Data munging

Segment 1 - Filtering and selecting data

import numpy as np
import pandas as pd
​
from pandas import Series, DataFrame
Selecting and retrieving data

series_obj = Series(np.arange(8), index=['row 1', 'row 2','row 3','row 4','row 5', 'row 6', 'row 7', 'row 8'])
series_obj
row 1    0
row 2    1
row 3    2
row 4    3
row 5    4
row 6    5
row 7    6
row 8    7
dtype: int32

series_obj['row 7']
# ['label-index']
# ♔┈♔┈♔┈( WHAT THIS DOES ) ┈♔┈♔┈♔
# When you write square brackets with a label-index inside them, this tells Python to select and 
# retrieve all records with that label-index.
series_obj['row 7']
6

series_obj[[0,7]]
# [integer index] 
# ♔┈♔┈♔┈( WHAT THIS DOES )┈♔┈♔┈♔
# When you write square brackets with an integer index inside them, this tells Python to select and 
# retrieve all records with the specified integer index.
series_obj[[0,7]]
row 1    0
row 8    7
dtype: int32

np.random.seed(25)
DF_obj = DataFrame(np.random.rand(36).reshape((6,6)), 
                   index=['row 1', 'row 2', 'row 3', 'row 4', 'row 5', 'row 6'],
                   columns=['column 1', 'column 2', 'column 3', 'column 4', 'column 5', 'column 6'])
DF_obj
column 1	column 2	column 3	column 4	column 5	column 6
row 1	0.870124	0.582277	0.278839	0.185911	0.411100	0.117376
row 2	0.684969	0.437611	0.556229	0.367080	0.402366	0.113041
row 3	0.447031	0.585445	0.161985	0.520719	0.326051	0.699186
row 4	0.366395	0.836375	0.481343	0.516502	0.383048	0.997541
row 5	0.514244	0.559053	0.034450	0.719930	0.421004	0.436935
row 6	0.281701	0.900274	0.669612	0.456069	0.289804	0.525819

# object_name.ix[[row indexes], [column indexes]]
# ♔┈♔┈♔┈( WHAT THIS DOES )┈♔┈♔┈♔
# When you call the .ix[] special indexer, and pass in a set of row and colum indexes, this tells 
# Python to select and retrieve only those specific rows and columns.
DF_obj.ix[['row 2', 'row 5'], ['column 5', 'column 2']]
column 5	column 2
row 2	0.402366	0.437611
row 5	0.421004	0.559053
Data slicing

# ['starting label-index':'ending label-index'] 
# ♔┈♔┈♔┈( WHAT THIS DOES )┈♔┈♔┈♔
# Data slicing allows you to select and retrieve all records from the starting label-index, to the 
# ending label-index, and every record in between.
series_obj['row 3':'row 7']
row 3    2
row 4    3
row 5    4
row 6    5
row 7    6
dtype: int32
Comparing with scalars

​
# object_name < scalar value
# ♔┈♔┈♔┈( WHAT THIS DOES )┈♔┈♔┈♔
# You can use comparison operators (like greater than or less than) to return True / False values for 
# all records, to indicate how each element compares to a scalar value. 
DF_obj < .2
column 1	column 2	column 3	column 4	column 5	column 6
row 1	False	False	False	True	False	True
row 2	False	False	False	False	False	True
row 3	False	False	True	False	False	False
row 4	False	False	False	False	False	False
row 5	False	False	True	False	False	False
row 6	False	False	False	False	False	False
Filtering with scalars

# object_name[object_name > scalar value] 
# ♔┈♔┈♔┈( WHAT THIS DOES )┈♔┈♔┈♔
# You can also use comparison operators and scalar values for indexing, to return only the records 
# that satisfy the comparison expression you write.
series_obj[series_obj > 6]
row 8    7
dtype: int32
Setting values with scalars

# ['label-index', 'label-index', 'label-index'] = scalar value
# ♔┈♔┈♔┈( WHAT THIS DOES )┈♔┈♔┈♔
# Setting is where you select all records associated with the specified label-indexes and set those 
# values equal to a scalar.
series_obj['row 1', 'row 5', 'row 8'] = 8

series_obj
row 1    8
row 2    1
row 3    2
row 4    3
row 5    8
row 6    5
row 7    6
row 8    8
dtype: int32

​