How to Remove All Non-Numeric Characters from All the Values in a Particular Column in Pandas Dataframe

Pandas dataframe strip non-numeric characters

You can do this way using str.replace(r"[a-zA-Z]",'') to remove the alphabet characters. If you need you can add more characters on this class to remove those also.

import pandas as pd

df = pd.read_csv("test.csv", names=['Accuracy', 'Error rate', 'Not classified'])

df['Accuracy'] = df['Error rate'].str.replace(r"[a-zA-Z]",'')
df['Error rate'] = df['Error rate'].str.replace(r"[a-zA-Z]",'')
df['Not classified'] = df['Not classified'].str.replace(r"[a-zA-Z]",'')
print(df)

DEMO: https://repl.it/@SanyAhmed/EarnestTatteredRepo

Remove non-numeric in df column with different datatypes

This should work : df['Volumne'] = df['Volume'].str.replace(r'[^0-9.]', '')

How can I strip off all non-numeric characters in a Pandas Series

You have 2 different problems here:

• first is to extract digits from the column cells
• second is to make a list if you have more than one digit

Just chain both operations:

df[col].str.findall(r'\d').apply(lambda x: x[0] if len(x) == 1 else '' if len(x) == 0 else x)

With you example it gives:

0         4
1         4
2         4
3    [3, 4]
4         4
5         4

How do I remove all non- numerical numbers from entire data frame: Debugging

I think problem is need specify columns for replace and replace empty value to NaN or 0 if not numeric like second last Size value:

cols = ['Size','Installs']
df[cols] = df[cols].replace('[^\d.]', '', regex = True).replace('',np.nan).astype(float)

print (df)
       Rating  Reviews  Size    Installs  Type  Price
0         4.1      159  19.0     10000.0  Free      0
1         3.9      967  14.0    500000.0  Free      0
2         4.7    87510   8.7   5000000.0  Free      0
3         4.5   215644  25.0  50000000.0  Free      0
4         4.3      967   2.8    100000.0  Free      0
10836     4.5       38  53.0      5000.0  Free      0
10837     5.0        4   3.6       100.0  Free      0
10838     0.0        3   9.5      1000.0  Free      0
10839     4.5      114   NaN      1000.0  Free      0
10840     4.5   398307  19.0  10000000.0  Free      0

Delete all but numerical values in a column(s) using Pandas

Just an extra information, I did like this and got the opposite :D

import pandas as pd
df.replace(to_replace=r'[^a-zA-Z#]', value='', regex=True)

    Size    Total
  0   TB    TB
  1   G      G
  2   A      A

Since you changed your question, I did like this, maybe someone could have a better answer.

df['Size'] = df['Size'].str.replace("[^[^0-9]+", " ") 
df['Total'] = df['Total'].str.replace("[^[^0-9]+", " ") 
df

output:

   Size Total   ID
0   110  200    A
1   100  300    B
2   500  700    C

How do I remove non-numeric values from specific column in pandas?

Those are actually integers, just represented in a different base (base 16, also known as hexadecimal). The int() function takes an optional second argument for the base. We can check if a string consists only of numeric characters, and if so use 10 as the base, 16 otherwise:

df.DstPort.apply(lambda x: int(x, 10 if x.isnumeric() else 16))

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