我是 python 的新手。我正在嘗試解決錯誤我有一個數據框（reprex）-import pandas as pd df Out[29]: Id ServiceSubCodeKey PrintDate 0 1895650 2 2018-07-27 1 1895650 4 2018-08-13 2 1896355 2 2018-08-10 3 1897675 9 2018-08-13 4 1897843 2 2018-08-10 5 2178737 3 2019-06-14 6 2178737 4 2019-06-14 7 2178737 7 2019-06-14 8 2178737 1 2019-06-14 9 2178750 699 2019-06-14columns = ( pd.get_dummies(df["ServiceSubCodeKey"]) .reindex(range(df.ServiceSubCodeKey.min(), df.ServiceSubCodeKey.max()+1), axis=1, fill_value=0) # now it has all digits .astype(str) )codes = pd.Series( [int(''.join(row)) for row in columns.itertuples(index=False)], index=df.index)codes = ( codes.groupby(df.Id).transform('sum').astype('str') .str.pad(width=columns.shape[1], fillchar='0') .str.rstrip('0') # this will remove trailing 0's )print(codes)df = df.assign(one_hot_ssc=codes)OverflowError: int too large to convert to float當我嘗試對其進行故障排除時，此錯誤發生在該部分codes = pd.Series( [int(''.join(row)) for row in columns.itertuples(index=False)], index=df.index)如果我將最后一個服務子代碼更改為 60 或更低的數字而不是 699，此錯誤就會消失。這個錯誤有什么解決辦法嗎？我希望它甚至可以用于 5 位數字。尋找永久解決方案
1 回答
墨色風雨
TA貢獻1853條經驗 獲得超6個贊
罪魁禍首似乎是 pandas 試圖將值轉換為浮點數。
[int(''.join(row)) for row in columns.itertuples(index=False)]有效，但將其轉換為系列卻pd.Series無效。我不知道為什么 pandas 試圖將
intstofloats解決方法是，以熊貓沒有機會嘗試將其轉換
ints為floats.dfg[0]是list一個int以下代碼也適用于
'ServiceSubCodeKey'等于99999
import pandas as pd
# this will create codes
codes_values = [int(''.join(r)) for r in columns.itertuples(index=False)]
codes = pd.Series({'test': codes_values}).explode()
codes.index = df.index
# groupby and aggregate the values into lists
dfg = codes.groupby(df.Id).agg(list).reset_index()
# sum the lists; doing this with a pandas function also does not work, so no .sum or .apply
summed_lists = list()
for r, v in dfg.iterrows():
summed_lists.append(str(sum(v[0])))
# assign the list of strings to a column
dfg['sums'] = summed_lists
# perform the remainder of the functions on the sums column
dfg['final'] = dfg.sums.str.pad(width=columns.shape[1], fillchar='0').str.rstrip('0')
# display(dfg.final)
0 0101
1 01
2 000000001
3 01
4 1011001
5 0000000000000000000000000000000000000000000000...
Name: final, dtype: object
# merge df and dfg.final
dfm = pd.merge(df, dfg[['Id', 'final']], on='Id')
# display(dfm)
Id ServiceSubCodeKey PrintDate final
0 1895650 2 2018-07-27 0101
1 1895650 4 2018-08-13 0101
2 1896355 2 2018-08-10 01
3 1897675 9 2018-08-13 000000001
4 1897843 2 2018-08-10 01
5 2178737 3 2019-06-14 1011001
6 2178737 4 2019-06-14 1011001
7 2178737 7 2019-06-14 1011001
8 2178737 1 2019-06-14 1011001
9 2178750 99999 2019-06-14 ...000000001
添加回答
舉報
0/150
提交
取消