pandas to_csv：将 pandas 写入 csv 时抑制 csv 文件中的科学记数法

Posted 2023-02-25

【中文标题】pandas to_csv：将 pandas 写入 csv 时抑制 csv 文件中的科学记数法

【英文标题】：pandas to_csv: suppress scientific notation in csv file when writing pandas to csv

【发布时间】：2014-05-24 14:46:34

【问题描述】：

我正在将 pandas df 写入 csv。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。字符串在 csv 文件中大多表示为字符串，因为它们应该是。但是，偶尔出现的字符串（例如 '104E59'）会被转换为科学计数法（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（即 pandas -> csv -> software_new），而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'] 中的所有元素在生成的 csv 中表示为字符串或不转换为科学计数法？

这是我用来将 pandas df 写入 csv 的代码：

df.to_csv('df.csv', encoding='utf-8')

我还检查了问题列的dtype：

for df.dtype, df['problem_column'] is an object

【参考方案1】：

对于python 3.xx (Python 3.7.2)&

In [2]: pd.__version__Out[2]: '0.23.4':

Options and Settings

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

数据输出：

df.dtypes # check datatype for columns

[output]:
0    float64
1    float64
2    float64
3    float64
dtype: object

数据框：

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

现在使用 float_format='%.15f' 参数写入 to_csv

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

文件输出：

,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

现在使用 float_format='%f' 参数写入 to_csv

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

For more details check pandas.DataFrame.to_csv

【参考方案2】：

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

对于 to_csv 的工作方式类似：

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

【讨论】：

似乎不适用于 pandas 0.17.1：TypeError：不支持的操作数类型为 %：'builtin_function_or_method' 和 'float'

@user1637894 对我来说仍然适用于 0.17.1 :s。在 python 2.7 和 3.4 上测试了几个不同的 numpy 版本。

@user1637894 我建议您在 pandas 的 github 上发布您的问题！

【参考方案3】：

如果您想将值用作列表中的格式化字符串，例如作为 csvfile csv.writier 的一部分，则可以在创建列表之前对数字进行格式化：

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())