pandas 用python处理96孔板中的数据标签
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dealing with data labels in 96 well plate with python
提问by Moritz
I do have data from a 96 well plate (mostly in excel):
我确实有来自 96 孔板的数据（主要在 excel 中）：
A 96 well plate, schematic provided by http://www.cellsignet.com:
一个 96 孔板，示意图由http://www.cellsignet.com提供：
(source: cellsignet.com)
（来源：cellsignet.com）
Out of each cell we can do some experiment and read values from it, the data looks like:
在每个单元格中，我们可以做一些实验并从中读取值，数据如下所示：
1 2 3 4 . .
A 9.1 8.7 5.6 4.5
B 8.7 8.5 5.4 4.3
C 4.3 4.5 7.6 6.7
D 4.1 6.0 7.0 6.1
.
I also have excel files with the sample names:
我还有带有示例名称的 excel 文件：
1 2 3 4 . .
A l1 l2 l3 l4
B l1 l2 l3 l4
C ds1 ds2 ds3 ds4
D ds1 ds2 ds3 ds4
.
The duplicate entries are two wells with the same sample loaded.
重复条目是加载了相同样品的两个孔。
I would like to read in the data (no problem) and assign the labels to the data points and group the data according to the labels. In pandas i can read in the data and group it according to the column and row headers. But how can i group according to the sample names ?
我想读入数据（没问题）并将标签分配给数据点并根据标签对数据进行分组。在 Pandas 中，我可以读入数据并根据列和行标题对其进行分组。但是我如何根据样本名称分组？
采纳答案by CT Zhu
I will suggest just make a DataFramewith two columns, one stores the names, the other stores the readings.
我建议只DataFrame用两列制作一个，一个存储名称，另一个存储读数。
In [20]:
print data_df
print name_df
1 2 3 4
A 9.1 8.7 5.6 4.5
B 8.7 8.5 5.4 4.3
C 4.3 4.5 7.6 6.7
D 4.1 6.0 7.0 6.1
[4 rows x 4 columns]
1 2 3 4
A l1 l2 l3 l4
B l1 l2 l3 l4
C ds1 ds2 ds3 ds4
D ds1 ds2 ds3 ds4
[4 rows x 4 columns]
In [21]:
final_df=pd.DataFrame({'Name':name_df.values.ravel(), 'Reading':data_df.values.ravel()})
#if you have additional readings, i.e. from a different assay,
#from a different wavelength, add them there, as:
#'OTHER_Reading':OTHER_data_df.values.ravel()
print final_df
Name Reading
0 l1 9.1
1 l2 8.7
2 l3 5.6
3 l4 4.5
4 l1 8.7
5 l2 8.5
6 l3 5.4
7 l4 4.3
8 ds1 4.3
9 ds2 4.5
10 ds3 7.6
11 ds4 6.7
12 ds1 4.1
13 ds2 6.0
14 ds3 7.0
15 ds4 6.1
[16 rows x 2 columns]
This way you can do some calculations rather easily, such as:
通过这种方式，您可以相当轻松地进行一些计算，例如：
In [22]:
print final_df.groupby('Name').mean()
Reading
Name
ds1 4.20
ds2 5.25
ds3 7.30
ds4 6.40
l1 8.90
l2 8.60
l3 5.50
l4 4.40
[8 rows x 1 columns]
