What Is “Wrong Format” in Pandas?

Wrong format refers to data stored in an incorrect data type or structure. Common examples include:

  • Numbers stored as strings

  • Dates stored as plain text

  • Mixed values in a single column

  • Extra symbols (₹, %, commas) in numeric fields

  • Inconsistent text casing or spacing

Cleaning wrong formats is essential before analysis or modeling.

Why Fixing Wrong Formats Is Important

Wrong formats can:

  • Break calculations and comparisons

  • Cause errors in sorting and filtering

  • Produce incorrect statistics

  • Prevent proper date/time operations

Correct formats ensure accurate analysis and reliable results.

Load Sample Data

Python 
import pandas as pd df = pd.read_csv("data.csv") print(df.info()) # Output: # Shows columns with incorrect data types

1. Fix Numeric Columns Stored as Strings

Convert String to Integer or Float

Python 
df["Age"] = df["Age"].astype(int) df["Salary"] = df["Salary"].astype(float) print(df.dtypes) # Output: # Correct numeric data types

Handle Invalid Numeric Values

If a column contains non-numeric text:

Python 
df["Marks"] = pd.to_numeric(df["Marks"], errors="coerce") print(df) # Output: # Invalid values converted to NaN

2. Remove Symbols from Numeric Data

Columns may include currency symbols, commas, or percentages.

Python 
df["Salary"] = df["Salary"].str.replace(",", "") df["Salary"] = df["Salary"].str.replace("₹", "") df["Salary"] = df["Salary"].astype(float) print(df["Salary"]) # Output: # Clean numeric salary values

3. Fix Date Columns

Convert String to Date

Python 
df["JoinDate"] = pd.to_datetime(df["JoinDate"]) print(df["JoinDate"].dtype) # Output: # datetime64[ns]

Handle Invalid Dates

Python 
df["DOB"] = pd.to_datetime(df["DOB"], errors="coerce") print(df) # Output: # Invalid dates converted to NaT

4. Standardize Text Format

Convert to Lowercase / Uppercase

Python 
df["City"] = df["City"].str.lower() print(df["City"]) # Output: # All values in lowercase

Remove Extra Spaces

Python 
df["Name"] = df["Name"].str.strip() print(df["Name"]) # Output: # Leading and trailing spaces removed

5. Fix Boolean Columns

Boolean values may appear as "Yes", "No", "Y", "N".

Python 
df["Active"] = df["Active"].replace({"Yes": True, "No": False}) print(df["Active"]) # Output: # Boolean values

6. Fix Mixed Data Types in a Column

Python 
df["Score"] = pd.to_numeric(df["Score"], errors="coerce") print(df["Score"]) # Output: # Non-numeric values converted to NaN

7. Rename Columns for Consistency

Python 
df.columns = df.columns.str.lower().str.replace(" ", "_") print(df.columns) # Output: # Clean, consistent column names

8. Verify Cleaned Formats

Always recheck after cleaning:

Python 
print(df.info()) print(df.head()) # Output: # Updated data types and values

Best Practices for Fixing Wrong Formats

  • Always inspect data using info() and head()

  • Fix formats before analysis

  • Use errors="coerce" for safe conversions

  • Standardize text early

  • Validate results after cleaning