我在" my_list"中有一个词典列表。如下:
my_list=[{'Id': '100', 'A': [val1, val2], 'B': [val3, val4], 'C': [val5,val6]},
{'Id': '200', 'A': [val7, val8], 'B': [val9, val10], 'C':
[val11,val12],
{'Id': '300', 'A': [val13, val14], 'B': [val15, val16], 'C':
[val17,val18]}]
我想将此列表写入CSV文件,如下所示:
ID, A, AA, B, BB, C, CC
100, val1, val2, val3, val4, val5, val6
200, val7, val8, val9, val10, val11, val12
300, val13, val14, val15, val16, val17, val18
有谁知道我该怎么处理它?</ p>
答案 0 :(得分:1)
Tablib应该做的伎俩
我将这个例子留在他们的首页(你可以适应.csv格式):
>>> data = tablib.Dataset(headers=['First Name', 'Last Name', 'Age'])
>>> for i in [('Kenneth', 'Reitz', 22), ('Bessie', 'Monke', 21)]:
... data.append(i)
>>> print(data.export('json'))
[{"Last Name": "Reitz", "First Name": "Kenneth", "Age": 22}, {"Last Name": "Monke", "First Name": "Bessie", "Age": 21}]
>>> print(data.export('yaml'))
- {Age: 22, First Name: Kenneth, Last Name: Reitz}
- {Age: 21, First Name: Bessie, Last Name: Monke}
>>> data.export('xlsx')
<censored binary data>
>>> data.export('df')
First Name Last Name Age
0 Kenneth Reitz 22
1 Bessie Monke 21
答案 1 :(得分:0)
你可以这样做......(根据需要用csv writerow替换打印)
print(['ID', 'A', 'AA', 'B', 'BB', 'C', 'CC'])
for row in my_list:
out_row = []
out_row.append(row['Id'])
for v in row['A']:
out_row.append(v)
for v in row['B']:
out_row.append(v)
for v in row['C']:
out_row.append(v)
print(out_row)
答案 2 :(得分:0)
您可以使用pandas来解决问题:
my_list = [{'Id': '100', 'A': [val1, val2], 'B': [val3, val4], 'C': [val5, val6]},
{'Id': '200', 'A': [val7, val8], 'B': [val9, val10], 'C': [val11, val12]},
{'Id': '300', 'A': [val13, val14], 'B': [val15, val16], 'C': [val17, val18]}]
index = ['Id', 'A', 'AA', 'B', 'BB', 'C', 'CC']
df = pd.DataFrame(data=my_list)
for letter in ['A', 'B', 'C']:
first = []
second = []
for a in df[letter].values.tolist():
first.append(a[0])
second.append(a[1])
df[letter] = first
df[letter * 2] = second
df = df.reindex_axis(index, axis=1)
df.to_csv('out.csv')
这会产生以下输出dataframe
:
Id A AA B BB C CC
0 100 1 2 3 4 5 6
1 200 7 8 9 10 11 12
2 300 13 14 15 16 17 18
这是out.csv
- 文件:
,Id,A,AA,B,BB,C,CC
0,100,1,2,3,4,5,6
1,200,7,8,9,10,11,12
2,300,13,14,15,16,17,18
请参阅有关csv
- 功能(csv)的pandas文档。
将DataFrame写入以逗号分隔的值(csv)文件