<p>如果默认情况下创建了从键向<code>Series</code>传递字典的索引:</p>
<pre><code>country_capital_dict = {'Germany':'Berlin', 'US':'Washington',
'Italy':'Rome', 'France':'Paris',
'Russia':'Moscow','Spain':'Madrid',
'Austria':'Vienna','Greece':'Athens'}
country_capital_series = pd.Series(country_capital_dict)
print(country_capital_series)
Germany Berlin
US Washington
Italy Rome
France Paris
Russia Moscow
Spain Madrid
Austria Vienna
Greece Athens
dtype: object
</code></pre>
<p>如果需要更改索引,您可以指定它:</p>
<pre><code>country_capital_series.index = ['a','b','c','d','e','f','g','h']
print(country_capital_series)
a Berlin
b Washington
c Rome
d Paris
e Moscow
f Madrid
g Vienna
h Athens
dtype: object
</code></pre>
<p>或仅将字典的值传递给<code>Series</code>:</p>
<pre><code>country_capital_series = pd.Series(country_capital_dict.values(),
index = ['a','b','c','d','e','f','g','h'])
print(country_capital_series)
a Berlin
b Washington
c Rome
d Paris
e Moscow
f Madrid
g Vienna
h Athens
dtype: object
</code></pre>
<p>获取所有缺失值的原因是列表中的索引和字典键中的索引不匹配-因为不同的熊猫尝试用列表中的“新”更改原始索引,但不知道新值,所以分配了所有“N”:</p>
<pre><code>country_capital_series = pd.Series(country_capital_dict,
index = ['a','b','c','d','e','f','g','h'])
print(country_capital_series)
a NaN
b NaN
c NaN
d NaN
e NaN
f NaN
g NaN
h NaN
dtype: object
</code></pre>
<p>如果仅为某些匹配的值分配了NAN(仅针对不匹配的值):</p>
<pre><code>country_capital_series = pd.Series(country_capital_dict,
index = ['a','Germany','c','d','e','Austria','g','h'])
print(country_capital_series)
a NaN
Germany Berlin
c NaN
d NaN
e NaN
Austria Vienna
g NaN
h NaN
dtype: object
</code></pre>