python - 尝试将字符串转换为整数的 Pandas 错误

Question

要求:

DataFrame 中的一个特定列是“混合”类型。它的值可以是 "123456" 或 "ABC12345"。

正在使用 xlsxwriter 将此数据框写入 Excel。

对于像 "123456" 这样的值，Pandas 将其转换为 123456.0(使其看起来像一个 float )

我们需要将其作为 123456(即 + 整数)放入 xlsx 中，以防值是完全数字。

努力:

代码片段如下所示

import pandas as pd
import numpy as np
import xlsxwriter
import os
import datetime
import sys
excel_name = str(input("Please Enter Spreadsheet Name :\n").strip())

print("excel entered :   "   , excel_name)
df_header = ['DisplayName','StoreLanguage','Territory','WorkType','EntryType','TitleInternalAlias',
         'TitleDisplayUnlimited','LocalizationType','LicenseType','LicenseRightsDescription',
         'FormatProfile','Start','End','PriceType','PriceValue','SRP','Description',
         'OtherTerms','OtherInstructions','ContentID','ProductID','EncodeID','AvailID',
         'Metadata', 'AltID', 'SuppressionLiftDate','SpecialPreOrderFulfillDate','ReleaseYear','ReleaseHistoryOriginal','ReleaseHistoryPhysicalHV',
          'ExceptionFlag','RatingSystem','RatingValue','RatingReason','RentalDuration','WatchDuration','CaptionIncluded','CaptionExemption','Any','ContractID',
          'ServiceProvider','TotalRunTime','HoldbackLanguage','HoldbackExclusionLanguage']
first_pass_drop_duplicate = df_m_d.drop_duplicates(['StoreLanguage','Territory','TitleInternalAlias','LocalizationType','LicenseType',
                                   'LicenseRightsDescription','FormatProfile','Start','End','PriceType','PriceValue','ContentID','ProductID',
                                   'AltID','ReleaseHistoryPhysicalHV','RatingSystem','RatingValue','CaptionIncluded'], keep=False) 
# We need to keep integer AltID  as is

first_pass_drop_duplicate.loc[first_pass_drop_duplicate['AltID']] =   first_pass_drop_duplicate['AltID'].apply(lambda x : str(int(x)) if str(x).isdigit() == True else x)

我试过了:

1. using `dataframe.astype(int).astype(str)` # works as long as value is not alphanumeric
2.importing re and using pure python `re.compile()` and `replace()` -- does not work
3.reading DF row by row in a for loop !!! Kills the machine as dataframe can have 300k+ records

每次，我都会得到错误:

raise KeyError('%s not in index' % objarr[mask])
KeyError: '[ 102711. 102711. 102711. 102711. 102711. 102711. 102711. 102711.\n 102711. 102711. 102711. 102711. 102711. 102711. 102711. 102711.\n 102711. 102711. 102711. 102711. 102711. 102711. 102711. 102711.\n 102711. 102711. 102711. 102711. 102711. 102711. 102711. 102711.\n 102711. 102711. 102711. 102711. 102711. 102711. 102711. 102711.\n 102711. 102711. 102711. 102711. 102711. 102711. 102711. 102711.\n 102711. 102711. 102711. 102711. 102711. 102711. 102711. 102711.\n 102711. 102711. 102711. 102711. 102711. 102711. 102711. 102711.\n 5337. 5337. 5337. 5337. 5337. 5337. 5337. 5337.\n 5337. 5337. 5337. 5337. 5337. 5337. 5337. 5337.\n 5337. 5337. 5337. 5337. 5337. 5337. 5337. 5337.\n 5337. 5337. 5337. 5337. 5337. 5337. 5337. 5337.\n 5337. 5337. 5337. 5337. 5337. 5337. 5337. 5337.\n 5337. 5337. 2124. 2124. 2124. 2124. 2124. 2124.\n 2124. 2124. 6643. 6643. 6643. 6643. 6643. 6643.\n 6643. 6643. 6643. 6643. 6643. 6643. 6643. 6643.\n 6643. 6643. 6643. 6643. 6643. 6643. 6643. 6643.\n 6643. 6643. 6643. 6643. 6643. 6643. 6643. 6643.] not in index'

我是 python/pandas 的新手，非常感谢任何帮助，解决方案。

Answer 1

我认为你需要 to_numeric :

df = pd.DataFrame({'AltID':['123456','ABC12345','123456'],
                   'B':[4,5,6]})

print (df)
      AltID  B
0    123456  4
1  ABC12345  5
2    123456  6

df.ix[df.AltID.str.isdigit(), 'AltID']  = pd.to_numeric(df.AltID, errors='coerce')

print (df)
      AltID  B
0    123456  4
1  ABC12345  5
2    123456  6

print (df['AltID'].apply(type))
0    <class 'float'>
1      <class 'str'>
2    <class 'float'>
Name: AltID, dtype: object