CSV文件中的Python分析列和单元格中的数据

Question

I am trying to create a code for the following data:

I have imported the data using the code:

import csv
import itertools
import pandas as pd

input_file="computation.csv"
cmd=pd.read_csv(input_file)
subset = cmd[['Carbon A', 'Carbon B']]
carbon_pairs = [tuple(y) for y in subset.values]
c_pairs = carbon_pairs

I want to create a code that has the output:

1 is connected to
  2
  4
  6
  7 
  8
2 is connected to
  1
  4
  5

Note that for 'carbon' 2, I would like it to repeat that it is connected to carbon 1. I was thinking that some permutation would be able to show this, but I am very unsure where to start. Basically, the code needs to output:

for every cell with the same value, print adjacent cell

Answer 1

You can get your desired output without the pandas dependency with the following function (Python 2), which will allow you to pass in any filename you want, and control with indices (zero-based) you're trying to query. This solution assumes that the data is sorted as in the example you provided.

import csv

def printAdjacentNums(filename, firstIdx, secondIdx):
    with open(filename, 'rb') as csvfile:
        # handle header line
        header = next(csvfile)
        reader = csv.reader(csvfile)
        current_val = ''
        current_adj = []
        # dict of lists for lookback
        lookback = {}
        for row in reader:
            if current_val == '':
                current_val = row[firstIdx]
            if row[firstIdx] == current_val:
                current_adj.append(row[secondIdx])
            else:
                # check lookback
                for k, v in lookback.items():
                    if current_val in v:
                        current_adj.append(k)

                # print what we need to
                print current_val + ' is connected to'
                for i in current_adj:
                    print i

                # append current vals to lookback
                lookback[current_val] = current_adj

                # reassign
                current_val = row[firstIdx]
                current_adj = [row[secondIdx]]

     # print final set
    for k, v in lookback.items():
        if current_val in v:
            current_adj.append(k)
    print current_val + ' is connected to'
    for i in current_adj:
        print i

Then call it like so, based on your example:

printAdjacentNums('computation.csv', 0, 1)

Answer 2

Starting from the end of your question:

c_pairs = [(1, 2), (1, 4), (1, 6), (1, 7), (1, 8), (2, 1), (2, 4), (2, 5)]

You presumably want to end up with something more like:

groups = {1: [2, 4, 6, 7, 8], 2: [1, 4, 5]}

There are many ways to obtain this.

A very fast way, if you know your data is sorted, is to use itertools.groupby , eg:

first_item = lambda (a, b): a
for key, items in itertools.groupby(c_pairs, first_item):
    print '%s is connected to' % key
    for (a, b) in items:
        print '  %s' % b

it is still probably the fastest way if your data is not sorted, simply sort it first:

c_pairs = sorted(c_pairs, key=first_item)

A more do-it-yourself solution is to use defaultdict or a standard dictionary to create a mapping from one to the other.

groups = collections.defaultdict(list)
for a, b in c_pairs:
    groups[a].append(b)

which is equivalent to without collections:

groups = {}
for a, b in c_pairs:
    groups.setdefault(a, [])  # many ways to do this as well
    groups[a].append(b)