如何遍历.csv文件并提取python中的某些值？

Question

I'm trying to loop through the 11th column in a CSV file and search for the term "abc" (as an example). For every "abc" it finds, I want it to return the value of the first column of the same row, unless it's empty. If it's empty, I want it to go up the first column row by row until it finds a cell that's not empty and return the value of that cell.

I've already imported the needed CSV file. Here's my code trying to do the above.

for row in csvReader:
    if row[10] == 'abc':
        colAVal = row
        while colAVal[0] == '' and colAVal != 0:
            colAVal -= 1
        print(colAVal[0])

My question is does this code do what it's supposed to do?

And for the second part of what I'm trying to do, I want to be able to manipulate the values that it returns - is there a way of storing these values so that that I can write code that does something for every colAVal[0] that the first part returned?

Answer 1

What you have there won't quite do what you want. Involking

colAVal -= 1

does not give you the previous row in an iterator. In languages with a more standard for loop, you could instead access the data you want by going backwards on the current iterator row until you found what you wanted, but in python this is not the recommended approach. Python's for loop is more of a for each loop, and as such once you've gone from one row to the next, the previous is inaccessable without saving it or accessing it directly by row count on the input data object. Mixing these kinds of access is highly not recommended, and can get confusing fast.

You also have two questions in you question above, and I'll try my best to answer both.

Given a dataset that looks like the following:

col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11,col12
0,0,0,0,0,0,0,0,0,0,abc,0
1,1,1,1,1,1,1,1,1,1,1,1
2,2,2,2,2,2,2,2,2,2,2,2
3,3,3,3,3,3,3,3,3,3,3,3
4,4,4,4,4,4,4,4,4,4,4,4
,5,5,5,5,5,5,5,5,5,abc,5
,6,6,6,6,6,6,6,6,6,abc,6
7,7,7,7,7,7,7,7,7,7,7,7

you would expect the answers to be 0, 4, and 4, if I'm understanding your question correctly. You could accomplish that and save the data for later use with something like the following:

#! /usr/bin/env python

import csv

results = []

with open('example.csv') as file_handler:

    for row in csv.reader(file_handler):

        if row[0] != '' and row[0] != 0:

            lastValidFirstColumn = row[0]

        if row[10] == 'abc':

            results.append(lastValidFirstColumn)

print(results)
# prints ['0', '4', '4']

the data you want if I understood correctly is now stored in the results variable. Its not too difficult to write it to file or do other manipulations for it, and I'd recommend looking them up yourself, it'd be a better learning experience.

Answer 2

You can do this in pandas pretty easily

import pandas as pd
import numpy as np
df = pd.read_csv('my.csv', header=None)

Using a made up csv, we have these values

    0       1   2   3   4   5   6   7   8   9   10
0   20.0    b   a   b   a   b   a   b   a   b   abc
1   NaN     c   d   c   d   c   d   c   d   c   def
2   10.0    d   e   d   e   d   e   d   e   d   ghi
3   NaN     e   f   e   f   e   f   e   f   e   abc

df['has_abc'] = np.where(df[10]=='abc', df.ffill()[0], np.nan)
df.dropna(subset=['has_abc'], inplace=True)

Output

    0       1   2   3   4   5   6   7   8   9   10  has_abc
0   20.0    b   a   b   a   b   a   b   a   b   abc 20.0
3   NaN     e   f   e   f   e   f   e   f   e   abc 10.0