Pandas，对于每个分组，枚举字符串列并转换为计数器字典

Question

我正在尝试为任何输入熊猫数据帧自动构建 networkx 图。

数据帧如下所示：

FeatureID       BC         chrom       pos        ftm_call
1_1_1           GCTATT     12          25398138   NRAS_3
1_1_1           GCCTAT     12          25398160   NRAS_3
1_1_1           GCCTAT     12          25398073   NRAS_3
1_1_1           GATCCT     12          25398128   NRAS_3
1_1_1           GATCCT     12          25398107   NRAS_3

这是我需要整理的算法：

• 按特征 ID 分组
• 对于每个特征 ID，选择具有与ftm_call匹配的"名称"属性的图形
• 对于组中的每一行，枚举起始位置等于 pos 列中的值的 BC 列
• 对于 BC 中的每个字母，检查该字母是否已在该位置的图形中找到，如果没有，则添加权重为 1。如果已经存在，则在重量上加 1

到目前为止，这是我所拥有的：

import pandas as pd
import numpy as np
import networkx as nx
from collections import defaultdict
# read in test basecalls
hamming_df = pd.read_csv("./test_data.txt", sep="t")
hamming_df = hamming_df[["FeatureID", "BC", "chrom", "pos"]]
# initiate graphs 
G = nx.DiGraph(name="G")
KRAS = nx.DiGraph(name="KRAS")
NRAS_3 = nx.DiGraph(name="NRAS_3")
# list of reference graphs
ref_graph_list = [G, KRAS, NRAS_3]
def add_basecalls(row):
basecall = row.BC.astype(str)
target = row.name[1]
pos = row["pos"]
chrom = row["chrom"]
# initialize counter dictionary
d = defaultdict()
# select graph that matches ftm call
graph = [f for f in ref_graph_list if f.graph["name"] == target]
stuff = hamming_df.groupby(["FeatureID", "ftm_call"])  
stuff.apply(add_basecalls)

但这不是将条形码作为我可以枚举的字符串拉出，而是将它们作为一个系列拉出来，我被困住了。

所需的输出是包含以下信息的图表，例如第一个具有虚构计数的 BC"GCTATT"：

FeatureID    chrom    pos         Nucleotide    Weight
1_1_1        12       25398138       G            10
1_1_1        12       25398138       C            22
1_1_1        12       25398139       T            12
1_1_1        12       25398140       A            15
1_1_1        12       25398141       T            18
1_1_1        12       25398142       T            22                     

提前感谢！

Answer 1

您可能需要一个带有axis=1的额外apply来解析每个组的行：

import pandas as pd
import numpy as np
import networkx as nx
from collections import defaultdict
# initiate graphs
GRAPHS = {"G": nx.DiGraph(name="G"),
"KRAS": nx.DiGraph(name="KRAS"),
"NRAS_3": nx.DiGraph(name="NRAS_3"), # notice that test_data.txt has "NRAS_3" not "KRAS_3"
}
WEIGHT_DICT = defaultdict()
def update_weight_for_row(row, target_graph):
pos = row["pos"]
chrom = row["chrom"]
for letter in row.BC:
print(letter)
# now you have access to letters in BC per row
# and can update graph weights as desired
def add_basecalls(grp):
# select graph that matches ftm_call
target = grp.name[1]
target_graph = GRAPHS[target]
grp.apply(lambda row: update_weight_for_row(row, target_graph), axis=1)
# read in test basecalls
hamming_df = pd.read_csv("./test_data.txt", sep="t")
hamming_df2 = hamming_df[["FeatureID", "BC", "chrom", "pos"]]  # Why is this line needed?
stuff = hamming_df.groupby(["FeatureID", "ftm_call"])  
stuff.apply(lambda grp: add_basecalls(grp))