我在这个问题上花了很多时间，但距离解决方案还差得很远。
我有一个输出为的数据框
RegionID AreaID Year Jan Feb Mar Apr May Jun
0 20.0 1.0 2020.0 1174.0 1056.0 1051.0 1107.0 1097.0 1118.0
1 19.0 2.0 2020.0 460.0 451.0 421.0 421.0 420.0 457.0
2 20.0 3.0 2020.0 2723.0 2594.0 2590.0 2399.0 2377.0 2331.0
3 21.0 4.0 2020.0 863.0 859.0 813.0 785.0 757.0 765.0
4 19.0 5.0 2020.0 4037.0 3942.0 4069.0 3844.0 3567.0 3721.0
5 19.0 6.0 2020.0 1695.0 1577.0 1531.0 1614.0 1671.0 1693.0
6 18.0 7.0 2020.0 1757.0 1505.0 1445.0 1514.0 1406.0 1444.0
7 18.0 8.0 2020.0 832.0 721.0 747.0 852.0 885.0 872.0
8 18.0 9.0 2020.0 2538.0 2000.0 2026.0 1981.0 1987.0 1949.0
9 21.0 10.0 2020.0 1145.0 1235.0 1114.0 1161.0 1150.0 1189.0
10 20.0 11.0 2020.0 551.0 497.0 503.0 472.0 505.0 532.0
11 19.0 12.0 2020.0 1664.0 1526.0 1389.0 1373.0 1384.0 1404.0
12 21.0 13.0 2020.0 381.0 351.0 299.0 286.0 297.0 319.0
13 21.0 14.0 2020.0 1733.0 1627.0 1567.0 1561.0 1498.0 1511.0
14 18.0 15.0 2020.0 1257.0 1257.0 1160.0 1172.0 1124.0 1113.0
I want to pivot this data so that I have a month combined field like below
RegionID AreaID Year Month Amout
20.0 1.0 2020 Jan 1174
20.0 1.0 2020 Feb 1056
20.0 1.0 2020 Mar 1051
Can this be done using pandas? I have been trying with the pivot_table but I cant get it to work.
最佳答案
我希望我已经很好地理解了你的问题。您可以.set_index()，然后.stack():
print(
df.set_index(["RegionID", "AreaID", "Year"])
.stack()
.reset_index()
.rename(columns={"level_3": "Month", 0: "Amount"})
)
打印:
RegionID AreaID Year Month Amount
0 20.0 1.0 2020.0 Jan 1174.0
1 20.0 1.0 2020.0 Feb 1056.0
2 20.0 1.0 2020.0 Mar 1051.0
3 20.0 1.0 2020.0 Apr 1107.0
4 20.0 1.0 2020.0 May 1097.0
5 20.0 1.0 2020.0 Jun 1118.0
6 19.0 2.0 2020.0 Jan 460.0
7 19.0 2.0 2020.0 Feb 451.0
8 19.0 2.0 2020.0 Mar 421.0
9 19.0 2.0 2020.0 Apr 421.0
10 19.0 2.0 2020.0 May 420.0
11 19.0 2.0 2020.0 Jun 457.0
...
或者:
print(
df.melt(
["RegionID", "AreaID", "Year"], var_name="Month", value_name="Amount"
)
)
关于python - 如何在 Python 中使用 Pivot_Table 操作 Dataframe，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/67844562/