pandas: find percentile stats of a given column

You can use the pandas.DataFrame.quantile() function, as shown below.

import pandas as pdimport randomA = [ random.randint(0,100) for i in range(10) ]B = [ random.randint(0,100) for i in range(10) ]df = pd.DataFrame({ 'field_A': A, 'field_B': B })df#    field_A  field_B# 0       90       72# 1       63       84# 2       11       74# 3       61       66# 4       78       80# 5       67       75# 6       89       47# 7       12       22# 8       43        5# 9       30       64df.field_A.mean()   # Same as df['field_A'].mean()# 54.399999999999999df.field_A.median() # 62.0# You can call `quantile(i)` to get the i'th quantile,# where `i` should be a fractional number.df.field_A.quantile(0.1) # 10th percentile# 11.9df.field_A.quantile(0.5) # same as median# 62.0df.field_A.quantile(0.9) # 90th percentile# 89.10000000000001

assume series s

s = pd.Series(np.arange(100))

Get quantiles for [.1, .2, .3, .4, .5, .6, .7, .8, .9]

s.quantile(np.linspace(.1, 1, 9, 0))0.1     9.90.2    19.80.3    29.70.4    39.60.5    49.50.6    59.40.7    69.30.8    79.20.9    89.1dtype: float64

OR

s.quantile(np.linspace(.1, 1, 9, 0), 'lower')0.1     90.2    190.3    290.4    390.5    490.6    590.7    690.8    790.9    89dtype: int32

I figured out below would work:

my_df.dropna().quantile([0.0, .9])