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)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:
