DeepDiff 5.2.2 documentation!


class deepdiff.diff.DeepDiff(t1, t2, cutoff_distance_for_pairs=0.3, cutoff_intersection_for_pairs=0.7, cache_size=0, cache_tuning_sample_size=0, cache_purge_level=1, exclude_paths=None, exclude_regex_paths=None, exclude_types=None, exclude_obj_callback=None, get_deep_distance=False, group_by=None, hasher=None, hashes=None, ignore_order=False, ignore_type_in_groups=None, ignore_string_type_changes=False, ignore_numeric_type_changes=False, ignore_type_subclasses=False, ignore_string_case=False, ignore_nan_inequality=False, ignore_private_variables=True, log_frequency_in_sec=0, math_epsilon=None, max_passes=10000000, max_diffs=None, number_format_notation='f', number_to_string_func=None, progress_logger=<bound method of <Logger deepdiff.diff (WARNING)>>, report_repetition=False, significant_digits=None, truncate_datetime=None, verbose_level=1, view='text', _original_type=None, _parameters=None, _shared_parameters=None, **kwargs)

Deep Difference of dictionaries, iterables, strings and almost any other object. It will recursively look for all the changes.


t1A dictionary, list, string or any python object that has __dict__ or __slots__

This is the first item to be compared to the second item

t2dictionary, list, string or almost any python object that has __dict__ or __slots__

The second item is to be compared to the first one

cutoff_distance_for_pairs1 >= float >= 0, default=0.3

Cutoff Distance For Pairs What is the threshold to consider 2 items as pairs. Note that it is only used when ignore_order = True.

cutoff_intersection_for_pairs1 >= float >= 0, default=0.7

Cutoff Intersection For Pairs What is the threshold to calculate pairs of items between 2 iterables. For example 2 iterables that have nothing in common, do not need their pairs to be calculated. Note that it is only used when ignore_order = True.

cache_sizeint >= 0, default=0

Cache Size Cache size to be used to improve the performance. A cache size of zero means it is disabled. Using the cache_size can dramatically improve the diff performance especially for the nested objects at the cost of more memory usage.

cache_purge_level: int, 0, 1, or 2. default=1

Cache Purge Level defines what objects in DeepDiff should be deleted to free the memory once the diff object is calculated. If this value is set to zero, most of the functionality of the diff object is removed and the most memory is released. A value of 1 preserves all the functionalities of the diff object. A value of 2 also preserves the cache and hashes that were calculated during the diff calculations. In most cases the user does not need to have those objects remained in the diff unless for investigation purposes.

cache_tuning_sample_sizeint >= 0, default = 0

Cache Tuning Sample Size This is an experimental feature. It works hands in hands with the Cache Size. When cache_tuning_sample_size is set to anything above zero, it will sample the cache usage with the passed sample size and decide whether to use the cache or not. And will turn it back on occasionally during the diffing process. This option can be useful if you are not sure if you need any cache or not. However you will gain much better performance with keeping this parameter zero and running your diff with different cache sizes and benchmarking to find the optimal cache size.

exclude_paths: list, default = None

Exclude Paths List of paths to exclude from the report. If only one item, you can path it as a string.

exclude_regex_paths: list, default = None

Exclude Regex Paths List of string regex paths or compiled regex paths objects to exclude from the report. If only one item, you can pass it as a string or regex compiled object.

exclude_types: list, default = None

Exclude Types List of object types to exclude from the report.

exclude_obj_callback: function, default = None

Exclude Obj Callback A function that takes the object and its path and returns a Boolean. If True is returned, the object is excluded from the results, otherwise it is included. This is to give the user a higher level of control than one can achieve via exclude_paths, exclude_regex_paths or other means.

get_deep_distance: Boolean, default = False

Get Deep Distance will get you the deep distance between objects. The distance is a number between 0 and 1 where zero means there is no diff between the 2 objects and 1 means they are very different. Note that this number should only be used to compare the similarity of 2 objects and nothing more. The algorithm for calculating this number may or may not change in the future releases of DeepDiff.

group_by: String, default=None

Group By can be used when dealing with list of dictionaries to convert them to group them by value defined in group_by. The common use case is when reading data from a flat CSV and primary key is one of the columns in the CSV. We want to use the primary key to group the rows instead of CSV row number.

hasher: default = DeepHash.sha256hex

Hash function to be used. If you don’t want SHA256, you can use your own hash function by passing hasher=hash. This is for advanced usage and normally you don’t need to modify it.

ignore_orderBoolean, default=False

Ignore Order ignores order of elements when comparing iterables (lists) Normally ignore_order does not report duplicates and repetition changes. In order to report repetitions, set report_repetition=True in addition to ignore_order=True

ignore_string_type_changes: Boolean, default = False

Ignore String Type Changes Whether to ignore string type changes or not. For example b”Hello” vs. “Hello” are considered the same if ignore_string_type_changes is set to True.

ignore_numeric_type_changes: Boolean, default = False

Ignore Numeric Type Changes Whether to ignore numeric type changes or not. For example 10 vs. 10.0 are considered the same if ignore_numeric_type_changes is set to True.

ignore_type_in_groups: Tuple or List of Tuples, default = None

Ignore Type In Groups ignores types when t1 and t2 are both within the same type group.

ignore_type_subclasses: Boolean, default = False

Ignore Type Subclasses ignore type (class) changes when dealing with the subclasses of classes that were marked to be ignored.

ignore_string_case: Boolean, default = False

Ignore String Case Whether to be case-sensitive or not when comparing strings. By settings ignore_string_case=False, strings will be compared case-insensitively.

ignore_nan_inequality: Boolean, default = False

Ignore Nan Inequality Whether to ignore float(‘nan’) inequality in Python.

ignore_private_variables: Boolean, default = True

Ignore Private Variables Whether to exclude the private variables in the calculations or not. It only affects variables that start with double underscores (__).

log_frequency_in_sec: Integer, default = 0

Log Frequency In Sec How often to log the progress. The default of 0 means logging progress is disabled. If you set it to 20, it will log every 20 seconds. This is useful only when running DeepDiff on massive objects that will take a while to run. If you are only dealing with small objects, keep it at 0 to disable progress logging.

max_passes: Integer, default = 10000000

Max Passes defined the maximum number of passes to run on objects to pin point what exactly is different. This is only used when ignore_order=True. A new pass is started each time 2 iterables are compared in a way that every single item that is different from the first one is compared to every single item that is different in the second iterable.

max_diffs: Integer, default = None

Max Diffs defined the maximum number of diffs to run on objects to pin point what exactly is different. This is only used when ignore_order=True

math_epsilon: Decimal, default = None

Math Epsilon uses Python’s built in Math.isclose. It defines a tolerance value which is passed to math.isclose(). Any numbers that are within the tolerance will not report as being different. Any numbers outside of that tolerance will show up as different.

number_format_notationstring, default=”f”

Number Format Notation is what defines the meaning of significant digits. The default value of “f” means the digits AFTER the decimal point. “f” stands for fixed point. The other option is “e” which stands for exponent notation or scientific notation.

number_to_string_funcfunction, default=None

Number To String Function is an advanced feature to give the user the full control into overriding how numbers are converted to strings for comparison. The default function is defined in and is called number_to_string. You can define your own function to do that.

progress_logger: log function, default =

Progress Logger defines what logging function to use specifically for progress reporting. This function is only used when progress logging is enabled which happens by setting log_frequency_in_sec to anything above zero.

report_repetitionBoolean, default=False

Reporting Repetitions reports repetitions when set True It only works when ignore_order is set to True too.

significant_digitsint >= 0, default=None

Significant Digits defines the number of digits AFTER the decimal point to be used in the comparison. However you can override that by setting the number_format_notation=”e” which will make it mean the digits in scientific notation.

truncate_datetime: string, default = None

Truncate Datetime can take value one of ‘second’, ‘minute’, ‘hour’, ‘day’ and truncate with this value datetime objects before hashing it

verbose_level: 2 >= int >= 0, default = 1

Higher verbose level shows you more details. For example verbose level 1 shows what dictionary item are added or removed. And verbose level 2 shows the value of the items that are added or removed too.

view: string, default = text

View Views are different “formats” of results. Each view comes with its own features. The choices are text (the default) and tree. The text view is the original format of the results. The tree view allows you to traverse through the tree of results. So you can traverse through the tree and see what items were compared to what.


A DeepDiff object that has already calculated the difference of the 2 items. The format of the object is chosen by the view parameter.

Supported data types

int, string, unicode, dictionary, list, tuple, set, frozenset, OrderedDict, NamedTuple, Numpy, custom objects and more!


Get some stats on internals of the DeepDiff run.

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