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 & lt; = q & lt; = 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
– & gt; 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.
– & gt; 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 :