pandas 如何将系列加入数据帧？
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How to join a Series to a DataFrame?
提问by Pythonista anonymous
Is there any way to join a Series to a DataFrame directly?
有没有办法将系列直接加入数据帧？
The join would be on a field of the dataframe and on the index of the series.
连接将在数据帧的字段和系列的索引上。
The only way I found was to convert the series to a dataframe first, as in the code below.
我发现的唯一方法是首先将系列转换为数据帧，如下面的代码所示。
import numpy as np
import pandas as pd
df = pd.DataFrame()
df['a'] = np.arange(0, 4)
df['b'] = np.arange(100, 104)
s = pd.Series(data=np.arange(100, 103))
# this doesn't work
# myjoin = pd.merge(df, s, how='left', left_on='a', right_index=True)
# this does
s = s.reset_index()
# s becomes a Dataframe
# note you cannot reset the index of a series inplace
myjoin = pd.merge(df, s, how='left', left_on='a', right_on='index')
print myjoin
回答by Alex
I guess http://pandas.pydata.org/pandas-docs/stable/generated/pandas.concat.htmlmight help.
我想http://pandas.pydata.org/pandas-docs/stable/generated/pandas.concat.html可能会有所帮助。
For example inner/outer join.
例如内/外连接。
pd.concat((df,s), axis=1)
Out[26]:
a b 0
0 0 100 100
1 1 101 101
2 2 102 102
3 3 103 NaN
In [27]: pd.concat((df,s), axis=1, join='inner')
Out[27]:
a b 0
0 0 100 100
1 1 101 101
2 2 102 102
回答by Ando Jurai
That's a very late answer, but what worked for me was building a dataframe with the columns you want to retrieve in your series, name this series as the index you need, append the series to the dataframe (if you have supplementary elements in the series they are added to the dataframe, which in some application may be convenient), then join the final dataframe by this index to the original dataframe you want to expand. Agreed it is not direct, but that's still the most convenient way if you have a lot of series, instead of transforming each in a dataframe first.
这是一个很晚的答案，但对我有用的是构建一个包含您要在系列中检索的列的数据框，将该系列命名为您需要的索引，将该系列附加到该数据框中（如果您在该系列中有补充元素它们被添加到数据帧中，这在某些应用程序中可能很方便），然后通过此索引将最终数据帧连接到您要扩展的原始数据帧。同意它不是直接的，但是如果您有很多系列，这仍然是最方便的方法，而不是首先在数据框中转换每个系列。
回答by Alex
Try concat():
尝试连接（）：
import numpy as np
import pandas as pd
df= pd.DataFrame()
df['a']= np.arange(0,4)
df['b']= np.arange(100,104)
s =pd.Series(data = np.arange(100,103))
new_df = pd.concat((df, s), axis=1)
print new_df
This prints:
这打印：
a b 0
0 0 100 100
1 1 101 101
2 2 102 102
3 3 103 NaN
