處理遺漏值
使用 pandas 重塑資料
Maria Eugenia Inzaugarat
Data Scientist
複習
- 堆疊與反堆疊 DataFrame：
- 所有欄索引層級
- 其中一個列索引層級
- 選擇要堆疊或反堆疊的層級
反堆疊會產生遺漏值
子群的標籤集合不相同
animals
jump run fly
class order name
Mammalia carnivora dog No Yes No
Diprotodontia Kangaroo Yes No No
Aves hervibora bird No No Yes
反堆疊會產生遺漏值
子群的標籤集合不相同
animals
jump run fly
class order name
Mammalia carnivora dog No Yes No <--
Diprotodontia Kangaroo Yes No No
Aves hervibora bird No No Yes
反堆疊會產生遺漏值
子群的標籤集合不相同
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
反堆疊會產生遺漏值
子群的標籤集合不相同
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
用反堆疊處理 NaN
animals.unstack(level='class', fill_value= )
用反堆疊處理 NaN
animals.unstack(level='class', fill_value='No')
用反堆疊處理 NaN
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
堆疊與遺漏值
原始 DataFrame 缺少某些索引與欄位值的組合
flowers
petals Stigma
number size
rose 40 NaN
Lily 8 5
堆疊與遺漏值
原始 DataFrame 缺少某些索引與欄位值的組合
flowers.stack()
Stigma petals
rose number NaN 40.0
Lily number NaN 8.0
size 5 NaN
堆疊與遺漏值
原始 DataFrame 缺少某些索引與欄位值的組合
flowers.stack(dropna=True)
Stigma petals
rose number NaN 40.0
Lily number NaN 8.0
size 5 NaN
堆疊與遺漏值
原始 DataFrame 缺少某些索引與欄位值的組合
flowers.stack(dropna=False)
Stigma petals
rose number NaN 40.0
size NaN NaN <--
Lily number NaN 8.0
size 5 NaN
用堆疊處理 NaN
flowers.stack(dropna=False).fillna(0)
Stigma petals
rose number 0 40.0
size 0 0
Lily number 0 8.0
size 5 0
一起來練習吧！
使用 pandas 重塑資料
