Skip to content
Related Articles

Related Articles

Improve Article

How to Get the Descriptive Statistics for Pandas DataFrame?

  • Difficulty Level : Basic
  • Last Updated : 10 Jul, 2020

describe() method in Python Pandas is used to compute descriptive statistical data like count, unique values, mean, standard deviation, minimum and maximum value and many more. In this article, let’s learn to get the descriptive statistics for Pandas DataFrame.

Syntax:
df[‘cname’].describe(percentiles = None, include = None, exclude = None)
df.describe(percentiles = None, include = None, exclude = None)

 Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.  

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning - Basic Level Course

Parameters:
percentiles: represents percentile value that has to be returned by the function. Default values are 0.25, 0.5 and 0.75
include: represents list of data types that has to be included
exclude: represents list of data types that has to be excluded



Example 1:




# Import package
from pandas import DataFrame
  
# Create DataFrame
cart = {'Product': ['Mobile', 'AC', 'Mobile', 'Sofa', 'Laptop'],
        'Price': [20000, 28000, 22000, 19000, 45000],
         'Year': [2014, 2015, 2016, 2017, 2018]
        }
df = DataFrame(cart, columns = ['Product', 'Price', 'Year'])
  
# Original DataFrame
print("Original DataFrame:\n", df)
  
# Describing descriptive statistics of Price
print("\nDescriptive statistics of Price:\n")
stats = df['Price'].describe()
print(stats)

Output:

Example 2:




# Import package
from pandas import DataFrame
  
# Create DataFrame
cart = {'Product': ['Mobile', 'AC', 'Mobile', 'Sofa', 'Laptop'],
        'Price': [20000, 28000, 22000, 19000, 45000],
         'Year': [2014, 2015, 2016, 2017, 2018]
        }
df = DataFrame(cart, columns = ['Product', 'Price', 'Year'])
  
# Original DataFrame
print("Original DataFrame:\n", df)
  
# Describing descriptive statistics of Year
print("\nDescriptive statistics of year:\n")
stats = df['Year'].describe()
print(stats)

Output:

Example 3:




# Import package
from pandas import DataFrame
  
# Create DataFrame
cart = {'Product': ['Mobile', 'AC', 'Mobile', 'Sofa', 'Laptop'],
        'Price': [20000, 28000, 22000, 19000, 45000],
         'Year': [2014, 2015, 2016, 2017, 2018]
        }
df = DataFrame(cart, columns = ['Product', 'Price', 'Year'])
  
# Original DataFrame
print("Original DataFrame:\n", df)
  
# Describing descriptive statistics of whole dataframe
print("\nDescriptive statistics of whole dataframe:\n")
stats = df.describe(include = 'all')
print(stats)

Output:

Example 4:
In this example, let’s print all the descriptive statistical data individually.




from pandas import DataFrame
  
# Create DataFrame
cart = {'Product': ['Mobile', 'AC', 'Mobile', 'Sofa', 'Laptop'],
        'Price': [20000, 28000, 22000, 19000, 45000],
         'Year': [2014, 2015, 2016, 2017, 2018]
        }
df = DataFrame(cart, columns = ['Product', 'Price', 'Year'])
  
# Original DataFrame
print("Original DataFrame:\n", df)
  
# Print Count of Price
print("\nCount of Price:\n")
counts = df['Price'].count()
print(counts)
  
# Print mean of Price
print("\nMean of Price:\n")
m = df['Price'].mean()
print(m)
  
# Print maximum value of Price
print("\nMaximum value of Price:\n")
mx = df['Price'].max()
print(m)
  
# Print standard deviation of Price
print("\nStandard deviation of Price:\n")
sd = df['Price'].std()
print(sd)

Output:




My Personal Notes arrow_drop_up
Recommended Articles
Page :