Python:出现“列表索引超出范围”错误;我知道为啥但不知道如何解决
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【中文标题】Python:出现“列表索引超出范围”错误;我知道为啥但不知道如何解决【英文标题】:Pyhthon: Getting "list index out of range" error; I know why but don't know how to resolve thisPython:出现“列表索引超出范围”错误;我知道为什么但不知道如何解决 【发布时间】:2021-10-28 07:16:20 【问题描述】:我目前正在从事一个数据科学项目。想法是从“glassdoor_jobs.csv”中清理数据,并以更易于理解的方式呈现。
import pandas as pd
df = pd.read_csv('glassdoor_jobs.csv')
#salary parsing
#Removing "-1" Ratings
#Clean up "Founded"
#state field
#Parse out job description
df['hourly'] = df['Salary Estimate'].apply(lambda x: 1 if 'per hour' in x.lower() else 0)
df['employer_provided'] = df['Salary Estimate'].apply(lambda x: 1 if 'employer provided salary' in x.lower() else 0)
df = df[df['Salary Estimate'] != '-1']
Salary = df['Salary Estimate'].apply(lambda x: x.split('(')[0])
minus_Kd = Salary.apply(lambda x: x.replace('K', '').replace('$',''))
minus_hr = minus_Kd.apply(lambda x: x.lower().replace('per hour', '').replace('employer provided salary:', ''))
df['min_salary'] = minus_hr.apply(lambda x: int(x.split('-')[0]))
df['max_salary'] = minus_hr.apply(lambda x: int(x.split('-')[1]))
我在最后一行收到错误。经过一番挖掘,我在 minus_hr 中发现,一些“Salary Estimate”只有一个数字而不是范围:
index | Salary Estimate |
---|---|
0 | 150 |
1 | 58 |
2 | 130 |
3 | 125-150 |
4 | 110-140 |
5 | 200 |
6 | 67- 77 |
等等。现在我想弄清楚如何解决“列表索引超出范围”的问题,并使 max_salary 与只有一个值的单元格的 min_salary 相同。
我也在尝试获取最低和最高工资之间的平均值,如果单元格只有一个值,则将该值设为平均值
所以最后,像索引 0 这样的东西看起来像:
index | min | max | average |
---|---|---|---|
0 | 150 | 150 | 150 |
【问题讨论】:
【参考方案1】:您必须在某处添加条件语句。
df['min_salary'] = minus_hr.apply(lambda x: int(x.split('-')[0]) if '-' in x else x)
上面可能会做,或者你可以定义一个函数。
def max_salary(cell_value):
if '-' in cell_value:
max_salary = split(cell_value, '-')[1]
else:
max_salary = cell_value
return max_salary
df['max_salary'] = minus_hr.apply(lambda x: max_salary(x))
def avg_salary(cell_value):
if '-' in cell_value:
salaries = split(cell_value,'-')
avg = sum(salaries)/len(salaries)
else:
avg = cell_value
return avg
df['avg_salary'] = minus_hr.apply(lambda x: avg_salary(x))
交换 min_salary 并重复
【讨论】:
所以按照你的第一个例子,我得到了最小值和最大值。我该怎么办平均?显然在当前状态下除以 2 是不可能的 已更新。如果这对您有用,您可以将其标记为答案吗?我以前从来没有回答过编码问题:) 所以平均工资部分比我想象的要容易;我所要做的就是:df['average_salary'] = (df.min_salary.astype(int) + df.max_salary.astype(int))/2
但是感谢您的回答。最小值和最大值真的很头疼,你帮了我!
赢家!当你被一个应该很简单但你无法弄清楚的想法卡住时,我讨厌它。另一方面,如果你能弄清楚,那就太好了。为加价干杯:D【参考方案2】:
在访问元素之前测试x.split('-')
的长度。
salaries = x.split('-')
if len(salaries) == 1:
# only one salary number is given, so assign the same value to min and max
df['min_salary'] = df['max_salary'] = minus_hr.apply(lambda x: int(salaries[0]))
else:
# two salary numbers are given
df['min_salary'] = minus_hr.apply(lambda x: int(salaries[0]))
df['max_salary'] = minus_hr.apply(lambda x: int(salaries[1]))
【讨论】:
salaries = x.split('-')
中未声明的变量 x 不会有问题
@ciaranhaines 啊,那是真的;我没有注意到原始代码在 lambda 上下文中。
是的,这看起来很有希望,但我一直在第一行遇到问题【参考方案3】:
如果you want to avoid.apply()
...
试试:
import numpy as np
# extract the two numbers (if there are two numbers) from the 'Salary Estimate' column
sals = df['Salary Estimate'].str.extractall(r'(?P<min_salary>\d+)[^0-9]*(?P<max_salary>\d*)?')
# reset the new frame's index
sals = sals.reset_index()
# join the extracted min/max salary columns to the original dataframe and fill any blanks with nan
df = df.join(sals[['min_salary', 'max_salary']].fillna(np.nan))
# fill any nan values in the 'max_salary' column with values from the 'min_salary' column
df['max_salary'] = df['max_salary'].fillna(df['min_salary'])
# set the type of the columns to int
df['min_salary'] = df['min_salary'].astype(int)
df['max_salary'] = df['max_salary'].astype(int)
# calculate the average
df['average_salary'] = df.loc[:,['min_salary', 'max_salary']].mean(axis=1).astype(int)
# see what you've got
print(df)
或者不使用正则表达式:
import numpy as np
# extract the two numbers (if there are two numbers) from the 'Salary Estimate' column
df['sals'] = df['Salary Estimate'].str.split('-')
# expand the list in sals to two columns filling with nan
df[['min_salary', 'max_salary']] = pd.DataFrame(df.sals.tolist()).fillna(np.nan)
# delete the sals column
del df['sals']
# # fill any nan values in the 'max_salary' column with values from the 'min_salary' column
df['max_salary'] = df['max_salary'].fillna(df['min_salary'])
# # set the type of the columns to int
df['min_salary'] = df['min_salary'].astype(int)
df['max_salary'] = df['max_salary'].astype(int)
# # calculate the average
df['average_salary'] = df.loc[:,['min_salary', 'max_salary']].mean(axis=1).astype(int)
# see you've got
print(df)
输出:
Salary Estimate min_salary max_salary average_salary
0 150 150 150 150
1 58 58 58 58
2 130 130 130 130
3 125-150 125 150 137
4 110-140 110 140 125
5 200 200 200 200
6 67- 77 67 77 72
【讨论】:
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