[PYTHON] pandas to_csv : csv에 팬더를 쓸 때 CSV 파일의 과학 표기법을 사용하지 않습니다.

pandas to_csv : csv에 팬더를 쓸 때 CSV 파일의 과학 표기법을 사용하지 않습니다.

나는 csv에 판다 df를 쓰고있다. 내가 csv 파일에 쓸 때 열 중 하나의 요소 중 일부가 과학 표기법 / 숫자로 잘못 변환되고 있습니다. 예를 들어, col_1에 '104D59'와 같은 문자열이 있습니다. 문자열은 대개 csv 파일에서 문자열로 표시됩니다. 그러나 '104E59'와 같은 임시 문자열은 과학 표기법 (예 : 1.04 E 61)으로 변환되어 이후의 CSV 파일에 정수로 표시됩니다.

csv 파일을 소프트웨어 패키지 (즉, pandas -> csv -> software_new)로 내보내려고하는데이 데이터 유형의 변경으로 인해 해당 내보내기에 문제가 발생했습니다.

csv에 df를 쓰는 방법이 있나요? df [ 'problem_col']의 모든 요소가 결과 CSV에서 문자열로 표시되거나 과학 표기법으로 변환되지 않도록할까요?

다음은 csv에 팬더 df를 작성하는 데 사용한 코드입니다. df.to_csv ( 'df.csv', 인코딩 = 'utf-8')

또한 문제 열의 dtype을 확인합니다. df.dtype에 대해 df [ 'problem_column']는 객체입니다.

해결법

1. ==============================

   1.float_format 인수를 사용하십시오.

   float_format 인수를 사용하십시오.

   In [11]: df = pd.DataFrame(np.random.randn(3, 3) * 10 ** 12)
   
   In [12]: df
   Out[12]:
                 0             1             2
   0  1.757189e+12 -1.083016e+12  5.812695e+11
   1  7.889034e+11  5.984651e+11  2.138096e+11
   2 -8.291878e+11  1.034696e+12  8.640301e+08
   
   In [13]: print(df.to_string(float_format='{:f}'.format))
                        0                     1                   2
   0 1757188536437.788086 -1083016404775.687134 581269533538.170288
   1  788903446803.216797   598465111695.240601 213809584103.112457
   2 -829187757358.493286  1034695767987.889160    864030095.691202
   

   to_csv와 비슷하게 작동합니다.

   df.to_csv('df.csv', float_format='{:f}'.format, encoding='utf-8')
   

2. ==============================

   2.옵션 및 설정

   옵션 및 설정

   데이터 프레임의 시각화를 위해 pandas.set_option

   import pandas as pd #import pandas package
   
   # for visualisation fo the float data once we read the float data:
   
   pd.set_option('display.html.table_schema', True) # to can see the dataframe/table as a html
   pd.set_option('display.precision', 5) # setting up the precision point so can see the data how looks, here is 5
   df = pd.DataFrame(np.random.randn(20,4)* 10 ** -12) # create random dataframe
   

   df.dtypes # check datatype for columns
   
   [output]:
   0    float64
   1    float64
   2    float64
   3    float64
   dtype: object
   

   df # output of the dataframe
   
   [output]:
   0   1   2   3
   0   -2.01082e-12    1.25911e-12 1.05556e-12 -5.68623e-13
   1   -6.87126e-13    1.91950e-12 5.25925e-13 3.72696e-13
   2   -1.48068e-12    6.34885e-14 -1.72694e-12    1.72906e-12
   3   -5.78192e-14    2.08755e-13 6.80525e-13 1.49018e-12
   4   -9.52408e-13    1.61118e-13 2.09459e-13 2.10940e-13
   5   -2.30242e-13    -1.41352e-13    2.32575e-12 -5.08936e-13
   6   1.16233e-12 6.17744e-13 1.63237e-12 1.59142e-12
   7   1.76679e-13 -1.65943e-12    2.18727e-12 -8.45242e-13
   8   7.66469e-13 1.29017e-13 -1.61229e-13    -3.00188e-13
   9   9.61518e-13 9.71320e-13 8.36845e-14 -6.46556e-13
   10  -6.28390e-13    -1.17645e-12    -3.59564e-13    8.68497e-13
   11  3.12497e-13 2.00065e-13 -1.10691e-12    -2.94455e-12
   12  -1.08365e-14    5.36770e-13 1.60003e-12 9.19737e-13
   13  -1.85586e-13    1.27034e-12 -1.04802e-12    -3.08296e-12
   14  1.67438e-12 7.40403e-14 3.28035e-13 5.64615e-14
   15  -5.31804e-13    -6.68421e-13    2.68096e-13 8.37085e-13
   16  -6.25984e-13    1.81094e-13 -2.68336e-13    1.15757e-12
   17  7.38247e-13 -1.76528e-12    -4.72171e-13    -3.04658e-13
   18  -1.06099e-12    -1.31789e-12    -2.93676e-13    -2.40465e-13
   19  1.38537e-12 9.18101e-13 5.96147e-13 -2.41401e-12
   

   df.to_csv('estc.csv',sep=',', float_format='%.15f') # write with precision .15
   

   ,0,1,2,3
   0,-0.000000000002011,0.000000000001259,0.000000000001056,-0.000000000000569
   1,-0.000000000000687,0.000000000001919,0.000000000000526,0.000000000000373
   2,-0.000000000001481,0.000000000000063,-0.000000000001727,0.000000000001729
   3,-0.000000000000058,0.000000000000209,0.000000000000681,0.000000000001490
   4,-0.000000000000952,0.000000000000161,0.000000000000209,0.000000000000211
   5,-0.000000000000230,-0.000000000000141,0.000000000002326,-0.000000000000509
   6,0.000000000001162,0.000000000000618,0.000000000001632,0.000000000001591
   7,0.000000000000177,-0.000000000001659,0.000000000002187,-0.000000000000845
   8,0.000000000000766,0.000000000000129,-0.000000000000161,-0.000000000000300
   9,0.000000000000962,0.000000000000971,0.000000000000084,-0.000000000000647
   10,-0.000000000000628,-0.000000000001176,-0.000000000000360,0.000000000000868
   11,0.000000000000312,0.000000000000200,-0.000000000001107,-0.000000000002945
   12,-0.000000000000011,0.000000000000537,0.000000000001600,0.000000000000920
   13,-0.000000000000186,0.000000000001270,-0.000000000001048,-0.000000000003083
   14,0.000000000001674,0.000000000000074,0.000000000000328,0.000000000000056
   15,-0.000000000000532,-0.000000000000668,0.000000000000268,0.000000000000837
   16,-0.000000000000626,0.000000000000181,-0.000000000000268,0.000000000001158
   17,0.000000000000738,-0.000000000001765,-0.000000000000472,-0.000000000000305
   18,-0.000000000001061,-0.000000000001318,-0.000000000000294,-0.000000000000240
   19,0.000000000001385,0.000000000000918,0.000000000000596,-0.000000000002414
   

   df.to_csv('estc.csv',sep=',', float_format='%f') # this will remove the extra zeros after the '.'
   

   자세한 내용은 pandas.DataFrame.to_csv를 확인하십시오.

3. ==============================

   3.값을 형식화 된 문자열 (예 : csvfile csv.writier의 일부)로 사용하려면 목록을 만들기 전에 숫자를 형식화 할 수 있습니다.

   값을 형식화 된 문자열 (예 : csvfile csv.writier의 일부)로 사용하려면 목록을 만들기 전에 숫자를 형식화 할 수 있습니다.

   with open('results_actout_file','w',newline='') as csvfile:
        resultwriter = csv.writer(csvfile, delimiter=',')
        resultwriter.writerow(header_row_list)
   
        resultwriter.writerow(df['label'].apply(lambda x: '%.17f' % x).values.tolist())
   

from https://stackoverflow.com/questions/22995762/pandas-to-csv-suppress-scientific-notation-in-csv-file-when-writing-pandas-to-c by cc-by-sa and MIT license