Pythonic/efficient way to strip whitespace from every Pandas Data frame cell that has a stringlike object in it
PythonPandasDataframePython Problem Overview
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'm doing:
def remove_whitespace( x ):
if isinstance( x, basestring ):
return x.strip()
else:
return x
my_data = my_data.applymap( remove_whitespace )
Is there a better or more idiomatic to Pandas way to do this?
Is there a more efficient way (perhaps by doing things column wise)?
I've tried searching for a definitive answer, but most questions on this topic seem to be how to strip whitespace from the column names themselves, or presume the cells are all strings.
Python Solutions
Solution 1 - Python
Stumbled onto this question while looking for a quick and minimalistic snippet I could use. Had to assemble one myself from posts above. Maybe someone will find it useful:
data_frame_trimmed = data_frame.apply(lambda x: x.str.strip() if x.dtype == "object" else x)
Solution 2 - Python
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
Solution 3 - Python
We want to:
-
Apply our function to each element in our dataframe - use
applymap
. -
Use
type(x)==str
(versusx.dtype == 'object'
) because Pandas will label columns asobject
for columns of mixed datatypes (anobject
column may containint
and/orstr
). -
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)
Solution 4 - Python
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 , ddd , 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 ddd 10 XXX
1 2.5 eee fff ggg 20 YYY
Solution 5 - Python
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)
Solution 6 - Python
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'.
Solution 7 - Python
This worked for me - applies it to the whole dataframe:
def panda_strip(x):
r =[]
for y in x:
if isinstance(y, str):
y = y.strip()
r.append(y)
return pd.Series(r)
df = df.apply(lambda x: panda_strip(x))
Solution 8 - Python
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)