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Basic statistics module.
This module provides functions for calculating statistics of data, including
averages, variance, and standard deviation.
Calculating averages
--------------------
================== ==================================================
Function Description
================== ==================================================
mean Arithmetic mean (average) of data.
fmean Fast, floating point arithmetic mean.
geometric_mean Geometric mean of data.
harmonic_mean Harmonic mean of data.
median Median (middle value) of data.
median_low Low median of data.
median_high High median of data.
median_grouped Median, or 50th percentile, of grouped data.
mode Mode (most common value) of data.
multimode List of modes (most common values of data).
quantiles Divide data into intervals with equal probability.
================== ==================================================
Calculate the arithmetic mean ("the average") of data:
>>> mean([-1.0, 2.5, 3.25, 5.75])
2.625
Calculate the standard median of discrete data:
>>> median([2, 3, 4, 5])
3.5
Calculate the median, or 50th percentile, of data grouped into class intervals
centred on the data values provided. E.g. if your data points are rounded to
the nearest whole number:
>>> median_grouped([2, 2, 3, 3, 3, 4]) #doctest: +ELLIPSIS
2.8333333333...
This should be interpreted in this way: you have two data points in the class
interval 1.5-2.5, three data points in the class interval 2.5-3.5, and one in
the class interval 3.5-4.5. The median of these data points is 2.8333...
Calculating variability or spread
---------------------------------
================== =============================================
Function Description
================== =============================================
pvariance Population variance of data.
variance Sample variance of data.
pstdev Population standard deviation of data.
stdev Sample standard deviation of data.
================== =============================================
Calculate the standard deviation of sample data:
>>> stdev([2.5, 3.25, 5.5, 11.25, 11.75]) #doctest: +ELLIPSIS
4.38961843444...
If you have previously calculated the mean, you can pass it as the optional
second argument to the four "spread" functions to avoid recalculating it:
>>> data = [1, 2, 2, 4, 4, 4, 5, 6]
>>> mu = mean(data)
>>> pvariance(data, mu)
2.5
Exceptions
----------
A single exception is defined: StatisticsError is a subclass of ValueError.
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|fS )aC _sum(data [, start]) -> (type, sum, count)
Return a high-precision sum of the given numeric data as a fraction,
together with the type to be converted to and the count of items.
If optional argument ``start`` is given, it is added to the total.
If ``data`` is empty, ``start`` (defaulting to 0) is returned.
Examples
--------
>>> _sum([3, 2.25, 4.5, -0.5, 1.0], 0.75)
(<class 'float'>, Fraction(11, 1), 5)
Some sources of round-off error will be avoided:
# Built-in sum returns zero.
>>> _sum([1e50, 1, -1e50] * 1000)
(<class 'float'>, Fraction(1000, 1), 3000)
Fractions and Decimals are also supported:
>>> from fractions import Fraction as F
>>> _sum([F(2, 3), F(7, 5), F(1, 4), F(5, 6)])
(<class 'fractions.Fraction'>, Fraction(63, 20), 4)
>>> from decimal import Decimal as D
>>> data = [D("0.1375"), D("0.2108"), D("0.3061"), D("0.0419")]
>>> _sum(data)
(<class 'decimal.Decimal'>, Fraction(6963, 10000), 4)
Mixed types are currently treated as an error, except that int is
allowed.
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rB c C s� | |kr | S |t ks |tkr | S | t kr,|S t|| �r:|S t| |�rH| S t| t �rV|S t|t �rd| S t| t�r|t|t�r||S t| t�r�t|t�r�| S d}t|| j|jf ��dS )z�Coerce types T and S to a common type, or raise TypeError.
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_find_rteq s rW �negative valuec c s$ | D ]}|dk rt |��|V qdS )z7Iterate over values, failing if any are less than zero.r N)r )r< �errmsgrA r& r& r' � _fail_neg s rZ c C sH t | �| krt| �} t| �}|dk r,td��t| �\}}}t|| |�S )a� Return the sample arithmetic mean of data.
>>> mean([1, 2, 3, 4, 4])
2.8
>>> from fractions import Fraction as F
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Fraction(13, 21)
>>> from decimal import Decimal as D
>>> mean([D("0.5"), D("0.75"), D("0.625"), D("0.375")])
Decimal('0.5625')
If ``data`` is empty, StatisticsError will be raised.
r( z%mean requires at least one data point)�iter�listrQ r r> rP )r7 r, r: r= r9 r& r& r' r ' s c st z t | �� W n0 tk
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rn td�d�Y nX dS )z�Convert data to floats and compute the arithmetic mean.
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>>> fmean([3.5, 4.0, 5.25])
4.25
r c 3 s t | dd�D ]\� }|V q d S )Nr( )r8 )� enumerate)�iterablerA �r, r& r' r9 O s zfmean.<locals>.countz&fmean requires at least one data pointN)rQ rF r �ZeroDivisionErrorr )r7 r9 r= r& r_ r' r A s
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r2 td�d�Y nX dS )aY Convert data to floats and compute the geometric mean.
Raises a StatisticsError if the input dataset is empty,
if it contains a zero, or if it contains a negative value.
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>>> round(geometric_mean([54, 24, 36]), 9)
36.0
zHgeometric mean requires a non-empty dataset containing positive numbersN)r r r3 r rM r )r7 r& r& r' r \ s �c C s� t | �| krt| �} d}t| �}|dk r2td��n<|dkrn| d }t|tjtf�rf|dk rbt|��|S td��z"t dd� t
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r� Y dS X t || |�S )a Return the harmonic mean of data.
The harmonic mean, sometimes called the subcontrary mean, is the
reciprocal of the arithmetic mean of the reciprocals of the data,
and is often appropriate when averaging quantities which are rates
or ratios, for example speeds. Example:
Suppose an investor purchases an equal value of shares in each of
three companies, with P/E (price/earning) ratios of 2.5, 3 and 10.
What is the average P/E ratio for the investor's portfolio?
>>> harmonic_mean([2.5, 3, 10]) # For an equal investment portfolio.
3.6
Using the arithmetic mean would give an average of about 5.167, which
is too high.
If ``data`` is empty, or any element is less than zero,
``harmonic_mean`` will raise ``StatisticsError``.
z.harmonic mean does not support negative valuesr( z.harmonic_mean requires at least one data pointr zunsupported typec s s | ]}d | V qdS )r( Nr& �r* rA r& r& r' r- � s z harmonic_mean.<locals>.<genexpr>)
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"c C s\ t | �} t| �}|dkr td��|d dkr8| |d S |d }| |d | | d S dS )aB Return the median (middle value) of numeric data.
When the number of data points is odd, return the middle data point.
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>>> median([1, 3, 5])
3
>>> median([1, 3, 5, 7])
4.0
r �no median for empty data� r( N�r5 rQ r )r7 r, rS r& r& r' r � s
c C sL t | �} t| �}|dkr td��|d dkr8| |d S | |d d S dS )a Return the low median of numeric data.
When the number of data points is odd, the middle value is returned.
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>>> median_low([1, 3, 5])
3
>>> median_low([1, 3, 5, 7])
3
r rd re r( Nrf �r7 r, r& r& r' r
� s c C s, t | �} t| �}|dkr td��| |d S )a Return the high median of data.
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>>> median_high([1, 3, 5])
3
>>> median_high([1, 3, 5, 7])
5
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r� t|�t|�d }Y nX t| |�}t | ||�}|}|| d } |||d | | S )a� Return the 50th percentile (median) of grouped continuous data.
>>> median_grouped([1, 2, 2, 3, 4, 4, 4, 4, 4, 5])
3.7
>>> median_grouped([52, 52, 53, 54])
52.5
This calculates the median as the 50th percentile, and should be
used when your data is continuous and grouped. In the above example,
the values 1, 2, 3, etc. actually represent the midpoint of classes
0.5-1.5, 1.5-2.5, 2.5-3.5, etc. The middle value falls somewhere in
class 3.5-4.5, and interpolation is used to estimate it.
Optional argument ``interval`` represents the class interval, and
defaults to 1. Changing the class interval naturally will change the
interpolated 50th percentile value:
>>> median_grouped([1, 3, 3, 5, 7], interval=1)
3.25
>>> median_grouped([1, 3, 3, 5, 7], interval=2)
3.5
This function does not check whether the data points are at least
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r rd r( re zexpected number but got %r)
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rB td�d�Y nX dS )ax Return the most common data point from discrete or nominal data.
``mode`` assumes discrete data, and returns a single value. This is the
standard treatment of the mode as commonly taught in schools:
>>> mode([1, 1, 2, 3, 3, 3, 3, 4])
3
This also works with nominal (non-numeric) data:
>>> mode(["red", "blue", "blue", "red", "green", "red", "red"])
'red'
If there are multiple modes with same frequency, return the first one
encountered:
>>> mode(['red', 'red', 'green', 'blue', 'blue'])
'red'
If *data* is empty, ``mode``, raises StatisticsError.
r( r zno mode for empty dataN)r[ r" � most_common�
IndexErrorr )r7 Zpairsr& r& r' r s c C s@ t t| ���� }tt|td�d�dg f�\}}tttd�|��S )a. Return a list of the most frequently occurring values.
Will return more than one result if there are multiple modes
or an empty list if *data* is empty.
>>> multimode('aabbbbbbbbcc')
['b']
>>> multimode('aabbbbccddddeeffffgg')
['b', 'd', 'f']
>>> multimode('')
[]
r( )�keyr )r" r[ ro �nextr r! r\ r3 )r7 ZcountsZmaxcountZ
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C sL |dk rt d��t| �} t| �}|dk r0t d��|dkr�|d }g }td|�D ]N}|| | }|| || }| | || | |d | | } |�| � qN|S |dk�r:|d }g }td|�D ]r}|| | }|dk r�dn||d kr�|d n|}|| || }| |d || | | | | } |�| � q�|S td|����dS ) a� Divide *data* into *n* continuous intervals with equal probability.
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Set *n* to 100 for percentiles which gives the 99 cuts points that
separate *data* in to 100 equal sized groups.
The *data* can be any iterable containing sample.
The cut points are linearly interpolated between data points.
If *method* is set to *inclusive*, *data* is treated as population
data. The minimum value is treated as the 0th percentile and the
maximum value is treated as the 100th percentile.
r( zn must be at least 1re z"must have at least two data pointsZ inclusivert zUnknown method: N)r r5 rQ �range�appendrM )
r7 r, ru Zld�m�resultrS �jZdeltaZ interpolatedr& r& r' r l s4 $
$$ c s� � dk r,t � fdd�| D ��\}}}||fS t| �� t � fdd�| D ��\}}}t � fdd�| D ��\}}}||d t| � 8 }||fS )a; Return sum of square deviations of sequence data.
If ``c`` is None, the mean is calculated in one pass, and the deviations
from the mean are calculated in a second pass. Otherwise, deviations are
calculated from ``c`` as given. Use the second case with care, as it can
lead to garbage results.
Nc 3 s | ]}|� d V qdS �re Nr& ra ��cr& r' r- � s z_ss.<locals>.<genexpr>c 3 s | ]}|� d V qdS r{ r& ra r| r& r' r- � s c 3 s | ]}|� V qd S r) r& ra r| r& r' r- � s re )r> r rQ )r7 r} r: r= r9 �UZtotal2Zcount2r& r| r' �_ss� s r c C sL t | �| krt| �} t| �}|dk r,td��t| |�\}}t||d |�S )a� Return the sample variance of data.
data should be an iterable of Real-valued numbers, with at least two
values. The optional argument xbar, if given, should be the mean of
the data. If it is missing or None, the mean is automatically calculated.
Use this function when your data is a sample from a population. To
calculate the variance from the entire population, see ``pvariance``.
Examples:
>>> data = [2.75, 1.75, 1.25, 0.25, 0.5, 1.25, 3.5]
>>> variance(data)
1.3720238095238095
If you have already calculated the mean of your data, you can pass it as
the optional second argument ``xbar`` to avoid recalculating it:
>>> m = mean(data)
>>> variance(data, m)
1.3720238095238095
This function does not check that ``xbar`` is actually the mean of
``data``. Giving arbitrary values for ``xbar`` may lead to invalid or
impossible results.
Decimals and Fractions are supported:
>>> from decimal import Decimal as D
>>> variance([D("27.5"), D("30.25"), D("30.25"), D("34.5"), D("41.75")])
Decimal('31.01875')
>>> from fractions import Fraction as F
>>> variance([F(1, 6), F(1, 2), F(5, 3)])
Fraction(67, 108)
re z*variance requires at least two data pointsr( �r[ r\ rQ r r rP )r7 �xbarr, r: �ssr& r& r' r � s & c C sH t | �| krt| �} t| �}|dk r,td��t| |�\}}t|| |�S )a, Return the population variance of ``data``.
data should be a sequence or iterable of Real-valued numbers, with at least one
value. The optional argument mu, if given, should be the mean of
the data. If it is missing or None, the mean is automatically calculated.
Use this function to calculate the variance from the entire population.
To estimate the variance from a sample, the ``variance`` function is
usually a better choice.
Examples:
>>> data = [0.0, 0.25, 0.25, 1.25, 1.5, 1.75, 2.75, 3.25]
>>> pvariance(data)
1.25
If you have already calculated the mean of the data, you can pass it as
the optional second argument to avoid recalculating it:
>>> mu = mean(data)
>>> pvariance(data, mu)
1.25
Decimals and Fractions are supported:
>>> from decimal import Decimal as D
>>> pvariance([D("27.5"), D("30.25"), D("30.25"), D("34.5"), D("41.75")])
Decimal('24.815')
>>> from fractions import Fraction as F
>>> pvariance([F(1, 4), F(5, 4), F(1, 2)])
Fraction(13, 72)
r( z*pvariance requires at least one data pointr� )r7 �mur, r: r� r& r& r' r � s # c C s8 t | |�}z
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r2 t�|� Y S X dS )z�Return the square root of the sample variance.
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>>> stdev([1.5, 2.5, 2.5, 2.75, 3.25, 4.75])
1.0810874155219827
N)r r r? r@ )r7 r� �varr& r& r' r s
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>>> pstdev([1.5, 2.5, 2.5, 2.75, 3.25, 4.75])
0.986893273527251
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zNormalDist.__init__c C s. t |ttf�st|�}t|�}| |t||��S )z5Make a normal distribution instance from sample data.)rb r\ �tupler r )�clsr7 r� r& r& r' � from_samples� s zNormalDist.from_samplesN)�seedc sB |dkrt jn
t �|�j� | j| j ��� ��fdd�t|�D �S )z=Generate *n* samples for a given mean and standard deviation.Nc s g | ]}� ����qS r& r& �r* rS ��gaussr� r� r& r' �
<listcomp>� s z&NormalDist.samples.<locals>.<listcomp>)�randomr� ZRandomr� r� rv )r� r, r� r& r� r' �samples� s zNormalDist.samplesc C s< | j d }|std��t|| j d d| �tt| � S )z4Probability density function. P(x <= X < x+dx) / dx� @z$pdf() not defined when sigma is zerog �)r� r r r� r r )r� rA r r& r& r' �pdf� s
zNormalDist.pdfc C s2 | j std��ddt|| j | j td� � S )z,Cumulative distribution function. P(X <= x)z$cdf() not defined when sigma is zeror� r� r� )r� r r r� r )r� rA r& r& r' �cdf� s zNormalDist.cdfc C s: |dks|dkrt d��| jdkr*t d��t|| j| j�S )aS Inverse cumulative distribution function. x : P(X <= x) = p
Finds the value of the random variable such that the probability of
the variable being less than or equal to that value equals the given
probability.
This function is also called the percent point function or quantile
function.
r� r� z$p must be in the range 0.0 < p < 1.0z-cdf() not defined when sigma at or below zero)r r� r� r� )r� r� r& r& r' �inv_cdf� s
zNormalDist.inv_cdfrs c s � �fdd�t d� �D �S )an Divide into *n* continuous intervals with equal probability.
Returns a list of (n - 1) cut points separating the intervals.
Set *n* to 4 for quartiles (the default). Set *n* to 10 for deciles.
Set *n* to 100 for percentiles which gives the 99 cuts points that
separate the normal distribution in to 100 equal sized groups.
c s g | ]}�� |� ��qS r& )r� r� �r, r� r& r' r� � s z(NormalDist.quantiles.<locals>.<listcomp>r( )rv )r� r, r& r� r' r � s zNormalDist.quantilesc C s t |t�std��| | }}|j|jf|j|jfk r>|| }}|j|j }}|rT|s\td��|| }t|j|j �}|s�dt|d|j t d� � S |j| |j| }|j|j t |d |t
|| � � } || | }
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� �t|� | �|� | � � S )a� Compute the overlapping coefficient (OVL) between two normal distributions.
Measures the agreement between two normal probability distributions.
Returns a value between 0.0 and 1.0 giving the overlapping area in
the two underlying probability density functions.
>>> N1 = NormalDist(2.4, 1.6)
>>> N2 = NormalDist(3.2, 2.0)
>>> N1.overlap(N2)
0.8035050657330205
z$Expected another NormalDist instancez(overlap() not defined when sigma is zeror� r� ) rb r rF r� r� r r r r r r r� ) r� �other�X�YZX_varZY_varZdvZdmrR �b�x1�x2r& r& r' �overlap� s"
( zNormalDist.overlapc C s | j S )z+Arithmetic mean of the normal distribution.�r� �r� r& r& r' r � s zNormalDist.meanc C s | j S )z,Return the median of the normal distributionr� r� r& r& r' r � s zNormalDist.medianc C s | j S )z�Return the mode of the normal distribution
The mode is the value x where which the probability density
function (pdf) takes its maximum value.
r� r� r& r& r' r � s zNormalDist.modec C s | j S )z.Standard deviation of the normal distribution.�r� r� r& r& r' r � s zNormalDist.stdevc C s
| j d S )z!Square of the standard deviation.r� r� r� r& r& r' r s zNormalDist.variancec C s8 t |t�r&t| j|j t| j|j��S t| j| | j�S )aj Add a constant or another NormalDist instance.
If *other* is a constant, translate mu by the constant,
leaving sigma unchanged.
If *other* is a NormalDist, add both the means and the variances.
Mathematically, this works only if the two distributions are
independent or if they are jointly normally distributed.
�rb r r� r r� �r� r� r& r& r' �__add__ s
zNormalDist.__add__c C s8 t |t�r&t| j|j t| j|j��S t| j| | j�S )as Subtract a constant or another NormalDist instance.
If *other* is a constant, translate by the constant mu,
leaving sigma unchanged.
If *other* is a NormalDist, subtract the means and add the variances.
Mathematically, this works only if the two distributions are
independent or if they are jointly normally distributed.
r� r� r& r& r' �__sub__ s
zNormalDist.__sub__c C s t | j| | jt|� �S )z�Multiply both mu and sigma by a constant.
Used for rescaling, perhaps to change measurement units.
Sigma is scaled with the absolute value of the constant.
�r r� r� r r� r& r& r' �__mul__% s zNormalDist.__mul__c C s t | j| | jt|� �S )z�Divide both mu and sigma by a constant.
Used for rescaling, perhaps to change measurement units.
Sigma is scaled with the absolute value of the constant.
r� r� r& r& r' � __truediv__- s zNormalDist.__truediv__c C s t | j| j�S )z Return a copy of the instance.�r r� r� �r� r& r& r' �__pos__5 s zNormalDist.__pos__c C s t | j | j�S )z(Negates mu while keeping sigma the same.r� r� r& r& r' �__neg__9 s zNormalDist.__neg__c C s
| | S )z<Subtract a NormalDist from a constant or another NormalDist.r& r� r& r& r' �__rsub__? s zNormalDist.__rsub__c C s& t |t�stS | j|jko$| j|jkS )zFTwo NormalDist objects are equal if their mu and sigma are both equal.)rb r �NotImplementedr� r� r� r& r& r' �__eq__E s
zNormalDist.__eq__c C s t | j| jf�S )zCNormalDist objects hash equal if their mu and sigma are both equal.)�hashr� r� r� r& r& r' �__hash__K s zNormalDist.__hash__c C s t | �j� d| j�d| j�d�S )Nz(mu=z, sigma=�))r2 r# r� r� r� r& r& r' �__repr__O s zNormalDist.__repr__)r� r� )rs ) r# r$ r% �__doc__� __slots__r� � classmethodr� r� r� r� r� r r� �propertyr r r r r r� r� r� r� r� r� �__radd__r� �__rmul__r� r� r� r& r& r& r' r � sF �
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