python - 为 Pandas 提供 python iterable 与 pd.Series for column 的区别

Question

传递 List 之间有什么区别？对比 pd.Series键入以创建新的数据框列？例如，从反复试验中我注意到:

# (1d) We can also give it a Series, which is quite similar to giving it a List
df['cost1'] = pd.Series([random.choice([1.99,2.99,3.99]) for i in range(len(df))])
df['cost2'] =           [random.choice([1.99,2.99,3.99]) for i in range(len(df))]
df['cost3'] = pd.Series([1,2,3]) # <== will pad length with `NaN`
df['cost4'] =           [1,2,3]  # <== this one will fail because not the same size
d

pd.Series还有其他原因吗？与传递标准 python 列表不同？数据帧可以接受任何可迭代的 python 还是对可以传递给它的内容有限制？最后，正在使用 pd.Series添加列的“正确”方法，还是可以与其他类型互换使用？

Answer 1

List分配给这里的数据框需要相同的长度
对于 pd.Series assign ，它将使用索引作为键匹配原始 DataFrame index ，然后在 Series 中用相同的索引填充值

df=pd.DataFrame([1,2,3],index=[9,8,7])
df['New']=pd.Series([1,2,3])
 # the default index is range index , which is from 0 to n 
 # since the dataframe index dose not match the series, then will return NaN 
df
Out[88]: 
   0  New
9  1  NaN
8  2  NaN
7  3  NaN

具有匹配索引的不同长度

df['New']=pd.Series([1,2],index=[9,8])
df
Out[90]: 
   0  New
9  1  1.0
8  2  2.0
7  3  NaN