I need to add double quotes to specific columns in a csv file that my script generates.
Below is the goofy way I thought of doing this. For these two fixed-width fields, it works:
df['DATE'] = df['DATE'].str.ljust(9,'"')
df['DATE'] = df['DATE'].str.rjust(10,'"')
df['DEPT CODE'] = df['DEPT CODE'].str.ljust(15,'"')
df[DEPT CODE'] = df['DEPT CODE'].str.rjust(16,'"')
For the following field, it doesn't. It has a variable length. So, if the value is shorter than the standard 6-digits, I get extra double-quotes: "5673"""
df['ID'] = df['ID'].str.ljust(7,'"')
df['ID'] = df['ID'].str.rjust(8,'"')
I have tried zfill, but the data in the column is a series-- I get "pandas.core.series.Series" when i run
print type(df['ID'])
and I have not been able to convert it to string using astype. I'm not sure why. I have not imported numpy.
I tried using len() to get the length of the ID number and pass it to str.ljust and str.rjust as its first argument, but I think it got hung up on the data not being a string.
Is there a simpler way to apply double-quotes as I need, or is the zfill going to be the way to go?
You can add a speech mark before / after:
In [11]: df = pd.DataFrame([["a"]], columns=["A"])
In [12]: df
Out[12]:
A
0 a
In [13]: '"' + df['A'] + '"'
Out[13]:
0 "a"
Name: A, dtype: object
Assigning this back:
In [14]: df['A'] = '"' + df.A + '"'
In [15]: df
Out[15]:
A
0 "a"
If it's for exporting to csv you can use the quoting kwarg:
In [21]: df = pd.DataFrame([["a"]], columns=["A"])
In [22]: df.to_csv()
Out[22]: ',A\n0,a\n'
In [23]: df.to_csv(quoting=1)
Out[23]: '"","A"\n"0","a"\n'