为列提供 pandas 一个 python 可迭代与 pd.Series 之间的区别

Question

通过 List 与 pd.Series 类型来创建新的 dataFrame 列之间有哪些区别？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 与通过标准 python 列表不同还有其他原因吗？数据框可以接受任何 python 可迭代对象还是对可以传递给它的内容有限制？最后，使用 pd.Series 'correct' 方式添加列，还是可以与其他类型互换使用？

Answer 1

List这里分配给dataframe需要相同的长度

对于pd.Series assign ，它将使用索引作为键来匹配原始DataFrame index，然后在Series[中使用相同索引填充值=17=]

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

匹配索引的长度不同

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

为列提供 pandas 一个 python 可迭代与 pd.Series 之间的区别

Difference between giving pandas a python iterable vs a pd.Series for column

python

pandas

dataframe

iterable