读取由 nan 行分割的数据帧，并将它们重新整形为 Python 中的多个数据帧

我有一个 excel 文件示例data1.xlsx from ，其中有一个Sheet1如下：

现在我想阅读它openpyxl or pandas，然后将它们转换为新的df1 and df2，我最终将它们保存为price and quantity sheet:

价格表：

and 数量表

我使用过的代码：

df = pd.read_excel('./data1.xlsx', sheet_name = 'Sheet1')
df_list = np.split(df, df[df.isnull().all(1)].index) 

for df in df_list:
    print(df, '\n')

Out:

         bj  Unnamed: 1  Unnamed: 2  Unnamed: 3 Unnamed: 4
0      year      2018.0      2019.0      2020.0        sum
1     price        12.0         4.0         5.0         21
2  quantity         5.0         5.0         3.0         13 

         bj  Unnamed: 1  Unnamed: 2  Unnamed: 3 Unnamed: 4
3       NaN         NaN         NaN         NaN        NaN
4        sh         NaN         NaN         NaN        NaN
5      year      2018.0      2019.0      2020.0        sum
6     price         5.0         6.0         7.0         18
7  quantity         7.0         5.0         4.0         16 

    bj  Unnamed: 1  Unnamed: 2  Unnamed: 3 Unnamed: 4
8  NaN         NaN         NaN         NaN        NaN 

          bj  Unnamed: 1  Unnamed: 2  Unnamed: 3 Unnamed: 4
9        NaN         NaN         NaN         NaN        NaN
10        gz         NaN         NaN         NaN        NaN
11      year      2018.0      2019.0      2020.0        sum
12     price         2.0         3.0         1.0          6
13  quantity         6.0         9.0         3.0         18 

     bj  Unnamed: 1  Unnamed: 2  Unnamed: 3 Unnamed: 4
14  NaN         NaN         NaN         NaN        NaN 

          bj  Unnamed: 1  Unnamed: 2  Unnamed: 3 Unnamed: 4
15       NaN         NaN         NaN         NaN        NaN
16        sz         NaN         NaN         NaN        NaN
17      year      2018.0      2019.0      2020.0        sum
18     price         8.0         2.0         3.0         13
19  quantity         5.0         4.0         3.0         12 

我怎样才能在Python中做到这一点？多谢。

Use:

#add header=None for default columns names
df = pd.read_excel('./data1.xlsx', sheet_name = 'Sheet1', header=None)

#convert columns by second row
df.columns = df.iloc[1].rename(None)

#create new column `city` by forward filling non missing values by second column
df.insert(0, 'city', df.iloc[:, 0].mask(df.iloc[:, 1].notna()).ffill())
#convert floats to integers 
df.columns = [int(x) if isinstance(x, float) else x for x in df.columns]
#convert column year to index
df = df.set_index('year')

print (df)
         city    2018    2019    2020  sum
year                                      
bj         bj     NaN     NaN     NaN  NaN
year       bj  2018.0  2019.0  2020.0  sum
price      bj    12.0     4.0     5.0   21
quantity   bj     5.0     5.0     3.0   13
NaN        bj     NaN     NaN     NaN  NaN
sh         sh     NaN     NaN     NaN  NaN
year       sh  2018.0  2019.0  2020.0  sum
price      sh     5.0     6.0     7.0   18
quantity   sh     7.0     5.0     4.0   16
NaN        sh     NaN     NaN     NaN  NaN
NaN        sh     NaN     NaN     NaN  NaN
gz         gz     NaN     NaN     NaN  NaN
year       gz  2018.0  2019.0  2020.0  sum
price      gz     2.0     3.0     1.0    6
quantity   gz     6.0     9.0     3.0   18
NaN        gz     NaN     NaN     NaN  NaN
NaN        gz     NaN     NaN     NaN  NaN
sz         sz     NaN     NaN     NaN  NaN
year       sz  2018.0  2019.0  2020.0  sum
price      sz     8.0     2.0     3.0   13
quantity   sz     5.0     4.0     3.0   12

df1 = df.loc['price'].reset_index(drop=True)
print (df1)
  city  2018  2019  2020 sum
0   bj  12.0   4.0   5.0  21
1   sh   5.0   6.0   7.0  18
2   gz   2.0   3.0   1.0   6
3   sz   8.0   2.0   3.0  13

df2 = df.loc['quantity'].reset_index(drop=True)
print (df2)
  city  2018  2019  2020 sum
0   bj   5.0   5.0   3.0  13
1   sh   7.0   5.0   4.0  16
2   gz   6.0   9.0   3.0  18
3   sz   5.0   4.0   3.0  12

最后写入DataFrames 到现有文件可以通过mode='a'范围，link https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_excel.html:

with pd.ExcelWriter('data1.xlsx', mode='a') as writer:  
    df1.to_excel(writer, sheet_name='price')
    df2.to_excel(writer, sheet_name='quantity')