如何快速将 csv 表导出到 python 字典？

Question

我有一个 csv 表，其中包含我希望导出到 Python 字典中的化合物及其属性和描述：

上表应转换为以下字典：

compound_data = {COMPOUND 1:{'Property 1':'Description 1', 'Property 2':'Description 2', 'Property 3':'Description 3', 'Property n':'Description n'}, COMPOUND 2:{'Property 1':'Description 1', 'Property 2':'Description 2', 'Property x':'Description x'}, COMPOUND n:{'Property y':'Description y', 'Property z':'Description z'}}

表中有数百种化合物，每种都具有可变数量的特性。 化合物可能具有共同的特性（例如沸点），或者它们可能具有独特的特性（例如电导率）。 我写了一些访问复合信息的代码，如下所示：

compound_data['COMPOUND 1']['Property 1'] = 'Description 1'

将表格读入 python 字典的最简单、最 Pythonic 的方法是什么？

Answer 1

这应该做。

import csv    

with open('csv_file.csv', 'r') as csv_file:
    csv_reader = csv.reader(csv_file)
    compound_dictionary = dict()
    last_compound_key = str()
    for row in csv_reader:
        if row[0] != '':
            last_compound_key = row[0]
            compound_dictionary[last_compound_key] = dict()

        compound_dictionary[last_compound_key][row[1]] = row[2]

print(compound_dictionary)

Answer 2

您可以在 Pandas 的帮助下轻松做到这一点。

import pandas as pd

# Read the csv file
df = pd.read_csv('check.csv')
# Fill the nan values with previous values in the first column
df['compound'] = df['compound'].fillna(method='ffill')

compounds = {}
# Iterate through the dataframe
for row in df.iterrows():
#     Check if the key has already added to the temp dictionary. If available then append else add new one
    if row[1]['compound'] in tmp.keys():
        tmp[row[1]['compound']][row[1]['property']] = row[1]['value']
    else:
        tmp[row[1]['compound']] = {row[1]['property']: row[1]['value']}

# The result is available as a dictionary
print(compounds)
print(compounds['compound a']['property a'])