使用Pandas读取CSV时如何在列中保持前导零? [英] How to keep leading zeros in a column when reading CSV with Pandas?

问题描述

我正在使用read_csv将研究数据导入Pandas数据框中.

I am importing study data into a Pandas data frame using read_csv.

我的主题代码是6个数字，其中包括出生日期.对于我的某些主题，这会导致代码的前导零(例如"010816").

My subject codes are 6 numbers coding, among others, the day of birth. For some of my subjects this results in a code with a leading zero (e.g. "010816").

当我导入Pandas时，会去除开头的零，并且该列的格式为int64.

When I import into Pandas, the leading zero is stripped of and the column is formatted as int64.

有没有一种方法可以不变地将该列作为字符串导入?

Is there a way to import this column unchanged maybe as a string?

我尝试为该列使用自定义转换器，但是它不起作用-似乎自定义转换发生在Pandas转换为int之前.

I tried using a custom converter for the column, but it does not work - it seems as if the custom conversion takes place before Pandas converts to int.

推荐答案

如中所示 Lev Landau 提出的这个问题/答案，可能有使用converters选项的简单解决方案对于read_csv函数中的特定列.

As indicated in this question/answer by Lev Landau, there could be a simple solution to use converters option for a certain column in read_csv function.

converters={'column_name': lambda x: str(x)}

您可以在pandas.io.parsers.read_csv

You can refer to more options of read_csv funtion in pandas.io.parsers.read_csv documentation.

假设我有csv文件projects.csv，如下所示:

Lets say I have csv file projects.csv like below:

project_name,project_id
Some Project,000245
Another Project,000478

例如下面的代码正在修剪前导零:

As for example below code is triming leading zeros:

import csv
from pandas import read_csv

dataframe = read_csv('projects.csv')
print dataframe

结果:

me@ubuntu:~$ python test_dataframe.py 
      project_name  project_id
0     Some Project         245
1  Another Project         478
me@ubuntu:~$

解决方案代码示例:

import csv
from pandas import read_csv

dataframe = read_csv('projects.csv', converters={'project_id': lambda x: str(x)})
print dataframe

必填结果:

me@ubuntu:~$ python test_dataframe.py 
      project_name project_id
0     Some Project     000245
1  Another Project     000478
me@ubuntu:~$

这篇关于使用Pandas读取CSV时如何在列中保持前导零?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IT屋！