使用熊猫将具有缺失值的csv数据读取到python中

Question

I have a CSV-file looking like this:

"row ID","label","val"
"Row0","5",6
"Row1","",6
"Row2","",6
"Row3","5",7
"Row4","5",8
"Row5",,9
"Row6","nan",
"Row7","nan",
"Row8","nan",0
"Row9","nan",3
"Row10","nan",

All quoted entries are strings. Non-quoted entries are numerical. Empty fields are missing values (NaN), Quoted empty fields still should be considered as empty strings. I tried to read it in with pandas read_csv but I cannot get it working the way I would like to have it... It still consideres ,"", and ,, as NaN, while it's not true for the first one.

d = pd.read_csv(csv_filename, sep=',', keep_default_na=False, na_values=[''], quoting = csv.QUOTE_NONNUMERIC)

Can anybody help? Is it possible at all?

Answer 1

You can try with numpy.genfromtxt and specify the missing_values parameter

http://docs.scipy.org/doc/numpy/reference/generated/numpy.genfromtxt.html

Answer 2

Maybe something like:

import pandas as pd
import csv
import numpy as np
d = pd.read_csv('test.txt', sep=',', keep_default_na=False, na_values=[''], quoting = csv.QUOTE_NONNUMERIC)
mask = d['label'] == 'nan'
d.label[mask] = np.nan

Answer 3

I found a way to get it more or less working. I just don't know, why I need to specify dtype=type(None) to get it working... Comments on this piece of code are very welcome!

import re
import pandas as pd
import numpy as np

# clear quoting characters
def filterTheField(s):
    m = re.match(r'^"?(.*)?"$', s.strip())
    if m:
        return m.group(1)
    else:
        return np.nan

file = 'test.csv'

y = np.genfromtxt(file, delimiter = ',', filling_values = np.nan, names = True, dtype = type(None), converters = {'row_ID': filterTheField, 'label': filterTheField,'val': float})

d = pd.DataFrame(y)

print(d)