我正在给csv写一个pandas df。当我将其写入csv文件时，其中一列中的某些元素被错误地转换为科学记数法/数字。例如，col_1中包含诸如“104D59”之类的字符串。字符串主要在csv文件中表示为字符串，因为它们应该是。但是，偶尔的字符串，例如'104E59'，正在转换为科学记数法（例如，1.04 E 61），并在随后的csv文件中表示为整数。
我正在尝试将csv文件导出到软件包中（例如，pandas - ＆gt; csv - ＆gt; software_new），这种数据类型的更改导致该导出出现问题。
有没有办法将df写入csv，确保df ['problem_col']中的所有元素都在结果csv中表示为字符串或未转换为科学记数法？
这是我用来将pandas df写入csv的代码： df.to_csv（'df.csv'，encoding ='utf-8'）
我还检查问题列的dtype： 对于df.dtype，df ['problem_column']是一个对象
答案 0 :(得分：7)
使用float_format参数：
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
其中与to_csv类似：
df.to_csv('df.csv', float_format='{:f}'.format, encoding='utf-8')
答案 1 :(得分：6)
对于python 3.xx（在3.6.5和3.7上测试）：
For visualization of the dataframe 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 '.'
答案 2 :(得分：0)
如果您希望将值用作列表中的格式化字符串，例如csvfile csv.writier的一部分，则可以在创建列表之前格式化数字：
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())