为什么elif语句在python中无限期运行

Question

我的elif命令正在无限期运行。我的elif逻辑很简单。我有两个API。1( 美国API-我希望API的需求应该在1985年之前工作。2( 澳大利亚API-此API仅适用于1985年&之后之后，我将这两个数据帧合并为一个数据帧。

要运行此逻辑，我们需要在终端中编写此命令。您可以在下面的命令中输入经度、纬度的任意值

python test.py-纬度88-经度75-起始年份1975-结束年份2021

有人能帮我弄清楚为什么这个elif逻辑无限期地运行吗？？？另外，请告诉我如何修复它。

import requests
import json
import argparse
import time
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
##
#   sample command: python test.py -latitude '' -longitude '' -startYear '' -endYear ''
##
parser = argparse.ArgumentParser(description="Process some integers.")
parser.add_argument("-latitude", help="Latitude(Degress)")
parser.add_argument("-longitude", help="Longitude(Degress)")
parser.add_argument("-startYear", help="Start of the Year")
parser.add_argument("-endYear", help="End of the Year")
parser.add_argument("--verbose", help="display processing information")
start = time.time()
def main(latitude,longitude,startYear,endYear,verbose):   
parameters = {  #Australia API
"latd": latitude, # [deg]
"latm": 00, # [deg]
"lats": 00, # [deg]
"lond": longitude, # [deg]
"lonm": 00, # [deg]
"lons": 00, # [deg]
"elev" : 00, # [km]
"year" : None, # [YYYY]
"month" : '07', # [MM]
"day": '01', # [DD]
"Ein": 'D'  # [Model]
}
parameters1 = {    #USA API
'lat1': latitude, # [deg]
'lon1': longitude, # [deg]
'model': 'IGRF', # [Model]
'startYear': None, # [year]
'startMonth': 7, # [month]
'startDay':1,  # [date] 
'resultFormat': 'json', # [format] 
}
hostname = "https://api.geomagnetism.ga.gov.au/agrf" #Australia API
hostname1 = "http://www.ngdc.noaa.gov/geomag-web/calculators/calculateDeclination?%s"  #USA API
df_1=pd.DataFrame()
df_2=pd.DataFrame()
for year in range(startYear, endYear): 
if endYear < 1985:
if  startYear < 1985:        
print('Good, this loop working')        
elif startYear < 1985:  # Loop is running indefinetly
for year in range(startYear, 1985): #USA API
try:
parameters1["year"] = year      #USA API
response = requests.get(hostname1, params= dict(parameters1, ps=str(year)))
# extract JSON payload of response as Python dictionary
json_payload = response.json()
# raise an Exception if we encoutnered any HTTP error codes like 404
response.raise_for_status()
except requests.exceptions.ConnectionError as e:
# handle any typo errors in url or endpoint, or just patchy internet connection
print(e)
except requests.exceptions.HTTPError as e:
# handle HTTP error codes in the response
print(e, json_payload['error'])
except requests.exceptions.RequestException as e:
# general error handling
print(e, json_payload['error'])
else:
json_payload = response.json()
#print(json.dumps(json_payload, indent=4, sort_keys=True))
df = pd.DataFrame(json_payload['result'])
new_row = {
"SourceFile": hostname1,
"Year": year,
"Magnetic Declination": df.iloc[0, 2],
"Latitude": -35,
"Longitude": 145
}
df_1 = df_1.append(new_row, ignore_index=True)
df_1 = df_1[['Year', 'Latitude', 'Longitude','Magnetic Declination','SourceFile']]
df_1.to_csv('magnetic_declination_usa.csv',index=False)  
for year in range(1985, endYear):
try:     #Australia API
parameters["year"] = year
response = requests.get(hostname, params= dict(parameters, ps=str(year)))
# extract JSON payload of response as Python dictionary
json_payload = response.json()
# raise an Exception if we encoutnered any HTTP error codes like 404
response.raise_for_status()
except requests.exceptions.ConnectionError as e:
# handle any typo errors in url or endpoint, or just patchy internet connection
print(e)
except requests.exceptions.HTTPError as e:
# handle HTTP error codes in the response
print(e, json_payload['error'])
except requests.exceptions.RequestException as e:
# general error handling
print(e, json_payload['error'])
else:
json_payload = response.json()
#print(json.dumps(json_payload, indent=4, sort_keys=True))
df = pd.DataFrame(json_payload)
new_row = {
"SourceFile": hostname,
"Year": year,
"Magnetic Declination": df.iloc[5, 3],
"Latitude": latitude,
"Longitude": longitude
}
df_2 = df_2.append(new_row, ignore_index=True)
df_2 = df_2[['Year', 'Latitude', 'Longitude','Magnetic Declination','SourceFile']]
df_2["Magnetic Declination"] = df_2["Magnetic Declination"].apply(lambda x: x.replace(" deg", ""))
df_2.to_csv('magnetic_declination_australia.csv',index=False)
df_3 = pd.concat([df_1,df_2], axis=0)            #Merge dataframe into one    
df_3.to_csv('Magnetic_Declination_(USA+Australia).csv',index=False) 
else:    # Case where endYear < 1985 and startYear > 1985 (probably an input error)
print("Invalid Year. Please check the startYear & EndYear.")

if __name__ == '__main__':
start = time.time()
args = parser.parse_args()
latitude = args.latitude
longitude = args.longitude
startYear = int(args.startYear)
endYear = int(args.endYear)
verbose = args.verbose
main(latitude,longitude,startYear,endYear,verbose)  # Calling Main Function
print("Processed time:", time.time() - start)  # Total Time

Answer 1

它将永远运行，因为您在循环中再次在循环中使用变量year。这会造成一个副作用，使循环繁忙：-(在嵌套循环时使用不同的变量名。