问题描述
是否可以为要由Python pandas
包方法 pandas.DataFrame.to_csv ?
Is it possible to specify a float precision specifically for each column to be printed by the Python pandas
package method pandas.DataFrame.to_csv?
如果我有一个pandas
数据帧,其排列方式如下:
If I have a pandas
dataframe that is arranged like this:
In [53]: df_data[:5]
Out[53]:
year month day lats lons vals
0 2012 6 16 81.862745 -29.834254 0.0
1 2012 6 16 81.862745 -29.502762 0.1
2 2012 6 16 81.862745 -29.171271 0.0
3 2012 6 16 81.862745 -28.839779 0.2
4 2012 6 16 81.862745 -28.508287 0.0
有一个float_format
选项可用于指定精度,但这会在打印时将该精度应用于数据框的所有列.
There is the float_format
option that can be used to specify a precision, but this applys that precision to all columns of the dataframe when printed.
当我这样使用时:
df_data.to_csv(outfile, index=False,
header=False, float_format='%11.6f')
我得到以下信息,其中vals
的精度不正确:
I get the following, where vals
is given an inaccurate precision:
2012,6,16, 81.862745, -29.834254, 0.000000
2012,6,16, 81.862745, -29.502762, 0.100000
2012,6,16, 81.862745, -29.171270, 0.000000
2012,6,16, 81.862745, -28.839779, 0.200000
2012,6,16, 81.862745, -28.508287, 0.000000
推荐答案
在将数据框导出到CSV文件之前,请更改列"vals"的类型
Change the type of column "vals" prior to exporting the data frame to a CSV file
df_data['vals'] = df_data['vals'].map(lambda x: '%2.1f' % x)
df_data.to_csv(outfile, index=False, header=False, float_format='%11.6f')
这篇关于使用pandas.DataFrame.to_csv()按列输出不同的精度?的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持!