Title: Descriptive Statistics For pandas Dataframe
Slug: pandas_dataframe_descriptive_stats
Summary: Descriptive Statistics For pandas Dataframe
Date: 2016-05-01 12:00
Category: Python
Tags: Data Wrangling
Authors: Chris Albon

Import modules



In [40]:

    
import pandas as pd

Create dataframe



In [41]:

    
data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'age': [42, 52, 36, 24, 73], 
        'preTestScore': [4, 24, 31, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70]}
df = pd.DataFrame(data, columns = ['name', 'age', 'preTestScore', 'postTestScore'])
df









    Out[41]:






  
    
      
      name
      age
      preTestScore
      postTestScore
    
  
  
    
      0
       Jason
       42
        4
       25
    
    
      1
       Molly
       52
       24
       94
    
    
      2
        Tina
       36
       31
       57
    
    
      3
        Jake
       24
        2
       62
    
    
      4
         Amy
       73
        3
       70
    
  

5 rows × 4 columns

The sum of all the ages



In [42]:

    
df['age'].sum()









    Out[42]:





227

Mean preTestScore



In [43]:

    
df['preTestScore'].mean()









    Out[43]:





12.800000000000001

Cumulative sum of preTestScores, moving from the rows from the top



In [44]:

    
df['preTestScore'].cumsum()









    Out[44]:





0     4
1    28
2    59
3    61
4    64
Name: preTestScore, dtype: int64

Summary statistics on preTestScore



In [45]:

    
df['preTestScore'].describe()









    Out[45]:





count     5.000000
mean     12.800000
std      13.663821
min       2.000000
25%       3.000000
50%       4.000000
75%      24.000000
max      31.000000
Name: preTestScore, dtype: float64

Count the number of non-NA values



In [46]:

    
df['preTestScore'].count()









    Out[46]:





5

Minimum value of preTestScore



In [47]:

    
df['preTestScore'].min()









    Out[47]:





2

Maximum value of preTestScore



In [48]:

    
df['preTestScore'].max()









    Out[48]:





31

Median value of preTestScore



In [49]:

    
df['preTestScore'].median()









    Out[49]:





4.0

Sample variance of preTestScore values



In [50]:

    
df['preTestScore'].var()









    Out[50]:





186.69999999999999

Sample standard deviation of preTestScore values



In [51]:

    
df['preTestScore'].std()









    Out[51]:





13.663820841916802

Skewness of preTestScore values



In [52]:

    
df['preTestScore'].skew()









    Out[52]:





0.74334524573267591

Kurtosis of preTestScore values



In [53]:

    
df['preTestScore'].kurt()









    Out[53]:





-2.4673543738411525

Correlation Matrix Of Values



In [54]:

    
df.corr()









    Out[54]:






  
    
      
      age
      preTestScore
      postTestScore
    
  
  
    
      age
       1.000000
      -0.105651
       0.328852
    
    
      preTestScore
      -0.105651
       1.000000
       0.378039
    
    
      postTestScore
       0.328852
       0.378039
       1.000000
    
  

3 rows × 3 columns

Covariance Matrix Of Values



In [55]:

    
df.cov()









    Out[55]:






  
    
      
      age
      preTestScore
      postTestScore
    
  
  
    
      age
       340.80
       -26.65
       151.20
    
    
      preTestScore
       -26.65
       186.70
       128.65
    
    
      postTestScore
       151.20
       128.65
       620.30
    
  

3 rows × 3 columns

	age	preTestScore	postTestScore
age	1.000000	-0.105651	0.328852
preTestScore	-0.105651	1.000000	0.378039
postTestScore	0.328852	0.378039	1.000000

	age	preTestScore	postTestScore
age	340.80	-26.65	151.20
preTestScore	-26.65	186.70	128.65
postTestScore	151.20	128.65	620.30