我有一段我目前不理解的代码，我不确定它是否按预期运行。如果有人可以提供有关如何重构此代码的一些信息，我们将不胜感激。
conn = psycopg2.connect(conn_string) cur = conn.cursor()
query = """
SELECT
users.id,
users.company_id,
users.state,
users.phase,
transactions.amount
FROM
cases
INNER JOIN transactions ON user.id = transactions.user_id
WHERE users.company_id IN (9, 15)
AND transactions.date < '2018-01-01'
"""
fields = ['id','company_id', 'state', 'phase','amount']
cur.execute(query) data = cur.fetchall()
internal = []
for d in data:
internal += [ [''] * 4 ]
internal[-1][0] = d[1]
internal[-1][1] = d[fields.index('phase')] == 'activated'
internal[-1][2] = d[fields.index('state')]
internal[-1][3] = 'success' if d[4] > 0 else 'fail'
print(internal)
答案 0 :(得分：0)
这将引导您逐步完成代码，注释总是适用于他们后面的行解释
# Imports and connection string setup
# ...
# You create a connection object and a cursor here, which are prerequisites to
# executing queries.
conn = psycopg2.connect(conn_string)
cur = conn.cursor()
# This is your query, however this is static and you would have to manipulate
# this query every time you want something different.
# If you want to dynamically change stuff look at the links
# posted below the code.
query = """
SELECT
users.id,
users.company_id,
users.state,
users.phase,
transactions.amount
FROM
cases
INNER JOIN transactions ON user.id = transactions.user_id
WHERE users.company_id IN (9, 15)
AND transactions.date < '2018-01-01'
"""
# The following line is unnecessary, although it may help understand what
# these columns are named
# To get all column names from the DB you can use
# colnames = [desc[0] for desc in curs.description]
# After you executed your query below
fields = ['id', 'company_id', 'state', 'phase', 'amount']
cur.execute(query)
# Here you fetch all data, which can be a lot (you may want to check
# memory consumption)
data = cur.fetchall()
# Setting up a result list and iterate over the results of the query
internal = []
for d in data:
# The next line is not needed, we do not need to append an empty list
# to the results as we can just append the final result for this row
# further += is overloading the operator, it is cleaner (IMHO) to
# use .append() here
internal += [[''] * 4]
# Now the remainder in the loop is interesting, we add an empty list
# of values to the end of the results (internal) and then use the last
# index to update the values
# Apart from printing the full results in the last step this will probably
# incur some overhead compared to creating the single row's result first
# and then appending/printing it
# This simply assigns the value of the current rows index 1 to the last
# item in internal (the empty list added before)
internal[-1][0] = d[1]
# Here we obtain the index of 'phase' from fields to know which column
# the value is
# If you never change the fields variable above, your can just simply index
# it directly.
# After knowing the index (fields.index('phase')) which would be 3 we do a
# comparison if it is 'activated' as a value
# So the result will be True or False for everything that is
# not 'activated'
internal[-1][1] = d[fields.index('phase')] == 'activated'
# Here we simply find the column idx for state (which is 2) and add the
# value.
internal[-1][2] = d[fields.index('state')]
# Here we check if the amount is > 0 and assign 'success' if so, otherwise
# 'fail'.
internal[-1][3] = 'success' if d[4] > 0 else 'fail'
# You are printing ALL results here, for simple check you may want to print
# only the first or last result as such:
# print(internal[0])
# or
# print(internal[-1])
print(internal)
[编辑]在此处添加重构结果“解析”（这只是其中一个选项）
conn = psycopg2.connect(conn_string)
cur = conn.cursor()
query = """
SELECT
users.id,
users.company_id,
users.state,
users.phase,
transactions.amount
FROM
cases
INNER JOIN transactions ON user.id = transactions.user_id
WHERE users.company_id IN (9, 15)
AND transactions.date < '2018-01-01'
"""
fields = ['id', 'company_id', 'state', 'phase', 'amount']
cur.execute(query)
data = cur.fetchall()
internal = []
for d in data:
# We simply create a list with all the same results and append it
row = [
d[1],
d[fields.index('phase')] == 'activated',
d[fields.index('state')],
'success' if d[4] > 0 else 'fail'
]
internal.append(row)
# print(internal)
print(internal[0])
可以对循环数据进行进一步的简化（从某种意义上说，使其更易于理解），但这些不会严重提高性能。