python replace multiple values from all columns in dataframe pandas

Question

Below is a sample of my table.

DataFrame

Below is the code for the table:

d = {'1978': ['10k', '20000'],
    '1979': ['30k', '2M'],
    '1980': ['60000', '20k'],
    '1981': ['10000', '1M'],
    '1982': ['15000', '70k'],
    '1983': ['12k', '8M']}
df = pd.DataFrame(data=d)

Actually, the one I am working has 60 columns and 200 rows. However, It's the same structure.

My goal is to replace "k" for "000" and "M" for "000000" for all the rows of the many columns.

So the output should be:

Expected DataFrame

If someone could share with me the code to get this desired output, I would really appreciate.

Whole Brain · Accepted Answer · 2021-06-22 00:29:56Z

5

You can use pandas.DataFrame.replace with a dictionary as argument and regex=True:

new_df = df.replace({'k':'000', "M": "000000"}, regex=True)

answered Jun 22, 2021 at 0:29

Whole Brain

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1 Comment

Victor Castro Over a year ago

Omg!! Thanks really much , It worked!! I really appreciate! thanks!!

Serg · Accepted Answer · 2021-06-22 00:31:44Z

1

Hi a quick solution is to convert the columns to string, do the character replacement and then convert back to int or float. This can be done columns by column:

import pandas as pd

d = {'1978': ['10k', '20000'], '1979': ['30k', '2M'], '1980': ['60000', '20k'], '1981': ['10000', '1M'], '1982': ['15000', '70k'], '1983': ['12k', '8M']}
df = pd.DataFrame(data=d)
for col in df.columns:
    df[col] = (
        df[col].astype(str)
        .str.replace("k", "000")
        .str.replace("M", "000000")
        .astype(int)
    )

or as a whole:

df = (
   df.astype(str)
   .str.replace("k", "000")
   .str.replace("M", "000000")
   .astype(int)
)

answered Jun 22, 2021 at 0:31

Serg

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1 Comment

Victor Castro Over a year ago

Hi, Serg!! I actually was trying to to exact this code!! thanks so much for your time and attention, it worked!! thankss

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