我有下表:
| Brand | Product | |
|---|---|---|
| 0 | Nike | Shoes |
| 1 | Nike | Socks |
| 2 | Adidas | Shoes |
| 3 | Adidas | Shoes |
| 4 | Adidas | Socks |
| 5 | Flight | Shorts |
我想使用Pandas GroupBy函数生成下表(行和列的总计)，以找出特定品牌-产品对出现的次数.
| Shoes | Socks | Shorts | Total | |
|---|---|---|---|---|
| Nike | 1 | 1 | 0 | 2 |
| Adidas | 2 | 1 | 0 | 3 |
| Flight | 0 | 0 | 1 | 1 |
| Total | 3 | 2 | 1 | 6 |
然后想要按百分比转换单元格:
| Shoes | Socks | Shorts | Total | |
|---|---|---|---|---|
| Nike | 50% | 50% | 0% | 33% |
| Adidas | 67% | 50% | 0% | 50% |
| Flight | 0% | 0% | 100% | 17% |
| Total | 100% | 100% | 100% | 100% |
最后，有没有办法将所有单元格编号乘以一个调整系数(例如，0.75)
试一试pd.crosstab:
out = pd.crosstab(df["Brand"], df["Product"])
out["Total"] = out.sum(axis=1)
out.index.name, out.columns.name = None, None
print(out)
打印:
Shoes Shorts Socks Total
Adidas 2 0 1 3
Flight 0 1 0 1
Nike 1 0 1 2
编辑:要获得百分比，可以在之后执行以下操作:
out.iloc[:, :-1] = (
out.iloc[:, :-1]
.div(out["Total"], axis=0)
.mul(100)
.round(0)
.astype(int)
.astype(str)
+ "%"
)
out["Total"] = (
out["Total"]
.div(out["Total"].sum())
.mul(100)
.round(0)
.astype(int)
.astype(str)
+ "%"
)
打印:
Shoes Shorts Socks Total
Adidas 67% 0% 33% 50%
Flight 0% 100% 0% 17%
Nike 50% 0% 50% 33%