Pythonic/efficient way to strip whitespace from every Pandas Data frame cell that has a stringlike object in it

Question

I\'m reading a CSV file into a DataFrame. I need to strip whitespace from all the stringlike cells, leaving the other cells unchanged in Python 2.7.

Here is what I\

Answer 1

When you call pandas.read_csv, you can use a regular expression that matches zero or more spaces followed by a comma followed by zero or more spaces as the delimiter.

For example, here's "data.csv":

In [19]: !cat data.csv
1.5, aaa,  bbb ,  ffffd     , 10 ,  XXX   
2.5, eee, fff  ,       ggg, 20 ,     YYY

(The first line ends with three spaces after XXX, while the second line ends at the last Y.)

The following uses pandas.read_csv() to read the files, with the regular expression ' *, *' as the delimiter. (Using a regular expression as the delimiter is only available in the "python" engine of read_csv().)

In [20]: import pandas as pd

In [21]: df = pd.read_csv('data.csv', header=None, delimiter=' *, *', engine='python')

In [22]: df
Out[22]: 
     0    1    2    3   4    5
0  1.5  aaa  bbb  ffffd  10  XXX
1  2.5  eee  fff  ggg  20  YYY

Answer 2

Here is a column-wise solution with pandas apply:

import numpy as np

def strip_obj(col):
    if col.dtypes == object:
        return (col.astype(str)
                   .str.strip()
                   .replace({'nan': np.nan}))
    return col

df = df.apply(strip_obj, axis=0)

This will convert values in object type columns to string. Should take caution with mixed-type columns. For example if your column is zip codes with 20001 and ' 21110 ' you will end up with '20001' and '21110'.

Answer 3

The "data['values'].str.strip()" answer above did not work for me, but I found a simple work around. I am sure there is a better way to do this. The str.strip() function works on Series. Thus, I converted the dataframe column into a Series, stripped the whitespace, replaced the converted column back into the dataframe. Below is the example code.

import pandas as pd
data = pd.DataFrame({'values': ['   ABC   ', '   DEF', '  GHI  ']})
print ('-----')
print (data)

data['values'].str.strip()
print ('-----')
print (data)

new = pd.Series([])
new = data['values'].str.strip()
data['values'] = new
print ('-----')
print (new)

Answer 4

You could use pandas' Series.str.strip() method to do this quickly for each string-like column:

>>> data = pd.DataFrame({'values': ['   ABC   ', '   DEF', '  GHI  ']})
>>> data
      values
0     ABC   
1        DEF
2      GHI  

>>> data['values'].str.strip()
0    ABC
1    DEF
2    GHI
Name: values, dtype: object

Answer 5

I found the following code useful and something that would likely help others. This snippet will allow you to delete spaces in a column as well as in the entire DataFrame, depending on your use case.

import pandas as pd

def remove_whitespace(x):
    try:
        # remove spaces inside and outside of string
        x = "".join(x.split())

    except:
        pass
    return x

# Apply remove_whitespace to column only
df.orderId = df.orderId.apply(remove_whitespace)
print(df)


# Apply to remove_whitespace to entire Dataframe
df = df.applymap(remove_whitespace)
print(df)

Answer 6

We want to:

1. Apply our function to each element in our dataframe - use applymap.

2. Use type(x)==str (versus x.dtype == 'object') because Pandas will label columns as object for columns of mixed datatypes (an object column may contain int and/or str).

3. Maintain the datatype of each element (we don't want to convert everything to a str and then strip whitespace).

Therefore, I've found the following to be the easiest:

df.applymap(lambda x: x.strip() if type(x)==str else x)