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 <= q <= 1, the quantile(s) to compute
axis : [{0, 1, ‘index’, ‘columns’} (default 0)] 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise
numeric_only : If False, the quantile of datetime and timedelta data will be computed as well
interpolation : {‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’}
Returns : quantiles : Series or DataFrame -> If q is an array, a DataFrame will be returned where the index is q, the columns are the columns of self, and the values are the quantiles. -> 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
# importing pandas as pd
import pandas as pd
# Creating the dataframe
df= pd.DataFrame({"A":[1,5,import1,import3,import5import6
import7import8import1,import5,import3,import1,import3import6
import7# Creating the dataframe0import5,import5,# Creating the dataframe5,import1,import3import6
import7df2import3,import1,df7,df9,# Creating the dataframe5=2
=3
df
Let’s use the dataframe.quantile() function to find the quantile of ‘.2’ for each column in the dataframe
Python3
=5
=6import5=8= pd.DataFrame({"A":[0pd.DataFrame({"A":[1
Output :
Example #2: Use quantile() function to find the (.1, .25, .5, .75) quantiles along the index axis.
Python3
# importing pandas as pd
import pandas as pd
# Creating the dataframe
df= pd.DataFrame({"A":[1,5,import1,import3,import5import6
import7import8import1,import5,import3,import1,import3import6
import7# Creating the dataframe0import5,import5,# Creating the dataframe5,import1,import3import6
import7df2import3,import1,df7,df9,# Creating the dataframe5=2
import15
import16
import171import19import20import195import19import24import25= pd.DataFrame({"A":[0pd.DataFrame({"A":[1