Write a program in Python to calculate the default float quantile value for all the element in a Series

A quantile represents the value below which a certain percentage of data falls. In pandas, the quantile() method calculates quantile values for a Series, with 0.5 being the default (median).

Understanding Quantiles

The quantile value of 0.5 represents the median − the middle value when data is sorted. For a Series with values [10, 20, 30, 40, 50], the 0.5 quantile (median) is 30.0.

Syntax

Series.quantile(q=0.5, interpolation='linear')

Parameters:

• q − Float between 0 and 1 (default is 0.5)
• interpolation − Method to use when quantile lies between two data points

Example

Let's calculate the default quantile (0.5) for a Series ?

import pandas as pd

data_list = [10, 20, 30, 40, 50]
data = pd.Series(data_list)

print("Original Series:")
print(data)
print("\nDefault quantile (0.5):")
print(data.quantile(0.5))

Original Series:
0    10
1    20
2    30
3    40
4    50
dtype: int64

Default quantile (0.5):
30.0

Different Quantile Values

You can calculate different quantiles by changing the parameter ?

import pandas as pd

data_list = [10, 20, 30, 40, 50]
data = pd.Series(data_list)

print("25th percentile (Q1):", data.quantile(0.25))
print("50th percentile (Q2/Median):", data.quantile(0.5))
print("75th percentile (Q3):", data.quantile(0.75))

25th percentile (Q1): 20.0
50th percentile (Q2/Median): 30.0
75th percentile (Q3): 40.0

Multiple Quantiles

Calculate multiple quantiles at once by passing a list ?

import pandas as pd

data_list = [10, 20, 30, 40, 50, 60, 70]
data = pd.Series(data_list)

quantiles = data.quantile([0.25, 0.5, 0.75])
print(quantiles)

0.25    25.0
0.50    40.0
0.75    55.0
dtype: float64

Conclusion

The quantile() method with default value 0.5 calculates the median of a Series. Use different quantile values to find percentiles and analyze data distribution effectively.