pd.read_tsv
时间: 2023-11-11 11:55:45 浏览: 106
pd.read_tsv函数不是pandas库中的一个内置函数,因此无法直接使用该函数来读取tsv文件。然而,你可以使用pd.read_csv函数来读取tsv文件。只需要将参数sep设置为'\t'即可指定分隔符为制表符。以下是一个示例代码:
```python
import pandas as pd
df = pd.read_csv('data.tsv', sep='\t')
```
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帮我解读下这个代码:import csv import os import numpy as np import pandas as pd import pymysql from pymysql import connect # %% # drug_table = pd.read_excel('./data/drug.xlsx') drug_table_an = pd.read_excel('./data/mimiciv_feature_info.xlsx', sheet_name='antibiotic') drug_table_sa = pd.read_excel('./data/mimiciv_feature_info.xlsx', sheet_name='sedatives_and_analgesics') drug_table_co = pd.read_excel('./data/mimiciv_feature_info.xlsx', sheet_name='anticoagulant') prescriptions = pd.read_csv('/data/check_in/EHR_data/MIMIC_III/CSV/PRESCRIPTIONS.csv') item = pd.read_csv('/data/check_in/EHR_data/MIMIC_III/CSV/D_ITEMS.csv') labitem = pd.read_csv('/data/check_in/EHR_data/MIMIC_III/CSV/D_LABITEMS.csv') columns_pre = prescriptions.columns.tolist() columns_item = item.columns.tolist() columns_labitem = labitem.columns.tolist() # drugs = (drug_table['anticoagulant'].to_list()+drug_table['antiplatelet'].to_list())[:-4] drugs = ['barbital' ,'zepam' ,'zolam' ,'zolpidem' ,'propofol' ,'dexmedetomidine' ,'pentobarbital' ,'clonazepam' ,'alprazolam' ,'estazolam' ,'Zolpidem Tartrate'] drug_test_tsv = open('drug_patients_sedative.csv', 'w', newline='', encoding='utf-8') drug_test = csv.writer(drug_test_tsv, delimiter=',') drug_test.writerow(columns_pre) item_test_tsv = open('item_patients_sedative.csv', 'w', newline='', encoding='utf-8') item_test = csv.writer(item_test_tsv, delimiter=',') item_test.writerow(columns_item) labitem_test_tsv = open('labitem_patients_sedative.csv', 'w', newline='', encoding='utf-8') labitem_test = csv.writer(labitem_test_tsv, delimiter=',') labitem_test.writerow(columns_labitem) # import pdb;pdb.set_trace() for drug in drugs: # print(type(drug)) sql = "select * FROM PRESCRIPTIONS where drug like '%"+ drug + "%' or drug_name_poe like '%"+ drug + "%' or drug_name_generic like '%"+ drug + "%'" print(sql) conn = connect(host='127.0.0.1', port=3306, user='root', passwd='root', db='mimiciii') cursor = conn.cursor() cursor.execute(sql) data_tmp = cursor.fetchall() # print(data_tmp is None) if len(data_tmp) != 0: for data_cur in data_tmp: print(data_cur[1], data_cur[2], data_cur[3], data_cur[7], data_cur[8], data_cur[9]) drug_test.writerow(list(data_cur)) # import pdb;pdb.set_trace() for drug in drugs: # print(type(drug)) sql = "select * FROM D_ITEMS where label like '%{}%'" .format(drug) print(sql) conn1 = connect(host='127.0.0.1', port=3306, user='root', passwd='root', db='mimiciii') cursor1 = conn1.cursor() cursor1.execute(sql) data_tmp = cursor1.fetchall() if len(data_tmp) != 0: for data_cur in data_tmp: print(data_cur[1], data_cur[2]) item_test.writerow(list(data_cur)) # import pdb;pdb.set_trace() for drug in drugs: # print(type(drug)) sql = "select * FROM D_LABITEMS where label like '%{}%'" .format(drug) print(sql) conn1 = connect(host='127.0.0.1', port=3306, user='root', passwd='root', db='mimiciii') cursor1 = conn1.cursor() cursor1.execute(sql) data_tmp = cursor1.fetchall() if len(data_tmp) != 0: for data_cur in data_tmp: print(data_cur[1], data_cur[2]) labitem_test.writerow(list(data_cur)) # import pdb;pdb.set_trace() # %% import pandas as pd drug = pd.read_csv('drug_patients_sedative.csv') print(drug.DRUG.unique()) # %% print(drug.DRUG_NAME_POE.unique()) # %% print(drug.DRUG_NAME_GENERIC.unique()) # %%
这段代码是Python语言的导入模块语句,它导入了csv、os、numpy、pandas和pymysql等模块,并从pymysql模块中导入connect函数。这些模块提供了各种功能,如读写CSV文件、操作操作系统、进行数值计算、处理数据和连接MySQL数据库等。
用pd.read_csv()函数在python中将一个存储序列信息的tsv文件变成一个fasa文件
在Python中,`pandas`库的`read_csv()`函数主要用于读取CSV文件,而TSV(Tab Separated Values)文件本质上也是文本文件,可以视为一种特殊的CSV文件,只是列之间的分隔符是制表符 `\t` 而不是逗号。
如果你有一个存储序列信息的TSV文件,并想将其转换为Fasta(`.fasta`)文件,通常这不是直接通过`read_csv`完成的,因为Fasta是一种专门用于生物序列存储的文件格式,它包含两条行,每条对应一个序列,第一行是描述(ID),第二行是序列本身。对于这样的转换,你需要编写一些特定的逻辑,比如解析TSV文件的内容,然后按照Fasta格式重新组织数据并保存。
下面是一个简化的例子,展示如何从TSV文件读取序列数据,然后生成Fasta文件:
```python
import pandas as pd
# 假设你已经有了一个名为 'sequences.tsv' 的TSV文件
data = pd.read_csv('sequences.tsv', sep='\t') # 用'\t'替换为你的实际分隔符
# 假设文件的第一列是ID,其余列是序列
sequences = data.iloc[:, 1:].values.flatten().tolist()
# 创建一个字典,键是ID,值是序列
seq_dict = {data.columns[0][i]: seq for i, seq in enumerate(sequences)}
# 将字典内容写入Fasta文件
with open('output.fasta', 'w') as f:
for id_, seq in seq_dict.items():
f.write(f">{id_}\n{seq}\n")
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