python - Pandas wide to long 附加字典

Question

我的数据框是这样的

>df
ds           A  B  C
01/01/2010   4  2  1
02/01/2010   2  9  3
03/01/2010   1  3  0

其中 A 和 B 属于类别 1，C 属于类别 2。

我想把它转换成:

ds           Category  Company  Value
01/01/2010      1         A      4
01/01/2010      1         B      2
01/01/2010      2         C      1

依此类推，以便稍后绘制。

Answer 1

使用DataFrame.melt :

df['ds'] = pd.to_datetime(df['ds'], format='%d/%m/%Y')

df = df.melt('ds', var_name='Company')

如果可以创建多个类别，则创建字典并按 Series.map 创建新列:

d = {1:['A','B'], 2:['C']}
#swap key values in dict
#http://stackoverflow.com/a/31674731/2901002
d1 = {k: oldk for oldk, oldv in d.items() for k in oldv}

df['Category'] = df['Company'].map(d1)

#alternative1
#df['Category'] = np.where(df['Company'] == 'C', 2, 1)
#alternative2
#df['Category'] = np.where(df['Company'].isin(['A','B']), 1, 2)

---

df = df.sort_values(['ds','Company']).reset_index(drop=True)

或DataFrame.set_index与 DataFrame.stack :

df['ds'] = pd.to_datetime(df['ds'], format='%d/%m/%Y')

df = df.set_index('ds').stack().rename_axis(('ds','Company')).reset_index(name='value')
df['Category'] = np.where(df['Company'] == 'C', 2, 1)

---

print (df)
          ds Company  value  Category
0 2010-01-01       A      4         1
1 2010-01-01       B      2         1
2 2010-01-01       C      1         2
3 2010-01-02       A      2         1
4 2010-01-02       B      9         1
5 2010-01-02       C      3         2
6 2010-01-03       A      1         1
7 2010-01-03       B      3         1
8 2010-01-03       C      0         2