Python 检查两个df之间是否存在相同的模式,并在模式中使用groupby
您好,我有一个df1文件,例如:Python 检查两个df之间是否存在相同的模式,并在模式中使用groupby,python,pandas,Python,Pandas,您好,我有一个df1文件,例如: Acc_number ACC1.1_CP_Sp1_1 ACC2.1_CP_Sp1_1 ACC3.1_CP_Sp1_1 ACC4.1_CP_Sp1_1 Cluster_nb SeqName Cluster1 YP_009216714 Cluster1 YP_002051918 Cluster1 JZSA01005235.1:37071-37973(-):Sp1_1 Cluster1 NW_014464344.1:68901-69716(-
Acc_number
ACC1.1_CP_Sp1_1
ACC2.1_CP_Sp1_1
ACC3.1_CP_Sp1_1
ACC4.1_CP_Sp1_1
Cluster_nb SeqName
Cluster1 YP_009216714
Cluster1 YP_002051918
Cluster1 JZSA01005235.1:37071-37973(-):Sp1_1
Cluster1 NW_014464344.1:68901-69716(-):Sp2_3
Cluster1 YP_001956729
Cluster1 ACC1.1_CP_Sp1_1
Cluster1 YP_009213712
Cluster2 ACC2.1_CP_Sp1_1
Cluster2 NR_014464231.1:35866-36717(-):Sp1_1
Cluster2 NR_014464232.1:35889-36788(-):Sp1_1
Cluster2 YP_009213728
Cluster3 ACC3.1_CP_Sp1_1
Cluster3 NK_014464231.1:35772-38898(-):Sp1_2
Cluster3 NZ_014464232.1:3533-78787(+):Sp1_2
Cluster3 YP_009213723
Cluster3 YP_009213739
和另一个df2,例如:
Acc_number
ACC1.1_CP_Sp1_1
ACC2.1_CP_Sp1_1
ACC3.1_CP_Sp1_1
ACC4.1_CP_Sp1_1
Cluster_nb SeqName
Cluster1 YP_009216714
Cluster1 YP_002051918
Cluster1 JZSA01005235.1:37071-37973(-):Sp1_1
Cluster1 NW_014464344.1:68901-69716(-):Sp2_3
Cluster1 YP_001956729
Cluster1 ACC1.1_CP_Sp1_1
Cluster1 YP_009213712
Cluster2 ACC2.1_CP_Sp1_1
Cluster2 NR_014464231.1:35866-36717(-):Sp1_1
Cluster2 NR_014464232.1:35889-36788(-):Sp1_1
Cluster2 YP_009213728
Cluster3 ACC3.1_CP_Sp1_1
Cluster3 NK_014464231.1:35772-38898(-):Sp1_2
Cluster3 NZ_014464232.1:3533-78787(+):Sp1_2
Cluster3 YP_009213723
Cluster3 YP_009213739
如果包含Acc\u编号[I]
的groupby
Cluster\u nb
在其(+或-:…
部分中还包含另一个具有相同扩展名的序列(在Acc\u编号
中后面的部分),我想检查df1中的每个Acc\u编号
比如说
for ACC1.1_CP_Sp1_1 as i
我通过做一个
df=df2.loc[df2['SeqName']==i]
Cluster_number=df['Cluster_nb'].iloc[0]
df3=df2.loc[df2['Cluster_nb']==Cluster_number]
print(df3)
Cluster_nb SeqName
Cluster1 YP_009216714
Cluster1 YP_002051918
Cluster1 JZSA01005235.1:37071-37973(-):Sp1_1
Cluster1 NW_014464344.1:68901-69716(-):Sp2_3
Cluster1 YP_001956729
df=df2.loc[df2['SeqName']==i]
Cluster_number=df['Cluster_nb'].iloc[0]
df3=df2.loc[df2['Cluster_nb']==Cluster_number]
print(df3)
Cluster3 ACC3.1_CP_Sp1_1
Cluster3 NK_014464231.1:35772-38898(-):Sp1_2
Cluster3 NZ_014464232.1:3533-78787(+):Sp1_2
Cluster3 YP_009213723
Cluster3 YP_009213739
第3行中的序列JZSA01005235.1:37071-37973(-):Sp1_1
在其末端具有相同的Sp1_1
模式
因此,这里的答案是肯定的,ACC1.1\u CP\u Sp1\u 1与另一个序列位于同一簇中,具有相同的结尾(但名称中有(-or+):
)
我通过做一个
df=df2.loc[df2['SeqName']==i]
Cluster_number=df['Cluster_nb'].iloc[0]
df3=df2.loc[df2['Cluster_nb']==Cluster_number]
print(df3)
Cluster_nb SeqName
Cluster1 YP_009216714
Cluster1 YP_002051918
Cluster1 JZSA01005235.1:37071-37973(-):Sp1_1
Cluster1 NW_014464344.1:68901-69716(-):Sp2_3
Cluster1 YP_001956729
df=df2.loc[df2['SeqName']==i]
Cluster_number=df['Cluster_nb'].iloc[0]
df3=df2.loc[df2['Cluster_nb']==Cluster_number]
print(df3)
Cluster3 ACC3.1_CP_Sp1_1
Cluster3 NK_014464231.1:35772-38898(-):Sp1_2
Cluster3 NZ_014464232.1:3533-78787(+):Sp1_2
Cluster3 YP_009213723
Cluster3 YP_009213739
我发现在集群中没有其他序列的结尾与ACC3.1\u CP\u Sp1\u 1
相同,因此答案是否定的
结果应总结在df3中:
Acc_number present cluster
ACC1.1_CP_Sp1_1 Yes Cluster1
ACC2.1_CP_Sp1_1 Yes Cluster2
ACC3.1_CP_Sp1_1 No NaN
ACC4.1_CP_Sp1_1 No NaN
非常感谢你的帮助
我试过:
for CP in df1['Acc_number']:
df=df2.loc[df2['SeqName']==CP]
try:
Cluster_number=df['Cluster_nb'].iloc[0]
df3=df2.loc[df2['Cluster_nb']==Cluster_number]
for a in df3['SeqName']:
if '(+)' in a or '(-)' in a:
if re.sub('.*_CP_','',CP) in a:
new_df=new_df.append({"Cluster":Cluster_number,"Acc_nb":CP,"present":'yes'}, ignore_index=True)
print(CP,'yes')
except:
continue
我在代码本身中做了评论;概述是为每行获取唯一标识符,合并数据帧并仅保留您感兴趣的列:
#create an 'ending' column
#where u split off the ends after ':'
df1['ending'] = df1.loc[df1.SeqName.str.contains(':'),'SeqName']
df1['ending'] = df1['ending'].str.split(':').str[-1]
#get the cluster number and add to the ending column
#it will serve as a unique identifier for each row
df1['ending'] = df1.Cluster_nb.str[-1].str.cat(df1['ending'],sep='_')
#get rid of null and duplicates; keep only relevant columns
df1 = df1.dropna().drop('SeqName',axis=1).drop_duplicates('ending')
#create ending column here as well
df['ending'] = df['Acc_number'].str.extract(r'((?<=ACC)\d)')
#merge acc_number with the ending to serve as unique identifier
df['ending'] = df['ending'].str.cat(df['Acc_number'].str.extract(r'((?<=P_).*)'),sep='_')
#merge both dataframes
(df
.merge(df1,on='ending',how='left')
#keep only relevant columns
.filter(['Acc_number','Cluster_nb'])
#create present column
.assign(present = lambda x: np.where(x.Cluster_nb.isna(),'no','yes'))
.rename(columns={'Cluster_nb':'cluster'})
)
Acc_number cluster present
0 ACC1.1_CP_Sp1_1 Cluster1 yes
1 ACC2.1_CP_Sp1_1 Cluster2 yes
2 ACC3.1_CP_Sp1_1 NaN no
3 ACC4.1_CP_Sp1_1 NaN no
#创建“结束”列
#你在“:”之后把两端分开
df1['ending']=df1.loc[df1.SeqName.str.contains(':'),'SeqName']
df1['ending']=df1['ending'].str.split(':').str[-1]
#获取群集编号并添加到结束列
#它将作为每行的唯一标识符
df1['ending']=df1.Cluster\u nb.str[-1].str.cat(df1['ending'],sep=''u1')
#消除空的和重复的;只保留相关列
df1=df1.dropna().drop('SeqName',axis=1)。drop_duplicates('ending'))
#在这里也创建结束列
df['ending']=df['Acc_number'].str.extract(r')(?