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How to use percentile method in locust

Best Python code snippet using locust

6. UC - Practice - Python Percentiles.py

Source:6. UC - Practice - Python Percentiles.py

...21plt.show()                                         # Display the histogram    2223# Calculate Percentile Values using numpy functions24# 50th Percentile25np.percentile(vals, 50)                            # Gives value at 50th percentile which is the Median2627# Compute the median28np.median(vals)                                    # Display the median of random values2930# 90th Percentile31np.percentile(vals, 90)                            # Gives value at 90th percentile3233# 20th Percentile34np.percentile(vals, 20)                            # Gives value at 20th percentile3536# 75th Percentile37np.percentile(vals, 75)                            # Gives value at 75th percentile which is the Q33839# 25th Percentile40np.percentile(vals, 25)                            # Gives value at 25th percentile which is the Q14142# Generate random numbers with normal distribution - Set 243vals = np.random.normal(5, 10, 10000)              # Generate random numbers with normal distribution; mu = 5, sigma = 104445# Segment the income data into 50 buckets, and plot it as a histogram46plt.hist(vals, 50)                                 # Segments the data into 50 buckets47plt.show()                                         # Display the histogram    4849# Calculate Percentile Values using numpy functions50# 50th Percentile51np.percentile(vals, 50)                            # Gives value at 50th percentile which is the Median5253# Compute the median54np.median(vals)                                    # Display the median of random values5556# 90th Percentile57np.percentile(vals, 90)                            # Gives value at 90th percentile5859# 20th Percentile60np.percentile(vals, 20)                            # Gives value at 20th percentile6162# 75th Percentile63np.percentile(vals, 50)                            # Gives value at 75th percentile which is the Q36465# 25th Percentile66np.percentile(vals, 25)                            # Gives value at 25th percentile which is the Q16768# Generate random numbers with normal distribution - Set 369vals = np.random.normal(-5, 10, 10000)             # Generate random numbers with normal distribution; mu = -5, sigma = 107071# Segment the income data into 50 buckets, and plot it as a histogram72plt.hist(vals, 50)                                 # Segments the data into 50 buckets73plt.show()                                         # Display the histogram    7475# Calculate Percentile Values using numpy functions76# 50th Percentile77np.percentile(vals, 50)                            # Gives value at 50th percentile which is the Median7879# Compute the median80np.median(vals)                                    # Display the median of random values8182# 90th Percentile83np.percentile(vals, 90)                            # Gives value at 90th percentile8485# 20th Percentile86np.percentile(vals, 20)                            # Gives value at 20th percentile8788# 75th Percentile89np.percentile(vals, 50)                            # Gives value at 75th percentile which is the Q39091# 25th Percentile92np.percentile(vals, 25)                            # Gives value at 25th percentile which is the Q19394# Generate random numbers with normal distribution - Set 495vals = np.random.normal(-50, 10, 100)              # Generate random numbers with normal distribution; mu = -50, sigma = 109697# Segment the income data into 50 buckets, and plot it as a histogram98plt.hist(vals, 50)                                 # Segments the data into 50 buckets99plt.show()                                         # Display the histogram    100101# Calculate Percentile Values using numpy functions102# 50th Percentile103np.percentile(vals, 50)                            # Gives value at 50th percentile which is the Median104105# Compute the median106np.median(vals)                                    # Display the median of random values107108# 90th Percentile109np.percentile(vals, 90)                            # Gives value at 90th percentile110111# 20th Percentile112np.percentile(vals, 20)                            # Gives value at 20th percentile113114# 75th Percentile115np.percentile(vals, 50)                            # Gives value at 75th percentile which is the Q3116117# 25th Percentile118np.percentile(vals, 25)                            # Gives value at 25th percentile which is the Q1119
...

test_percentiles.py

Source:test_percentiles.py

...16        "dask",17    ],18)19@percentile_internal_methods20def test_percentile(internal_method):21    d = da.ones((16,), chunks=(4,))22    qs = [0, 50, 100]23    assert_eq(24        da.percentile(d, qs, internal_method=internal_method),25        np.array([1, 1, 1], dtype=d.dtype),26    )27    x = np.array([0, 0, 5, 5, 5, 5, 20, 20])28    d = da.from_array(x, chunks=(3,))29    result = da.percentile(d, qs, internal_method=internal_method)30    assert_eq(result, np.array([0, 5, 20], dtype=result.dtype))31    assert same_keys(32        da.percentile(d, qs, internal_method=internal_method),33        da.percentile(d, qs, internal_method=internal_method),34    )35    assert not same_keys(36        da.percentile(d, qs, internal_method=internal_method),37        da.percentile(d, [0, 50], internal_method=internal_method),38    )39    if internal_method != "tdigest":40        x = np.array(["a", "a", "d", "d", "d", "e"])41        d = da.from_array(x, chunks=(3,))42        assert_eq(43            da.percentile(d, [0, 50, 100]), np.array(["a", "d", "e"], dtype=x.dtype)44        )45@pytest.mark.skip46def test_percentile_with_categoricals():47    try:48        import pandas as pd49    except ImportError:50        return51    x0 = pd.Categorical(["Alice", "Bob", "Charlie", "Dennis", "Alice", "Alice"])52    x1 = pd.Categorical(["Alice", "Bob", "Charlie", "Dennis", "Alice", "Alice"])53    dsk = {("x", 0): x0, ("x", 1): x1}54    x = da.Array(dsk, "x", chunks=((6, 6),))55    p = da.percentile(x, [50])56    assert (p.compute().categories == x0.categories).all()57    assert (p.compute().codes == [0]).all()58    assert same_keys(da.percentile(x, [50]), da.percentile(x, [50]))59@percentile_internal_methods60def test_percentiles_with_empty_arrays(internal_method):61    x = da.ones(10, chunks=((5, 0, 5),))62    assert_eq(63        da.percentile(x, [10, 50, 90], internal_method=internal_method),64        np.array([1, 1, 1], dtype=x.dtype),65    )66@percentile_internal_methods67def test_percentiles_with_empty_q(internal_method):68    x = da.ones(10, chunks=((5, 0, 5),))69    assert_eq(70        da.percentile(x, [], internal_method=internal_method),71        np.array([], dtype=x.dtype),72    )73@percentile_internal_methods74@pytest.mark.parametrize("q", [5, 5.0, np.int64(5), np.float64(5)])75def test_percentiles_with_scaler_percentile(internal_method, q):76    # Regression test to ensure da.percentile works with scalar percentiles77    # See #302078    d = da.ones((16,), chunks=(4,))79    assert_eq(80        da.percentile(d, q, internal_method=internal_method),81        np.array([1], dtype=d.dtype),82    )83@percentile_internal_methods84def test_unknown_chunk_sizes(internal_method):85    x = da.random.random(1000, chunks=(100,))86    x._chunks = ((np.nan,) * 10,)87    result = da.percentile(x, 50, internal_method=internal_method).compute()88    assert 0.1 < result < 0.989    a, b = da.percentile(x, [40, 60], internal_method=internal_method).compute()90    assert 0.1 < a < 0.991    assert 0.1 < b < 0.9...

stats.py

Source:stats.py

1import numpy as np2from .extmath import stable_cumsum3from .fixes import _take_along_axis4def _weighted_percentile(array, sample_weight, percentile=50):5    """Compute weighted percentile6    Computes lower weighted percentile. If `array` is a 2D array, the7    `percentile` is computed along the axis 0.8        .. versionchanged:: 0.249            Accepts 2D `array`.10    Parameters11    ----------12    array : 1D or 2D array13        Values to take the weighted percentile of.14    sample_weight: 1D or 2D array15        Weights for each value in `array`. Must be same shape as `array` or16        of shape `(array.shape[0],)`.17    percentile: int or float, default=5018        Percentile to compute. Must be value between 0 and 100....

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