如何在Python中切片列表

Question

I am at the trying to clean some data for a log regression in python and my list keeps showing up like

loansData['FICO.Score'][0:5]
Out[23]: 
81174    735-739
99592    715-719
80059    690-694
15825    695-699
33182    695-699

I want to be able to pick the lower fico range number how do I remove the first set of numbers and also remove the upper range?

Answer 1

It doesn't look like your Q's subject is germane to what you're actually asking about -- you're doing fine with the slicing of that one list, now what you have are, instead, strings (each an item from said list).

Anyway, assuming eg the first one is 81174 735-739 (exactly four spaces in the middle, none before, no tab, &c), the 735 is the string slice at [9:12]. So for example

[s[9:12] for s in loansData['FICO.Score'][0:5]]

should show what you want (could easily need tweaking by 1 or so if the hypotheses above about the extract string format are incorrect, but, that's the general idea).

Answer 2

这是一个示例，该示例会将您的数据子集化为仅包含FICO观测值（含“ 695-699”，“ 690-694”分数）。

loansData['FICO.Score'][loansData['FICO.Score'].isin(['695-699', ' 690-694'])]

Answer 3

This answer assumes the 5 digit number is the series index, and you want to return an array with the lower bound of each value.

import pandas as pd

data:

i=[81174,99592,80059,15825,33182]
d = ['735-739','715-719','690-694','695-699','695-699']

recreate frame:

df =pd.DataFrame(d, index=i, columns=['FICO.Score'])

slice string and convert to int:

df['FICO.Score'].apply(lambda x: int(x[:-4])).values

Calling values returns and array from the series(getting rid of the index)