如何在python中将嵌套字典转换为Dataframe

sd2nnvve  于 2021-07-14  发布在  Java
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我有一个包含多个序列的fasta文件，尝试计算每个序列的密码子数量，计算每个序列的rcsu值，并最终将其存储在一个Dataframe中。因此，我想制作一个嵌套字典，将序列名（specie_1，specie_2）存储为“super key”，将密码子存储为key及其相关的rcsu值（对于每个序列名），然后将其转换为一个带有pandas的Dataframe。我对Dataframe的外观有问题，它看起来不像我想要的那样。下面是python（版本3）代码：

from Bio import SeqIO
import pandas as pd

CG_nucleic_file = '/home/ju/Documents/data_128/CG_all_seq_per_species.fasta'

CodonsDict = {
    'TTT': 0, 'TTC': 0, 'TTA': 0, 'TTG': 0, 'CTT': 0,
    'CTC': 0, 'CTA': 0, 'CTG': 0, 'ATT': 0, 'ATC': 0,
    'ATA': 0, 'ATG': 0, 'GTT': 0, 'GTC': 0, 'GTA': 0,
    'GTG': 0, 'TAT': 0, 'TAC': 0, 'TAA': 0, 'TAG': 0,
    'CAT': 0, 'CAC': 0, 'CAA': 0, 'CAG': 0, 'AAT': 0,
    'AAC': 0, 'AAA': 0, 'AAG': 0, 'GAT': 0, 'GAC': 0,
    'GAA': 0, 'GAG': 0, 'TCT': 0, 'TCC': 0, 'TCA': 0,
    'TCG': 0, 'CCT': 0, 'CCC': 0, 'CCA': 0, 'CCG': 0,
    'ACT': 0, 'ACC': 0, 'ACA': 0, 'ACG': 0, 'GCT': 0,
    'GCC': 0, 'GCA': 0, 'GCG': 0, 'TGT': 0, 'TGC': 0,
    'TGA': 0, 'TGG': 0, 'CGT': 0, 'CGC': 0, 'CGA': 0,
    'CGG': 0, 'AGT': 0, 'AGC': 0, 'AGA': 0, 'AGG': 0,
    'GGT': 0, 'GGC': 0, 'GGA': 0, 'GGG': 0}

# this dictionary shows which codons encode the same AA

SynonymousCodons = {
    'CYS': ['TGT', 'TGC'],
    'ASP': ['GAT', 'GAC'],
    'SER': ['TCT', 'TCG', 'TCA', 'TCC', 'AGC', 'AGT'],
    'GLN': ['CAA', 'CAG'],
    'MET': ['ATG'],
    'ASN': ['AAC', 'AAT'],
    'PRO': ['CCT', 'CCG', 'CCA', 'CCC'],
    'LYS': ['AAG', 'AAA'],
    'STOP': ['TAG', 'TGA', 'TAA'],
    'THR': ['ACC', 'ACA', 'ACG', 'ACT'],
    'PHE': ['TTT', 'TTC'],
    'ALA': ['GCA', 'GCC', 'GCG', 'GCT'],
    'GLY': ['GGT', 'GGG', 'GGA', 'GGC'],
    'ILE': ['ATC', 'ATA', 'ATT'],
    'LEU': ['TTA', 'TTG', 'CTC', 'CTT', 'CTG', 'CTA'],
    'HIS': ['CAT', 'CAC'],
    'ARG': ['CGA', 'CGC', 'CGG', 'CGT', 'AGG', 'AGA'],
    'TRP': ['TGG'],
    'VAL': ['GTA', 'GTC', 'GTG', 'GTT'],
    'GLU': ['GAG', 'GAA'],
    'TYR': ['TAT', 'TAC']}

# DNA bases that can occupy each codon position

CodonBases = {'A' : 0, 'C' : 0, 'G' : 0, 'T' : 0}

def count_codons(fasta_file):
    with open(fasta_file, 'r') as handle:

        # make the codon dictionary local
        codon_count = CodonsDict.copy()

        # iterate over sequence and count all the codons in the FastaFile.
        for cur_record in SeqIO.parse(handle, "fasta"):
            # make sure the sequence is lower case
            if str(cur_record.seq).islower():
                dna_sequence = str(cur_record.seq).upper()
            else:
                dna_sequence = str(cur_record.seq)
                #print(dna_sequence)
            for i in range(0, len(dna_sequence), 3):
                codon = dna_sequence[i:i + 3]
                if codon in codon_count:
                    codon_count[codon] += 1
                #else: raise TypeError("illegal codon %s in gene: %s" % (codon, cur_record.id))
            #print(cur_record.id, codon_count)

            # now to calculate the index we first need to sum the number of times
            # synonymous codons were used all together.
            for aa in SynonymousCodons:
                total = 0.0
                # RCSU values are CodonCount/((1/num of synonymous codons) * sum of
                # all synonymous codons)
                rcsu = []
                codons = SynonymousCodons[aa]

                for codon in codons:
                    total += codon_count[codon]

                # calculate the RSCU value for each of the codons
                for codon in codons:
                    denominator = float(total) / len(codons)
                    rcsu.append(codon_count[codon] / denominator)
                #print(cur_record.id, codons, rcsu)

                # CREATE DICTIONARY
                sequences = [cur_record.id]
                dic = dict(zip(codons, rcsu))
                hyper_dic = dict.fromkeys(sequences, dic)
                #print(hyper_dic)

                # CREATE DATAFRAME
                df = pd.DataFrame.from_dict(hyper_dic)
                print(df)

count_codons(fasta_file=CG_nucleic_file)

打印df后，输出的一部分是：

SPECIE_1
GAA                         0.457874
GAG                         1.542126
                            SPECIE_1
TAC                         1.643541
TAT                         0.356459
                                  SPECIE_2
TGC                               1.322851
TGT                               0.677149
                                  SPECIE_2
GAC                               1.462735
GAT                               0.537265
                                  SPECIE_2
AGC                               3.089088
AGT                               0.474951
TCA                               0.618015
TCC                               0.869249
TCG                               0.652648
TCT                               0.296049
                                  SPECIE_2
CAA                               0.224834
CAG                               1.775166
                                  SPECIE_2
ATG                                    1.0
                                  SPECIE_2
AAC                               1.747474
AAT                               0.252526
                                  SPECIE_2
CCA                               0.709466
CCC                               1.174944
CCG                               1.734340
CCT                               0.381251
                                  SPECIE_2
AAA                               0.333452
AAG                               1.666548
                                  SPECIE_2
TAA                               0.439291
TAG                               0.272399
TGA                               2.288311
                                  SPECIE_2
ACA                               0.493505
ACC                               1.937182
ACG                               1.108230
ACT                               0.461084
                                  SPECIE_2
TTC                               1.569263
TTT                               0.430737
                                  SPECIE_2
GCA                               0.628793
GCC                               1.786888
GCG                               0.943387
GCT                               0.640932
                                  SPECIE_2
GGA                               1.186415
GGC                               1.497532
GGG                               0.551426
GGT                               0.764627
                                  SPECIE_2
ATA                               1.420639
ATC                               1.003165
ATT                               0.576195
                                  SPECIE_2
CTA                               0.272501
CTC                               2.970173
CTG                               1.074753
CTT                               1.036392
TTA                               0.295155
TTG                               0.351025
                                  SPECIE_2
CAC                               1.725651
CAT                               0.274349
                                  SPECIE_2
AGA                               1.119508
AGG                               3.383216
CGA                               0.047087
CGC                               1.099417
CGG                               0.175471
CGT                               0.175301
                                  SPECIE_2
TGG                                    1.0
                                  SPECIE_2
GTA                               0.292662
GTC                               1.662430
GTG                               0.616281
GTT                               1.428627
                                  Specie_2
GAA                               0.456977
GAG                               1.543023
                                  Specie_2
TAC                               1.644978
TAT                               0.355022

而不是：

SPECIE_1        SPECIE_2       other_species...
TGC         0.567           1.076          ...
...         ...             ...            ...
codons      value           value          value

我刚刚开始学习python，不知道如何解决这个问题。我认为这是因为print（hyper_dic）（也叫print（sequences））输出多个嵌套字典，而不是一个：

{'SPECIE_1': {'GAG': 1.5421256842253714, 'GAA': 0.4578743157746285}}
{'SPECIE_1': {'TAT': 0.3564585901175933, 'TAC': 1.6435414098824066}}
{'SPECIE_2': {'TGT': 0.6771493242971753, 'TGC': 1.3228506757028247}}
{'SPECIE_2': {'GAT': 0.5372647945111357, 'GAC': 1.4627352054888643}}
{'SPECIE_2': {'TCT': 0.29604880551912877, 'TCG': 0.6526483836022577, 'TCA': 0.6180147100746907, 'TCC': 0.8692491020012543, 'AGC': 3.089088317463937, 'AGT': 0.4749506813387308}}
{'SPECIE_2': {'CAA': 0.22483409972224053, 'CAG': 1.7751659002777596}}
{'SPECIE_2': {'ATG': 1.0}}
{'SPECIE_2': {'AAC': 1.7474737818764585, 'AAT': 0.2525262181235415}}
{'SPECIE_2': {'CCT': 0.38125098616066655, 'CCG': 1.734339768614896, 'CCA': 0.7094656154406038, 'CCC': 1.1749436297838336}}
{'SPECIE_2': {'AAG': 1.6665484206879835, 'AAA': 0.33345157931201647}}
{'SPECIE_2': {'TAG': 0.27239861292147266, 'TGA': 2.288310666240378, 'TAA': 0.4392907208381496}}
{'SPECIE_2': {'ACC': 1.9371817149218815, 'ACA': 0.49350453967609204, 'ACG': 1.1082300904926319, 'ACT': 0.4610836549093945}}
{'SPECIE_2': {'TTT': 0.4307370502428151, 'TTC': 1.5692629497571848}}
{'SPECIE_2': {'GCA': 0.6287929079476343, 'GCC': 1.7868879496959076, 'GCG': 0.9433872796618905, 'GCT': 0.6409318626945676}}
{'SPECIE_2': {'GGT': 0.764627289015375, 'GGG': 0.5514255653469322, 'GGA': 1.1864153566270856, 'GGC': 1.497531789010607}}
{'SPECIE_2': {'ATC': 1.003165304483394, 'ATA': 1.4206394112819318, 'ATT': 0.5761952842346741}}
{'SPECIE_2': {'TTA': 0.29515482086967093, 'TTG': 0.3510247956486068, 'CTC': 2.9701734327995384, 'CTT': 1.036392020419946, 'CTG': 1.0747534868880855, 'CTA': 0.272501443374153}}
{'SPECIE_2': {'CAT': 0.2743491778419728, 'CAC': 1.7256508221580271}}
{'SPECIE_2': {'CGA': 0.047087050675736423, 'CGC': 1.0994171275614661, 'CGG': 0.17547054940030157, 'CGT': 0.17530100519164918, 'AGG': 3.38321615088407, 'AGA': 1.1195081162867764}}
{'SPECIE_2': {'TGG': 1.0}}
{'SPECIE_2': {'GTA': 0.2926620254806615, 'GTC': 1.6624303723824536, 'GTG': 0.6162805781696535, 'GTT': 1.4286270239672312}}
{'SPECIE_2': {'GAG': 1.5430230245080025, 'GAA': 0.45697697549199756}}
{'SPECIE_2': {'TAT': 0.3550221815082481, 'TAC': 1.6449778184917518}}

我读了其他的文章，但找不到答案。有人能解决这个问题吗？先谢谢你

1条答案

按热度按时间
rqcrx0a6

rqcrx0a61#

它可以帮助您根据您希望看到的Dataframe来考虑数据的外观。所以如果你想看：

SPECIE_1        SPECIE_2       other_species...
TGC         0.567           1.076          ...
...         ...             ...            ...
codons      value           value          value

然后您需要一个dict列表作为表单的输入：

[
    {"codons": "TGC", "SPECIE_1": 0.567, "SPECIE_2": 1.076, "other_species"...},
.
.
.
]

从您想要的数据向后工作，您可以找到如何格式化数据—正确地获取一个dict并创建一个单行Dataframe可以帮助您完成这项工作，因为这样您就知道需要如何处理所有数据。

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