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pythonstringpandas
julesc
julesc

Reputation: 11

removing numbers from a column in python pandas

I want to remove all numbers within the entries of a certain column in a Python pandas dataframe. Unfortunately, commands like .join() and .find() are not iterable (when I define a function to iterate on the entries, it gives me a message that floating variables do not have .find and .join attributes). Are there any commands that take care of this in pandas?

def remove(data):

  for i in data if not i.isdigit():
    data=''         
    data=data.join(i)  
    return data

myfile['column_name']=myfile['column_name'].apply(remove())

Upvotes: 0

Views: 5475

Answers (3)

Scott Boston
Scott Boston

Reputation: 153510

Or look at using pd.to_numeric with errors='coerce' to cast the column as numeric and eliminate non-numeric values:

Using @Raidex setup:

s = pd.DataFrame({'x':['p','2','3','d','f','0']})
pd.to_numeric(s['x'], errors='coerce')

Output:

0    NaN
1    2.0
2    3.0
3    NaN
4    NaN
5    0.0
Name: x, dtype: float64

EDIT to handle either situation.

s['x'].where(~s['x'].str.isdigit())

Output:

0      p
1    NaN
2    NaN
3      d
4      f
5    NaN
Name: x, dtype: object

OR

s['x'].where(s['x'].str.isdigit())

Output:

0    NaN
1      2
2      3
3    NaN
4    NaN
5      0
Name: x, dtype: object

Upvotes: 0

Christian Sloper
Christian Sloper

Reputation: 7510

You can remove all numbers like this:

import pandas as pd

df = pd.DataFrame ( {'x' : ['1','2','C','4']})
df[ df["x"].str.isdigit()  ] = "NaN"

Upvotes: 2

ovo
ovo

Reputation: 457

Impossible to know for sure without a data sample, but your code implies data contains strings since you call isdigit on the elements.

Assuming the above, there are many ways to do what you want. One of them is conditional list comprehension:

import pandas as pd
s = pd.DataFrame({'x':['p','2','3','d','f','0']})
out = [ x if x.isdigit() else '' for x in s['x'] ]
# Output: ['', '2', '3', '', '', '0']

Upvotes: 0

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