How to retrieve records in a DataFrame (Python/Pandas) that contains leading or trail

[mmunozjr]

I would like to know how to write a formula that would identify/display records of string/object data type on a Pandas DataFrame that contains leading or trailing spaces.

The purpose for this is to get an audit on a Jupyter notebook of such records before applying any strip functions.

The goal is for the script to identify these records automatically without having to type the name of the columns manually. The scope should be any column of str/object data type that contains a value that includes either a leading or trailing spaces or both.

Please notice. I would like to see the resulting output in a dataframe format.

Link to sample datafram data:

Thank you!

[Pedroski55]

Why don't you try? Then, if you encounter problems, ask here?

I would just save your csv as Excel and use openpyxl. Seems easy!

I always think, maybe wrongly, pandas is for BIG DATA or rocket scientists!

I didn't find any leading or trailing spaces in your file.

When I put some in, I found them

mystring1 = ' a space in front'
mystring2 = 'a space behind '

[snippsat]

I always think, maybe wrongly, pandas is for BIG DATA or rocket scientists!

That's just wrong Pandas can be used for all kind of big or small tasks.

@mmunozjr som tips for this task.

import pandas as pd

d = {
    'Quarters' : ['quarter1','quarter2','quarter3','quarter4'],
     'Description': ['AA year', 'BB year', ' CC year', 'DD year '],
     'Revenue': [23.5, 54.6, 5.45, 41.87]
}

df = pd.DataFrame(d)

>>> df
   Quarters Description  Revenue
0  quarter1     AA year    23.50
1  quarter2     BB year    54.60
2  quarter3     CC year     5.45
3  quarter4    DD year     41.87

So CC and DD has leading and trailing space.
Pandas has Regex and method build in that can be used for this.
Eg with startswith and endswith

>>> df[df['Description'].str.startswith(' ')]
   Quarters Description  Revenue
2  quarter3     CC year     5.45
>>> df[df['Description'].str.endswith(' ')]
   Quarters Description  Revenue
3  quarter4    DD year     41.87

Regex.

>>> df['Description'].str.findall(r'^\s.*')
0            []
1            []
2    [ CC year]
3            []
Name: Description, dtype: object
>>> df['Description'].str.findall(r'.*\s$')
0            []
1            []
2            []
3    [DD year ]

# Find  both
>>> df['Description'].str.findall(r'(^[ ].*)|(.*[ ]$)')
0                []
1                []
2    [( CC year, )]
3    [(, DD year )]

>>> df['Description'].str.findall(r'(^[ ].*)|(.*[ ]$)').sum()
[(' CC year', ''), ('', 'DD year ')]

Look at How to use Regex in Pandas

[Pedroski55]

That's just wrong Pandas can be used for all kind of big or small tasks.

You are right!

This picture Plimpton322 on this webpage shows a Babylonian clay tablet from about 4000 years ago!

But it is really a df!

The column on the right is just a row number index!!!