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 pdimport pandas as pd# Creating the dataframedf = 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]})# Print the dataframedf |
Let’s use the dataframe.quantile() function to find the quantile of ‘.2’ for each column in the dataframe
Python3
# find the product over the index axisdf.quantile(.2, axis = 0) |
Output :
Example #2: Use quantile() function to find the (.1, .25, .5, .75) quantiles along the index axis.
Python3
# importing pandas as pdimport pandas as pd# Creating the dataframedf = 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]})# using quantile() function to# find the quantiles over the index axisdf.quantile([.1, .25, .5, .75], axis = 0) |
Output :
