给 pandas 一个可迭代的 python 和一个 pd.Series 的列之间的区别

Question

传递 List 与 pd.Series 类型以创建新的 dataFrame 列之间有什么区别？例如，通过反复试验，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 与传递标准 python 列表有什么不同吗？数据框可以采用任何可迭代的python，还是对可以传递给它的内容有限制？最后，使用pd.Series 是添加列的“正确”方式，还是可以与其他类型互换使用？

Answer 1

List 此处分配给数据帧需要相同的长度

对于pd.Series赋值，它会以索引为key匹配原来的DataFrameindex，然后在Series中填充相同索引的值

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

---

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