Write a Python code to fill all the missing values in a given dataframe

When working with datasets, missing values (NaN) are common. Pandas provides the interpolate() method to fill missing values using various interpolation techniques like linear, polynomial, or time-based methods.

Syntax

df.interpolate(method='linear', limit_direction='forward', limit=None)

Parameters

• method − Interpolation technique ('linear', 'polynomial', 'spline', etc.)

• limit_direction − Direction to fill ('forward', 'backward', 'both')

• limit − Maximum number of consecutive NaNs to fill

Example

Let's create a DataFrame with missing values and apply linear interpolation ?

import pandas as pd

df = pd.DataFrame({"Id": [1, 2, 3, None, 5],
                   "Age": [12, 12, 14, 13, None],
                   "Mark": [80, 90, None, 95, 85]})

print("Original DataFrame:")
print(df)
print("\nAfter interpolation:")
print(df.interpolate(method='linear', limit_direction='forward', limit=2))

Original DataFrame:
    Id   Age  Mark
0  1.0  12.0  80.0
1  2.0  12.0  90.0
2  3.0  14.0   NaN
3  NaN  13.0  95.0
4  5.0   NaN  85.0

After interpolation:
    Id   Age  Mark
0  1.0  12.0  80.0
1  2.0  12.0  90.0
2  3.0  14.0  92.5
3  4.0  13.0  95.0
4  5.0  13.0  85.0

How It Works

Linear interpolation estimates missing values by drawing a straight line between known values:

• Mark column − NaN at index 2 becomes 92.5 (midpoint between 90 and 95)

• Id column − NaN at index 3 becomes 4.0 (between 3 and 5)

• Age column − NaN at index 4 becomes 13.0 (forward fills from previous value)

Alternative Methods

You can also use fillna() for simpler filling strategies ?

import pandas as pd

df = pd.DataFrame({"Values": [1, None, 3, None, 5]})

# Forward fill
print("Forward fill:")
print(df.fillna(method='ffill'))

print("\nBackward fill:")
print(df.fillna(method='bfill'))

print("\nFill with mean:")
print(df.fillna(df.mean()))

Forward fill:
   Values
0     1.0
1     1.0
2     3.0
3     3.0
4     5.0

Backward fill:
   Values
0     1.0
1     3.0
2     3.0
3     5.0
4     5.0

Fill with mean:
   Values
0     1.0
1     3.0
2     3.0
3     3.0
4     5.0

Conclusion

Use interpolate() for intelligent missing value estimation based on surrounding data patterns. For simpler cases, fillna() with forward/backward fill or statistical measures works effectively.