假设我将此列表作为全局列表
dataset =[{'Major': 'Biology', 'GPA': '2.4', 'Name': 'Edward'},
{'Major': 'Physics', 'GPA': '2.9', 'Name':'Emily'},
{'Major':'Mathematics', 'GPA': '3.5', 'Name': 'Sarah'}]
并想要一个函数print（）将其打印为
name major GPA
===============================
edward Biology 2.4
Emily physics 2.9
sarah mathematics 3.5
答案 0 :(得分：6)
您可以使用模块tabulate。 以下代码与python 2兼容，对于python 3，请参阅注释。
>>> import tabulate
>>> dataset =[{'Major': 'Biology', 'GPA': '2.4', 'Name': 'Edward'}, {'Major': 'Physics', 'GPA': '2.9', 'Name': 'Emily'}, {'Major': 'Mathematics', 'GPA': '3.5', 'Name': 'Sarah'}]
>>> header = dataset[0].keys()
>>> rows = [x.values() for x in dataset]
>>> print tabulate.tabulate(rows, header)
Major GPA Name
----------- ----- ------
Biology 2.4 Edward
Physics 2.9 Emily
Mathematics 3.5 Sarah
您可以将tablefmt参数用于不同的表格格式
>>> print tabulate.tabulate(rows, header, tablefmt='grid')
+-------------+-------+--------+
| Major | GPA | Name |
+=============+=======+========+
| Biology | 2.4 | Edward |
+-------------+-------+--------+
| Physics | 2.9 | Emily |
+-------------+-------+--------+
| Mathematics | 3.5 | Sarah |
+-------------+-------+--------+
>>> print tabulate.tabulate(rows, header, tablefmt='rst')
=========== ===== ======
Major GPA Name
=========== ===== ======
Biology 2.4 Edward
Physics 2.9 Emily
Mathematics 3.5 Sarah
=========== ===== ======
答案 1 :(得分：2)
for dicts in dataset:
print(dicts.get('Name')),
print(dicts.get('Major')),
print(dicts.get('GPA')),
示例
>>> dataset =[{'Major': 'Biology', 'GPA': '2.4', 'Name': 'Edward'}, {'Major': 'Physics', 'GPA': '2.9', 'Name': 'Emily'}, {'Major': 'Mathematics', 'GPA': '3.5', 'Name': 'Sarah'}]
>>>
>>> for dicts in dataset:
... print(dicts.get('Name')),
... print(dicts.get('Major')),
... print(dicts.get('GPA')),
...
Edward Biology 2.4 Emily Physics 2.9 Sarah Mathematics 3.5
答案 2 :(得分：1)
这个怎么样：
from __future__ import print_function
dataset =[{'Major': 'Biology', 'GPA': '2.4', 'Name': 'Edward'},Physics', 'GPA': '2.9', 'Name': 'Emily'},Mathematics', 'GPA': '3.5', 'Name': 'Sarah'}]
[print("%s %s: %s\n"%(item['Name'],item['Major'],item['GPA'])) for item in dataset]
结果：
Edward Biology: 2.4
Emily Physics: 2.9
Sarah Mathematics: 3.5
答案 3 :(得分：0)
对于矩阵格式的结构化输出，我会这样做：
dataset =[{'Name': 'Edward', 'Major': 'Biology', 'GPA': '2.4'},
{'Name': 'Emily', 'Major': 'Physics', 'GPA': '2.9'},
{'Name': 'Sarah', 'Major': 'Mathematics', 'GPA': '3.5'}]
keys = []
vals = []
for data in dataset:
val = []
for k,v in data.items():
keys.append(k)
val.append(v)
vals.append(val)
print(list(dict.fromkeys(keys)))
for v in vals:
print(v)
['Name', 'Major', 'GPA']
['Edward', 'Biology', '2.4']
['Emily', 'Physics', '2.9']
['Sarah', 'Mathematics', '3.5']
这样，如果您愿意，也可以使用pandas：
import pandas as pd
dataset =[{'Name': 'Edward', 'Major': 'Biology', 'GPA': '2.4'},
{'Name': 'Emily', 'Major': 'Physics', 'GPA': '2.9'},
{'Name': 'Sarah', 'Major': 'Mathematics', 'GPA': '3.5'}]
keys = []
vals = []
for data in dataset:
val = []
for k,v in data.items():
keys.append(k)
val.append(v)
vals.append(val)
pd.DataFrame([v for v in vals], columns=list(dict.fromkeys(keys)))
Name Major GPA
0 Edward Biology 2.4
1 Emily Physics 2.9
2 Sarah Mathematics 3.5
当然，pandas有更简单的方法：
import pandas as pd
dataset =[{'Name': 'Edward', 'Major': 'Biology', 'GPA': '2.4'},
{'Name': 'Emily', 'Major': 'Physics', 'GPA': '2.9'},
{'Name': 'Sarah', 'Major': 'Mathematics', 'GPA': '3.5'}]
pd.DataFrame(dataset).reindex(columns=['Name','Major','GPA'])
Name Major GPA
0 Edward Biology 2.4
1 Emily Physics 2.9
2 Sarah Mathematics 3.5
答案 4 :(得分：0)
我对tabulate &co的行为感到不满，我为dicts编写了一个文档化，高性能的辅助函数。 timeit：<1毫秒，列表格式的21毫秒为纯格式。
它具有自定义转换器，格式设置，列排除和值替换的多功能功能。自从它的插件以来就非常快：最小的MWE是
print(tabulate_dict([{'col1':'v1'}, {'col1':'v2'}]))
col1
----
v1
v2
def tabulate_dict(dicts: List[Dict], keys: List[str] = None, pads: List[str] = None, fcodes: List[str] = None,
convert_headers: Dict[str, Callable] = None, header_names: List[str] = None,
skip_none_lines: bool = False, replace_values: Dict[str, Any] = None):
""" Generate ascii table from dictionary
dicts: input dictionary list; empty lists make keys OR header_names mandatory
keys: order list of keys to generate columns for; no key/dict-key should suffix with '____' else adjust code-suffix
pads: indicate padding direction and size, eg <10 to right pad alias left-align
fcodes: formating codes for respective column type, eg .3f
convert_headers: apply converters(dict) on column keys k, eg timestamps
header_names: supply for custom column headers instead of keys
skip_none_lines: skip line if contains None
replace_values: specify per column keys k a map from seen value to new value;
new value must comply with the columns fcode; CAUTION: modifies input (due speed)
"""
# optional arg prelude
if keys is None:
if len(dicts) > 0:
keys = dicts[0].keys()
elif header_names is not None:
keys = header_names
else:
raise ValueError('keys or header_names mandatory on empty input list')
if pads is None:
pads = [''] * len(keys)
elif len(pads) != len(keys):
raise ValueError(f'bad pad length {len(pads)}, expected: {len(keys)}')
if fcodes is None:
fcodes = [''] * len(keys)
elif len(fcodes) != len(fcodes):
raise ValueError(f'bad fcodes length {len(fcodes)}, expected: {len(keys)}')
if convert_headers is None:
convert_headers = {}
if header_names is None:
header_names = keys
if replace_values is None:
replace_values = {}
# build header
headline = '│'.join(f"{v:{pad}}" for v, pad in zip_longest(header_names, pads))
underline = '─' * len(headline)
# suffix special keys to apply converters to later on
marked_keys = [h + '____' if h in convert_headers else h for h in keys]
marked_values = {}
s = '│'.join(f"{{{h}:{pad}{fcode}}}" for h, pad, fcode in zip_longest(marked_keys, pads, fcodes))
lines = [headline, underline, ]
for d in dicts:
none_keys = [k for k, v in d.items() if v is None]
if skip_none_lines and none_keys:
continue
elif replace_values:
for k in d.keys():
if k in replace_values and d[k] in replace_values[k]:
d[k] = replace_values[k][d[k]]
if d[k] is None:
raise ValueError(f"bad or no mapping for key '{k}' is None. Use skip or change replace mapping.")
elif none_keys:
raise ValueError(f'keys {none_keys} are None in {d}. Do skip or use replace mapping.')
for h in convert_headers:
if h in keys:
converter = convert_headers[h]
marked_values[h + '____'] = converter(d)
line = s.format(**d, **marked_values)
lines.append(line)
return '\n'.join(lines)
计算列？ np：
print(tabulate_dict([{'col1':1}, {'col1':2}], keys=['col1', 'comp1'], pads=['<5', '>4'],convert_headers={'comp1': lambda d: d['col1']+1}))
输出是
col1 │comp1
───────────
1 │ 2
2 │ 3