Test data file我正在使用我从CSV文件创建的数据框。数据在整个数据中都有标题行，用于标识数据下方的行，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？
我需要通读这个数据框，从标题行（所以2001年，2002年等等）获取每年特定项目（比如Slippers）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。
我会很感激有关如何做到这一点的一些帮助？
答案 0 :(得分：2)
使用：
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