Question

我的数据框有问题。

我的df是：

product      power                   brand
product_1    3 x 1500W               brand_A
product_2    2x1000W + 1x100W
product 3    1x1500W + 1x500W        brand_B
product 4    500W

我需要乘以乘数乘以乘数（用幂减去）

我的df预期：

product      power               brand          new_product
product_1    1500W               brand_A        product_1_1
product_1    1500W               brand_A        product_1_2
product_1    1500W               brand_A        product_1_3
product_2    1000W                              product_2_1
product_2    1000W                              product_2_2
product_2    100W                               product_2_3
product 3    1500W               brand_B        product_3_1
product 3    500W                brand_B        product_3_2
product 4    500W                               product_4_1

感谢您的帮助

Answer 1

我将进行字符串提取和合并，然后执行一些清理任务：

df1 = (df.power.str.extractall('(\d+)\s?x\s?(\d+W)')
         .reset_index(level=1,drop=True)
      )

new_df = df.merge(df1[1].repeat(df1[0]), 
                  left_index=True, 
                  right_index=True,
                  how='outer')

# update the power column
new_df['power']= np.where(new_df[1].isna(), new_df['power'], new_df[1])

# drop the extra 1 column
new_df.drop(1, axis=1, inplace=True)

# new_product column
new_df['new_product'] = (new_df['product'] + '_' + 
                         new_df.groupby('product').cumcount().add(1).astype(str) )

输出：

     product  power    brand  new_product
0  product_1  1500W  brand_A  product_1_1
0  product_1  1500W  brand_A  product_1_2
0  product_1  1500W  brand_A  product_1_3
1  product_2  1000W     None  product_2_1
1  product_2  1000W     None  product_2_2
1  product_2   100W     None  product_2_3
2  product 3  1500W  brand_B  product 3_1
2  product 3   500W  brand_B  product 3_2
3  product 4   500W     None  product 4_1

Answer 2

@Quang Hoang是一个更正确的答案，因为它仅通过pandas方法实现。无论如何，我只使用普通的python留下一个解决方案：

import pandas as pd
import numpy as np 

cols = ['product', 'power', 'brand']

data = [
  ['product_1', '3 x 1500W', 'brand_A'],
  ['product_2', '2x1000W + 1x100W', np.nan],
  ['product 3', '1x1500W + 1x500W', 'brand_B'],
  ['product 4', '500W', np.nan]
]

df = pd.DataFrame(columns=cols, data=data)
print(df)

原始数据：

     product             power    brand
0  product_1         3 x 1500W  brand_A
1  product_2  2x1000W + 1x100W      NaN
2  product 3  1x1500W + 1x500W  brand_B
3  product 4              500W      NaN

数据争吵

items = df.power.values.tolist()
brands = df.brand.values.tolist()

res = zip(items, brands)

new_data = []

for idx, aux in enumerate(res):
  item, brand = aux
  for idx2, power_model in enumerate(item.split('+')):
      res = power_model.strip().split('x')
      if len(res) == 2:
        units, val = res
      else:
        units = 1
        val = res[0]

      for _ in range(int(units)):
        new_data.append(
            [
              f'product_{idx + 1}', 
              val,
              brand,
              f'product_{idx + 1}_{idx2 + 1}'
            ]
        )

new_cols = ['product', 'power', 'brand', 'new_product']
df2 = pd.DataFrame(columns=new_cols, data=new_data)

print(df2)

结果

     product   power    brand  new_product
0  product_1   1500W  brand_A  product_1_1
1  product_1   1500W  brand_A  product_1_1
2  product_1   1500W  brand_A  product_1_1
3  product_2   1000W      NaN  product_2_1
4  product_2   1000W      NaN  product_2_1
5  product_2    100W      NaN  product_2_2
6  product_3   1500W  brand_B  product_3_1
7  product_3    500W  brand_B  product_3_2
8  product_4    500W      NaN  product_4_1

使用条件熊猫数据框python复制行

2 个答案:

数据争吵

结果