從多個數據源在python中創建嵌套字典

Question

由於原始文章不清楚，所以將其完全改寫。我想要做的是逐行解析一些數據並創建一個字典。我想有更好的方法來組織這些數據。我試圖去解決這個問題的原始方式並沒有解釋幾件事情，所以我提出了這個問題。我通過逐行循環服務策略輸出來將接口，策略名稱的數據放在一起，然後拉出隊列，丟棄和無緩衝區丟棄。我遇到的問題是它沒有考慮額外的策略，因此數據的原始傳遞被覆蓋。

服務策略輸出：

GigabitEthernet11/1 

Service-policy output: Gi11_1 

Counters last updated 7191104 seconds ago 

Class-map: class-default (match-any) 
    0 packets, 0 bytes 
    30 second offered rate 0000 bps, drop rate 0000 bps 
    Match: any 
    Queueing 
    queue limit 33025 packets 
    (queue depth/total drops/no-buffer drops) 0/0/0 
    (pkts output/bytes output) 0/0 
    shape (average) cir 500000000, bc 2000000, be 2000000 
    target shape rate 500000000 

    Service-policy : child 

    Counters last updated 7191104 seconds ago 

    Class-map: class-default (match-any) 
     0 packets, 0 bytes 
     30 second offered rate 0000 bps, drop rate 0000 bps 
     Match: any 
     Queueing 
     queue limit 33025 packets 
     (queue depth/total drops/no-buffer drops) 0/0/0 
     (pkts output/bytes output) 0/0 
     bandwidth remaining ratio 100 

for ints, int_strings in zip(int_names, int_output): 
    counts.setdefault(ints, {}) 

    for line in int_strings.splitlines(): 
     matchpolicy = re.search(r'(Service-policy.*)', line) 
     matchdrops = re.findall(r'total drops.*', line) 
     if matchpolicy: 
      spolicies = matchpolicy.group(0) 
      counts[ints]['Policy'] = spolicies 
     if matchdrops: 
      regx = re.search(r'\s(\d+)\/(\d+)\/(\d+)', line) 
      counts[ints]['queue'] = int(regx.group(1)) 
      counts[ints]['drops'] = int(regx.group(2)) 
      counts[ints]['no-buffer'] = int(regx.group(3)) 

return counts 

我試圖創建一個額外的深度級別的字典，但我對數得到一個關鍵的錯誤[整數] [spolicies]線。從我讀的內容來看，我認爲這是嵌套字典的工作方式，但我想我誤解了。

for ints, int_strings in zip(int_names, int_output): 
    counts.setdefault(ints, {}) 

    for line in int_strings.splitlines(): 
     matchpolicy = re.search(r'(Service-policy.*)', line) 
     matchdrops = re.findall(r'total drops.*', line) 
     if matchpolicy: 
      spolicies = matchpolicy.group(0) 
      counts[ints][spolicies] 
     if matchdrops: 
      regx = re.search(r'\s(\d+)\/(\d+)\/(\d+)', line) 
      counts[ints][spolicies]['queue'] = int(regx.group(1)) 
      counts[ints][spolicies]['drops'] = int(regx.group(2)) 
      counts[ints][spolicies]['no-buffer'] = int(regx.group(3)) 

return counts 

無論哪種方式，我猜想有可能是一個更好的方式來組織這個數據，所以我可以以後通過它更容易。有任何想法嗎？

Answer 1

labels = ["depth", "drops", "buffer_drops"] 
values = ['0', '14996', '0', '0', '2100', '0'] 
keys=['Gi1','Gi2'] 

values_grouped_by_3 = list(zip(*[iter(values)]*3)) 
data = dict(zip(keys,[dict(zip(labels,vals)) for vals in values_grouped_by_3])) 

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