Data Wrangling in Python – How to Do Descriptive Statistics For pandas Dataframe

Descriptive Statistics For pandas Dataframe

Import modules

import pandas as pd

Create dataframe

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'])
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


Mean preTestScore


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

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

Summary statistics on preTestScore

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


Minimum value of preTestScore


Maximum value of preTestScore


Median value of preTestScore


Sample variance of preTestScore values


Sample standard deviation of preTestScore values


Skewness of preTestScore values


Kurtosis of preTestScore values


Correlation Matrix Of Values

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

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


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