Reshaping Wide Data to Long Format in Pandas
In the realm of data manipulation, reshaping data from wide to long format often arises as a necessity. Consider the following pandas dataframe:
| AA | BB | CC | |
|---|---|---|---|
| 05/03 | 1 | 2 | 3 |
| 06/03 | 4 | 5 | 6 |
| 07/03 | 7 | 8 | 9 |
| 08/03 | 5 | 7 | 1 |
To transform this into the desired long format:
| AA | 05/03 | 1 |
| AA | 06/03 | 4 |
| AA | 07/03 | 7 |
| AA | 08/03 | 5 |
| BB | 05/03 | 2 |
| BB | 06/03 | 5 |
| BB | 07/03 | 8 |
| BB | 08/03 | 7 |
| CC | 05/03 | 3 |
| CC | 06/03 | 6 |
| CC | 07/03 | 9 |
| CC | 08/03 | 1 |
We employ the pandas.melt or pandas.DataFrame.melt function, which elegantly transforms wide data into long format.
import pandas as pd
df = pd.DataFrame({
'date' : ['05/03', '06/03', '07/03', '08/03'],
'AA' : [1, 4, 7, 5],
'BB' : [2, 5, 8, 7],
'CC' : [3, 6, 9, 1]
}).set_index('date')
df = df.reset_index()
pd.melt(df, id_vars='date', value_vars=['AA', 'BB', 'CC'])Alternatively, one can omit the reset_index step by calling melt with ignore_index=False:
dfm = df.melt(ignore_index=False).reset_index()
Resulting in the desired long format:
| date | variable | value | |
|---|---|---|---|
| 0 | 05/03 | AA | 1 |
| 1 | 06/03 | AA | 4 |
| 2 | 07/03 | AA | 7 |
| 3 | 08/03 | AA | 5 |
| 4 | 05/03 | BB | 2 |
| 5 | 06/03 | BB | 5 |
| 6 | 07/03 | BB | 8 |
| 7 | 08/03 | BB | 7 |
| 8 | 05/03 | CC | 3 |
| 9 | 06/03 | CC | 6 |
| 10 | 07/03 | CC | 9 |
| 11 | 08/03 | CC | 1 |
This transformation enables efficient merging with other dataframes based on shared dates and column names.
以上是如何在 Pandas 中將寬資料轉換為長格式？的詳細內容。更多資訊請關注PHP中文網其他相關文章！
