问题描述
我有一个 df，我想根据“Name”和“Plan”列中的值将其拆分为多个 df。对于低于 df，我希望拆分为 6 个 dfs，其中第 1 行和第 6 行将在同一个 df 中
df：
City State Name Plan Price
A CA Star Inn CTS 50
B CA 1 Inn KVG 100
C IN GS Hotel KHA 25
D FL HJ Resort 2QN 45
E AL PQ Inn POI 55
A CA Star Inn CTS 80
A CA Star Inn MNB 65
期望的输出
df1：
City State Name Plan Price
A CA Star Inn CTS 50
A CA Star Inn CTS 80
df2：
City State Name Plan Price
B CA 1 Inn KVG 100
依此类推，直到 df6...
解决方法
此示例将按 Name 和 Plan 拆分数据框并打印它们：
dataframes = []
for _,d in df.groupby(["Name","Plan"]):
dataframes.append(d)
# print it:
for d in dataframes:
print(d)
print("-" * 80)
打印：
City State Name Plan Price
1 B CA 1_Inn KVG 100
--------------------------------------------------------------------------------
City State Name Plan Price
2 C IN GS_Hotel KHA 25
--------------------------------------------------------------------------------
City State Name Plan Price
3 D FL HJ_Resort 2QN 45
--------------------------------------------------------------------------------
City State Name Plan Price
4 E AL PQ_Inn POI 55
--------------------------------------------------------------------------------
City State Name Plan Price
0 A CA Star_Inn CTS 50
5 A CA Star_Inn CTS 80
--------------------------------------------------------------------------------
City State Name Plan Price
6 A CA Star_Inn MNB 65
--------------------------------------------------------------------------------
在 Pandas 中使用 group_by 你会得到一个 Grouper 对象：
grouped = df.groupby(["Name","Plan"])
当您迭代时，它将为您提供一个元组，其中第一个元素是组（在本例中为 ("Name","Plan")），第二个元素是拆分的 dfs：
grouped = df.groupby(["Name","Plan"])
for _,split_df in grouped:
print(split_df)
print("-----")
会给你：
City State Name Plan Price
1 B CA 1 Inn KVG 100
-----
City State Name Plan Price
2 C IN GS Hotel KHA 25
-----
City State Name Plan Price
3 D FL HJ Resort 2QN 45
-----
City State Name Plan Price
4 E AL PQ Inn POI 55
-----
City State Name Plan Price
0 A CA Star Inn CTS 50
5 A CA Star Inn CTS 80
-----
City State Name Plan Price
6 A CA Star Inn MNB 65
-----