Filter pandas Dataframes
Import modules
import pandas as pd
Create Dataframe
data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],
'year': [2012, 2012, 2013, 2014, 2014],
'reports': [4, 24, 31, 2, 3],
'coverage': [25, 94, 57, 62, 70]}
df = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df
coverage | name | reports | year | |
---|---|---|---|---|
Cochice | 25 | Jason | 4 | 2012 |
Pima | 94 | Molly | 24 | 2012 |
Santa Cruz | 57 | Tina | 31 | 2013 |
Maricopa | 62 | Jake | 2 | 2014 |
Yuma | 70 | Amy | 3 | 2014 |
View Column
df['name']
Cochice Jason
Pima Molly
Santa Cruz Tina
Maricopa Jake
Yuma Amy
Name: name, dtype: object
View Two Columns
df[['name', 'reports']]
name | reports | |
---|---|---|
Cochice | Jason | 4 |
Pima | Molly | 24 |
Santa Cruz | Tina | 31 |
Maricopa | Jake | 2 |
Yuma | Amy | 3 |
View First Two Rows
df[:2]
coverage | name | reports | year | |
---|---|---|---|---|
Cochice | 25 | Jason | 4 | 2012 |
Pima | 94 | Molly | 24 | 2012 |
View Rows Where Coverage Is Greater Than 50
df[df['coverage'] > 50]
coverage | name | reports | year | |
---|---|---|---|---|
Pima | 94 | Molly | 24 | 2012 |
Santa Cruz | 57 | Tina | 31 | 2013 |
Maricopa | 62 | Jake | 2 | 2014 |
Yuma | 70 | Amy | 3 | 2014 |
View Rows Where Coverage Is Greater Than 50 And Reports Less Than 4
df[(df['coverage'] > 50) & (df['reports'] < 4)]
coverage | name | reports | year | |
---|---|---|---|---|
Maricopa | 62 | Jake | 2 | 2014 |
Yuma | 70 | Amy | 3 | 2014 |
Python Example for Beginners
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- 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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