Python 熊猫到csv：将熊猫写入csv时，在csv文件中禁止使用科学符号

我正在给csv写一个熊猫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符号/数字。例如，

colu_1

中有字符串，如

'104D59'

。字符串在csv文件中主要表示为字符串，这是它们应该有的。但是，偶尔出现的字符串，如

'104E59'

，正在转换为科学符号（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（即熊猫->csv->软件新）中，数据类型的此更改导致该导出出现问题

是否有办法将df写入csv，以确保

df['problem\u col']

中的所有元素在生成的csv中表示为字符串，或者不转换为科学符号

以下是我用来将熊猫df写入csv的代码：

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

我还检查问题列的数据类型：

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

---

使用

float\u格式

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

这与to_csv的工作原理类似：

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

---

如果要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())

对于python 3.xx（

python 3.7.2

）&

输入[2]：pd.\uuuuuu版本\uuuuu

输出[2]：“0.23.4”

：

数据输出： 数据帧： 现在使用float_format='%.15f'参数写入_csv 文件输出： 现在使用float\u format='%f'参数写入\u csv

---

从0.17.1开始似乎不起作用：TypeError:不支持%的操作数类型：“'builtin_function_或_method'和'float'@user1637894在0.17.1:s时仍然适用于我。在Python2.7和3.4上测试，有几个不同的numpy版本。@user1637894我建议将您的问题发布在pandas的github上！

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 '.'