在不进行并行编程的情况下,我可以使用下面的代码在 key
列上合并左右数据帧,但它会太慢,因为它们都非常大。有什么方法可以有效地并行化吗?
我有 64 个内核,所以实际上我可以使用其中的 63 个来合并这两个数据帧。
left = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3']})
right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']})
result = pd.merge(left, right, on='key')
输出将是:
left:
A B key
0 A0 B0 K0
1 A1 B1 K1
2 A2 B2 K2
3 A3 B3 K3
right:
C D key
0 C0 D0 K0
1 C1 D1 K1
2 C2 D2 K2
3 C3 D3 K3
result:
A B key C D
0 A0 B0 K0 C0 D0
1 A1 B1 K1 C1 D1
2 A2 B2 K2 C2 D2
3 A3 B3 K3 C3 D3
我想并行执行此操作,以便快速完成。
最佳答案
Docs说:
什么绝对有效?
Cleverly parallelizable operations (also fast):
Join on index: dd.merge(df1, df2, left_index=True, right_index=True)
或者:
Operations requiring a shuffle (slow-ish, unless on index)
Set index: df.set_index(df.x)
Join not on the index: pd.merge(df1, df2, on='name')
您还可以检查如何 Create Dask DataFrames .
示例
import pandas as pd
left = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3']})
right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']})
result = pd.merge(left, right, on='key')
print result
A B key C D
0 A0 B0 K0 C0 D0
1 A1 B1 K1 C1 D1
2 A2 B2 K2 C2 D2
3 A3 B3 K3 C3 D3
import dask.dataframe as dd
#Construct a dask objects from a pandas objects
left1 = dd.from_pandas(left, npartitions=3)
right1 = dd.from_pandas(right, npartitions=3)
#merge on key
print dd.merge(left1, right1, on='key').compute()
A B key C D
0 A3 B3 K3 C3 D3
1 A1 B1 K1 C1 D1
0 A2 B2 K2 C2 D2
1 A0 B0 K0 C0 D0
#first set indexes and then merge by them
print dd.merge(left1.set_index('key').compute(),
right1.set_index('key').compute(),
left_index=True,
right_index=True)
A B C D
key
K0 A0 B0 C0 D0
K1 A1 B1 C1 D1
K2 A2 B2 C2 D2
K3 A3 B3 C3 D3
关于python - 如何并行合并两个 Pandas 数据框(多线程或多处理),我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/35785109/