英文:
Item to column with value from other column
问题
如何在Python中将DataFrame从这种格式：
| date | item_code | qty |
|---|---|---|
| 2023-01-01 | HTA-01 | 16 |
| 2023-01-01 | HTA-02 | 29 |
| 2023-01-01 | HTB-07 | 1 |
| 2023-01-02 | HTA-01 | 18 |
| 2023-01-02 | HTA-02 | 34 |
转换成这种格式：
| date | HTA-01 | HTA-02 | HTB-07 |
|---|---|---|---|
| 2023-01-01 | 16 | 29 | 1 |
| 2023-01-02 | 18 | 34 | nan |
每个唯一的item_code值都成为一列，其值来自qty列。以下是示例数据：
data = {'date': ['2023-01-01', '2023-01-01', '2023-01-01', '2023-01-02', '2023-01-02'],
'item_code': ['HTA-01', 'HTA-02', 'HTB-07', 'HTA-01', 'HTA-02'],
'qty': ['16', '29', '1', '18', '34']}
df = pd.DataFrame(data)
英文:
How to change dataframe like this
| date | item_code | qty |
|---|---|---|
| 2023-01-01 | HTA-01 | 16 |
| 2023-01-01 | HTA-02 | 29 |
| 2023-01-01 | HTB-07 | 1 |
| 2023-01-02 | HTA-01 | 18 |
| 2023-01-02 | HTA-02 | 34 |
to this in python
| date | HTA-01 | HTA-02 | HTB-07 |
|---|---|---|---|
| 2023-01-01 | 16 | 29 | 1 |
| 2023-01-02 | 18 | 34 | nan |
Each unique value from item_code become column. The value from its is from qty column. This is sample data to try
data = { 'date': ['2023-01-01','2023-01-01','2023-01-01','2023-01-02', '2023-01-02'],
'item_code':['HTA-01', 'HTA-02', 'HTB-07', 'HTA-01', 'HTA-02'],
'qty':['16', '29', '1', '18', '34'],
}
df = pd.DataFrame(data2)
答案1
得分: 1
使用 pivot：
data = { 'date': ['2023-01-01','2023-01-01','2023-01-01','2023-01-02', '2023-01-02'],
'item_code':['HTA-01', 'HTA-02', 'HTB-07', 'HTA-01', 'HTA-02'],
'qty':['16', '29', '1', '18', '34'],
}
df = pd.DataFrame(data)
df.pivot(index='date', columns='item_code')
#输出
# qty
#item_code HTA-01 HTA-02 HTB-07
#date
#2023-01-01 16 29 1
#2023-01-02 18 34 NaN
英文:
Use pivot:
data = { 'date': ['2023-01-01','2023-01-01','2023-01-01','2023-01-02', '2023-01-02'],
'item_code':['HTA-01', 'HTA-02', 'HTB-07', 'HTA-01', 'HTA-02'],
'qty':['16', '29', '1', '18', '34'],
}
df = pd.DataFrame(data)
df.pivot(index='date', columns='item_code')
#Output
# qty
#item_code HTA-01 HTA-02 HTB-07
#date
#2023-01-01 16 29 1
#2023-01-02 18 34 NaN
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