rnalysis.filtering.FoldChangeFilter.describeο
- FoldChangeFilter.describe(percentiles: float | List[float] = (0.01, 0.25, 0.5, 0.75, 0.99)) DataFrame ο
Generate descriptive statistics that summarize the central tendency, dispersion and shape of the datasetβs distribution, excluding NaN values. For more information see the documentation of pandas.DataFrame.describe.
- Parameters:
percentiles (list-like of floats (default=(0.01, 0.25, 0.5, 0.75, 0.99))) β The percentiles to include in the output. All should fall between 0 and 1. The default is [.25, .5, .75], which returns the 25th, 50th, and 75th percentiles.
- Returns:
Summary statistics of the dataset.
- Return type:
Series or DataFrame
- Examples:
>>> from rnalysis import filtering >>> import numpy as np >>> counts = filtering.Filter('tests/test_files/counted.csv') >>> counts.describe() cond1 cond2 cond3 cond4 count 22.000000 22.000000 22.000000 22.000000 mean 2515.590909 2209.227273 4230.227273 3099.818182 std 4820.512674 4134.948493 7635.832664 5520.394522 min 0.000000 0.000000 0.000000 0.000000 1% 0.000000 0.000000 0.000000 0.000000 25% 6.000000 6.250000 1.250000 0.250000 50% 57.500000 52.500000 23.500000 21.000000 75% 2637.000000 2479.000000 6030.500000 4669.750000 99% 15054.950000 12714.290000 21955.390000 15603.510000 max 15056.000000 12746.000000 22027.000000 15639.000000
>>> # show the deciles (10%, 20%, 30%... 90%) of the columns >>> counts.describe(percentiles=np.arange(0.1, 1, 0.1)) cond1 cond2 cond3 cond4 count 22.000000 22.000000 22.000000 22.000000 mean 2515.590909 2209.227273 4230.227273 3099.818182 std 4820.512674 4134.948493 7635.832664 5520.394522 min 0.000000 0.000000 0.000000 0.000000 10% 0.000000 0.200000 0.000000 0.000000 20% 1.400000 3.200000 1.000000 0.000000 30% 15.000000 15.700000 2.600000 1.000000 40% 28.400000 26.800000 14.000000 9.000000 50% 57.500000 52.500000 23.500000 21.000000 60% 82.000000 106.800000 44.000000 33.000000 70% 484.200000 395.500000 305.000000 302.500000 80% 3398.600000 3172.600000 7981.400000 6213.000000 90% 8722.100000 7941.800000 16449.500000 12129.900000 max 15056.000000 12746.000000 22027.000000 15639.000000