如何遍历这本字典而不是对键进行硬编码

问题描述

到目前为止，我有这个代码(来自 cs50/pset6/DNA):

So far, I have this code (from cs50/pset6/DNA):

import csv

data_dict = {}
with open(argv[1]) as data_file:
    reader = csv.DictReader(data_file)
    for record in reader:
        # `record` is a dictionary of column-name & value
        name = record["name"]
        data = {
            "AGATC": record["AGATC"],
            "AATG": record["AATG"],
            "TATC": record["TATC"],
        }

        data_dict[name] = data

print(data_dict)

输出

{'Alice': {'AATG': '8', 'AGATC': '2', 'TATC': '3'},
     'Bob': {'AATG': '1', 'AGATC': '4', 'TATC': '5'},
 'Charlie': {'AATG': '2', 'AGATC': '3', 'TATC': '5'}}

这里是 csv 文件:

Here is the csv file:

name,AGATC,AATG,TATC
Alice,2,8,3
Bob,4,1,5
Charlie,3,2,5

但我的目标是实现完全相同的目标，而不是对键 AATG 等进行硬编码，而且因为我将使用包含更多值的更大的数据库，我希望能够遍历数据，而不是这样做:

But my goal is to achieve the exact same thing, but instead of hardcoding the keys AATG, etc., and also because I'll use a much much bigger database that contains more values, I want to be able to loop through the data, instead of doing this:

data = {
            "AGATC": record["AGATC"],
            "AATG": record["AATG"],
            "TATC": record["TATC"],
        }

你能帮我吗?谢谢

推荐答案

您也可以尝试使用 Pandas.

You could also try using pandas.

使用您的示例数据作为 .csv 文件:

Using your example data as .csv file:

pandas.read_csv('example.csv', index_col = 0).transpose().to_dict()

输出:

{'Alice': {'AGATC': 2, 'AATG': 8, 'TATC': 3},
 'Bob': {'AGATC': 4, 'AATG': 1, 'TATC': 5},
 'Charlie': {'AGATC': 3, 'AATG': 2, 'TATC': 5}}

index_col = 0 因为你有我设置为索引的名称列(以便以后成为字典中的顶级键)

index_col = 0 because you have names column which I set as index (so that later becomes top level keys in dictionary)

.transpose() 所以顶级键是名称而不是特征(AGATC、AATG 等)

.transpose() so top level keys are names and not features (AGATC, AATG, etc.)

.to_dict() 将 pandas.DataFrame 转换为 python 字典

.to_dict() to transform pandas.DataFrame to python dictionary

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