I am importing study data into a Pandas data frame using read_csv
.
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").
When I import into Pandas, the leading zero is stripped of and the column is formatted as int64
.
Is there a way to import this column unchanged maybe as a string?
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.
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:~$
here is a shorter, robust and fully working solution:
simply define a mapping (dictionary) between variable names and desired data type:
dtype_dic= {'subject_id': str,
'subject_number' : 'float'}
use that mapping with pd.read_csv()
:
df = pd.read_csv(yourdata, dtype = dtype_dic)
et voila!
If you have a lot of columns and you don't know which ones contain leading zeros that might be missed, or you might just need to automate your code. You can do the following:
df = pd.read_csv("your_file.csv", nrows=1) # Just take the first row to extract the columns' names
col_str_dic = {column:str for column in list(df)}
df = pd.read_csv("your_file.csv", dtype=col_str_dic) # Now you can read the compete file
You could also do:
df = pd.read_csv("your_file.csv", dtype=str)
By doing this you will have all your columns as strings and you won't lose any leading zeros.
I don't think you can specify a column type the way you want (if there haven't been changes reciently and if the 6 digit number is not a date that you can convert to datetime). You could try using np.genfromtxt()
and create the DataFrame
from there.
EDIT: Take a look at Wes Mckinney's blog, there might be something for you. It seems to be that there is a new parser from pandas 0.10
coming in November.
You can use converters
to convert number to fixed width if you know the width.
For example, if the width is 5, then
data = pd.read_csv('text.csv', converters={'column1': lambda x: f"{x:05}"})
This will do the trick. It works for pandas==0.23.0 and also read_excel
.
Python3.6 or higher required.
来源:https://stackoverflow.com/questions/13250046/pandas-csv-import-keep-leading-zeros-in-a-column