Pandas Dataframe（来自 CSV）在整个数据中具有多个标题行

Question

Test data file我正在使用从 CSV 文件创建的数据框。数据在整个数据中都有标题行，这些标题行标识了该数据下方的行，直到下一个标题行。

数据看起来像这样。

2001|     |colour |Price | Quantity sold<br>
Shoes|<br>
Blank  | High heal Shoes| red |£22|44<br>
Blank  | Low heal Shoes|red |£22|44<br>
Slippers|<br>
Blank  | High heal Slippers| red |£22|44<br>
Blank  | High heal Slippers| blue |£22|44<br>
Blank  | Low heal Slippers| red |£22|44<br>
2002|   |colour |Price | Quantity sold<br>
Shoes|<br>
Blank  | High heal Shoes| red |£22|44<br>
Blank  | Low heal Shoes|red |£22|44<br>
Slippers|<br>
Blank  | High heal Slippers| red |£22|44<br>
Blank  | High heal Slippers| blue |£22|44<br>
Blank  | Low heal Slippers| red |£22|44<br>

这是什么类型的结构？

我需要通读此数据框，从标题行（如 2001 年、2002 年等）中获取每年特定项目（例如拖鞋）的所有数据。即使在每个数据行旁边添加相应年份的行也会有所帮助。

我会很感激一些关于如何做到这一点的帮助？

Answer 1

用途：

df = pd.read_csv('test.csv')

#get value of first column (here 2001)
col = df.columns[0]

#forward fill last previous value
df[col] = df[col].ffill()
#convert first column to numeric
num = pd.to_numeric(df[col], errors='coerce')
#forward fill again, first group replace by value of first column name
df['Year'] = num.ffill().fillna(col)
#change columns names 
df = df.rename(columns={col:'Shoes', 'Unnamed: 1':'Names'})
#remove unnecessary rows
df = df[num.isnull() & df['colour'].notnull()].reset_index(drop=True)

print (df)
           Shoes       Names  colour price Quantity sold  Year
0   Type A shoes  Sub type A     red    22             5  2001
1   Type A shoes  Sub type A   green    11             5  2001
2   Type A shoes  Sub type A  yellow    44             5  2001
3   Type A shoes  Sub type B     red    33             5  2001
4   Type A shoes  Sub type B   green    66             5  2001
5   Type A shoes  Sub type B  yellow    22             5  2001
6   Type B shoes  Sub type A     red    11             5  2001
7   Type B shoes  Sub type A   green    44             5  2001
8   Type B shoes  Sub type A  yellow    33             5  2001
9   Type B shoes  Sub type B     red    66             5  2001
10  Type B shoes  Sub type B   green    21             5  2001
11  Type B shoes  Sub type B  yellow    22             5  2001
12  Type A shoes  Sub type A     red    22             5  2002
13  Type A shoes  Sub type A   green    11             5  2002
14  Type A shoes  Sub type A  yellow    44             5  2002
15  Type A shoes  Sub type B     red    33             5  2002
16  Type A shoes  Sub type B   green    66             5  2002
17  Type A shoes  Sub type B  yellow    22             5  2002
18  Type B shoes  Sub type A     red    11             5  2002
19  Type B shoes  Sub type A   green    44             5  2002
20  Type B shoes  Sub type A  yellow    33             5  2002
21  Type B shoes  Sub type B     red    66             5  2002
22  Type B shoes  Sub type B   green    21             5  2002
23  Type B shoes  Sub type B  yellow    22             5  2002

编辑：

df = pd.read_csv('testV2.csv', sep='\t')
#print (df)

#get value of first column (here 2001)
col = df.columns[0]

#forward fill last previous value
df[col] = df[col].ffill()
#convert first column to numeric
num = pd.to_numeric(df[col], errors='coerce')
#forward fill again, first group replace by value of first column name
df['Year'] = num.ffill().fillna(col)
#change columns names 
df = df.rename(columns={col:'Top Category', 'Unnamed: 1':'Names'})
#remove unnecessary rows
df = df[num.isnull() & (df['Top Category'] != 'Top Category')].reset_index(drop=True)

---

print (df)

   Top Category   Names Colour Price Sold  Year
0        Item 1  Type 1      -     2  NaN  2001
1        Item 2  Type 1      -     2  NaN  2001
2        Item 3  Type 1    red     2    5  2001
3        Item 3  Type 2   blue     2    5  2001
4        Item 3  Type 3  green     2    5  2001
5        item 4  Type 1    red     2    5  2001
6        item 4  Type 2   blue     3  NaN  2001
7        item 4  Type 3  green     3  NaN  2001
8        Item 1  Type 1      -     3  NaN  2002
9        Item 2  Type 1      -     3  NaN  2002
10       Item 3  Type 1    red     3    5  2002
11       Item 3  Type 2   blue     3    5  2002
12       Item 3  Type 3  green     3    5  2002
13        Item4  Type 1    red     3  NaN  2002
14        Item4  Type 2   blue     3  NaN  2002
15        Item4  Type 3  green     3  NaN  2002
16       Item 1  Type 1      -     3  NaN  2003
17       Item 2  Type 1      -     3  NaN  2003
18       Item 3  Type 1    red     3    5  2003
19       Item 3  Type 2   blue     3    5  2003
20       Item 3  Type 3  green     3    5  2003
21        Item4  Type 1    red     3  NaN  2003
22        Item4  Type 2   blue     3  NaN  2003
23        Item4  Type 3  green     3  NaN  2003