熊猫:在ID上分割dataframe，并使用生成的文件名写入csv

I have a pandas dataframe I would like to iterate over. For instance a simplified version of my dataframe can be:

我有一只熊猫，我想去看看。例如，我的dataframe的简化版本可以是:

chr    start    end    Gene    Value   MoreData
chr1    123    123    HAPPY    41.1    3.4
chr1    125    129    HAPPY    45.9    4.5
chr1    140    145    HAPPY    39.3   4.1
chr1    342    355    SAD    34.2    9.0
chr1    360    361    SAD    44.3    8.1
chr1    390    399    SAD    29.0   7.2
chr1    400    411    SAD    35.6   6.5
chr1    462    470    LEG    20.0    2.7

I would like to iterate over each unique gene and create a new file named:

我想对每个独特的基因进行迭代，并创建一个名为:

for Gene in df: ## this is where I need the most help

    OutFileName = Gene+".pdf"

For the above example I should get three iterations with 3 outfiles and 3 dataframes:

对于上面的例子，我应该用3个outfile和3个dataframes进行3次迭代:

HAPPY.pdf

chr1    123    123    HAPPY    41.1    3.4 
chr1    125    129    HAPPY    45.9    4.5 
chr1    140    145    HAPPY    39.3   4.1

SAD.pdf

chr1    342    355    SAD    34.2    9.0 
chr1    360    361    SAD  44.3    8.1 
chr1    390    399    SAD    29.0   7.2 
chr1    400    411    SAD    35.6   6.5

Leg.pdf

chr1    462    470    LEG    20.0    2.7

the resulting data frame contents split up by chunks will be sent to another function that will perform the analysis and return the contents to be written to file.

由块分割的结果数据框架内容将被发送到另一个函数，该函数将执行分析并返回要写入文件的内容。

1 个解决方案

#1

You can obtain the unique values calling unique, iterate over this, build the filename and write this out to csv:

您可以获得称为unique的惟一值，遍历此值，构建文件名并将其写入csv:

In [78]:
genes = df['Gene'].unique()
for gene in genes:
    outfilename = gene + '.pdf'
    print(outfilename)
    df[df['Gene'] == gene].to_csv(outfilename)
HAPPY.pdf
SAD.pdf
LEG.pdf

A more pandas-thonic method is to groupby on 'Gene' and then iterate over the groups:

一种更直接的方法是在“基因”上进行分组，然后在组上进行迭代:

In [93]:

gp = df.groupby('Gene')
# groups() returns a dict with 'Gene':indices as k:v pair
for g in gp.groups.items():
    print(df.loc[g[1]])   

    chr  start  end   Gene  Value  MoreData
0  chr1    123  123  HAPPY   41.1       3.4
1  chr1    125  129  HAPPY   45.9       4.5
2  chr1    140  145  HAPPY   39.3       4.1
    chr  start  end Gene  Value  MoreData
3  chr1    342  355  SAD   34.2       9.0
4  chr1    360  361  SAD   44.3       8.1
5  chr1    390  399  SAD   29.0       7.2
6  chr1    400  411  SAD   35.6       6.5
    chr  start  end Gene  Value  MoreData
7  chr1    462  470  LEG     20       2.7

#1