Python | Pandas dataframe.quantile ()

The Pandas function dataframe.quantile() returns values ​​in the specified quantile along the requested axis, numpy.percentile.

Note. In each set of values, a variable that divides the frequency distribution into equal groups, each of which contains 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
interpolatoin: {’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 the function quantile () to find the quantile value ".2"

# import pandas as pd import pandas as pd   # Create data frame 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 ]})   # Print the data frame df

Let’s use the dataframe.quantile () function to find the quantile & # 39; .2 & # 39; for each column in the data frame.

# find product by index axis df.quantile (. 2 , axis = 0 )

Output:

Example # 2: Use the quantile () function to find quantile () .1, .25, .5, .75) along the index axis .

# import pandas as pd import pandas as pd   # Create data frame 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 ]})   # using the quantile function () for # find quantiles along the index axis df.quantile ([. 1 ,. 25  ,. 5 ,. 75 ], axis = 0 )

Exit :