对于每个分组依据的熊猫，都会在字符串列中枚举并转换为反词典

Question

I'm trying to automate building a networkx graph for any input pandas dataframe.

The dataframe looks like this:

  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

Here's the algorithm I need to sort out:

• Group by FeatureID
• For each FeatureID, select graph with "name" attribute that matches ftm_call
• For each row in group, enumerate over the BC column where starting position equals the value in the pos column
• For every letter in BC, check if that letter is already found in the graph at that position, and if not, add with weight of 1. If already there, add 1 to weight

So far, here is what I have:

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)

But this isn't pulling out the barcodes as strings that I can just enumerate across, it's pulling them out as a series and I'm stuck.

Desired output is a graph containing the following information, example shown for the first BC "GCTATT" with fictitious counts:

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                     

Thanks in advance!

Answer 1

You probably need an additional apply with axis=1 to parse the rows for each group:

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))