Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas dataframe.quantile[] function return values at the given quantile over requested axis, a numpy.percentile. Note : In each of any set of values of a variate which divide a frequency distribution into equal groups, each containing the same fraction of the total population.
Syntax: DataFrame.quantile[q=0.5, axis=0, numeric_only=True, interpolation=’linear’]
Parameters : q : float or array-like, default 0.5 [50% quantile]. 0 If q is a float, a Series will be returned where the index is the columns of self and the values are the quantiles.
Example #1: Use quantile[] function to find the value of “.2” quantile
Python3
import pandas as pd
df = pd.DataFrame[{"A":[1, 5, 3, 4, 2],
"B":[3, 2, 4, 3, 4],
"C":[2, 2, 7, 3, 4],
"D":[4, 3, 6, 12, 7]}]
df
Let’s use the dataframe.quantile[] function to find the quantile of ‘.2’ for each column in the dataframe
Python3
df.quantile[.2, axis = 0]
Output :
Example #2: Use quantile[] function to find the [.1, .25, .5, .75] quantiles along the index axis.
Python3
import pandas as pd
df =
pd.DataFrame[{"A":[1, 5, 3, 4, 2],
"B":[3, 2, 4, 3, 4],
"C":[2, 2, 7, 3, 4],
"D":[4, 3, 6, 12, 7]}]
df.quantile[[.1, .25, .5, .75], axis =
0]
Output :