大佬教程收集整理的这篇文章主要介绍了如何关联两个列表或列？，大佬教程大佬觉得挺不错的，现在分享给大家，也给大家做个参考。
代码/程序：
# 1. Represent all unique feature types (column 2).
lines = f.read().split('\n')
# Set features
unique_features = set((i.split('\t')[1]) for i in lines)
unique_features = sorted(unique_features)
qty_features = [str(i.split('\t')[1]) for i in lines]
for feature in unique_features:
print(feature,qty_features.count(feature))
# 2. For every feature type (column 2) it stores all feature names (column 4) that had that type.
# Create a List of types and names
types = List(unique_features)
names = List([str(i.split('\t')[3]) for i in lines])
data = dict()
for i in range(len(types)):
data[types[i]] = names[i]
print(data)
我在评论后的代码中有问题：
"# 2. Demonstrate the use of a data structure that associates the feature names to feature types".
我正在尝试获取与第 2 列关联的文件第 4 列中的数据。
期望的输出：
对于每个特征类型（第 2 列），我想获取与特征类型相关联的所有特征名称（第 4 列）。
{'ORF': ['YAL069W','YAL068W-A','YAL068C','YAL067W-A' ... ] }
{'CDS': ['YAL068W-A','YAL067W-A','YAL067C' ... ] }
以及第 2 列中的所有唯一值：
(ARS
ARS_consensus_sequence
CDS
LTR_retrotransposon
ORF
W_region
X_element
X_element_combinatorial_repeat
X_region
Y_prime_element
Y_region
Z1_region
...
etc etc)
属于它们的第 4 列的每个值都存储在它们中，就像对它们进行分类一样......
我得到的代码是
{'ARS': 'YAL069W','ARS_consensus_sequence': '','CDS': 'YAL068W-A','LTR_retrotransposon': '','ORF': 'ARS102','W_region': 'TEL01L','X_element': '','X_element_combinatorial_repeat': '','X_region': '','Y_prime_element': 'YAL068C','Y_region': '','Z1_region': 'YAL067W-A','Z2_region': '','blocked_reading_frame': 'ARS103','centromere': 'YAL067C','centromere_DNA_Element_I': '','centromere_DNA_Element_II': 'YAL066W','centromere_DNA_Element_III': '','external_transcribed_spacer_region': 'YAL065C','five_prime_UTR_intron': '','gene_group': 'YAL064W-B','intein_enCoding_region': '','internal_transcribed_spacer_region': 'YAL064C-A','intron': '','long_terminal_repeat': 'YAL064W','mating_type_region': '','matrix_attachment_site': 'YALWdelta1','ncRNA_gene': 'YAL063C-A','non_transcribed_region': '','nonCoding_exon': 'YAL063C','not in systematic sequence of S288C': '','not physically mapped': 'ARS104','origin_of_replication': '','plus_1_translational_frameshift': 'YAL062W','pseudogene': '','rRNA_gene': 'YAL061W','silent_mating_type_cassette_array': '','snRNA_gene': 'YAL060W','snorNA_gene': '','tRNA_gene': 'YAL059W','telomerase_RNA_gene': '','telomere': 'YAL059C-A','telomeric_repeat': '','transposable_element_gene': 'YAL058W'}
在该输出中，它打印的是已经设置的“unique_features”，但由于功能名称与其不对应，它甚至没有将它们全部打印出来。
EG。文件：
S000036595 nonCoding_exon snR18 1 142367 142468 W 2011-02-03 2000-05-19|2007-05-08
S000000002 ORF VerifIEd YAL002W VPS8 CORVET complex membrane-binding subunit VPS8|VPL8|VPT8|FUN15 chromosome 1 L000003013 1 143707 147531 W 2011-02-03 2004-01-14|1996-07-31 Membrane-binding component of the CORVET complex; involved in endosomal vesicle tethering and fusion in the endosome to vacuole protein targeting pathway; interacts with Vps21p; contains RING finger motif
S000031737 CDS YAL002W 1 143707 147531 W 2011-02-03 2004-01-14|1996-07-31
S000121255 ARS ARS108 ARSI-147 chromosome 1 1 147398 147717 2014-11-18 2014-11-18|2007-03-07 autonomously Replicating Sequence
S000000001 ORF VerifIEd YAL001C TFC3 transcription factor TFIIIC subunit TFC3|tau 138|TSV115|FUN24 chromosome 1 L000000641|L000002287 1 151166 147594 C -1 2011-02-03 1996-07-31 Subunit of RNA polymerase III transcription initiation factor complex; part of the TauB domain of TFIIIC that binds DNA at the BoxB promoter sites of tRNA and similar genes; cooperates with Tfc6p in DNA binding; largest of six subunits of the RNA polymerase III transcription initiation factor complex (TFIIIC)
S000030735 CDS YAL001C 1 151006 147594 C 2011-02-03 1996-07-31
S000030734 CDS YAL001C 1 151166 151097 C 2011-02-03 1996-07-31
S000030736 intron YAL001C 1 151096 151007 C 2011-02-03 1996-07-31
您的代码效率低下，因为它反复拆分所有行。在下面的代码中，这仅在首次从文件中读入时执行一次。此外，在读取之后，它们会被转置为行列，因为大部分处理是针对每列中的内容完成的。
from pprint import pprint,pp
NUMCOLS = 4 # Number columns of interest.
filepath = 'SGD_features.tab'
# Retrieve only number of columns needed from each row of data.
with open(filepath) as file:
rows = [row.split('\t',NUMCOLS)[:NUMCOLS] for row in file.read().splitlines()]
cols = list(zip(*rows)) # Transpose rows and columns for further processing.
unique_features = sorted(set(cols[1]))
print('Quantities of each unique feature')
qty_features = cols[1]
for feature in unique_features:
print(f' {qty_features.count(feature)} - {feature}')
feature_dict = {key: [] for key in unique_features} # Initialize to empty lists.
for col1,col3 in zip(cols[1],cols[3]):
if col3:
feature_dict[col1].append(col3)
print()
print('Feature dictionary')
pp(feature_dict,indent=1)
处理该 SGD_features.tab 文件的前 15 行的输出，您在问题下的评论中提供了链接（不是中显示的示例数据）它）：
Quantities of each unique feature
2 - ARS
4 - CDS
5 - ORF
1 - X_element
1 - X_element_combinatorial_repeat
1 - telomere
1 - telomeric_repeat
Feature dictionary
{'ARS': ['ARS102','ARS103'],'CDS': [],'ORF': ['YAL069W','YAL068W-A','YAL068C','YAL067W-A','YAL067C'],'X_element': [],'X_element_combinatorial_repeat': [],'telomere': ['TEL01L'],'telomeric_repeat': []}
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