輸入字典
{'basename_AM1.csv': ['AM1286', 'AM1287', 'AM1288']}
我有以下格式的大型 csv 檔案 basename_AM1.csv 我有以下格式的大型 csv 檔案
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
現在我需要通過搜索/過濾 csv 檔案為給定的 input_dict 創建一個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}}
我提出了以下邏輯，但是對于 100 個樣本的 input_dict，這需要很長時間，有人可以建議優化和最快的方法來實作這一點
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']
uj5u.com熱心網友回復：
IIUC，您可以使用：
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())
如果你想要 100 在對角線上：
import numpy as np
a = df2.to_numpy()
np.fill_diagonal(a, 100)
df2 = pd.DataFrame(a, index=lst, columns=lst)
輸出：
{'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}}
uj5u.com熱心網友回復：
Pandas 有一個內置的 read_csv 方法。
檔案位于：https ://pandas.pydata.org/docs/reference/api/pandas.read_csv.html
轉載請註明出處，本文鏈接：https://www.uj5u.com/net/478336.html
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