我的前端是位置import pymongo
import config
import pandas as pd
import numpy as np
from datetime import datetime
from config import logger
client = pymongo.MongoClient(config.IP)
try:
client.server_info()
except pymongo.errors.ServerSelectionTimeoutError as e:
logger.error("Unable to connect to %s. Error: %s" % (config.IP, e))
client = None
# connect to database (or create if not exists)
mydb = client[config.DB_NAME]
# connect to collections (or create if not exists)
movie_collection = mydb[config.DB_MOVIE_COLLECTION]
actors_collection = mydb[config.DB_ACTOR_COLLECTION]
def read_data(file):
'''
returns Dataframe with read csv data
'''
df = pd.read_csv(file, sep='\t')
df.replace('\\N', np.nan, inplace=True)
return df
def insert_to_collection(collection, data):
collection.insert(data)
def fill_movie_data():
'''
iterates over movie Dataframe
process values and creates dict structure
with specific attributes to insert into MongoDB movie collection
'''
# load data to pandas Dataframe
logger.info("Reading movie data to Dataframe")
data = read_data('datasets/title.basics.tsv')
for index, row in data.iterrows():
result_dict = {}
id_ = row['tconst']
title = row['primaryTitle']
# check value of movie year (if not NaN)
if not pd.isnull(row['endYear']) and not pd.isnull(row['startYear']):
year = list([row['startYear'], row['endYear']])
elif not pd.isnull(row['startYear']):
year = int(row['startYear'])
else:
year = None
# check value of movie duration (if not NaN)
if not pd.isnull(row['runtimeMinutes']):
try:
duration = int(row['runtimeMinutes'])
except ValueError:
duration = None
else:
duration = None
# check value of genres (if not NaN)
if not pd.isnull(row['genres']):
genres = row['genres'].split(',')
else:
genres = None
result_dict['_id'] = id_
result_dict['primary_title'] = title
# if both years have values
if isinstance(year, list):
result_dict['year_start'] = int(year[0])
result_dict['year_end'] = int(year[1])
# if start_year has value
elif year:
result_dict['year'] = year
if duration:
result_dict['duration'] = duration
if genres:
result_dict['genres'] = genres
insert_to_collection(movie_collection, result_dict)
def fill_actors_data():
'''
iterates over actors Dataframe
process values, creates dict structure
with new fields to insert into MongoDB actors collection
'''
logger.info("Inserting data to actors collection")
# load data to pandas Dataframe
logger.info("Reading actors data to Dataframe")
data = read_data('datasets/name.basics.tsv')
logger.info("Inserting data to actors collection")
for index, row in data.iterrows():
result_dict = {}
id_ = row['nconst']
name = row['primaryName']
# if no birth year and death year value
if pd.isnull(row['birthYear']):
yob = None
alive = False
# if both birth and death year have value
elif not pd.isnull(row['birthYear']) and not pd.isnull(row['deathYear']):
yob = int(row['birthYear'])
death = int(row['deathYear'])
age = death - yob
alive = False
# if only birth year has value
else:
yob = int(row['birthYear'])
current_year = datetime.now().year
age = current_year - yob
alive = True
if not pd.isnull(row['knownForTitles']):
movies = row['knownForTitles'].split(',')
result_dict['_id'] = id_
result_dict['name'] = name
result_dict['yob'] = yob
result_dict['alive'] = alive
result_dict['age'] = age
result_dict['movies'] = movies
insert_to_collection(actors_collection, result_dict)
# update movie documents with list of actors ids
movie_collection.update_many({"_id": {"$in": movies}}, {"$push": { "people": id_}})
# if collections are empty, fill it with data
if movie_collection.count() == 0:
fill_movie_data()
if actors_collection.count() == 0:
fill_actors_data()
上服务的静态站点，而我的后端是位置/上的REST API。我已经配置/core/来反向代理后端，但是根据日志，它正在尝试将其用作静态站点。
nginx.conf这是我的2018/12/17 08:47:32 [error] 6#6: *12 open() "/usr/share/nginx/html/core/v1/api/user/login" failed (2: No such file or directory)
：
nginx.conf发生了什么，如何解决？