使用 Pandas 读取 CSV 时如何在列中保留前导零? [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.
I am importing study data into a Pandas data frame using read_csv
.
我的主题代码是 6 个数字编码,其中包括出生日期.对于我的一些主题,这会导致代码带有前导零(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").
当我导入到 Pandas 中时,前导零被去除,列的格式为 int64
.
When I import into Pandas, the leading zero is stripped of and the column is formatted as int64
.
有没有办法将这一列原样导入为字符串?
Is there a way to import this column unchanged maybe as a string?
我尝试为列使用自定义转换器,但它不起作用 - 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
如所示这个问题/答案来自Lev Landau,可能有一个简单的解决方案来使用转换器
选项.read_csv
函数中特定列的
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)}
可以参考pandas.io.parsers.read_csv中read_csv
函数的更多选项文档.
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
要求的结果:
me@ubuntu:~$ python test_dataframe.py
project_name project_id
0 Some Project 000245
1 Another Project 000478
me@ubuntu:~$
更新,因为它可以帮助他人:
Update as it helps others:
要将所有列作为str,可以这样做(来自评论):
To have all columns as str, one can do this (from the comment):
pd.read_csv('sample.csv', dtype = str)
要将大多数或选择性列作为str,可以这样做:
To have most or selective columns as str, one can do this:
# lst of column names which needs to be string
lst_str_cols = ['prefix', 'serial']
# use dictionary comprehension to make dict of dtypes
dict_dtypes = {x : 'str' for x in lst_str_cols}
# use dict on dtypes
pd.read_csv('sample.csv', dtype=dict_dtypes)
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