在 Python Pandas 中同时融化多个列
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Simultaneously melt multiple columns in Python Pandas
提问by Martin Petrov
wondering if pd.melt supports melting multiple columns. I have the below examples trying to have the value_vars as list of lists but i am getting an error:
想知道 pd.melt 是否支持熔化多个列。我有以下示例试图将 value_vars 作为列表列表，但出现错误：
ValueError: Location based indexing can only have [labels (MUST BE IN THE INDEX), slices of labels (BOTH endpoints included! Can be slices of integers if the index is integers), listlike of labels, boolean] types
ValueError: Location based indexing can only have [labels (MUST BE IN THE INDEX), slices of labels (BOTH endpoints included! Can be slices of integers if the index is integers), listlike of labels, boolean] types
Using pandas 0.23.1.
使用Pandas 0.23.1。
df = pd.DataFrame({'City': ['Houston', 'Austin', 'Hoover'],
'State': ['Texas', 'Texas', 'Alabama'],
'Name':['Aria', 'Penelope', 'Niko'],
'Mango':[4, 10, 90],
'Orange': [10, 8, 14],
'Watermelon':[40, 99, 43],
'Gin':[16, 200, 34],
'Vodka':[20, 33, 18]},
columns=['City', 'State', 'Name', 'Mango', 'Orange', 'Watermelon', 'Gin', 'Vodka'])
Desired output:
期望的输出：
City State Fruit Pounds Drink Ounces
0 Houston Texas Mango 4 Gin 16.0
1 Austin Texas Mango 10 Gin 200.0
2 Hoover Alabama Mango 90 Gin 34.0
3 Houston Texas Orange 10 Vodka 20.0
4 Austin Texas Orange 8 Vodka 33.0
5 Hoover Alabama Orange 14 Vodka 18.0
6 Houston Texas Watermelon 40 nan NaN
7 Austin Texas Watermelon 99 nan NaN
8 Hoover Alabama Watermelon 43 nan NaN
I tried that and i get the aforementioned error:
我试过了，我得到了上述错误：
df.melt(id_vars=['City', 'State'],
value_vars=[['Mango', 'Orange', 'Watermelon'], ['Gin', 'Vodka']],var_name=['Fruit', 'Drink'],
value_name=['Pounds', 'Ounces'])
回答by jezrael
Use double meltfor each catogories and then concat, but because duplicted values add cumcountfor unique triplesin MultiIndex:
melt对每个类别使用 double ，然后concat，但因为重复的值添加cumcount为 unique triplesin MultiIndex：
df1 = df.melt(id_vars=['City', 'State'],
value_vars=['Mango', 'Orange', 'Watermelon'],
var_name='Fruit', value_name='Pounds')
df2 = df.melt(id_vars=['City', 'State'],
value_vars=['Gin', 'Vodka'],
var_name='Drink', value_name='Ounces')
df1 = df1.set_index(['City', 'State', df1.groupby(['City', 'State']).cumcount()])
df2 = df2.set_index(['City', 'State', df2.groupby(['City', 'State']).cumcount()])
df3 = (pd.concat([df1, df2],axis=1)
.sort_index(level=2)
.reset_index(level=2, drop=True)
.reset_index())
print (df3)
City State Fruit Pounds Drink Ounces
0 Austin Texas Mango 10 Gin 200.0
1 Hoover Alabama Mango 90 Gin 34.0
2 Houston Texas Mango 4 Gin 16.0
3 Austin Texas Orange 8 Vodka 33.0
4 Hoover Alabama Orange 14 Vodka 18.0
5 Houston Texas Orange 10 Vodka 20.0
6 Austin Texas Watermelon 99 NaN NaN
7 Hoover Alabama Watermelon 43 NaN NaN
8 Houston Texas Watermelon 40 NaN NaN
