Pandas: Clean object column with mixed data of a given DataFrame using regular expression

Last update on May 01 2025 12:54:19 (UTC/GMT +8 hours)

76. Clean Object Column with Regex

Write a Pandas program to clean object column with mixed data of a given DataFrame using regular expression.

Sample Solution :

Python Code :

import pandas as pd
d = {"agent": ["a001", "a002", "a003", "a003", "a004"], "purchase":[4500.00, 7500.00, "$3000.25", "$1250.35", "9000.00"]}
df = pd.DataFrame(d)
print("Original dataframe:")
print(df)
print("\nData Types:")
print(df["purchase"].apply(type))
df["purchase"] = df["purchase"].replace("[$,]", "", regex = True).astype("float")
print("\nNew Data Types:")
print(df["purchase"].apply(type))

Sample Output:

Original dataframe:
  agent  purchase
0  a001      4500
1  a002      7500
2  a003  $3000.25
3  a003  $1250.35
4  a004   9000.00

Data Types:
0    <class 'float'>
1    <class 'float'>
2      <class 'str'>
3      <class 'str'>
4      <class 'str'>
Name: purchase, dtype: object

New Data Types:
0    <class 'float'>
1    <class 'float'>
2    <class 'float'>
3    <class 'float'>
4    <class 'float'>
Name: purchase, dtype: object

For more Practice: Solve these Related Problems:

Write a Pandas program to clean a column of mixed-format strings using regular expressions and then convert it to numeric type.
Write a Pandas program to extract numeric values from a column containing mixed characters using regex and then update the column.
Write a Pandas program to apply a regex pattern to remove currency symbols from a column and then cast the column to float.
Write a Pandas program to replace unwanted characters in a string column using regex and then verify the transformation by printing the unique values.

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Python-Pandas Code Editor:

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