Python 是否可以将 Series 附加到 DataFrame 的行而不先创建列表？
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Is it possible to append Series to rows of DataFrame without making a list first?
提问by O.rka
I have some data I'm trying to organize into a DataFramein Pandas. I was trying to make each row a Seriesand append it to the DataFrame. I found a way to do it by appending the Seriesto an empty listand then converting the listof Seriesto a DataFrame
我有一些数据我试图组织成一个DataFramein Pandas。我试图使每一行 aSeries并将其附加到DataFrame. 我找到了一种方法，通过将 the 附加Series到一个空list然后将listof转换Series为一个DataFrame
e.g. DF = DataFrame([series1,series2],columns=series1.index)
例如 DF = DataFrame([series1,series2],columns=series1.index)
This listto DataFramestep seems to be excessive. I've checked out a few examples on here but none of the Seriespreserved the Indexlabels from the Seriesto use them as column labels.
这list要DataFrame一步似乎过度。我在这里查看了一些示例，但没有一个示例Series保留了 中的Index标签Series以将它们用作列标签。
My long way where columns are id_names and rows are type_names:
我很长的路是列是 id_names 行是 type_names：
Is it possible to append Series to rows of DataFrame without making a list first?
是否可以将 Series 附加到 DataFrame 的行而不先创建列表？
#!/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
Then I tried
然后我试过了
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()
Empty DataFrame
空数据帧
Tried Insert a row to pandas dataframeStill getting an empty dataframe :/
尝试向 Pandas 数据框插入一行仍然得到一个空的数据框：/
I am trying to get the Series to be the rows, where the index of the Series becomes the column labels of the DataFrame
我试图让系列成为行，其中系列的索引成为 DataFrame 的列标签
采纳答案by Anand S Kumar
Maybe an easier way would be to add the pandas.Seriesinto the pandas.DataFramewith ignore_index=Trueargument to DataFrame.append(). Example -
也许更简单的方法是将 加入pandas.Series到pandas.DataFramewithignore_index=True参数中DataFrame.append()。例子 -
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)
Demo -
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
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 -
您的代码中的另一个问题DataFrame.append()是未就地，它返回附加的数据帧，您需要将其分配回原始数据帧才能工作。例子 -
DF = DF.append(SR_row,ignore_index=True)
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 -
为了保留标签，您可以使用您的解决方案来包含系列的名称，并将附加的 DataFrame 分配回DF. 例子 -
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()
回答by BrenBarn
DataFrame.appenddoes not modify the DataFrame in place. You need to do df = df.append(...)if you want to reassign it back to the original variable.
DataFrame.append不会就地修改 DataFrame。df = df.append(...)如果要将其重新分配回原始变量，则需要这样做。
回答by Selah
Something like this could work...
像这样的东西可以工作......
mydf.loc['newindex'] = myseries
Here is an example where I used it...
这是我使用它的一个例子......
stats = df[['bp_prob', 'ICD9_prob', 'meds_prob', 'regex_prob']].describe()
stats
Out[32]:
bp_prob ICD9_prob meds_prob regex_prob
count 171.000000 171.000000 171.000000 171.000000
mean 0.179946 0.059071 0.067020 0.126812
std 0.271546 0.142681 0.152560 0.207014
min 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 0.013116
75% 0.309019 0.065248 0.066667 0.192954
max 1.000000 1.000000 1.000000 1.000000
medians = df[['bp_prob', 'ICD9_prob', 'meds_prob', 'regex_prob']].median()
stats.loc['median'] = medians
stats
Out[36]:
bp_prob ICD9_prob meds_prob regex_prob
count 171.000000 171.000000 171.000000 171.000000
mean 0.179946 0.059071 0.067020 0.126812
std 0.271546 0.142681 0.152560 0.207014
min 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 0.013116
75% 0.309019 0.065248 0.066667 0.192954
max 1.000000 1.000000 1.000000 1.000000
median 0.000000 0.000000 0.000000 0.013116
回答by Vijay Ganesh Srinivasan
Try using this command. See the example given below:
尝试使用此命令。请参阅下面给出的示例：
df.loc[len(df)] = ['Product 9',99,9.99,8.88,1.11]
df
回答by tmldwn
Convert the series to a dataframe and transpose it, then append normally.
将系列转换为数据帧并转置，然后正常追加。
srs = srs.to_frame().T
df = df.append(srs)
