CODEWITHSUNDEEP
pythonpandasdata-cleaning
Pearl
Pearl

Reputation: 87

PANDAS how to drop columns that have numbers and combine the rest of the columns

I have a DataFrame:

     A          B       C
  0 PQ          None    None
  1 Chieti      None    None
  2 Gainesville None    None
  3 Wenzhou     325027  None
  4 Boston      None    None
  5 D-53127     Bonn    None
  6 SE-11282    Stockholm None
  7 Birmingham  None    None
  8 Miami       None    None
  9 Los         Angeles None

I want to combine the text in all columns and create a new column which is column 'D' here .However I do not want to include the column if there are numbers or digits in that column . My final output should look like this:

     A          B       C       D
  0 PQ          None    None    PQ
  1 Chieti      None    None    Chieti
  2 Gainesville None    None    Gainesville
  3 Wenzhou     325027  None    Wenzhou  
  4 Boston      None    None    Boston
  5 D-53127     Bonn    None    Bonn
  6 SE-11282    Stockholm None  Stockholm
  7 Birmingham  None    None    Birmingham
  8 Miami       None    None    Miami
  9 Los         Angeles None    LosAngeles

Could the knowledgeable folks in Stackoverflow give me an insight into solving this using PANDAS/python

Upvotes: 0

Views: 239

Answers (2)

user2047399
user2047399

Reputation:

You could do this by applying a function over the rows with apply(...,axis=1).

import pandas as pd

def row_func(row):
    def num_there(s):
        return any(i.isdigit() for i in s)
    result = []
    for x in row:
        if x and not num_there(str(x)):
            result.append(str(x))
    return ''.join(result)


df['D'] = df.apply(row_func, axis=1)

Upvotes: 1

Allen Qin
Allen Qin

Reputation: 19947

You can mask the cells containing digits can then concat all columns"

df['D'] = df[~df.fillna('').apply(lambda x: x.str.contains('\d'))]\
            .fillna('').apply(lambda x: ''.join(x), axis=1)
df
Out[810]: 
             A          B     C            D
0           PQ       None  None           PQ
1       Chieti       None  None       Chieti
2  Gainesville       None  None  Gainesville
3      Wenzhou     325027  None      Wenzhou
4       Boston       None  None       Boston
5      D-53127       Bonn  None         Bonn
6     SE-11282  Stockholm  None    Stockholm
7   Birmingham       None  None   Birmingham
8        Miami       None  None        Miami
9          Los    Angeles  None   LosAngeles

Upvotes: 1

Related Questions