1.2. Statistics

  • statistics module

1.2.1. Mean

Table 78. Mean

Function

Description

statistics.mean()

Arithmetic mean ('average') of data

statistics.fmean()

faster, floating point variant of statistics.mean(), since Python 3.8

statistics.harmonic_mean()

Harmonic mean of data

statistics.geometric_mean()

Since Python 3.8

Listing 499. Arithmetic mean ('average') of data
from statistics import mean


mean([1, 2, 3, 4, 4])           # 2.8
mean([-1.0, 2.5, 3.25, 5.75])   # 2.625
Listing 500. Harmonic mean of data
from statistics import harmonic_mean


harmonic_mean([2.5, 3, 10])     # 3.6

1.2.2. Median

Table 79. Median

Function

Description

statistics.median()

Median (middle value) of data

statistics.median_low()

Low median of data

statistics.median_high()

High median of data

statistics.median_grouped()

Median, or 50th percentile, of grouped data

Listing 501. Median (middle value) of data
from statistics import median


median([1, 3, 5])               # 3
median([1, 3, 5, 7])            # 4.0
  • The low median is always a member of the data set.

  • When the number of data points is odd, the middle value is returned.

  • When it is even, the smaller of the two middle values is returned.

Listing 502. Low median of data
from statistics import median_low


median_low([1, 3, 5])           # 3
median_low([1, 3, 5, 7])        # 3
  • The high median is always a member of the data set.

  • When the number of data points is odd, the middle value is returned.

  • When it is even, the larger of the two middle values is returned.

Listing 503. High median of data
from statistics import median_high


median_high([1, 3, 5])          # 3
median_high([1, 3, 5, 7])       # 5
  • Median of grouped continuous data.

  • Calculated using interpolation as the 50th percentile.

Listing 504. Median, or 50th percentile, of grouped data
from statistics import median_grouped


median_grouped([52, 52, 53, 54])              # 52.5
median_grouped([1, 3, 3, 5, 7], interval=1)   # 3.25
median_grouped([1, 3, 3, 5, 7], interval=2)   # 3.5

1.2.3. Mode

Table 80. Mode

Function

Description

statistics.mode()

Mode (most common value) of discrete data

statistics.multimode()

returns a list of the most common values, since Python 3.8

statistics.quantiles()

divides data or a distribution in to equiprobable intervals (e.g. quartiles, deciles, or percentiles), since Python 3.8

Listing 505. Mode (most common value) of discrete data
from statistics import mode


mode([1, 1, 2, 3, 3, 3, 3, 4])                                  # 3
mode(["red", "blue", "blue", "red", "green", "red", "red"])     # 'red'

1.2.4. Distribution

Table 81. Distribution

Function

Description

statistics.NormalDist

tool for creating and manipulating normal distributions of a random variable

1.2.5. Standard Deviation

Table 82. Standard Deviation

Function

Description

statistics.pstdev()

Population standard deviation of data

statistics.stdev()

Sample standard deviation of data

Listing 506. Sample standard deviation of data
from statistics import stdev


stdev([1.5, 2.5, 2.5, 2.75, 3.25, 4.75])
# 1.0810874155219827
  • Population standard deviation

  • Is the square root of the population variance

Listing 507. Population standard deviation
from statistics import pstdev


pstdev([1.5, 2.5, 2.5, 2.75, 3.25, 4.75])
# 0.986893273527251

1.2.6. Variance

Table 83. Variance

Function

Description

statistics.pvariance()

Population variance of data

statistics.variance()

Sample variance of data

Listing 508. Sample variance of data
from statistics import variance


variance([2.75, 1.75, 1.25, 0.25, 0.5, 1.25, 3.5])
# 1.3720238095238095
Listing 509. Population variance of data
from statistics import pvariance


pvariance([0.0, 0.25, 0.25, 1.25, 1.5, 1.75, 2.75, 3.25])
# 1.25

1.2.7. Example

temperature_feb = NormalDist.from_samples([4, 12, -3, 2, 7, 14])

temperature_feb.mean    # 6.0
temperature_feb.stdev   # 6.356099432828281

# Chance of being under 3 degrees
temperature_feb.cdf(3)  # 0.3184678262814532

# Relative chance of being 7 degrees versus 10 degrees
temperature_feb.pdf(7) / temperature_feb.pdf(10)  # 1.2039930378537762


el_niño = NormalDist(4, 2.5)

# Add in a climate effect
temperature_feb += el_niño

temperature_feb                 # NormalDist(mu=10.0, sigma=6.830080526611674)

# Convert to Fahrenheit
temperature_feb * (9/5) + 32    # NormalDist(mu=50.0, sigma=12.294144947901014)

# Generate random samples
temperature_feb.samples(3)      # [7.672102882379219, 12.000027119750287, 4.647488369766392]

1.2.8. Assignments

1.2.8.1. Column Stats

English
  1. Use INPUT: List[tuple] from listing (see below)

  2. For columns:

    • Sepal length,

    • Sepal width,

    • Petal length,

    • Petal width.

  3. Print calculated values:

    • mean,

    • median,

    • standard deviation,

    • variance.

Polish
  1. Użyj INPUT: List[tuple] z listingu (patrz poniżej)

  2. Dla kolumn:

    • Sepal length,

    • Sepal width,

    • Petal length,

    • Petal width.

  3. Wypisz wyliczone wartości:

    • średnią,

    • medianę,

    • odchylenie standardowe,

    • wariancję.

Non-functional requirements
  1. Use statistics

Input
INPUT = [
    ('Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species'),
    (5.8, 2.7, 5.1, 1.9, 'virginica'),
    (5.1, 3.5, 1.4, 0.2, 'setosa'),
    (5.7, 2.8, 4.1, 1.3, 'versicolor'),
    (6.3, 2.9, 5.6, 1.8, 'virginica'),
    (6.4, 3.2, 4.5, 1.5, 'versicolor'),
    (4.7, 3.2, 1.3, 0.2, 'setosa'),
    (7.0, 3.2, 4.7, 1.4, 'versicolor'),
    (7.6, 3.0, 6.6, 2.1, 'virginica'),
    (4.9, 3.0, 1.4, 0.2, 'setosa'),
    (4.9, 2.5, 4.5, 1.7, 'virginica'),
    (7.1, 3.0, 5.9, 2.1, 'virginica'),
    (4.6, 3.4, 1.4, 0.3, 'setosa'),
    (5.4, 3.9, 1.7, 0.4, 'setosa'),
    (5.7, 2.8, 4.5, 1.3, 'versicolor'),
    (5.0, 3.6, 1.4, 0.3, 'setosa'),
    (5.5, 2.3, 4.0, 1.3, 'versicolor'),
    (6.5, 3.0, 5.8, 2.2, 'virginica'),
    (6.5, 2.8, 4.6, 1.5, 'versicolor'),
    (6.3, 3.3, 6.0, 2.5, 'virginica'),
    (6.9, 3.1, 4.9, 1.5, 'versicolor'),
    (4.6, 3.1, 1.5, 0.2, 'setosa'),
]

1.2.8.2. Iris Stats

English
  1. Create dict OUTPUT: Dict[str, dict]

  2. For each species calculate for numerical values:

    • mean,

    • median,

    • standard deviation,

    • variance.

  3. Save data to OUTPUT dict

Polish
  1. Stwórz słownik OUTPUT: Dict[str, dict]

  2. Dla każdego gatunku wylicz dla wartości numerycznych:

    • średnią,

    • medianę,

    • odchylenie standardowe,

    • wariancję.

  3. Dane zapisz w słowniku OUTPUT

Non-functional requirements
  1. Use statistics

Input
INPUT = [
    ('Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species'),
    (5.8, 2.7, 5.1, 1.9, 'virginica'),
    (5.1, 3.5, 1.4, 0.2, 'setosa'),
    (5.7, 2.8, 4.1, 1.3, 'versicolor'),
    (6.3, 2.9, 5.6, 1.8, 'virginica'),
    (6.4, 3.2, 4.5, 1.5, 'versicolor'),
    (4.7, 3.2, 1.3, 0.2, 'setosa'),
    (7.0, 3.2, 4.7, 1.4, 'versicolor'),
    (7.6, 3.0, 6.6, 2.1, 'virginica'),
    (4.9, 3.0, 1.4, 0.2, 'setosa'),
    (4.9, 2.5, 4.5, 1.7, 'virginica'),
    (7.1, 3.0, 5.9, 2.1, 'virginica'),
    (4.6, 3.4, 1.4, 0.3, 'setosa'),
    (5.4, 3.9, 1.7, 0.4, 'setosa'),
    (5.7, 2.8, 4.5, 1.3, 'versicolor'),
    (5.0, 3.6, 1.4, 0.3, 'setosa'),
    (5.5, 2.3, 4.0, 1.3, 'versicolor'),
    (6.5, 3.0, 5.8, 2.2, 'virginica'),
    (6.5, 2.8, 4.6, 1.5, 'versicolor'),
    (6.3, 3.3, 6.0, 2.5, 'virginica'),
    (6.9, 3.1, 4.9, 1.5, 'versicolor'),
    (4.6, 3.1, 1.5, 0.2, 'setosa'),
]
Output
OUTPUT: Dict[str, dict] = {
    'setosa': {
        'Sepal length': {'mean': 4.9,
                         'median': 4.9,
                         'stdev': 0.2943920288775951,
                         'values': [5.1, 4.7, 4.9, 4.6, 5.4, 5.0, 4.6],
                         'variance': 0.08666666666666677},
        'Sepal width':  {'mean': 3.3857142857142857,
                         'median': 3.4,
                         'stdev': 0.31320159337914943,
                         'values': [3.5, 3.2, 3.0, 3.4, 3.9, 3.6, 3.1],
                         'variance': 0.09809523809523807}},
        'Petal length': {'mean': 1.4428571428571428,
                         'median': 1.4,
                         'stdev': 0.12724180205607036,
                         'values': [1.4, 1.3, 1.4, 1.4, 1.7, 1.4, 1.5],
                         'variance': 0.01619047619047619},
        'Petal width':  {'mean': 0.2571428571428572,
                         'median': 0.2,
                         'stdev': 0.07867957924694431,
                         'values': [0.2, 0.2, 0.2, 0.3, 0.4, 0.3, 0.2],
                         'variance': 0.006190476190476191},
    'virginica': {...},
    'versicolor': {...},
}