如何获取 Pandas DataFrame 的描述性统计数据？

Python Pandas 中的describe()方法用于计算描述性统计数据，如计数、唯一值、均值、标准差、最小值和最大值等等。在本文中，让我们学习获取 Pandas DataFrame 的描述性统计数据。

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

示例 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)

输出：

示例 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)

输出：

示例 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)

输出：

示例 4：
在这个例子中，让我们单独打印所有的描述性统计数据。

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)

输出：