将列表作为带有索引的新行附加到 Pandas DataFrame [英] Append list to pandas DataFrame as new row with index

问题描述

尽管有许多关于将数据附加到数据帧的堆栈溢出问题，但我无法真正找到以下问题的答案.我正在寻找一个直接的解决方案来附加一个列表作为数据帧的最后一行.想象一下我有一个简单的数据框:

Despite of the numerous stack overflow questions on appending data to a dataframe I could not really find an answer to the following. I am looking for a straight forward solution to append a list as last row of a dataframe. Imagine I have a simple dataframe:

 indexlist=['one']
 columnList=list('ABC')
 values=np.array([1,2,3])
 # take care, the values array is a 3x1 size array. 
 # row has to be 1x3 so we have to reshape it

values=values.reshape(1,3)
df3=pd.DataFrame(values,index=indexlist,columns=columnList)
print(df3)

     A  B  C
one  1  2  3

经过一些操作后，我得到以下列表:

After some operations I get the following list:

listtwo=[4,5,6]

我想将它附加到数据帧的末尾.我将该列表更改为一个系列:

I want to append it at the end of the dataframe. I change that list into a series:

oseries=pd.Series(listtwo)
print(type(oseries))
oseries.name="two"

现在，这不起作用:

df3.append(oseries)

因为它给出:

A   B   C   0   1   2
one 1.0 2.0 3.0 NaN NaN NaN
two NaN NaN NaN 5.0 6.0 7.0

我想要 A B 和 C 下的值.

I would like to have the values under A B and C.

我也试过:

df3.append(oseries, columns=list('ABC'))  *** not working ***
df3.append(oseries, ignore_index=True)  *** working but wrong result
df3.append(oseries, ignore_index=False) *** working but wrong result

df3.loc[oseries.name]=oseries adds a row with NaN values

我要找的是a) 如何将列表添加到特定索引名称b)即使我没有索引名称(将其留空)，我如何简单地从列表中添加一行值

what I am looking for is a) how can I add a list to a particular index name b) how can I simple add a row of values out of a list even if I don't have a name for index (leave it empty)

推荐答案

使用 loc 就地分配:

df.loc['two'] = [4, 5, 6]
# df.loc['two', :] = [4, 5, 6]
df
     A  B  C
one  1  2  3
two  4  5  6

或者，使用 df.append，第二个参数是具有适当索引和名称的 Series 对象:

Or, use df.append with the second argument being a Series object having appropriate index and name:

s = pd.Series(dict(zip(df.columns, [4, 5, 6])).rename('two'))
df2 = df.append(s)

df2
     A  B  C
one  1  2  3
two  4  5  6

<小时>

如果您要附加到没有索引的 DataFrame(即具有数字索引)，您可以在找到索引的最大值并增加 1 后使用 loc:

df4 = pd.DataFrame(np.array([1,2,3]).reshape(1,3), columns=list('ABC'))
df4

   A  B  C
0  1  2  3

df4.loc[df4.index.max() + 1, :] = [4, 5, 6]
df4
     A    B    C
0  1.0  2.0  3.0
1  4.0  5.0  6.0

或者，使用 append 和 ignore_index=True:

df4.append(pd.Series(dict(zip(df4.columns, [4, 5, 6]))), ignore_index=True)

   A  B  C
0  1  2  3
1  4  5  6

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