是否可以在不首先创建列表的情况下将Series添加到DataFrame的行? [英] Is it possible to append Series to rows of DataFrame without making a list first?

问题描述

我有一些数据要整理成Pandas中的DataFrame.我试图将每一行都设为Series并将其附加到DataFrame.我找到了一种方法，将Series附加到空的list上，然后将Series的list转换为DataFrame

I have some data I'm trying to organize into a DataFrame in Pandas. I was trying to make each row a Series and append it to the DataFrame. I found a way to do it by appending the Series to an empty list and then converting the list of Series to a DataFrame

例如DF = DataFrame([series1,series2],columns=series1.index)

此list至DataFrame步骤似乎过多.我在这里检查了一些示例，但是Series都没有保留Series中的Index标签以用作列标签.

This list to DataFrame step seems to be excessive. I've checked out a few examples on here but none of the Series preserved the Index labels from the Series to use them as column labels.

我很长的路要走，列是id_names，行是type_names:

My long way where columns are id_names and rows are type_names:

是否可以在不首先创建列表的情况下将Series添加到DataFrame的行?

#!/usr/bin/python

DF = DataFrame()
for sample,data in D_sample_data.items():
    SR_row = pd.Series(data.D_key_value)
    DF.append(SR_row)
DF.head()

TypeError: Can only append a Series if ignore_index=True or if the Series has a name

然后我尝试了

DF = DataFrame()
for sample,data in D_sample_data.items():
    SR_row = pd.Series(data.D_key_value,name=sample)
    DF.append(SR_row)
DF.head()

空DataFrame

Empty DataFrame

尝试将行插入熊猫数据框 仍然得到一个空的数据框:/

Tried Insert a row to pandas dataframe Still getting an empty dataframe :/

我正在尝试让Series成为行，其中Series的索引成为DataFrame的列标签

推荐答案

也许更简单的方法是将pandas.Series添加到pandas.DataFrame中，并且将ignore_index=True参数设置为DataFrame.append().示例-

Maybe an easier way would be to add the pandas.Series into the pandas.DataFrame with ignore_index=True argument to DataFrame.append(). Example -

DF = DataFrame()
for sample,data in D_sample_data.items():
    SR_row = pd.Series(data.D_key_value)
    DF = DF.append(SR_row,ignore_index=True)

演示-

In [1]: import pandas as pd

In [2]: df = pd.DataFrame([[1,2],[3,4]],columns=['A','B'])

In [3]: df
Out[3]:
   A  B
0  1  2
1  3  4

In [5]: s = pd.Series([5,6],index=['A','B'])

In [6]: s
Out[6]:
A    5
B    6
dtype: int64

In [36]: df.append(s,ignore_index=True)
Out[36]:
   A  B
0  1  2
1  3  4
2  5  6

---

代码中的另一个问题是 DataFrame.append() 是如果不是原地，它将返回附加的数据帧，您需要将其分配回原始数据帧才能正常工作.示例-

---

Another issue in your code is that DataFrame.append() is not in-place, it returns the appended dataframe, you would need to assign it back to your original dataframe for it to work. Example -

DF = DF.append(SR_row,ignore_index=True)

---

要保留标签，您可以使用解决方案为系列添加名称，并将附加的DataFrame分配回DF.示例-

---

To preserve the labels, you can use your solution to include name for the series along with assigning the appended DataFrame back to DF. Example -

DF = DataFrame()
for sample,data in D_sample_data.items():
    SR_row = pd.Series(data.D_key_value,name=sample)
    DF = DF.append(SR_row)
DF.head()

这篇关于是否可以在不首先创建列表的情况下将Series添加到DataFrame的行?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！