使用pandas过滤csv并创建矩阵的最快方法

Question

输入字典

{'basename_AM1.csv': ['AM1286', 'AM1287', 'AM1288']}

我有以下格式的大型csv文件basename_AM1.csv我有以下格式的大型csv文件

basename_AM1.csv

ID1           ID2   Score
0        AM1287       AM1286  97.55
1        AM1288       AM1286  78.91
2        AM1289       AM1286  95.38
3        AM1290       AM1286  94.83
4        AM1291       AM1286  82.91

现在我需要通过搜索/过滤csv文件

为给定的input_dict创建如下所示的相似性字典

{'AM1286': {'AM1286': 0, 'AM287': 97.55, 'AM288': 78.91},
'AM1287': {'AM1286': 97.55, 'AM1287': 100.0, 'AM1288': 78.91},
'AM1288': {'AM1286': 78.91, 'AM1287': 78.91, 'AM1288': 100.0}}

我已经提出了下面的逻辑，但对于100个样本的input_dict，这需要太长时间，有人可以建议优化和最快的方式来实现这个

for key,value in input_dict.items():
base_name_df = pd.read_csv('csv_file_path')
base_name_df.columns = "ID1","ID2","Score"
if os.path.exists('csv_file_path'):
for id1 in range(len(value)):
for id2 in range(len(value)):
scan_df = base_name_df[(base_name_df['ID1'] == value[id1]) & (base_name_df['ID2'] == value[id2])]
if not scan_df.empty:
scan_df = scan_df.groupby(['LIMSID1','LIMSID2'], as_index=False)['Score'].max()
final_dict[value[id1]][value[id2]] = scan_df.iloc[0]['Score']

Answer 1

iuc，可以使用:

input_dict = {'basename_AM1.csv': ['AM1286', 'AM1287', 'AM1288']}
import pandas as pd
for fname, lst in input_dict.items():
df = pd.read_csv(fname, sep='s+', names=['ID1', 'ID2', 'score'])
df2 = df.pivot('ID1', 'ID2', 'score').reindex(index=lst, columns=lst)
df2 = df2.combine_first(df2.T).fillna(0)
# print for example
print(df2.to_dict())

如果你想在对角线上设置100:

import numpy as np
a = df2.to_numpy()
np.fill_diagonal(a, 100)
df2 = pd.DataFrame(a, index=lst, columns=lst)

输出:

{'AM1286': {'AM1286': 0.0, 'AM1287': 97.55, 'AM1288': 78.91},
'AM1287': {'AM1286': 97.55, 'AM1287': 0.0, 'AM1288': 0.0},
'AM1288': {'AM1286': 78.91, 'AM1287': 0.0, 'AM1288': 0.0}}

Answer 2

Pandas有一个内置的read_csv方法

文档可在:https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html