Handling missing data
Rimodellare i dati con pandas
Maria Eugenia Inzaugarat
Data Scientist
Review
- Stack and unstack DataFrames:
- All columns index levels
- A row index level
- Choose which levels to stack or unstack
Unstacking leads to missing values
Subgroups do not have the same set of labels
animals
jump run fly
class order name
Mammalia carnivora dog No Yes No
Diprotodontia Kangaroo Yes No No
Aves hervibora bird No No Yes
Unstacking leads to missing values
Subgroups do not have the same set of labels
animals
jump run fly
class order name
Mammalia carnivora dog No Yes No <--
Diprotodontia Kangaroo Yes No No
Aves hervibora bird No No Yes
Unstacking leads to missing values
Subgroups do not have the same set of labels
animals.unstack(level='class')
jump run fly
clas Aves Mammalia Aves Mammalia Aves Mammalia
order name
Diprotodontia Kangaroo NaN Yes NaN No NaN No
carnivora Dog NaN No NaN Yes NaN No
Charadriiformes Avocet No NaN No NaN Yes NaN
Unstacking leads to missing values
Subgroups do not have the same set of labels
animals.unstack(level='class')
jump run fly
clas Aves Mammalia Aves Mammalia Aves Mammalia
order name
Diprotodontia Kangaroo NaN Yes NaN No NaN No
-----------------------------
carnivora Dog NaN <-- No NaN Yes NaN No
-----------------------------
Charadriiformes Avocet No NaN No NaN Yes NaN
Handling NaN with unstack
animals.unstack(level='class', fill_value= )
Handling NaN with unstack
animals.unstack(level='class', fill_value='No')
Handling NaN with unstack
animals.unstack(level='class', fill_value='No').sort_index(level=['order', 'name'], ascending=[True, False])
jump run fly
clas Aves Mammalia Aves Mammalia Aves Mammalia
order name
Diprotodontia Kangaroo No Yes No No No No
carnivora Dog No No No Yes No No
Charadriiformes Avocet No No No No Yes No
Stack and missing values
Combinations of index and column values missing from the original DataFrame
flowers
petals Stigma
number size
rose 40 NaN
Lily 8 5
Stack and missing values
Combinations of index and column values missing from the original DataFrame
flowers.stack()
Stigma petals
rose number NaN 40.0
Lily number NaN 8.0
size 5 NaN
Stack and missing values
Combinations of index and column values missing from the original DataFrame
flowers.stack(dropna=True)
Stigma petals
rose number NaN 40.0
Lily number NaN 8.0
size 5 NaN
Stack and missing values
Combinations of index and column values missing from the original DataFrame
flowers.stack(dropna=False)
Stigma petals
rose number NaN 40.0
size NaN NaN <--
Lily number NaN 8.0
size 5 NaN
Handling NaN with stack
flowers.stack(dropna=False).fillna(0)
Stigma petals
rose number 0 40.0
size 0 0
Lily number 0 8.0
size 5 0
Let's practice!
Rimodellare i dati con pandas
