列表寫成CSV Python的

Question

假設我有一個像這樣的字典數據集的列表，

data_set = [ 
    {'Active rate': [0.98, 0.97, 0.96]}, 
    {'Operating Expense': [3.104, 3.102, 3.101]} 
] 

我需要遍歷字典的列表，並把鑰匙作爲列標題，它的價值爲行，將其寫入CSV文件。

Active rate Operating Expense 
0.98   3.104 
0.97   3.102 
0.96   3.101 

這是我試過

data_set = [ 
    {'Active rate': [0.98, 0.931588, 0.941192]}, 
    {'Operating Expense': [3.104, 2.352, 2.304]} 
] 

import csv 

with open('names.csv', 'w') as csvfile: 
    fieldnames = ['Active rate', 'Operating Expense'] 
    writer = csv.DictWriter(csvfile, fieldnames=fieldnames) 

    writer.writeheader() 
    writer.writerow({'Active rate': 0.98, 'Operating Expense': 3.102}) 
    writer.writerow({'Active rate': 0.97, 'Operating Expense': 3.11}) 
    writer.writerow({'Active rate': 0.96, 'Operating Expense': 3.109}) 

爲了簡便起見，我已經減少了按鍵2和3

如何解決這個問題的值列表？

感謝

Answer 1

data_set = [ 
    {'Active rate': [0.98, 0.97, 0.96]}, 
    {'Operating Expense': [3.104, 3.102, 3.101]} 
] 

首先，只是一個簡短的評論，你的初始數據結構並不一定有意義。你使用的是一個字典列表，但每個字典似乎只使用一個密鑰，這似乎是擊敗了它的目的。

其他更有意義的數據結構可能是這樣的（每個字典結構都使用，就像你現在所用的那樣，對於一個標籤/值對，但至少該字典用於告訴標籤和值）：

data_set = [ 
    {'label': 'Active rate', 'values': [0.98, 0.97, 0.96]}, 
    {'label': 'Operating Expense', 'values': [3.104, 3.102, 3.101]} 
] 

或者，也許會更好，一個OrderedDict，讓您在初始數據集的順序和鍵/值映射好處：

from collections import OrderedDict 
data_set = OrderedDict() 
data_set['Active rate'] = [0.98, 0.97, 0.96] 
data_set['Operating Expense'] = [3.104, 3.102, 3.101] 

當然，我們不」 t總是選擇我們得到的數據結構，所以讓我們假設你c不會改變它。然後，您的問題就成爲交換初始數據集中行和列角色的問題。實際上，您希望同時遍歷多個列表，爲此，zip非常有用。

import csv 

fieldnames = [] 
val_lists = [] 
for d in data_set: 
    # Find the only used key. 
    # This is a bit awkward because of the initial data structure. 
    k = d.keys()[0] 
    fieldnames.append(k) 
    val_lists.append(d[k]) 

with open('names.csv', 'w') as csvfile: 
    writer = csv.writer(csvfile)  
    writer.writerow(fieldnames) 

    for row in zip(*val_lists): 
     # This picks one item from each list and builds a list. 
     # The first row will be [0.98, 3.104] 
     # The second row will be [0.97, 3.102] 
     # ... 
     writer.writerow(row) 

注意，沒有必要爲DictWriter當你使用zip，因爲這將意味着你需要重建一個字典，沒有任何實際的好處。

Answer 2

以下辦法應該爲你提供的數據結構的工作：

import csv 

data_set = [ 
    {'Active rate': [0.98, 0.97, 0.96]}, 
    {'Operating Expense': [3.104, 3.102, 3.101]} 
] 

fieldnames = ['Active rate', 'Operating Expense'] 
rows = [] 

for field in fieldnames: 
    for data in data_set: 
     try: 
      rows.append(data[field]) 
      break 
     except KeyError, e: 
      pass 

with open('names.csv', 'wb') as f_output: 
    csv_output = csv.writer(f_output) 
    csv_output.writerow(fieldnames) 
    csv_output.writerows(zip(*rows)) 

給你以下CSV輸出文件：

Active rate,Operating Expense 
0.98,3.104 
0.97,3.102 
0.96,3.101 

Answer 3

d1 = {'Active rate': [0.98, 0.931588, 0.941192]} 
d2 = {'Operating Expense': [3.104, 2.352, 2.304]} 

with open('names.csv', 'w') as csvfile: 
    fieldnames = zip(d1, d2)[0] 
    writer = csv.DictWriter(csvfile, fieldnames=fieldnames) 
    writer.writeheader() 

    for row in zip(d1['Active rate'], d2['Operating Expense']): 
     writer.writerow(dict(zip(fieldnames, row))) 

出於性能考慮，你可能想使用itertools.izip超過zip根據列表的長度。

Answer 4

（這個答案具有使用外部庫的劣勢，但）

pandas已經非常有力和簡單的工具來處理的CSV文件。您可以使用to_csv。

注意你的數據結構是笨拙的結構，所以我們首先將其轉換爲更直觀的結構

data_set2 = { x.keys()[0] : x.values()[0] for x in data_set } 

import pandas as pd 
df = pd.DataFrame(data_set2) 
df.to_csv('names.csv', index = False) 

Answer 5

此代碼將幫助您不必連接到一定數量類型的字典裏面data_set

我添加了另一個帶有'損失'鍵的字典，用於測試

import csv 

data_set = [ 
    {'Active rate': [0.98, 0.97, 0.96]}, 
    {'Operating Expense': [3.104, 3.102, 3.101]}, 
    {'Losses': [1.14, 2.28, 3.42]} 
] 

headers = [d.keys()[0] for d in data_set] 

with open('names.csv', 'w') as csvfile: 
    writer = csv.DictWriter(csvfile, fieldnames=headers) 
    writer.writeheader() 
    for item in zip(*[x.values()[0] for x in data_set]): 
     more_results = list() 
     more_results.append(headers) 
     more_results.append(item) 
     writer.writerow(dict(zip(*more_results))) 

輸出：