<p>您可以尝试<code>list-conprehension</code>,然后使用<code>data[::2]</code>选择每2个元素:</p>
<pre><code>data = [x.split("=")[1] for x in input_str.split(", ")]
df = pd.DataFrame({"age": data[1::2], "key": data[::2]})
print(df)
# age key
# 0 58 IAfpK
# 1 64 WNVdi
# 2 47 jp9zt
# 3 68 0Sr4C
# 4 76 CGEqo
# .. .. ...
# 295 13 lRf1j
# 296 50 0iJGV
# 297 5 cFCfU
# 298 48 J8an1
# 299 5 dkSlj
</code></pre>
<p><strong>解释</strong>:</p>
<ol>
<li>使用<a href="https://docs.python.org/fr/2.7/library/stdtypes.html#str.split" rel="nofollow noreferrer">^{<cd3>}</a>:<code>input_str.split(", ")</code>分割数据以标识每个元素</li>
<li>分解每个元素以选择<code>=</code>之后的值:<code>[x.split("=")[1] for x in input_str.split(", ")]</code></li>
<li>通过每两个元素选择一个来创建数据帧:<code>df = pd.DataFrame({"age": data[1::2], "key": data[::2]})</code></li>
</ol>
<hr/>
<p><strong>完整插图</strong>:</p>
<pre><code>r = requests.get('https://coderbyte.com/api/challenges/json/age-counting')
input_str = r.json().get('data')
print(input_str.split(", "))
# ['key=IAfpK', 'age=58', 'key=WNVdi', 'age=64', ... 'key=dkSlj', 'age=5']
print([x.split("=") for x in input_str.split(", ")])
# [['key', 'IAfpK'], ['age', '58'], ['key', 'WNVdi'], ['age', '64'], ... , ['key', 'dkSlj'], ['age', '5']]
print([x.split("=")[1] for x in input_str.split(", ")])
# ['IAfpK', '58', 'WNVdi', '64', ..., 'dkSlj', '5']
data = [x.split("=")[1] for x in input_str.split(", ")]
print(data[1::2])
# ['58', '64', ... , '5']
df = pd.DataFrame({"age": data[1::2], "key": data[::2]})
print(df)
# age key
# 0 58 IAfpK
# 1 64 WNVdi
# 2 47 jp9zt
# 3 68 0Sr4C
# 4 76 CGEqo
# .. .. ...
# 295 13 lRf1j
# 296 50 0iJGV
# 297 5 cFCfU
# 298 48 J8an1
# 299 5 dkSlj
# [300 rows x 2 columns]
</code></pre>