How can I remove extra whitespace from strings when parsing a csv file in Pandas?

I have the following file named 'data.csv':

    1997,Ford,E350
    1997, Ford , E350
    1997,Ford,E350,"Super, luxurious truck"
    1997,Ford,E350,"Super ""luxurious"" truck"
    1997,Ford,E350," Super luxurious truck "
    "1997",Ford,E350
    1997,Ford,E350
    2000,Mercury,Cougar

And I would like to parse it into a pandas DataFrame so that the DataFrame looks as follows:

       Year     Make   Model              Description
    0  1997     Ford    E350                     None
    1  1997     Ford    E350                     None
    2  1997     Ford    E350   Super, luxurious truck
    3  1997     Ford    E350  Super "luxurious" truck
    4  1997     Ford    E350    Super luxurious truck
    5  1997     Ford    E350                     None
    6  1997     Ford    E350                     None
    7  2000  Mercury  Cougar                     None

The best I could do was:

    pd.read_table("data.csv", sep=r',', names=["Year", "Make", "Model", "Description"])

Which gets me:

    Year     Make   Model              Description
 0  1997     Ford    E350                     None
 1  1997    Ford     E350                     None
 2  1997     Ford    E350   Super, luxurious truck
 3  1997     Ford    E350  Super "luxurious" truck
 4  1997     Ford    E350   Super luxurious truck 
 5  1997     Ford    E350                     None
 6  1997     Ford    E350                     None
 7  2000  Mercury  Cougar                     None

How can I get the DataFrame without those whitespaces?

Solution 1:

You could use converters:

import pandas as pd

def strip(text):
    try:
        return text.strip()
    except AttributeError:
        return text

def make_int(text):
    return int(text.strip('" '))

table = pd.read_table("data.csv", sep=r',',
                      names=["Year", "Make", "Model", "Description"],
                      converters = {'Description' : strip,
                                    'Model' : strip,
                                    'Make' : strip,
                                    'Year' : make_int})
print(table)

yields

   Year     Make   Model              Description
0  1997     Ford    E350                     None
1  1997     Ford    E350                     None
2  1997     Ford    E350   Super, luxurious truck
3  1997     Ford    E350  Super "luxurious" truck
4  1997     Ford    E350    Super luxurious truck
5  1997     Ford    E350                     None
6  1997     Ford    E350                     None
7  2000  Mercury  Cougar                     None

Solution 2:

Adding parameter skipinitialspace=True to read_table worked for me.

So try:

pd.read_table("data.csv", 
              sep=r',', 
              names=["Year", "Make", "Model", "Description"], 
              skipinitialspace=True)

Same thing works in pd.read_csv().

Solution 3:

Well, the whitespace is in your data, so you can't read in the data without reading in the whitespace. However, after you've read it in, you could strip out the whitespace by doing, e.g., df["Make"] = df["Make"].map(str.strip) (where df is your dataframe).

Solution 4:

I don't have enough reputation to leave a comment, but the answer above suggesting using the map function along with strip won't work if you have NaN values, since strip only works on chars and NaN are floats.

There is a built-in pandas function to do this, which I used: pd.core.strings.str_strip(df['Description'])
where df is your dataframe. In my case I used it on a dataframe with ~1.2 million rows and it was very fast.