How to keep leading zeros in a column when reading CSV with Pandas?

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)}

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

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

project_name,project_id
Some Project,000245
Another Project,000478

As for example below code is triming leading zeros:

import csv
from pandas import read_csv

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

Result:

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

Solution code example:

import csv
from pandas import read_csv

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

Required result:

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

Update as it helps others:

To have all columns as str, one can do this (from the comment):

pd.read_csv('sample.csv', dtype = str)

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)