Descriptive Statistics For pandas Dataframe
Import modules
import pandas as pdCreate 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'])
df| 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
df['age'].sum()227
Mean preTestScore
df['preTestScore'].mean()12.800000000000001
Cumulative sum of preTestScores, moving from the rows from the top
df['preTestScore'].cumsum()0 4
1 28
2 59
3 61
4 64
Name: preTestScore, dtype: int64
Summary statistics on preTestScore
df['preTestScore'].describe()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
df['preTestScore'].count()5
Minimum value of preTestScore
df['preTestScore'].min()2
Maximum value of preTestScore
df['preTestScore'].max()31
Median value of preTestScore
df['preTestScore'].median()4.0
Sample variance of preTestScore values
df['preTestScore'].var()186.69999999999999
Sample standard deviation of preTestScore values
df['preTestScore'].std()13.663820841916802
Skewness of preTestScore values
df['preTestScore'].skew()0.74334524573267591
Kurtosis of preTestScore values
df['preTestScore'].kurt()-2.4673543738411525
Correlation Matrix Of Values
df.corr()| 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
df.cov()| 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
Python Example for Beginners
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There are two sides to machine learning:
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- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
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