use when performing a database query. See the documentation on automatic database routing in multi database configurations. DATE_FORMAT Default: 'N j, Y' (e.g. Feb. 4, 2003) The default formatting to use for displaying date fields in any part of the system. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See allowed date format strings. See also DATETIME_FORMAT, TIME_FORMAT and SHORT_DATE_FORMAT. DATE_INPUT_FORMATS Default: [
'%Y-%m-%d', '%m/%d/%Y', '%m/%d/%y', # '2006-10-25', '10/25/2006', '10/25/06'
'%b %d %Y', '%b %d, %Y', # 'Oct 25 2006', 'Oct 25, 2006'
'%d %b %Y', '%d %b, %Y', # '25 Oct 2006', '25 Oct, 2006'
'%B %d %Y', '%B %d, %Y', # 'October 25 2006', 'October 25, 2006'
'%d %B %Y', '%d %B, %Y', # '25 October 2006', '25 October, 2006'
]
A list of formats that will be accepted when inputting data on a date field. Formats will be tried in order, using the first valid one. Note that these format strings use Python’s datetime module syntax, not the format strings from the date template filter. When USE_L10N is True, the locale-dictated format has higher precedence and will be applied instead. See also DATETIME_INPUT_FORMATS and TIME_INPUT_FORMATS. DATETIME_FORMAT Default: 'N j, Y, P' (e.g. Feb. 4, 2003, 4 p.m.) The default formatting to use for displaying datetime fields in any part of the system. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See allowed date format strings. See also DATE_FORMAT, TIME_FORMAT and SHORT_DATETIME_FORMAT. DATETIME_INPUT_FORMATS Default: [
'%Y-%m-%d %H:%M:%S', # '2006-10-25 14:30:59'
'%Y-%m-%d %H:%M:%S.%f', # '2006-10-25 14:30:59.000200'
'%Y-%m-%d %H:%M', # '2006-10-25 14:30'
'%m/%d/%Y %H:%M:%S', # '10/25/2006 14:30:59'
'%m/%d/%Y %H:%M:%S.%f', # '10/25/2006 14:30:59.000200'
'%m/%d/%Y %H:%M', # '10/25/2006 14:30'
'%m/%d/%y %H:%M:%S', # '10/25/06 14:30:59'
'%m/%d/%y %H:%M:%S.%f', # '10/25/06 14:30:59.000200'
'%m/%d/%y %H:%M', # '10/25/06 14:30'
]
A list of formats that will be accepted when inputting data on a datetime field. Formats will be tried in order, using the first valid one. Note that these format strings use Python’s datetime module syntax, not the format strings from the date template filter. Date-only formats are not included as datetime fields will automatically try DATE_INPUT_FORMATS in last resort. When USE_L10N is True, the locale-dictated format has higher precedence and will be applied instead. See also DATE_INPUT_FORMATS and TIME_INPUT_FORMATS. DEBUG Default: False A boolean that turns on/off debug mode. Never deploy a site into production with DEBUG turned on. One of the main features of debug mode is the display of detailed error pages. If your app raises an exception when DEBUG is True, Django will display a detailed traceback, including a lot of metadata about your environment, such as all the currently defined Django settings (from settings.py). As a security measure, Django will not include settings that might be sensitive, such as SECRET_KEY. Specifically, it will exclude any setting whose name includes any of the following: 'API' 'KEY' 'PASS' 'SECRET' 'SIGNATURE' 'TOKEN' Note that these are partial matches. 'PASS' will also match PASSWORD, just as 'TOKEN' will also match TOKENIZED and so on. Still, note that there are always going to be sections of your debug output that are inappropriate for public consumption. File paths, configuration options and the like all give attackers extra information about your server. It is also important to remember that when running with DEBUG turned on, Django will remember every SQL query it executes. This is useful when you’re debugging, but it’ll rapidly consume memory on a production server. Finally, if DEBUG is False, you also need to properly set the ALLOWED_HOSTS setting. Failing to do so will result in all requests being returned as “Bad Request (400)”. Note The default settings.py file created by django-admin
startproject sets DEBUG = True for convenience. DEBUG_PROPAGATE_EXCEPTIONS Default: False If set to True, Django’s exception handling of view functions (handler500, or the debug view if DEBUG is True) and logging of 500 responses (django.request) is skipped and exceptions propagate upward. This can be useful for some test setups. It shouldn’t be used on a live site unless you want your web server (instead of Django) to generate “Internal Server Error” responses. In that case, make sure your server doesn’t show the stack trace or other sensitive information in the response. DECIMAL_SEPARATOR Default: '.' (Dot) Default decimal separator used when formatting decimal numbers. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See also NUMBER_GROUPING, THOUSAND_SEPARATOR and USE_THOUSAND_SEPARATOR. DEFAULT_AUTO_FIELD New in Django 3.2. Default: 'django.db.models.AutoField' Default primary key field type to use for models that don’t have a field with primary_key=True. Migrating auto-created through tables The value of DEFAULT_AUTO_FIELD will be respected when creating new auto-created through tables for many-to-many relationships. Unfortunately, the primary keys of existing auto-created through tables cannot currently be updated by the migrations framework. This means that if you switch the value of DEFAULT_AUTO_FIELD and then generate migrations, the primary keys of the related models will be updated, as will the foreign keys from the through table, but the primary key of the auto-created through table will not be migrated. In order to address this, you should add a RunSQL operation to your migrations to perform the required ALTER TABLE step. You can check the existing table name through sqlmigrate, dbshell, or with the field’s remote_field.through._meta.db_table property. Explicitly defined through models are already handled by the migrations system. Allowing automatic migrations for the primary key of existing auto-created through tables may be implemented at a later date. DEFAULT_CHARSET Default: 'utf-8' Default charset to use for all HttpResponse objects, if a MIME type isn’t manually specified. Used when constructing the Content-Type header. DEFAULT_EXCEPTION_REPORTER Default: 'django.views.debug.ExceptionReporter' Default exception reporter class to be used if none has been assigned to the HttpRequest instance yet. See Custom error reports. DEFAULT_EXCEPTION_REPORTER_FILTER Default: 'django.views.debug.SafeExceptionReporterFilter' Default exception reporter filter class to be used if none has been assigned to the HttpRequest instance yet. See Filtering error reports. DEFAULT_FILE_STORAGE Default: 'django.core.files.storage.FileSystemStorage' Default file storage class to be used for any file-related operations that don’t specify a particular storage system. See Managing files. DEFAULT_FROM_EMAIL Default: 'webmaster@localhost' Default email address to use for various automated correspondence from the site manager(s). This doesn’t include error messages sent to ADMINS and MANAGERS; for that, see SERVER_EMAIL. DEFAULT_INDEX_TABLESPACE Default: '' (Empty string) Default tablespace to use for indexes on fields that don’t specify one, if the backend supports it (see Tablespaces). DEFAULT_TABLESPACE Default: '' (Empty string) Default tablespace to use for models that don’t specify one, if the backend supports it (see Tablespaces). DISALLOWED_USER_AGENTS Default: [] (Empty list) List of compiled regular expression objects representing User-Agent strings that are not allowed to visit any page, systemwide. Use this for bots/crawlers. This is only used if CommonMiddleware is installed (see Middleware). EMAIL_BACKEND Default: 'django.core.mail.backends.smtp.EmailBackend' The backend to use for sending emails. For the list of available backends see Sending email. EMAIL_FILE_PATH Default: Not defined The directory used by the file email backend to store output files. EMAIL_HOST Default: 'localhost' The host to use for sending email. See also EMAIL_PORT. EMAIL_HOST_PASSWORD Default: '' (Empty string) Password to use for the SMTP server defined in EMAIL_HOST. This setting is used in conjunction with EMAIL_HOST_USER when authenticating to the SMTP server. If either of these settings is empty, Django won’t attempt authentication. See also EMAIL_HOST_USER. EMAIL_HOST_USER Default: '' (Empty string) Username to use for the SMTP server defined in EMAIL_HOST. If empty, Django won’t attempt authentication. See also EMAIL_HOST_PASSWORD. EMAIL_PORT Default: 25 Port to use for the SMTP server defined in EMAIL_HOST. EMAIL_SUBJECT_PREFIX Default: '[Django] ' Subject-line prefix for email messages sent with django.core.mail.mail_admins or django.core.mail.mail_managers. You’ll probably want to include the trailing space. EMAIL_USE_LOCALTIME Default: False Whether to send the SMTP Date header of email messages in the local time zone (True) or in UTC (False). EMAIL_USE_TLS Default: False Whether to use a TLS (secure) connection when talking to the SMTP server. This is used for explicit TLS connections, generally on port 587. If you are experiencing hanging connections, see the implicit TLS setting EMAIL_USE_SSL. EMAIL_USE_SSL Default: False Whether to use an implicit TLS (secure) connection when talking to the SMTP server. In most email documentation this type of TLS connection is referred to as SSL. It is generally used on port 465. If you are experiencing problems, see the explicit TLS setting EMAIL_USE_TLS. Note that EMAIL_USE_TLS/EMAIL_USE_SSL are mutually exclusive, so only set one of those settings to True. EMAIL_SSL_CERTFILE Default: None If EMAIL_USE_SSL or EMAIL_USE_TLS is True, you can optionally specify the path to a PEM-formatted certificate chain file to use for the SSL connection. EMAIL_SSL_KEYFILE Default: None If EMAIL_USE_SSL or EMAIL_USE_TLS is True, you can optionally specify the path to a PEM-formatted private key file to use for the SSL connection. Note that setting EMAIL_SSL_CERTFILE and EMAIL_SSL_KEYFILE doesn’t result in any certificate checking. They’re passed to the underlying SSL connection. Please refer to the documentation of Python’s ssl.wrap_socket() function for details on how the certificate chain file and private key file are handled. EMAIL_TIMEOUT Default: None Specifies a timeout in seconds for blocking operations like the connection attempt. FILE_UPLOAD_HANDLERS Default: [
'django.core.files.uploadhandler.MemoryFileUploadHandler',
'django.core.files.uploadhandler.TemporaryFileUploadHandler',
]
A list of handlers to use for uploading. Changing this setting allows complete customization – even replacement – of Django’s upload process. See Managing files for details. FILE_UPLOAD_MAX_MEMORY_SIZE Default: 2621440 (i.e. 2.5 MB). The maximum size (in bytes) that an upload will be before it gets streamed to the file system. See Managing files for details. See also DATA_UPLOAD_MAX_MEMORY_SIZE. FILE_UPLOAD_DIRECTORY_PERMISSIONS Default: None The numeric mode to apply to directories created in the process of uploading files. This setting also determines the default permissions for collected static directories when using the collectstatic management command. See collectstatic for details on overriding it. This value mirrors the functionality and caveats of the FILE_UPLOAD_PERMISSIONS setting. FILE_UPLOAD_PERMISSIONS Default: 0o644 The numeric mode (i.e. 0o644) to set newly uploaded files to. For more information about what these modes mean, see the documentation for os.chmod(). If None, you’ll get operating-system dependent behavior. On most platforms, temporary files will have a mode of 0o600, and files saved from memory will be saved using the system’s standard umask. For security reasons, these permissions aren’t applied to the temporary files that are stored in FILE_UPLOAD_TEMP_DIR. This setting also determines the default permissions for collected static files when using the collectstatic management command. See collectstatic for details on overriding it. Warning Always prefix the mode with 0o . If you’re not familiar with file modes, please note that the 0o prefix is very important: it indicates an octal number, which is the way that modes must be specified. If you try to use 644, you’ll get totally incorrect behavior. FILE_UPLOAD_TEMP_DIR Default: None The directory to store data to (typically files larger than FILE_UPLOAD_MAX_MEMORY_SIZE) temporarily while uploading files. If None, Django will use the standard temporary directory for the operating system. For example, this will default to /tmp on *nix-style operating systems. See Managing files for details. FIRST_DAY_OF_WEEK Default: 0 (Sunday) A number representing the first day of the week. This is especially useful when displaying a calendar. This value is only used when not using format internationalization, or when a format cannot be found for the current locale. The value must be an integer from 0 to 6, where 0 means Sunday, 1 means Monday and so on. FIXTURE_DIRS Default: [] (Empty list) List of directories searched for fixture files, in addition to the fixtures directory of each application, in search order. Note that these paths should use Unix-style forward slashes, even on Windows. See Providing data with fixtures and Fixture loading. FORCE_SCRIPT_NAME Default: None If not None, this will be used as the value of the SCRIPT_NAME environment variable in any HTTP request. This setting can be used to override the server-provided value of SCRIPT_NAME, which may be a rewritten version of the preferred value or not supplied at all. It is also used by django.setup() to set the URL resolver script prefix outside of the request/response cycle (e.g. in management commands and standalone scripts) to generate correct URLs when SCRIPT_NAME is not /. FORM_RENDERER Default: 'django.forms.renderers.DjangoTemplates' The class that renders forms and form widgets. It must implement the low-level render API. Included form renderers are:
'django.forms.renderers.DjangoTemplates'
'django.forms.renderers.Jinja2'
FORMAT_MODULE_PATH Default: None A full Python path to a Python package that contains custom format definitions for project locales. If not None, Django will check for a formats.py file, under the directory named as the current locale, and will use the formats defined in this file. For example, if FORMAT_MODULE_PATH is set to mysite.formats, and current language is en (English), Django will expect a directory tree like: mysite/
formats/
__init__.py
en/
__init__.py
formats.py
You can also set this setting to a list of Python paths, for example: FORMAT_MODULE_PATH = [
'mysite.formats',
'some_app.formats',
]
When Django searches for a certain format, it will go through all given Python paths until it finds a module that actually defines the given format. This means that formats defined in packages farther up in the list will take precedence over the same formats in packages farther down. Available formats are: DATE_FORMAT DATE_INPUT_FORMATS
DATETIME_FORMAT, DATETIME_INPUT_FORMATS DECIMAL_SEPARATOR FIRST_DAY_OF_WEEK MONTH_DAY_FORMAT NUMBER_GROUPING SHORT_DATE_FORMAT SHORT_DATETIME_FORMAT THOUSAND_SEPARATOR TIME_FORMAT TIME_INPUT_FORMATS YEAR_MONTH_FORMAT IGNORABLE_404_URLS Default: [] (Empty list) List of compiled regular expression objects describing URLs that should be ignored when reporting HTTP 404 errors via email (see How to manage error reporting). Regular expressions are matched against request's full paths (including query string, if any). Use this if your site does not provide a commonly requested file such as favicon.ico or robots.txt. This is only used if BrokenLinkEmailsMiddleware is enabled (see Middleware). INSTALLED_APPS Default: [] (Empty list) A list of strings designating all applications that are enabled in this Django installation. Each string should be a dotted Python path to: an application configuration class (preferred), or a package containing an application. Learn more about application configurations. Use the application registry for introspection Your code should never access INSTALLED_APPS directly. Use django.apps.apps instead. Application names and labels must be unique in INSTALLED_APPS Application names — the dotted Python path to the application package — must be unique. There is no way to include the same application twice, short of duplicating its code under another name. Application labels — by default the final part of the name — must be unique too. For example, you can’t include both django.contrib.auth and myproject.auth. However, you can relabel an application with a custom configuration that defines a different label. These rules apply regardless of whether INSTALLED_APPS references application configuration classes or application packages. When several applications provide different versions of the same resource (template, static file, management command, translation), the application listed first in INSTALLED_APPS has precedence. INTERNAL_IPS Default: [] (Empty list) A list of IP addresses, as strings, that: Allow the debug() context processor to add some variables to the template context. Can use the admindocs bookmarklets even if not logged in as a staff user. Are marked as “internal” (as opposed to “EXTERNAL”) in AdminEmailHandler emails. LANGUAGE_CODE Default: 'en-us' A string representing the language code for this installation. This should be in standard language ID format. For example, U.S. English is "en-us". See also the list of language identifiers and Internationalization and localization. USE_I18N must be active for this setting to have any effect. It serves two purposes: If the locale middleware isn’t in use, it decides which translation is served to all users. If the locale middleware is active, it provides a fallback language in case the user’s preferred language can’t be determined or is not supported by the website. It also provides the fallback translation when a translation for a given literal doesn’t exist for the user’s preferred language. See How Django discovers language preference for more details. LANGUAGE_COOKIE_AGE Default: None (expires at browser close) The age of the language cookie, in seconds. LANGUAGE_COOKIE_DOMAIN Default: None The domain to use for the language cookie. Set this to a string such as "example.com" for cross-domain cookies, or use None for a standard domain cookie. Be cautious when updating this setting on a production site. If you update this setting to enable cross-domain cookies on a site that previously used standard domain cookies, existing user cookies that have the old domain will not be updated. This will result in site users being unable to switch the language as long as these cookies persist. The only safe and reliable option to perform the switch is to change the language cookie name permanently (via the LANGUAGE_COOKIE_NAME setting) and to add a middleware that copies the value from the old cookie to a new one and then deletes the old one. LANGUAGE_COOKIE_HTTPONLY Default: False Whether to use HttpOnly flag on the language cookie. If this is set to True, client-side JavaScript will not be able to access the language cookie. See SESSION_COOKIE_HTTPONLY for details on HttpOnly. LANGUAGE_COOKIE_NAME Default: 'django_language' The name of the cookie to use for the language cookie. This can be whatever you want (as long as it’s different from the other cookie names in your application). See Internationalization and localization. LANGUAGE_COOKIE_PATH Default: '/' The path set on the language cookie. This should either match the URL path of your Django installation or be a parent of that path. This is useful if you have multiple Django instances running under the same hostname. They can use different cookie paths and each instance will only see its own language cookie. Be cautious when updating this setting on a production site. If you update this setting to use a deeper path than it previously used, existing user cookies that have the old path will not be updated. This will result in site users being unable to switch the language as long as these cookies persist. The only safe and reliable option to perform the switch is to change the language cookie name permanently (via the LANGUAGE_COOKIE_NAME setting), and to add a middleware that copies the value from the old cookie to a new one and then deletes the one. LANGUAGE_COOKIE_SAMESITE Default: None The value of the SameSite flag on the language cookie. This flag prevents the cookie from being sent in cross-site requests. See SESSION_COOKIE_SAMESITE for details about SameSite. LANGUAGE_COOKIE_SECURE Default: False Whether to use a secure cookie for the language cookie. If this is set to True, the cookie will be marked as “secure”, which means browsers may ensure that the cookie is only sent under an HTTPS connection. LANGUAGES Default: A list of all available languages. This list is continually growing and including a copy here would inevitably become rapidly out of date. You can see the current list of translated languages by looking in django/conf/global_settings.py. The list is a list of two-tuples in the format (language code, language name) – for example, ('ja', 'Japanese'). This specifies which languages are available for language selection. See Internationalization and localization. Generally, the default value should suffice. Only set this setting if you want to restrict language selection to a subset of the Django-provided languages. If you define a custom LANGUAGES setting, you can mark the language names as translation strings using the gettext_lazy() function. Here’s a sample settings file: from django.utils.translation import gettext_lazy as _
LANGUAGES = [
('de', _('German')),
('en', _('English')),
]
LANGUAGES_BIDI Default: A list of all language codes that are written right-to-left. You can see the current list of these languages by looking in django/conf/global_settings.py. The list contains language codes for languages that are written right-to-left. Generally, the default value should suffice. Only set this setting if you want to restrict language selection to a subset of the Django-provided languages. If you define a custom LANGUAGES setting, the list of bidirectional languages may contain language codes which are not enabled on a given site. LOCALE_PATHS Default: [] (Empty list) A list of directories where Django looks for translation files. See How Django discovers translations. Example: LOCALE_PATHS = [
'/home/www/project/common_files/locale',
'/var/local/translations/locale',
]
Django will look within each of these paths for the <locale_code>/LC_MESSAGES directories containing the actual translation files. LOGGING Default: A logging configuration dictionary. A data structure containing configuration information. The contents of this data structure will be passed as the argument to the configuration method described in LOGGING_CONFIG. Among other things, the default logging configuration passes HTTP 500 server errors to an email log handler when DEBUG is False. See also Configuring logging. You can see the default logging configuration by looking in django/utils/log.py. LOGGING_CONFIG Default: 'logging.config.dictConfig' A path to a callable that will be used to configure logging in the Django project. Points at an instance of Python’s dictConfig configuration method by default. If you set LOGGING_CONFIG to None, the logging configuration process will be skipped. MANAGERS Default: [] (Empty list) A list in the same format as ADMINS that specifies who should get broken link notifications when BrokenLinkEmailsMiddleware is enabled. MEDIA_ROOT Default: '' (Empty string) Absolute filesystem path to the directory that will hold user-uploaded files. Example: "/var/www/example.com/media/" See also MEDIA_URL. Warning MEDIA_ROOT and STATIC_ROOT must have different values. Before STATIC_ROOT was introduced, it was common to rely or fallback on MEDIA_ROOT to also serve static files; however, since this can have serious security implications, there is a validation check to prevent it. MEDIA_URL Default: '' (Empty string) URL that handles the media served from MEDIA_ROOT, used for managing stored files. It must end in a slash if set to a non-empty value. You will need to configure these files to be served in both development and production environments. If you want to use {{ MEDIA_URL }} in your templates, add 'django.template.context_processors.media' in the 'context_processors' option of TEMPLATES. Example: "http://media.example.com/" Warning There are security risks if you are accepting uploaded content from untrusted users! See the security guide’s topic on User-uploaded content for mitigation details. Warning MEDIA_URL and STATIC_URL must have different values. See MEDIA_ROOT for more details. Note If MEDIA_URL is a relative path, then it will be prefixed by the server-provided value of SCRIPT_NAME (or / if not set). This makes it easier to serve a Django application in a subpath without adding an extra configuration to the settings. MIDDLEWARE Default: None A list of middleware to use. See Middleware. MIGRATION_MODULES Default: {} (Empty dictionary) A dictionary specifying the package where migration modules can be found on a per-app basis. The default value of this setting is an empty dictionary, but the default package name for migration modules is migrations. Example: {'blog': 'blog.db_migrations'}
In this case, migrations pertaining to the blog app will be contained in the blog.db_migrations package. If you provide the app_label argument, makemigrations will automatically create the package if it doesn’t already exist. When you supply None as a value for an app, Django will consider the app as an app without migrations regardless of an existing migrations submodule. This can be used, for example, in a test settings file to skip migrations while testing (tables will still be created for the apps’ models). To disable migrations for all apps during tests, you can set the MIGRATE to False instead. If MIGRATION_MODULES is used in your general project settings, remember to use the migrate --run-syncdb option if you want to create tables for the app. MONTH_DAY_FORMAT Default: 'F j' The default formatting to use for date fields on Django admin change-list pages – and, possibly, by other parts of the system – in cases when only the month and day are displayed. For example, when a Django admin change-list page is being filtered by a date drilldown, the header for a given day displays the day and month. Different locales have different formats. For example, U.S. English would say “January 1,” whereas Spanish might say “1 Enero.” Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT, DATETIME_FORMAT, TIME_FORMAT and YEAR_MONTH_FORMAT. NUMBER_GROUPING Default: 0 Number of digits grouped together on the integer part of a number. Common use is to display a thousand separator. If this setting is 0, then no grouping will be applied to the number. If this setting is greater than 0, then THOUSAND_SEPARATOR will be used as the separator between those groups. Some locales use non-uniform digit grouping, e.g. 10,00,00,000 in en_IN. For this case, you can provide a sequence with the number of digit group sizes to be applied. The first number defines the size of the group preceding the decimal delimiter, and each number that follows defines the size of preceding groups. If the sequence is terminated with -1, no further grouping is performed. If the sequence terminates with a 0, the last group size is used for the remainder of the number. Example tuple for en_IN: NUMBER_GROUPING = (3, 2, 0)
Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See also DECIMAL_SEPARATOR, THOUSAND_SEPARATOR and USE_THOUSAND_SEPARATOR. PREPEND_WWW Default: False Whether to prepend the “www.” subdomain to URLs that don’t have it. This is only used if CommonMiddleware is installed (see Middleware). See also APPEND_SLASH. ROOT_URLCONF Default: Not defined A string representing the full Python import path to your root URLconf, for example "mydjangoapps.urls". Can be overridden on a per-request basis by setting the attribute urlconf on the incoming HttpRequest object. See How Django processes a request for details. SECRET_KEY Default: '' (Empty string) A secret key for a particular Django installation. This is used to provide cryptographic signing, and should be set to a unique, unpredictable value. django-admin startproject automatically adds a randomly-generated SECRET_KEY to each new project. Uses of the key shouldn’t assume that it’s text or bytes. Every use should go through force_str() or force_bytes() to convert it to the desired type. Django will refuse to start if SECRET_KEY is not set. Warning Keep this value secret. Running Django with a known SECRET_KEY defeats many of Django’s security protections, and can lead to privilege escalation and remote code execution vulnerabilities. The secret key is used for: All sessions if you are using any other session backend than django.contrib.sessions.backends.cache, or are using the default get_session_auth_hash(). All messages if you are using CookieStorage or FallbackStorage. All PasswordResetView tokens. Any usage of cryptographic signing, unless a different key is provided. If you rotate your secret key, all of the above will be invalidated. Secret keys are not used for passwords of users and key rotation will not affect them. Note The default settings.py file created by django-admin
startproject creates a unique SECRET_KEY for convenience. SECURE_CONTENT_TYPE_NOSNIFF Default: True If True, the SecurityMiddleware sets the X-Content-Type-Options: nosniff header on all responses that do not already have it. SECURE_CROSS_ORIGIN_OPENER_POLICY New in Django 4.0. Default: 'same-origin' Unless set to None, the SecurityMiddleware sets the Cross-Origin Opener Policy header on all responses that do not already have it to the value provided. SECURE_HSTS_INCLUDE_SUBDOMAINS Default: False If True, the SecurityMiddleware adds the includeSubDomains directive to the HTTP Strict Transport Security header. It has no effect unless SECURE_HSTS_SECONDS is set to a non-zero value. Warning Setting this incorrectly can irreversibly (for the value of SECURE_HSTS_SECONDS) break your site. Read the HTTP Strict Transport Security documentation first. SECURE_HSTS_PRELOAD Default: False If True, the SecurityMiddleware adds the preload directive to the HTTP Strict Transport Security header. It has no effect unless SECURE_HSTS_SECONDS is set to a non-zero value. SECURE_HSTS_SECONDS Default: 0 If set to a non-zero integer value, the SecurityMiddleware sets the HTTP Strict Transport Security header on all responses that do not already have it. Warning Setting this incorrectly can irreversibly (for some time) break your site. Read the HTTP Strict Transport Security documentation first. SECURE_PROXY_SSL_HEADER Default: None A tuple representing an HTTP header/value combination that signifies a request is secure. This controls the behavior of the request object’s is_secure() method. By default, is_secure() determines if a request is secure by confirming that a requested URL uses https://. This method is important for Django’s CSRF protection, and it may be used by your own code or third-party apps. If your Django app is behind a proxy, though, the proxy may be “swallowing” whether the original request uses HTTPS or not. If there is a non-HTTPS connection between the proxy and Django then is_secure() would always return False – even for requests that were made via HTTPS by the end user. In contrast, if there is an HTTPS connection between the proxy and Django then is_secure() would always return True – even for requests that were made originally via HTTP. In this situation, configure your proxy to set a custom HTTP header that tells Django whether the request came in via HTTPS, and set SECURE_PROXY_SSL_HEADER so that Django knows what header to look for. Set a tuple with two elements – the name of the header to look for and the required value. For example: SECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https')
This tells Django to trust the X-Forwarded-Proto header that comes from our proxy, and any time its value is 'https', then the request is guaranteed to be secure (i.e., it originally came in via HTTPS). You should only set this setting if you control your proxy or have some other guarantee that it sets/strips this header appropriately. Note that the header needs to be in the format as used by request.META – all caps and likely starting with HTTP_. (Remember, Django automatically adds 'HTTP_' to the start of x-header names before making the header available in request.META.) Warning Modifying this setting can compromise your site’s security. Ensure you fully understand your setup before changing it. Make sure ALL of the following are true before setting this (assuming the values from the example above): Your Django app is behind a proxy. Your proxy strips the X-Forwarded-Proto header from all incoming requests. In other words, if end users include that header in their requests, the proxy will discard it. Your proxy sets the X-Forwarded-Proto header and sends it to Django, but only for requests that originally come in via HTTPS. If any of those are not true, you should keep this setting set to None and find another way of determining HTTPS, perhaps via custom middleware. SECURE_REDIRECT_EXEMPT Default: [] (Empty list) If a URL path matches a regular expression in this list, the request will not be redirected to HTTPS. The SecurityMiddleware strips leading slashes from URL paths, so patterns shouldn’t include them, e.g. SECURE_REDIRECT_EXEMPT = [r'^no-ssl/$', …]. If SECURE_SSL_REDIRECT is False, this setting has no effect. SECURE_REFERRER_POLICY Default: 'same-origin' If configured, the SecurityMiddleware sets the Referrer Policy header on all responses that do not already have it to the value provided. SECURE_SSL_HOST Default: None If a string (e.g. secure.example.com), all SSL redirects will be directed to this host rather than the originally-requested host (e.g. www.example.com). If SECURE_SSL_REDIRECT is False, this setting has no effect. SECURE_SSL_REDIRECT Default: False If True, the SecurityMiddleware redirects all non-HTTPS requests to HTTPS (except for those URLs matching a regular expression listed in SECURE_REDIRECT_EXEMPT). Note If turning this to True causes infinite redirects, it probably means your site is running behind a proxy and can’t tell which requests are secure and which are not. Your proxy likely sets a header to indicate secure requests; you can correct the problem by finding out what that header is and configuring the SECURE_PROXY_SSL_HEADER setting accordingly. SERIALIZATION_MODULES Default: Not defined A dictionary of modules containing serializer definitions (provided as strings), keyed by a string identifier for that serialization type. For example, to define a YAML serializer, use: SERIALIZATION_MODULES = {'yaml': 'path.to.yaml_serializer'}
SERVER_EMAIL Default: 'root@localhost' The email address that error messages come from, such as those sent to ADMINS and MANAGERS. Why are my emails sent from a different address? This address is used only for error messages. It is not the address that regular email messages sent with send_mail() come from; for that, see DEFAULT_FROM_EMAIL. SHORT_DATE_FORMAT Default: 'm/d/Y' (e.g. 12/31/2003) An available formatting that can be used for displaying date fields on templates. Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT and SHORT_DATETIME_FORMAT. SHORT_DATETIME_FORMAT Default: 'm/d/Y P' (e.g. 12/31/2003 4 p.m.) An available formatting that can be used for displaying datetime fields on templates. Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT and SHORT_DATE_FORMAT. SIGNING_BACKEND Default: 'django.core.signing.TimestampSigner' The backend used for signing cookies and other data. See also the Cryptographic signing documentation. SILENCED_SYSTEM_CHECKS Default: [] (Empty list) A list of identifiers of messages generated by the system check framework (i.e. ["models.W001"]) that you wish to permanently acknowledge and ignore. Silenced checks will not be output to the console. See also the System check framework documentation. TEMPLATES Default: [] (Empty list) A list containing the settings for all template engines to be used with Django. Each item of the list is a dictionary containing the options for an individual engine. Here’s a setup that tells the Django template engine to load templates from the templates subdirectory inside each installed application: TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'APP_DIRS': True,
},
]
The following options are available for all backends. BACKEND Default: Not defined The template backend to use. The built-in template backends are: 'django.template.backends.django.DjangoTemplates' 'django.template.backends.jinja2.Jinja2' You can use a template backend that doesn’t ship with Django by setting BACKEND to a fully-qualified path (i.e. 'mypackage.whatever.Backend'). NAME Default: see below The alias for this particular template engine. It’s an identifier that allows selecting an engine for rendering. Aliases must be unique across all configured template engines. It defaults to the name of the module defining the engine class, i.e. the next to last piece of BACKEND, when it isn’t provided. For example if the backend is 'mypackage.whatever.Backend' then its default name is 'whatever'. DIRS Default: [] (Empty list) Directories where the engine should look for template source files, in search order. APP_DIRS Default: False Whether the engine should look for template source files inside installed applications. Note The default settings.py file created by django-admin
startproject sets 'APP_DIRS': True. OPTIONS Default: {} (Empty dict) Extra parameters to pass to the template backend. Available parameters vary depending on the template backend. See DjangoTemplates and Jinja2 for the options of the built-in backends. TEST_RUNNER Default: 'django.test.runner.DiscoverRunner' The name of the class to use for starting the test suite. See Using different testing frameworks. TEST_NON_SERIALIZED_APPS Default: [] (Empty list) In order to restore the database state between tests for TransactionTestCases and database backends without transactions, Django will serialize the contents of all apps when it starts the test run so it can then reload from that copy before running tests that need it. This slows down the startup time of the test runner; if you have apps that you know don’t need this feature, you can add their full names in here (e.g. 'django.contrib.contenttypes') to exclude them from this serialization process. THOUSAND_SEPARATOR Default: ',' (Comma) Default thousand separator used when formatting numbers. This setting is used only when USE_THOUSAND_SEPARATOR is True and NUMBER_GROUPING is greater than 0. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See also NUMBER_GROUPING, DECIMAL_SEPARATOR and USE_THOUSAND_SEPARATOR. TIME_FORMAT Default: 'P' (e.g. 4 p.m.) The default formatting to use for displaying time fields in any part of the system. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See allowed date format strings. See also DATE_FORMAT and DATETIME_FORMAT. TIME_INPUT_FORMATS Default: [
'%H:%M:%S', # '14:30:59'
'%H:%M:%S.%f', # '14:30:59.000200'
'%H:%M', # '14:30'
]
A list of formats that will be accepted when inputting data on a time field. Formats will be tried in order, using the first valid one. Note that these format strings use Python’s datetime module syntax, not the format strings from the date template filter. When USE_L10N is True, the locale-dictated format has higher precedence and will be applied instead. See also DATE_INPUT_FORMATS and DATETIME_INPUT_FORMATS. TIME_ZONE Default: 'America/Chicago' A string representing the time zone for this installation. See the list of time zones. Note Since Django was first released with the TIME_ZONE set to 'America/Chicago', the global setting (used if nothing is defined in your project’s settings.py) remains 'America/Chicago' for backwards compatibility. New project templates default to 'UTC'. Note that this isn’t necessarily the time zone of the server. For example, one server may serve multiple Django-powered sites, each with a separate time zone setting. When USE_TZ is False, this is the time zone in which Django will store all datetimes. When USE_TZ is True, this is the default time zone that Django will use to display datetimes in templates and to interpret datetimes entered in forms. On Unix environments (where time.tzset() is implemented), Django sets the os.environ['TZ'] variable to the time zone you specify in the TIME_ZONE setting. Thus, all your views and models will automatically operate in this time zone. However, Django won’t set the TZ environment variable if you’re using the manual configuration option as described in manually configuring settings. If Django doesn’t set the TZ environment variable, it’s up to you to ensure your processes are running in the correct environment. Note Django cannot reliably use alternate time zones in a Windows environment. If you’re running Django on Windows, TIME_ZONE must be set to match the system time zone. USE_DEPRECATED_PYTZ New in Django 4.0. Default: False A boolean that specifies whether to use pytz, rather than zoneinfo, as the default time zone implementation. Deprecated since version 4.0: This transitional setting is deprecated. Support for using pytz will be removed in Django 5.0. USE_I18N Default: True A boolean that specifies whether Django’s translation system should be enabled. This provides a way to turn it off, for performance. If this is set to False, Django will make some optimizations so as not to load the translation machinery. See also LANGUAGE_CODE, USE_L10N and USE_TZ. Note The default settings.py file created by django-admin
startproject includes USE_I18N = True for convenience. USE_L10N Default: True A boolean that specifies if localized formatting of data will be enabled by default or not. If this is set to True, e.g. Django will display numbers and dates using the format of the current locale. See also LANGUAGE_CODE, USE_I18N and USE_TZ. Changed in Django 4.0: In older versions, the default value is False. Deprecated since version 4.0: This setting is deprecated. Starting with Django 5.0, localized formatting of data will always be enabled. For example Django will display numbers and dates using the format of the current locale. USE_THOUSAND_SEPARATOR Default: False A boolean that specifies whether to display numbers using a thousand separator. When set to True and USE_L10N is also True, Django will format numbers using the NUMBER_GROUPING and THOUSAND_SEPARATOR settings. These settings may also be dictated by the locale, which takes precedence. See also DECIMAL_SEPARATOR, NUMBER_GROUPING and THOUSAND_SEPARATOR. USE_TZ Default: False Note In Django 5.0, the default value will change from False to True. A boolean that specifies if datetimes will be timezone-aware by default or not. If this is set to True, Django will use timezone-aware datetimes internally. When USE_TZ is False, Django will use naive datetimes in local time, except when parsing ISO 8601 formatted strings, where timezone information will always be retained if present. See also TIME_ZONE, USE_I18N and USE_L10N. Note The default settings.py file created by django-admin startproject includes USE_TZ = True for convenience. USE_X_FORWARDED_HOST Default: False A boolean that specifies whether to use the X-Forwarded-Host header in preference to the Host header. This should only be enabled if a proxy which sets this header is in use. This setting takes priority over USE_X_FORWARDED_PORT. Per RFC 7239#section-5.3, the X-Forwarded-Host header can include the port number, in which case you shouldn’t use USE_X_FORWARDED_PORT. USE_X_FORWARDED_PORT Default: False A boolean that specifies whether to use the X-Forwarded-Port header in preference to the SERVER_PORT META variable. This should only be enabled if a proxy which sets this header is in use. USE_X_FORWARDED_HOST takes priority over this setting. WSGI_APPLICATION Default: None The full Python path of the WSGI application object that Django’s built-in servers (e.g. runserver) will use. The django-admin
startproject management command will create a standard wsgi.py file with an application callable in it, and point this setting to that application. If not set, the return value of django.core.wsgi.get_wsgi_application() will be used. In this case, the behavior of runserver will be identical to previous Django versions. YEAR_MONTH_FORMAT Default: 'F Y' The default formatting to use for date fields on Django admin change-list pages – and, possibly, by other parts of the system – in cases when only the year and month are displayed. For example, when a Django admin change-list page is being filtered by a date drilldown, the header for a given month displays the month and the year. Different locales have different formats. For example, U.S. English would say “January 2006,” whereas another locale might say “2006/January.” Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT, DATETIME_FORMAT, TIME_FORMAT and MONTH_DAY_FORMAT. X_FRAME_OPTIONS Default: 'DENY' The default value for the X-Frame-Options header used by XFrameOptionsMiddleware. See the clickjacking protection documentation. Auth Settings for django.contrib.auth. AUTHENTICATION_BACKENDS Default: ['django.contrib.auth.backends.ModelBackend'] A list of authentication backend classes (as strings) to use when attempting to authenticate a user. See the authentication backends documentation for details. AUTH_USER_MODEL Default: 'auth.User' The model to use to represent a User. See Substituting a custom User model. Warning You cannot change the AUTH_USER_MODEL setting during the lifetime of a project (i.e. once you have made and migrated models that depend on it) without serious effort. It is intended to be set at the project start, and the model it refers to must be available in the first migration of the app that it lives in. See Substituting a custom User model for more details. LOGIN_REDIRECT_URL Default: '/accounts/profile/' The URL or named URL pattern where requests are redirected after login when the LoginView doesn’t get a next GET parameter. LOGIN_URL Default: '/accounts/login/' The URL or named URL pattern where requests are redirected for login when using the login_required() decorator, LoginRequiredMixin, or AccessMixin. LOGOUT_REDIRECT_URL Default: None The URL or named URL pattern where requests are redirected after logout if LogoutView doesn’t have a next_page attribute. If None, no redirect will be performed and the logout view will be rendered. PASSWORD_RESET_TIMEOUT Default: 259200 (3 days, in seconds) The number of seconds a password reset link is valid for. Used by the PasswordResetConfirmView. Note Reducing the value of this timeout doesn’t make any difference to the ability of an attacker to brute-force a password reset token. Tokens are designed to be safe from brute-forcing without any timeout. This timeout exists to protect against some unlikely attack scenarios, such as someone gaining access to email archives that may contain old, unused password reset tokens. PASSWORD_HASHERS See How Django stores passwords. Default: [
'django.contrib.auth.hashers.PBKDF2PasswordHasher',
'django.contrib.auth.hashers.PBKDF2SHA1PasswordHasher',
'django.contrib.auth.hashers.Argon2PasswordHasher',
'django.contrib.auth.hashers.BCryptSHA256PasswordHasher',
]
AUTH_PASSWORD_VALIDATORS Default: [] (Empty list) The list of validators that are used to check the strength of user’s passwords. See Password validation for more details. By default, no validation is performed and all passwords are accepted. Messages Settings for django.contrib.messages. MESSAGE_LEVEL Default: messages.INFO Sets the minimum message level that will be recorded by the messages framework. See message levels for more details. Important If you override MESSAGE_LEVEL in your settings file and rely on any of the built-in constants, you must import the constants module directly to avoid the potential for circular imports, e.g.: from django.contrib.messages import constants as message_constants
MESSAGE_LEVEL = message_constants.DEBUG
If desired, you may specify the numeric values for the constants directly according to the values in the above constants table. MESSAGE_STORAGE Default: 'django.contrib.messages.storage.fallback.FallbackStorage' Controls where Django stores message data. Valid values are: 'django.contrib.messages.storage.fallback.FallbackStorage' 'django.contrib.messages.storage.session.SessionStorage' 'django.contrib.messages.storage.cookie.CookieStorage' See message storage backends for more details. The backends that use cookies – CookieStorage and FallbackStorage – use the value of SESSION_COOKIE_DOMAIN, SESSION_COOKIE_SECURE and SESSION_COOKIE_HTTPONLY when setting their cookies. MESSAGE_TAGS Default: {
messages.DEBUG: 'debug',
messages.INFO: 'info',
messages.SUCCESS: 'success',
messages.WARNING: 'warning',
messages.ERROR: 'error',
}
This sets the mapping of message level to message tag, which is typically rendered as a CSS class in HTML. If you specify a value, it will extend the default. This means you only have to specify those values which you need to override. See Displaying messages above for more details. Important If you override MESSAGE_TAGS in your settings file and rely on any of the built-in constants, you must import the constants module directly to avoid the potential for circular imports, e.g.: from django.contrib.messages import constants as message_constants
MESSAGE_TAGS = {message_constants.INFO: ''}
If desired, you may specify the numeric values for the constants directly according to the values in the above constants table. Sessions Settings for django.contrib.sessions. SESSION_CACHE_ALIAS Default: 'default' If you’re using cache-based session storage, this selects the cache to use. SESSION_COOKIE_AGE Default: 1209600 (2 weeks, in seconds) The age of session cookies, in seconds. SESSION_COOKIE_DOMAIN Default: None The domain to use for session cookies. Set this to a string such as "example.com" for cross-domain cookies, or use None for a standard domain cookie. To use cross-domain cookies with CSRF_USE_SESSIONS, you must include a leading dot (e.g. ".example.com") to accommodate the CSRF middleware’s referer checking. Be cautious when updating this setting on a production site. If you update this setting to enable cross-domain cookies on a site that previously used standard domain cookies, existing user cookies will be set to the old domain. This may result in them being unable to log in as long as these cookies persist. This setting also affects cookies set by django.contrib.messages. SESSION_COOKIE_HTTPONLY Default: True Whether to use HttpOnly flag on the session cookie. If this is set to True, client-side JavaScript will not be able to access the session cookie. HttpOnly is a flag included in a Set-Cookie HTTP response header. It’s part of the RFC 6265#section-4.1.2.6 standard for cookies and can be a useful way to mitigate the risk of a client-side script accessing the protected cookie data. This makes it less trivial for an attacker to escalate a cross-site scripting vulnerability into full hijacking of a user’s session. There aren’t many good reasons for turning this off. Your code shouldn’t read session cookies from JavaScript. SESSION_COOKIE_NAME Default: 'sessionid' The name of the cookie to use for sessions. This can be whatever you want (as long as it’s different from the other cookie names in your application). SESSION_COOKIE_PATH Default: '/' The path set on the session cookie. This should either match the URL path of your Django installation or be parent of that path. This is useful if you have multiple Django instances running under the same hostname. They can use different cookie paths, and each instance will only see its own session cookie. SESSION_COOKIE_SAMESITE Default: 'Lax' The value of the SameSite flag on the session cookie. This flag prevents the cookie from being sent in cross-site requests thus preventing CSRF attacks and making some methods of stealing session cookie impossible. Possible values for the setting are:
'Strict': prevents the cookie from being sent by the browser to the target site in all cross-site browsing context, even when following a regular link. For example, for a GitHub-like website this would mean that if a logged-in user follows a link to a private GitHub project posted on a corporate discussion forum or email, GitHub will not receive the session cookie and the user won’t be able to access the project. A bank website, however, most likely doesn’t want to allow any transactional pages to be linked from external sites so the 'Strict' flag would be appropriate.
'Lax' (default): provides a balance between security and usability for websites that want to maintain user’s logged-in session after the user arrives from an external link. In the GitHub scenario, the session cookie would be allowed when following a regular link from an external website and be blocked in CSRF-prone request methods (e.g. POST).
'None' (string): the session cookie will be sent with all same-site and cross-site requests.
False: disables the flag. Note Modern browsers provide a more secure default policy for the SameSite flag and will assume Lax for cookies without an explicit value set. SESSION_COOKIE_SECURE Default: False Whether to use a secure cookie for the session cookie. If this is set to True, the cookie will be marked as “secure”, which means browsers may ensure that the cookie is only sent under an HTTPS connection. Leaving this setting off isn’t a good idea because an attacker could capture an unencrypted session cookie with a packet sniffer and use the cookie to hijack the user’s session. SESSION_ENGINE Default: 'django.contrib.sessions.backends.db' Controls where Django stores session data. Included engines are: 'django.contrib.sessions.backends.db' 'django.contrib.sessions.backends.file' 'django.contrib.sessions.backends.cache' 'django.contrib.sessions.backends.cached_db' 'django.contrib.sessions.backends.signed_cookies' See Configuring the session engine for more details. SESSION_EXPIRE_AT_BROWSER_CLOSE Default: False Whether to expire the session when the user closes their browser. See Browser-length sessions vs. persistent sessions. SESSION_FILE_PATH Default: None If you’re using file-based session storage, this sets the directory in which Django will store session data. When the default value (None) is used, Django will use the standard temporary directory for the system. SESSION_SAVE_EVERY_REQUEST Default: False Whether to save the session data on every request. If this is False (default), then the session data will only be saved if it has been modified – that is, if any of its dictionary values have been assigned or deleted. Empty sessions won’t be created, even if this setting is active. SESSION_SERIALIZER Default: 'django.contrib.sessions.serializers.JSONSerializer' Full import path of a serializer class to use for serializing session data. Included serializers are: 'django.contrib.sessions.serializers.PickleSerializer' 'django.contrib.sessions.serializers.JSONSerializer' See Session serialization for details, including a warning regarding possible remote code execution when using PickleSerializer. Sites Settings for django.contrib.sites. SITE_ID Default: Not defined The ID, as an integer, of the current site in the django_site database table. This is used so that application data can hook into specific sites and a single database can manage content for multiple sites. Static Files Settings for django.contrib.staticfiles. STATIC_ROOT Default: None The absolute path to the directory where collectstatic will collect static files for deployment. Example: "/var/www/example.com/static/" If the staticfiles contrib app is enabled (as in the default project template), the collectstatic management command will collect static files into this directory. See the how-to on managing static files for more details about usage. Warning This should be an initially empty destination directory for collecting your static files from their permanent locations into one directory for ease of deployment; it is not a place to store your static files permanently. You should do that in directories that will be found by staticfiles’s finders, which by default, are 'static/' app sub-directories and any directories you include in STATICFILES_DIRS). STATIC_URL Default: None URL to use when referring to static files located in STATIC_ROOT. Example: "static/" or "http://static.example.com/" If not None, this will be used as the base path for asset definitions (the Media class) and the staticfiles app. It must end in a slash if set to a non-empty value. You may need to configure these files to be served in development and will definitely need to do so in production. Note If STATIC_URL is a relative path, then it will be prefixed by the server-provided value of SCRIPT_NAME (or / if not set). This makes it easier to serve a Django application in a subpath without adding an extra configuration to the settings. STATICFILES_DIRS Default: [] (Empty list) This setting defines the additional locations the staticfiles app will traverse if the FileSystemFinder finder is enabled, e.g. if you use the collectstatic or findstatic management command or use the static file serving view. This should be set to a list of strings that contain full paths to your additional files directory(ies) e.g.: STATICFILES_DIRS = [
"/home/special.polls.com/polls/static",
"/home/polls.com/polls/static",
"/opt/webfiles/common",
]
Note that these paths should use Unix-style forward slashes, even on Windows (e.g. "C:/Users/user/mysite/extra_static_content"). Prefixes (optional) In case you want to refer to files in one of the locations with an additional namespace, you can optionally provide a prefix as (prefix, path) tuples, e.g.: STATICFILES_DIRS = [
# ...
("downloads", "/opt/webfiles/stats"),
]
For example, assuming you have STATIC_URL set to 'static/', the collectstatic management command would collect the “stats” files in a 'downloads' subdirectory of STATIC_ROOT. This would allow you to refer to the local file '/opt/webfiles/stats/polls_20101022.tar.gz' with '/static/downloads/polls_20101022.tar.gz' in your templates, e.g.: <a href="{% static 'downloads/polls_20101022.tar.gz' %}">
STATICFILES_STORAGE Default: 'django.contrib.staticfiles.storage.StaticFilesStorage' The file storage engine to use when collecting static files with the collectstatic management command. A ready-to-use instance of the storage backend defined in this setting can be found at django.contrib.staticfiles.storage.staticfiles_storage. For an example, see Serving static files from a cloud service or CDN. STATICFILES_FINDERS Default: [
'django.contrib.staticfiles.finders.FileSystemFinder',
'django.contrib.staticfiles.finders.AppDirectoriesFinder',
]
The list of finder backends that know how to find static files in various locations. The default will find files stored in the STATICFILES_DIRS setting (using django.contrib.staticfiles.finders.FileSystemFinder) and in a static subdirectory of each app (using django.contrib.staticfiles.finders.AppDirectoriesFinder). If multiple files with the same name are present, the first file that is found will be used. One finder is disabled by default: django.contrib.staticfiles.finders.DefaultStorageFinder. If added to your STATICFILES_FINDERS setting, it will look for static files in the default file storage as defined by the DEFAULT_FILE_STORAGE setting. Note When using the AppDirectoriesFinder finder, make sure your apps can be found by staticfiles by adding the app to the INSTALLED_APPS setting of your site. Static file finders are currently considered a private interface, and this interface is thus undocumented. Core Settings Topical Index Cache CACHES CACHE_MIDDLEWARE_ALIAS CACHE_MIDDLEWARE_KEY_PREFIX CACHE_MIDDLEWARE_SECONDS Database DATABASES DATABASE_ROUTERS DEFAULT_INDEX_TABLESPACE DEFAULT_TABLESPACE Debugging DEBUG DEBUG_PROPAGATE_EXCEPTIONS Email ADMINS DEFAULT_CHARSET DEFAULT_FROM_EMAIL EMAIL_BACKEND EMAIL_FILE_PATH EMAIL_HOST EMAIL_HOST_PASSWORD EMAIL_HOST_USER EMAIL_PORT EMAIL_SSL_CERTFILE EMAIL_SSL_KEYFILE EMAIL_SUBJECT_PREFIX EMAIL_TIMEOUT EMAIL_USE_LOCALTIME EMAIL_USE_TLS MANAGERS SERVER_EMAIL Error reporting DEFAULT_EXCEPTION_REPORTER DEFAULT_EXCEPTION_REPORTER_FILTER IGNORABLE_404_URLS MANAGERS SILENCED_SYSTEM_CHECKS File uploads DEFAULT_FILE_STORAGE FILE_UPLOAD_HANDLERS FILE_UPLOAD_MAX_MEMORY_SIZE FILE_UPLOAD_PERMISSIONS FILE_UPLOAD_TEMP_DIR MEDIA_ROOT MEDIA_URL Forms FORM_RENDERER Globalization (i18n/l10n) DATE_FORMAT DATE_INPUT_FORMATS DATETIME_FORMAT DATETIME_INPUT_FORMATS DECIMAL_SEPARATOR FIRST_DAY_OF_WEEK FORMAT_MODULE_PATH LANGUAGE_CODE LANGUAGE_COOKIE_AGE LANGUAGE_COOKIE_DOMAIN LANGUAGE_COOKIE_HTTPONLY LANGUAGE_COOKIE_NAME LANGUAGE_COOKIE_PATH LANGUAGE_COOKIE_SAMESITE LANGUAGE_COOKIE_SECURE LANGUAGES LANGUAGES_BIDI LOCALE_PATHS MONTH_DAY_FORMAT NUMBER_GROUPING SHORT_DATE_FORMAT SHORT_DATETIME_FORMAT THOUSAND_SEPARATOR TIME_FORMAT TIME_INPUT_FORMATS TIME_ZONE USE_I18N USE_L10N USE_THOUSAND_SEPARATOR USE_TZ YEAR_MONTH_FORMAT HTTP DATA_UPLOAD_MAX_MEMORY_SIZE DATA_UPLOAD_MAX_NUMBER_FIELDS DEFAULT_CHARSET DISALLOWED_USER_AGENTS FORCE_SCRIPT_NAME INTERNAL_IPS MIDDLEWARE Security SECURE_CONTENT_TYPE_NOSNIFF SECURE_CROSS_ORIGIN_OPENER_POLICY SECURE_HSTS_INCLUDE_SUBDOMAINS SECURE_HSTS_PRELOAD SECURE_HSTS_SECONDS SECURE_PROXY_SSL_HEADER SECURE_REDIRECT_EXEMPT SECURE_REFERRER_POLICY SECURE_SSL_HOST SECURE_SSL_REDIRECT SIGNING_BACKEND USE_X_FORWARDED_HOST USE_X_FORWARDED_PORT WSGI_APPLICATION Logging LOGGING LOGGING_CONFIG Models ABSOLUTE_URL_OVERRIDES FIXTURE_DIRS INSTALLED_APPS Security Cross Site Request Forgery Protection CSRF_COOKIE_DOMAIN CSRF_COOKIE_NAME CSRF_COOKIE_PATH CSRF_COOKIE_SAMESITE CSRF_COOKIE_SECURE CSRF_FAILURE_VIEW CSRF_HEADER_NAME CSRF_TRUSTED_ORIGINS CSRF_USE_SESSIONS SECRET_KEY X_FRAME_OPTIONS Serialization DEFAULT_CHARSET SERIALIZATION_MODULES Templates TEMPLATES Testing Database: TEST
TEST_NON_SERIALIZED_APPS TEST_RUNNER URLs APPEND_SLASH PREPEND_WWW ROOT_URLCONF | |
doc_26950 |
One-vs-one multiclass strategy This strategy consists in fitting one classifier per class pair. At prediction time, the class which received the most votes is selected. Since it requires to fit n_classes * (n_classes - 1) / 2 classifiers, this method is usually slower than one-vs-the-rest, due to its O(n_classes^2) complexity. However, this method may be advantageous for algorithms such as kernel algorithms which don’t scale well with n_samples. This is because each individual learning problem only involves a small subset of the data whereas, with one-vs-the-rest, the complete dataset is used n_classes times. Read more in the User Guide. Parameters
estimatorestimator object
An estimator object implementing fit and one of decision_function or predict_proba.
n_jobsint, default=None
The number of jobs to use for the computation: the n_classes * (
n_classes - 1) / 2 OVO problems are computed in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details. Attributes
estimators_list of n_classes * (n_classes - 1) / 2 estimators
Estimators used for predictions.
classes_numpy array of shape [n_classes]
Array containing labels.
n_classes_int
Number of classes
pairwise_indices_list, length = len(estimators_), or None
Indices of samples used when training the estimators. None when estimator’s pairwise tag is False. Deprecated since version 0.24: The _pairwise attribute is deprecated in 0.24. From 1.1 (renaming of 0.25) and onward, pairwise_indices_ will use the pairwise estimator tag instead. Examples >>> from sklearn.datasets import load_iris
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.multiclass import OneVsOneClassifier
>>> from sklearn.svm import LinearSVC
>>> X, y = load_iris(return_X_y=True)
>>> X_train, X_test, y_train, y_test = train_test_split(
... X, y, test_size=0.33, shuffle=True, random_state=0)
>>> clf = OneVsOneClassifier(
... LinearSVC(random_state=0)).fit(X_train, y_train)
>>> clf.predict(X_test[:10])
array([2, 1, 0, 2, 0, 2, 0, 1, 1, 1])
Methods
decision_function(X) Decision function for the OneVsOneClassifier.
fit(X, y) Fit underlying estimators.
get_params([deep]) Get parameters for this estimator.
partial_fit(X, y[, classes]) Partially fit underlying estimators
predict(X) Estimate the best class label for each sample in X.
score(X, y[, sample_weight]) Return the mean accuracy on the given test data and labels.
set_params(**params) Set the parameters of this estimator.
decision_function(X) [source]
Decision function for the OneVsOneClassifier. The decision values for the samples are computed by adding the normalized sum of pair-wise classification confidence levels to the votes in order to disambiguate between the decision values when the votes for all the classes are equal leading to a tie. Parameters
Xarray-like of shape (n_samples, n_features)
Returns
Yarray-like of shape (n_samples, n_classes) or (n_samples,) for binary classification.
Changed in version 0.19: output shape changed to (n_samples,) to conform to scikit-learn conventions for binary classification.
fit(X, y) [source]
Fit underlying estimators. Parameters
X(sparse) array-like of shape (n_samples, n_features)
Data.
yarray-like of shape (n_samples,)
Multi-class targets. Returns
self
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values.
partial_fit(X, y, classes=None) [source]
Partially fit underlying estimators Should be used when memory is inefficient to train all data. Chunks of data can be passed in several iteration, where the first call should have an array of all target variables. Parameters
X(sparse) array-like of shape (n_samples, n_features)
Data.
yarray-like of shape (n_samples,)
Multi-class targets.
classesarray, shape (n_classes, )
Classes across all calls to partial_fit. Can be obtained via np.unique(y_all), where y_all is the target vector of the entire dataset. This argument is only required in the first call of partial_fit and can be omitted in the subsequent calls. Returns
self
predict(X) [source]
Estimate the best class label for each sample in X. This is implemented as argmax(decision_function(X), axis=1) which will return the label of the class with most votes by estimators predicting the outcome of a decision for each possible class pair. Parameters
X(sparse) array-like of shape (n_samples, n_features)
Data. Returns
ynumpy array of shape [n_samples]
Predicted multi-class targets.
score(X, y, sample_weight=None) [source]
Return the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters
Xarray-like of shape (n_samples, n_features)
Test samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights. Returns
scorefloat
Mean accuracy of self.predict(X) wrt. y.
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Estimator parameters. Returns
selfestimator instance
Estimator instance. | |
doc_26951 |
Creates a setuptools.Extension for C++. Convenience method that creates a setuptools.Extension with the bare minimum (but often sufficient) arguments to build a C++ extension. All arguments are forwarded to the setuptools.Extension constructor. Example >>> from setuptools import setup
>>> from torch.utils.cpp_extension import BuildExtension, CppExtension
>>> setup(
name='extension',
ext_modules=[
CppExtension(
name='extension',
sources=['extension.cpp'],
extra_compile_args=['-g']),
],
cmdclass={
'build_ext': BuildExtension
}) | |
doc_26952 | tf.compat.v1.keras.layers.LSTMCell(
units, activation='tanh',
recurrent_activation='hard_sigmoid', use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros', unit_forget_bias=True,
kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None,
kernel_constraint=None, recurrent_constraint=None, bias_constraint=None,
dropout=0.0, recurrent_dropout=0.0, **kwargs
)
Arguments
units Positive integer, dimensionality of the output space.
activation Activation function to use. Default: hyperbolic tangent (tanh). If you pass None, no activation is applied (ie. "linear" activation: a(x) = x).
recurrent_activation Activation function to use for the recurrent step. Default: hard sigmoid (hard_sigmoid). If you pass None, no activation is applied (ie. "linear" activation: a(x) = x).
use_bias Boolean, whether the layer uses a bias vector.
kernel_initializer Initializer for the kernel weights matrix, used for the linear transformation of the inputs.
recurrent_initializer Initializer for the recurrent_kernel weights matrix, used for the linear transformation of the recurrent state.
bias_initializer Initializer for the bias vector.
unit_forget_bias Boolean. If True, add 1 to the bias of the forget gate at initialization. Setting it to true will also force bias_initializer="zeros". This is recommended in Jozefowicz et al., 2015
kernel_regularizer Regularizer function applied to the kernel weights matrix.
recurrent_regularizer Regularizer function applied to the recurrent_kernel weights matrix.
bias_regularizer Regularizer function applied to the bias vector.
kernel_constraint Constraint function applied to the kernel weights matrix.
recurrent_constraint Constraint function applied to the recurrent_kernel weights matrix.
bias_constraint Constraint function applied to the bias vector.
dropout Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
recurrent_dropout Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Call arguments:
inputs: A 2D tensor.
states: List of state tensors corresponding to the previous timestep.
training: Python boolean indicating whether the layer should behave in training mode or in inference mode. Only relevant when dropout or recurrent_dropout is used. Methods get_dropout_mask_for_cell View source
get_dropout_mask_for_cell(
inputs, training, count=1
)
Get the dropout mask for RNN cell's input. It will create mask based on context if there isn't any existing cached mask. If a new mask is generated, it will update the cache in the cell.
Args
inputs The input tensor whose shape will be used to generate dropout mask.
training Boolean tensor, whether its in training mode, dropout will be ignored in non-training mode.
count Int, how many dropout mask will be generated. It is useful for cell that has internal weights fused together.
Returns List of mask tensor, generated or cached mask based on context.
get_initial_state View source
get_initial_state(
inputs=None, batch_size=None, dtype=None
)
get_recurrent_dropout_mask_for_cell View source
get_recurrent_dropout_mask_for_cell(
inputs, training, count=1
)
Get the recurrent dropout mask for RNN cell. It will create mask based on context if there isn't any existing cached mask. If a new mask is generated, it will update the cache in the cell.
Args
inputs The input tensor whose shape will be used to generate dropout mask.
training Boolean tensor, whether its in training mode, dropout will be ignored in non-training mode.
count Int, how many dropout mask will be generated. It is useful for cell that has internal weights fused together.
Returns List of mask tensor, generated or cached mask based on context.
reset_dropout_mask View source
reset_dropout_mask()
Reset the cached dropout masks if any. This is important for the RNN layer to invoke this in it call() method so that the cached mask is cleared before calling the cell.call(). The mask should be cached across the timestep within the same batch, but shouldn't be cached between batches. Otherwise it will introduce unreasonable bias against certain index of data within the batch. reset_recurrent_dropout_mask View source
reset_recurrent_dropout_mask()
Reset the cached recurrent dropout masks if any. This is important for the RNN layer to invoke this in it call() method so that the cached mask is cleared before calling the cell.call(). The mask should be cached across the timestep within the same batch, but shouldn't be cached between batches. Otherwise it will introduce unreasonable bias against certain index of data within the batch. | |
doc_26953 | tf.cosh Compat aliases for migration See Migration guide for more details. tf.compat.v1.cosh, tf.compat.v1.math.cosh
tf.math.cosh(
x, name=None
)
Given an input tensor, this function computes hyperbolic cosine of every element in the tensor. Input range is [-inf, inf] and output range is [1, inf]. x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 2, 10, float("inf")])
tf.math.cosh(x) ==> [inf 4.0515420e+03 1.1276259e+00 1.5430807e+00 1.8106556e+00 3.7621956e+00 1.1013233e+04 inf]
Args
x A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.
name A name for the operation (optional).
Returns A Tensor. Has the same type as x. | |
doc_26954 | A for loop. target holds the variable(s) the loop assigns to, as a single Name, Tuple or List node. iter holds the item to be looped over, again as a single node. body and orelse contain lists of nodes to execute. Those in orelse are executed if the loop finishes normally, rather than via a break statement.
type_comment
type_comment is an optional string with the type annotation as a comment.
>>> print(ast.dump(ast.parse("""
... for x in y:
... ...
... else:
... ...
... """), indent=4))
Module(
body=[
For(
target=Name(id='x', ctx=Store()),
iter=Name(id='y', ctx=Load()),
body=[
Expr(
value=Constant(value=Ellipsis))],
orelse=[
Expr(
value=Constant(value=Ellipsis))])],
type_ignores=[]) | |
doc_26955 |
Defaults to removing last label, but any index can be supplied | |
doc_26956 |
Estimate the transformation from a set of corresponding points. You can determine the over-, well- and under-determined parameters with the total least-squares method. Number of source and destination coordinates must match. Parameters
src(N, 2) array
Source coordinates.
dst(N, 2) array
Destination coordinates. Returns
successbool
True, if model estimation succeeds. | |
doc_26957 | The HTTP status of the response, as an integer. For a full list of defined codes, see the IANA status code registry. | |
doc_26958 | Returns the age of session cookies, in seconds. Defaults to SESSION_COOKIE_AGE. | |
doc_26959 | See Migration guide for more details. tf.compat.v1.igammac, tf.compat.v1.math.igammac
tf.math.igammac(
a, x, name=None
)
The upper regularized incomplete Gamma function is defined as: \(Q(a, x) = Gamma(a, x) / Gamma(a) = 1 - P(a, x)\) where \(Gamma(a, x) = int_{x}^{\infty} t^{a-1} exp(-t) dt\) is the upper incomplete Gama function. Note, above P(a, x) (Igamma) is the lower regularized complete Gamma function.
Args
a A Tensor. Must be one of the following types: float32, float64.
x A Tensor. Must have the same type as a.
name A name for the operation (optional).
Returns A Tensor. Has the same type as a. | |
doc_26960 | unlock()
Three locking mechanisms are used—dot locking and, if available, the flock() and lockf() system calls. | |
doc_26961 | tf.data.experimental.choose_from_datasets(
datasets, choice_dataset
)
For example, given the following datasets: datasets = [tf.data.Dataset.from_tensors("foo").repeat(),
tf.data.Dataset.from_tensors("bar").repeat(),
tf.data.Dataset.from_tensors("baz").repeat()]
# Define a dataset containing `[0, 1, 2, 0, 1, 2, 0, 1, 2]`.
choice_dataset = tf.data.Dataset.range(3).repeat(3)
result = tf.data.experimental.choose_from_datasets(datasets, choice_dataset)
The elements of result will be: "foo", "bar", "baz", "foo", "bar", "baz", "foo", "bar", "baz"
Args
datasets A list of tf.data.Dataset objects with compatible structure.
choice_dataset A tf.data.Dataset of scalar tf.int64 tensors between 0 and len(datasets) - 1.
Returns A dataset that interleaves elements from datasets according to the values of choice_dataset.
Raises
TypeError If the datasets or choice_dataset arguments have the wrong type. | |
doc_26962 |
Set the coordinates of the label. By default, the x coordinate of the y label and the y coordinate of the x label are determined by the tick label bounding boxes, but this can lead to poor alignment of multiple labels if there are multiple axes. You can also specify the coordinate system of the label with the transform. If None, the default coordinate system will be the axes coordinate system: (0, 0) is bottom left, (0.5, 0.5) is center, etc. | |
doc_26963 | See torch.inverse() | |
doc_26964 |
Return a callable that handles preprocessing, tokenization and n-grams generation. Returns
analyzer: callable
A function to handle preprocessing, tokenization and n-grams generation. | |
doc_26965 | The local filesystem path where the file can be opened using Python’s standard open(). For storage systems that aren’t accessible from the local filesystem, this will raise NotImplementedError instead. | |
doc_26966 | Computes the element-wise logical OR of the given input tensors. Zeros are treated as False and nonzeros are treated as True. Parameters
input (Tensor) – the input tensor.
other (Tensor) – the tensor to compute OR with Keyword Arguments
out (Tensor, optional) – the output tensor. Example: >>> torch.logical_or(torch.tensor([True, False, True]), torch.tensor([True, False, False]))
tensor([ True, False, True])
>>> a = torch.tensor([0, 1, 10, 0], dtype=torch.int8)
>>> b = torch.tensor([4, 0, 1, 0], dtype=torch.int8)
>>> torch.logical_or(a, b)
tensor([ True, True, True, False])
>>> torch.logical_or(a.double(), b.double())
tensor([ True, True, True, False])
>>> torch.logical_or(a.double(), b)
tensor([ True, True, True, False])
>>> torch.logical_or(a, b, out=torch.empty(4, dtype=torch.bool))
tensor([ True, True, True, False]) | |
doc_26967 |
Step could be called after every batch update Example >>> scheduler = CosineAnnealingWarmRestarts(optimizer, T_0, T_mult)
>>> iters = len(dataloader)
>>> for epoch in range(20):
>>> for i, sample in enumerate(dataloader):
>>> inputs, labels = sample['inputs'], sample['labels']
>>> optimizer.zero_grad()
>>> outputs = net(inputs)
>>> loss = criterion(outputs, labels)
>>> loss.backward()
>>> optimizer.step()
>>> scheduler.step(epoch + i / iters)
This function can be called in an interleaved way. Example >>> scheduler = CosineAnnealingWarmRestarts(optimizer, T_0, T_mult)
>>> for epoch in range(20):
>>> scheduler.step()
>>> scheduler.step(26)
>>> scheduler.step() # scheduler.step(27), instead of scheduler(20) | |
doc_26968 | Compile a source file to byte-code and write out the byte-code cache file. The source code is loaded from the file named file. The byte-code is written to cfile, which defaults to the PEP 3147/PEP 488 path, ending in .pyc. For example, if file is /foo/bar/baz.py cfile will default to /foo/bar/__pycache__/baz.cpython-32.pyc for Python 3.2. If dfile is specified, it is used as the name of the source file in error messages instead of file. If doraise is true, a PyCompileError is raised when an error is encountered while compiling file. If doraise is false (the default), an error string is written to sys.stderr, but no exception is raised. This function returns the path to byte-compiled file, i.e. whatever cfile value was used. The doraise and quiet arguments determine how errors are handled while compiling file. If quiet is 0 or 1, and doraise is false, the default behaviour is enabled: an error string is written to sys.stderr, and the function returns None instead of a path. If doraise is true, a PyCompileError is raised instead. However if quiet is 2, no message is written, and doraise has no effect. If the path that cfile becomes (either explicitly specified or computed) is a symlink or non-regular file, FileExistsError will be raised. This is to act as a warning that import will turn those paths into regular files if it is allowed to write byte-compiled files to those paths. This is a side-effect of import using file renaming to place the final byte-compiled file into place to prevent concurrent file writing issues. optimize controls the optimization level and is passed to the built-in compile() function. The default of -1 selects the optimization level of the current interpreter. invalidation_mode should be a member of the PycInvalidationMode enum and controls how the generated bytecode cache is invalidated at runtime. The default is PycInvalidationMode.CHECKED_HASH if the SOURCE_DATE_EPOCH environment variable is set, otherwise the default is PycInvalidationMode.TIMESTAMP. Changed in version 3.2: Changed default value of cfile to be PEP 3147-compliant. Previous default was file + 'c' ('o' if optimization was enabled). Also added the optimize parameter. Changed in version 3.4: Changed code to use importlib for the byte-code cache file writing. This means file creation/writing semantics now match what importlib does, e.g. permissions, write-and-move semantics, etc. Also added the caveat that FileExistsError is raised if cfile is a symlink or non-regular file. Changed in version 3.7: The invalidation_mode parameter was added as specified in PEP 552. If the SOURCE_DATE_EPOCH environment variable is set, invalidation_mode will be forced to PycInvalidationMode.CHECKED_HASH. Changed in version 3.7.2: The SOURCE_DATE_EPOCH environment variable no longer overrides the value of the invalidation_mode argument, and determines its default value instead. Changed in version 3.8: The quiet parameter was added. | |
doc_26969 | This method is called to create the default OPTIONS response. This can be changed through subclassing to change the default behavior of OPTIONS responses. Changelog New in version 0.7. Return type
flask.wrappers.Response | |
doc_26970 | padded boundary mode pad -> bool If set True, then the text boundary rectangle will be inflated to match that of font.Font. Otherwise, the boundary rectangle is just large enough for the text. | |
doc_26971 |
Bases: matplotlib.projections.geo.GeoAxes Build an Axes in a figure. Parameters
figFigure
The Axes is built in the Figure fig.
rect[left, bottom, width, height]
The Axes is built in the rectangle rect. rect is in Figure coordinates.
sharex, shareyAxes, optional
The x or y axis is shared with the x or y axis in the input Axes.
frameonbool, default: True
Whether the Axes frame is visible.
box_aspectfloat, optional
Set a fixed aspect for the Axes box, i.e. the ratio of height to width. See set_box_aspect for details. **kwargs
Other optional keyword arguments:
Property Description
adjustable {'box', 'datalim'}
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
anchor (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', ...}
animated bool
aspect {'auto', 'equal'} or float
autoscale_on bool
autoscalex_on bool
autoscaley_on bool
axes_locator Callable[[Axes, Renderer], Bbox]
axisbelow bool or 'line'
box_aspect float or None
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
facecolor or fc color
figure Figure
frame_on bool
gid str
in_layout bool
label object
navigate bool
navigate_mode unknown
path_effects AbstractPathEffect
picker None or bool or float or callable
position [left, bottom, width, height] or Bbox
prop_cycle unknown
rasterization_zorder float or None
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
title str
transform Transform
url str
visible bool
xbound unknown
xlabel str
xlim (bottom: float, top: float)
xmargin float greater than -0.5
xscale {"linear", "log", "symlog", "logit", ...} or ScaleBase
xticklabels unknown
xticks unknown
ybound unknown
ylabel str
ylim (bottom: float, top: float)
ymargin float greater than -0.5
yscale {"linear", "log", "symlog", "logit", ...} or ScaleBase
yticklabels unknown
yticks unknown
zorder float Returns
Axes
The new Axes object. classInvertedLambertTransform(center_longitude, center_latitude, resolution)[source]
Bases: matplotlib.projections.geo._GeoTransform Create a new geographical transform. Resolution is the number of steps to interpolate between each input line segment to approximate its path in curved space. has_inverse=True
True if this transform has a corresponding inverse transform.
inverted()[source]
Return the corresponding inverse transformation. It holds x == self.inverted().transform(self.transform(x)). The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy.
transform_non_affine(xy)[source]
Apply only the non-affine part of this transformation. transform(values) is always equivalent to transform_affine(transform_non_affine(values)). In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op. Parameters
valuesarray
The input values as NumPy array of length input_dims or shape (N x input_dims). Returns
array
The output values as NumPy array of length input_dims or shape (N x output_dims), depending on the input.
classLambertTransform(center_longitude, center_latitude, resolution)[source]
Bases: matplotlib.projections.geo._GeoTransform The base Lambert transform. Create a new Lambert transform. Resolution is the number of steps to interpolate between each input line segment to approximate its path in curved Lambert space. has_inverse=True
True if this transform has a corresponding inverse transform.
inverted()[source]
Return the corresponding inverse transformation. It holds x == self.inverted().transform(self.transform(x)). The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy.
transform_non_affine(ll)[source]
Apply only the non-affine part of this transformation. transform(values) is always equivalent to transform_affine(transform_non_affine(values)). In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op. Parameters
valuesarray
The input values as NumPy array of length input_dims or shape (N x input_dims). Returns
array
The output values as NumPy array of length input_dims or shape (N x output_dims), depending on the input.
cla()[source]
Clear the Axes.
name='lambert'
set(*, adjustable=<UNSET>, agg_filter=<UNSET>, alpha=<UNSET>, anchor=<UNSET>, animated=<UNSET>, aspect=<UNSET>, autoscale_on=<UNSET>, autoscalex_on=<UNSET>, autoscaley_on=<UNSET>, axes_locator=<UNSET>, axisbelow=<UNSET>, box_aspect=<UNSET>, clip_box=<UNSET>, clip_on=<UNSET>, clip_path=<UNSET>, facecolor=<UNSET>, frame_on=<UNSET>, gid=<UNSET>, in_layout=<UNSET>, label=<UNSET>, latitude_grid=<UNSET>, longitude_grid=<UNSET>, longitude_grid_ends=<UNSET>, navigate=<UNSET>, path_effects=<UNSET>, picker=<UNSET>, position=<UNSET>, prop_cycle=<UNSET>, rasterization_zorder=<UNSET>, rasterized=<UNSET>, sketch_params=<UNSET>, snap=<UNSET>, title=<UNSET>, transform=<UNSET>, url=<UNSET>, visible=<UNSET>, xbound=<UNSET>, xlabel=<UNSET>, xlim=<UNSET>, xmargin=<UNSET>, xscale=<UNSET>, xticklabels=<UNSET>, xticks=<UNSET>, ybound=<UNSET>, ylabel=<UNSET>, ylim=<UNSET>, ymargin=<UNSET>, yscale=<UNSET>, yticklabels=<UNSET>, yticks=<UNSET>, zorder=<UNSET>)[source]
Set multiple properties at once. Supported properties are
Property Description
adjustable {'box', 'datalim'}
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
anchor (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', ...}
animated bool
aspect {'auto', 'equal'} or float
autoscale_on bool
autoscalex_on bool
autoscaley_on bool
axes_locator Callable[[Axes, Renderer], Bbox]
axisbelow bool or 'line'
box_aspect float or None
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
facecolor or fc color
figure Figure
frame_on bool
gid str
in_layout bool
label object
latitude_grid unknown
longitude_grid unknown
longitude_grid_ends unknown
navigate bool
navigate_mode unknown
path_effects AbstractPathEffect
picker None or bool or float or callable
position [left, bottom, width, height] or Bbox
prop_cycle unknown
rasterization_zorder float or None
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
title str
transform Transform
url str
visible bool
xbound unknown
xlabel str
xlim unknown
xmargin float greater than -0.5
xscale unknown
xticklabels unknown
xticks unknown
ybound unknown
ylabel str
ylim unknown
ymargin float greater than -0.5
yscale unknown
yticklabels unknown
yticks unknown
zorder float | |
doc_26972 |
Return whether face is colored. | |
doc_26973 |
Bases: matplotlib.tri.triinterpolate.TriInterpolator Cubic interpolator on a triangular grid. In one-dimension - on a segment - a cubic interpolating function is defined by the values of the function and its derivative at both ends. This is almost the same in 2D inside a triangle, except that the values of the function and its 2 derivatives have to be defined at each triangle node. The CubicTriInterpolator takes the value of the function at each node - provided by the user - and internally computes the value of the derivatives, resulting in a smooth interpolation. (As a special feature, the user can also impose the value of the derivatives at each node, but this is not supposed to be the common usage.) Parameters
triangulationTriangulation
The triangulation to interpolate over.
z(npoints,) array-like
Array of values, defined at grid points, to interpolate between.
kind{'min_E', 'geom', 'user'}, optional
Choice of the smoothing algorithm, in order to compute the interpolant derivatives (defaults to 'min_E'): if 'min_E': (default) The derivatives at each node is computed to minimize a bending energy. if 'geom': The derivatives at each node is computed as a weighted average of relevant triangle normals. To be used for speed optimization (large grids). if 'user': The user provides the argument dz, no computation is hence needed.
trifinderTriFinder, optional
If not specified, the Triangulation's default TriFinder will be used by calling Triangulation.get_trifinder.
dztuple of array-likes (dzdx, dzdy), optional
Used only if kind ='user'. In this case dz must be provided as (dzdx, dzdy) where dzdx, dzdy are arrays of the same shape as z and are the interpolant first derivatives at the triangulation points. Notes This note is a bit technical and details how the cubic interpolation is computed. The interpolation is based on a Clough-Tocher subdivision scheme of the triangulation mesh (to make it clearer, each triangle of the grid will be divided in 3 child-triangles, and on each child triangle the interpolated function is a cubic polynomial of the 2 coordinates). This technique originates from FEM (Finite Element Method) analysis; the element used is a reduced Hsieh-Clough-Tocher (HCT) element. Its shape functions are described in [1]. The assembled function is guaranteed to be C1-smooth, i.e. it is continuous and its first derivatives are also continuous (this is easy to show inside the triangles but is also true when crossing the edges). In the default case (kind ='min_E'), the interpolant minimizes a curvature energy on the functional space generated by the HCT element shape functions - with imposed values but arbitrary derivatives at each node. The minimized functional is the integral of the so-called total curvature (implementation based on an algorithm from [2] - PCG sparse solver): \[E(z) = \frac{1}{2} \int_{\Omega} \left( \left( \frac{\partial^2{z}}{\partial{x}^2} \right)^2 + \left( \frac{\partial^2{z}}{\partial{y}^2} \right)^2 + 2\left( \frac{\partial^2{z}}{\partial{y}\partial{x}} \right)^2 \right) dx\,dy\] If the case kind ='geom' is chosen by the user, a simple geometric approximation is used (weighted average of the triangle normal vectors), which could improve speed on very large grids. References 1
Michel Bernadou, Kamal Hassan, "Basis functions for general Hsieh-Clough-Tocher triangles, complete or reduced.", International Journal for Numerical Methods in Engineering, 17(5):784 - 789. 2.01. 2
C.T. Kelley, "Iterative Methods for Optimization". Methods
`__call__` (x, y) (Returns interpolated values at (x, y) points.)
`gradient` (x, y) (Returns interpolated derivatives at (x, y) points.) gradient(x, y)[source]
Returns a list of 2 masked arrays containing interpolated derivatives at the specified (x, y) points. Parameters
x, yarray-like
x and y coordinates of the same shape and any number of dimensions. Returns
dzdx, dzdynp.ma.array
2 masked arrays of the same shape as x and y; values corresponding to (x, y) points outside of the triangulation are masked out. The first returned array contains the values of \(\frac{\partial z}{\partial x}\) and the second those of \(\frac{\partial z}{\partial y}\). | |
doc_26974 |
The number of outputs. Data attribute containing the number of arguments the ufunc treats as output. Notes Since all ufuncs can take output arguments, this will always be (at least) 1. Examples >>> np.add.nout
1
>>> np.multiply.nout
1
>>> np.power.nout
1
>>> np.exp.nout
1 | |
doc_26975 |
Alias for set_facecolor. | |
doc_26976 | Implementation of Loader.load_module(). Deprecated since version 3.4: use exec_module() instead. | |
doc_26977 |
Whether the intervals are closed on the left-side, right-side, both or neither. | |
doc_26978 | symbol.sym_name
Dictionary mapping the numeric values of the constants defined in this module back to name strings, allowing more human-readable representation of parse trees to be generated. | |
doc_26979 |
Create a qat module from a float module or qparams_dict Args: mod a float module, either produced by torch.quantization utilities or directly from user | |
doc_26980 |
Return cross-section from the Series/DataFrame. This method takes a key argument to select data at a particular level of a MultiIndex. Parameters
key:label or tuple of label
Label contained in the index, or partially in a MultiIndex.
axis:{0 or ‘index’, 1 or ‘columns’}, default 0
Axis to retrieve cross-section on.
level:object, defaults to first n levels (n=1 or len(key))
In case of a key partially contained in a MultiIndex, indicate which levels are used. Levels can be referred by label or position.
drop_level:bool, default True
If False, returns object with same levels as self. Returns
Series or DataFrame
Cross-section from the original Series or DataFrame corresponding to the selected index levels. See also DataFrame.loc
Access a group of rows and columns by label(s) or a boolean array. DataFrame.iloc
Purely integer-location based indexing for selection by position. Notes xs can not be used to set values. MultiIndex Slicers is a generic way to get/set values on any level or levels. It is a superset of xs functionality, see MultiIndex Slicers. Examples
>>> d = {'num_legs': [4, 4, 2, 2],
... 'num_wings': [0, 0, 2, 2],
... 'class': ['mammal', 'mammal', 'mammal', 'bird'],
... 'animal': ['cat', 'dog', 'bat', 'penguin'],
... 'locomotion': ['walks', 'walks', 'flies', 'walks']}
>>> df = pd.DataFrame(data=d)
>>> df = df.set_index(['class', 'animal', 'locomotion'])
>>> df
num_legs num_wings
class animal locomotion
mammal cat walks 4 0
dog walks 4 0
bat flies 2 2
bird penguin walks 2 2
Get values at specified index
>>> df.xs('mammal')
num_legs num_wings
animal locomotion
cat walks 4 0
dog walks 4 0
bat flies 2 2
Get values at several indexes
>>> df.xs(('mammal', 'dog'))
num_legs num_wings
locomotion
walks 4 0
Get values at specified index and level
>>> df.xs('cat', level=1)
num_legs num_wings
class locomotion
mammal walks 4 0
Get values at several indexes and levels
>>> df.xs(('bird', 'walks'),
... level=[0, 'locomotion'])
num_legs num_wings
animal
penguin 2 2
Get values at specified column and axis
>>> df.xs('num_wings', axis=1)
class animal locomotion
mammal cat walks 0
dog walks 0
bat flies 2
bird penguin walks 2
Name: num_wings, dtype: int64 | |
doc_26981 | set the mouse cursor position set_pos([x, y]) -> None Set the current mouse position to arguments given. If the mouse cursor is visible it will jump to the new coordinates. Moving the mouse will generate a new pygame.MOUSEMOTION event. | |
doc_26982 | Name Meaning
NUL
SOH Start of heading, console interrupt
STX Start of text
ETX End of text
EOT End of transmission
ENQ Enquiry, goes with ACK flow control
ACK Acknowledgement
BEL Bell
BS Backspace
TAB Tab
HT Alias for TAB: “Horizontal tab”
LF Line feed
NL Alias for LF: “New line”
VT Vertical tab
FF Form feed
CR Carriage return
SO Shift-out, begin alternate character set
SI Shift-in, resume default character set
DLE Data-link escape
DC1 XON, for flow control
DC2 Device control 2, block-mode flow control
DC3 XOFF, for flow control
DC4 Device control 4
NAK Negative acknowledgement
SYN Synchronous idle
ETB End transmission block
CAN Cancel
EM End of medium
SUB Substitute
ESC Escape
FS File separator
GS Group separator
RS Record separator, block-mode terminator
US Unit separator
SP Space
DEL Delete Note that many of these have little practical significance in modern usage. The mnemonics derive from teleprinter conventions that predate digital computers. The module supplies the following functions, patterned on those in the standard C library:
curses.ascii.isalnum(c)
Checks for an ASCII alphanumeric character; it is equivalent to isalpha(c) or
isdigit(c).
curses.ascii.isalpha(c)
Checks for an ASCII alphabetic character; it is equivalent to isupper(c) or
islower(c).
curses.ascii.isascii(c)
Checks for a character value that fits in the 7-bit ASCII set.
curses.ascii.isblank(c)
Checks for an ASCII whitespace character; space or horizontal tab.
curses.ascii.iscntrl(c)
Checks for an ASCII control character (in the range 0x00 to 0x1f or 0x7f).
curses.ascii.isdigit(c)
Checks for an ASCII decimal digit, '0' through '9'. This is equivalent to c in string.digits.
curses.ascii.isgraph(c)
Checks for ASCII any printable character except space.
curses.ascii.islower(c)
Checks for an ASCII lower-case character.
curses.ascii.isprint(c)
Checks for any ASCII printable character including space.
curses.ascii.ispunct(c)
Checks for any printable ASCII character which is not a space or an alphanumeric character.
curses.ascii.isspace(c)
Checks for ASCII white-space characters; space, line feed, carriage return, form feed, horizontal tab, vertical tab.
curses.ascii.isupper(c)
Checks for an ASCII uppercase letter.
curses.ascii.isxdigit(c)
Checks for an ASCII hexadecimal digit. This is equivalent to c in
string.hexdigits.
curses.ascii.isctrl(c)
Checks for an ASCII control character (ordinal values 0 to 31).
curses.ascii.ismeta(c)
Checks for a non-ASCII character (ordinal values 0x80 and above).
These functions accept either integers or single-character strings; when the argument is a string, it is first converted using the built-in function ord(). Note that all these functions check ordinal bit values derived from the character of the string you pass in; they do not actually know anything about the host machine’s character encoding. The following two functions take either a single-character string or integer byte value; they return a value of the same type.
curses.ascii.ascii(c)
Return the ASCII value corresponding to the low 7 bits of c.
curses.ascii.ctrl(c)
Return the control character corresponding to the given character (the character bit value is bitwise-anded with 0x1f).
curses.ascii.alt(c)
Return the 8-bit character corresponding to the given ASCII character (the character bit value is bitwise-ored with 0x80).
The following function takes either a single-character string or integer value; it returns a string.
curses.ascii.unctrl(c)
Return a string representation of the ASCII character c. If c is printable, this string is the character itself. If the character is a control character (0x00–0x1f) the string consists of a caret ('^') followed by the corresponding uppercase letter. If the character is an ASCII delete (0x7f) the string is '^?'. If the character has its meta bit (0x80) set, the meta bit is stripped, the preceding rules applied, and '!' prepended to the result.
curses.ascii.controlnames
A 33-element string array that contains the ASCII mnemonics for the thirty-two ASCII control characters from 0 (NUL) to 0x1f (US), in order, plus the mnemonic SP for the space character. | |
doc_26983 |
Computes the cumulative distribution function by inverting the transform(s) and computing the score of the base distribution. | |
doc_26984 | Return the value returned by the call. If the call hasn’t yet completed then this method will wait up to timeout seconds. If the call hasn’t completed in timeout seconds, then a concurrent.futures.TimeoutError will be raised. timeout can be an int or float. If timeout is not specified or None, there is no limit to the wait time. If the future is cancelled before completing then CancelledError will be raised. If the call raised, this method will raise the same exception. | |
doc_26985 |
Tries to return a string representing a frequency guess, generated by infer_freq. Returns None if it can’t autodetect the frequency. | |
doc_26986 | Returns a datetime of the creation time of the file. For storage systems unable to return the creation time this will raise NotImplementedError. If USE_TZ is True, returns an aware datetime, otherwise returns a naive datetime in the local timezone. | |
doc_26987 | Get current state of a generator-iterator. Possible states are:
GEN_CREATED: Waiting to start execution. GEN_RUNNING: Currently being executed by the interpreter. GEN_SUSPENDED: Currently suspended at a yield expression. GEN_CLOSED: Execution has completed. New in version 3.2. | |
doc_26988 |
Deprecated since version 1.21: This decorator is retained for compatibility with the nose testing framework, which is being phased out. Please use the nose2 or pytest frameworks instead. Make function raise KnownFailureException exception if given condition is true. If the condition is a callable, it is used at runtime to dynamically make the decision. This is useful for tests that may require costly imports, to delay the cost until the test suite is actually executed. Parameters
fail_conditionbool or callable
Flag to determine whether to mark the decorated test as a known failure (if True) or not (if False).
msgstr, optional
Message to give on raising a KnownFailureException exception. Default is None. Returns
decoratorfunction
Decorator, which, when applied to a function, causes KnownFailureException to be raised when fail_condition is True, and the function to be called normally otherwise. Notes The decorator itself is decorated with the nose.tools.make_decorator function in order to transmit function name, and various other metadata. | |
doc_26989 | See Migration guide for more details. tf.compat.v1.raw_ops.Atan
tf.raw_ops.Atan(
x, name=None
)
The tf.math.atan operation returns the inverse of tf.math.tan, such that if y = tf.math.tan(x) then, x = tf.math.atan(y).
Note: The output of tf.math.atan will lie within the invertible range of tan, i.e (-pi/2, pi/2).
For example: # Note: [1.047, 0.785] ~= [(pi/3), (pi/4)]
x = tf.constant([1.047, 0.785])
y = tf.math.tan(x) # [1.731261, 0.99920404]
tf.math.atan(y) # [1.047, 0.785] = x
Args
x A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, int16, int32, int64, complex64, complex128.
name A name for the operation (optional).
Returns A Tensor. Has the same type as x. | |
doc_26990 |
Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees deg and sample points (x, y). The pseudo-Vandermonde matrix is defined by \[V[..., (deg[1] + 1)*i + j] = He_i(x) * He_j(y),\] where 0 <= i <= deg[0] and 0 <= j <= deg[1]. The leading indices of V index the points (x, y) and the last index encodes the degrees of the HermiteE polynomials. If V = hermevander2d(x, y, [xdeg, ydeg]), then the columns of V correspond to the elements of a 2-D coefficient array c of shape (xdeg + 1, ydeg + 1) in the order \[c_{00}, c_{01}, c_{02} ... , c_{10}, c_{11}, c_{12} ...\] and np.dot(V, c.flat) and hermeval2d(x, y, c) will be the same up to roundoff. This equivalence is useful both for least squares fitting and for the evaluation of a large number of 2-D HermiteE series of the same degrees and sample points. Parameters
x, yarray_like
Arrays of point coordinates, all of the same shape. The dtypes will be converted to either float64 or complex128 depending on whether any of the elements are complex. Scalars are converted to 1-D arrays.
deglist of ints
List of maximum degrees of the form [x_deg, y_deg]. Returns
vander2dndarray
The shape of the returned matrix is x.shape + (order,), where \(order = (deg[0]+1)*(deg[1]+1)\). The dtype will be the same as the converted x and y. See also
hermevander, hermevander3d, hermeval2d, hermeval3d
Notes New in version 1.7.0. | |
doc_26991 | tf.experimental.numpy.amin(
a, axis=None, keepdims=None
)
Unsupported arguments: out, initial, where. See the NumPy documentation for numpy.amin. | |
doc_26992 | tf.math.softmax
tf.nn.softmax(
logits, axis=None, name=None
)
This function performs the equivalent of softmax = tf.exp(logits) / tf.reduce_sum(tf.exp(logits), axis)
Args
logits A non-empty Tensor. Must be one of the following types: half, float32, float64.
axis The dimension softmax would be performed on. The default is -1 which indicates the last dimension.
name A name for the operation (optional).
Returns A Tensor. Has the same type and shape as logits.
Raises
InvalidArgumentError if logits is empty or axis is beyond the last dimension of logits. | |
doc_26993 |
Hide the entire index, or specific keys in the index from rendering. This method has dual functionality:
if subset is None then the entire index, or specified levels, will be hidden whilst displaying all data-rows. if a subset is given then those specific rows will be hidden whilst the index itself remains visible.
Changed in version 1.3.0. Deprecated since version 1.4.0: This method should be replaced by hide(axis="index", **kwargs) Parameters
subset:label, array-like, IndexSlice, optional
A valid 1d input or single key along the index axis within DataFrame.loc[<subset>, :], to limit data to before applying the function.
level:int, str, list
The level(s) to hide in a MultiIndex if hiding the entire index. Cannot be used simultaneously with subset. New in version 1.4.0.
names:bool
Whether to hide the index name(s), in the case the index or part of it remains visible. New in version 1.4.0. Returns
self:Styler
See also Styler.hide
Hide the entire index / columns, or specific rows / columns. | |
doc_26994 | Return a casefolded copy of the string. Casefolded strings may be used for caseless matching. Casefolding is similar to lowercasing but more aggressive because it is intended to remove all case distinctions in a string. For example, the German lowercase letter 'ß' is equivalent to "ss". Since it is already lowercase, lower() would do nothing to 'ß'; casefold() converts it to "ss". The casefolding algorithm is described in section 3.13 of the Unicode Standard. New in version 3.3. | |
doc_26995 |
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | |
doc_26996 | sys.stdout
sys.stderr
File objects used by the interpreter for standard input, output and errors:
stdin is used for all interactive input (including calls to input());
stdout is used for the output of print() and expression statements and for the prompts of input(); The interpreter’s own prompts and its error messages go to stderr. These streams are regular text files like those returned by the open() function. Their parameters are chosen as follows:
The character encoding is platform-dependent. Non-Windows platforms use the locale encoding (see locale.getpreferredencoding()). On Windows, UTF-8 is used for the console device. Non-character devices such as disk files and pipes use the system locale encoding (i.e. the ANSI codepage). Non-console character devices such as NUL (i.e. where isatty() returns True) use the value of the console input and output codepages at startup, respectively for stdin and stdout/stderr. This defaults to the system locale encoding if the process is not initially attached to a console. The special behaviour of the console can be overridden by setting the environment variable PYTHONLEGACYWINDOWSSTDIO before starting Python. In that case, the console codepages are used as for any other character device. Under all platforms, you can override the character encoding by setting the PYTHONIOENCODING environment variable before starting Python or by using the new -X utf8 command line option and PYTHONUTF8 environment variable. However, for the Windows console, this only applies when PYTHONLEGACYWINDOWSSTDIO is also set. When interactive, the stdout stream is line-buffered. Otherwise, it is block-buffered like regular text files. The stderr stream is line-buffered in both cases. You can make both streams unbuffered by passing the -u command-line option or setting the PYTHONUNBUFFERED environment variable. Changed in version 3.9: Non-interactive stderr is now line-buffered instead of fully buffered. Note To write or read binary data from/to the standard streams, use the underlying binary buffer object. For example, to write bytes to stdout, use sys.stdout.buffer.write(b'abc'). However, if you are writing a library (and do not control in which context its code will be executed), be aware that the standard streams may be replaced with file-like objects like io.StringIO which do not support the buffer attribute. | |
doc_26997 |
Call self as a function. | |
doc_26998 |
Alias for get_linestyle. | |
doc_26999 |
Tuple of bytes to step in each dimension when traversing an array. The byte offset of element (i[0], i[1], ..., i[n]) in an array a is: offset = sum(np.array(i) * a.strides)
A more detailed explanation of strides can be found in the “ndarray.rst” file in the NumPy reference guide. See also numpy.lib.stride_tricks.as_strided
Notes Imagine an array of 32-bit integers (each 4 bytes): x = np.array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]], dtype=np.int32)
This array is stored in memory as 40 bytes, one after the other (known as a contiguous block of memory). The strides of an array tell us how many bytes we have to skip in memory to move to the next position along a certain axis. For example, we have to skip 4 bytes (1 value) to move to the next column, but 20 bytes (5 values) to get to the same position in the next row. As such, the strides for the array x will be (20, 4). Examples >>> y = np.reshape(np.arange(2*3*4), (2,3,4))
>>> y
array([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]])
>>> y.strides
(48, 16, 4)
>>> y[1,1,1]
17
>>> offset=sum(y.strides * np.array((1,1,1)))
>>> offset/y.itemsize
17
>>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)
>>> x.strides
(32, 4, 224, 1344)
>>> i = np.array([3,5,2,2])
>>> offset = sum(i * x.strides)
>>> x[3,5,2,2]
813
>>> offset / x.itemsize
813 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.