Forward fill and backward fill in Pandas - Mini Project: Build & Apply

Forward fill and backward fill

📖 Scenario: Imagine you have a small table of daily temperatures, but some days are missing values. You want to fill in those missing days by using the last known temperature or the next known temperature.

🎯 Goal: You will create a pandas DataFrame with some missing temperature values. Then, you will add variables to control filling methods. Next, you will apply forward fill and backward fill to fill missing values. Finally, you will print the filled DataFrames.

📋 What You'll Learn

Create a pandas DataFrame called temps with dates as index and temperature values including NaN

Create a variable called fill_method to choose between forward fill and backward fill

Use the fillna() method with method=fill_method to fill missing values

Print the filled DataFrame

💡 Why This Matters

🌍 Real World

Data often has missing values. Filling missing data helps keep analysis accurate and complete, like filling missing temperature readings in weather data.

💼 Career

Data scientists and analysts frequently clean data by filling missing values to prepare datasets for modeling and reporting.

1

Create the temperature DataFrame with missing values

Import pandas as pd. Create a DataFrame called temps with the dates '2024-06-01' to '2024-06-05' as the index. The temperature values should be 20.5, NaN, 22.0, NaN, and 23.5 respectively.

Pandas

# Import pandas and create the DataFrame temps with missing values
# Your code here

Hint

Use pd.date_range to create the dates index. Use np.nan for missing values.

2

Create a variable to select fill method

Create a variable called fill_method and set it to the string 'ffill' to represent forward fill.

Pandas

import pandas as pd
import numpy as np
dates = pd.date_range('2024-06-01', '2024-06-05')
temps = pd.DataFrame({'Temperature': [20.5, np.nan, 22.0, np.nan, 23.5]}, index=dates)
# Create fill_method variable and set to 'ffill'
# Your code here

Hint

Set fill_method exactly to the string 'ffill'.

3

Apply forward fill or backward fill to fill missing values

Create a new DataFrame called filled_temps by applying temps.fillna(method=fill_method) to fill missing values using the method stored in fill_method.

Pandas

import pandas as pd
import numpy as np
dates = pd.date_range('2024-06-01', '2024-06-05')
temps = pd.DataFrame({'Temperature': [20.5, np.nan, 22.0, np.nan, 23.5]}, index=dates)
fill_method = 'ffill'
# Create filled_temps by filling missing values using fill_method
# Your code here

Hint

Use the fillna() method with the method parameter set to fill_method.

4

Print the filled DataFrame

Print the filled_temps DataFrame to see the result of forward fill.

Pandas

import pandas as pd
import numpy as np
dates = pd.date_range('2024-06-01', '2024-06-05')
temps = pd.DataFrame({'Temperature': [20.5, np.nan, 22.0, np.nan, 23.5]}, index=dates)
fill_method = 'ffill'
filled_temps = temps.fillna(method=fill_method)
# Print the filled_temps DataFrame
# Your code here

Hint

Use print(filled_temps) to show the DataFrame.

Practice

1. What does the forward fill method do in pandas when handling missing data?

easy

A. It copies the next known value backward to fill missing spots.

B. It copies the last known value forward to fill missing spots.

C. It replaces missing values with zero.

D. It removes rows with missing values.

Solution

1. Step 1: Understand forward fill concept

   Forward fill means filling missing data by copying the last valid value forward.

2. Step 2: Compare options with definition

   It copies the last known value forward to fill missing spots. matches this exactly, while others describe different methods.

3. Final Answer:

   It copies the last known value forward to fill missing spots. -> Option B

4. Quick Check:

   Forward fill = copy last known value forward [OK]

Hint: Forward fill copies previous value downwards [OK]

Common Mistakes:

• Confusing forward fill with backward fill
• Thinking forward fill removes missing data
• Assuming forward fill replaces with zero

2. Which of the following is the correct pandas syntax to perform a backward fill on a DataFrame df?

easy

A. df.fillna(method='backfill')

B. df.fillna(method='ffill')

C. df.fillna(backward=True)

D. df.fillna(direction='backward')

Solution

1. Step 1: Recall pandas fillna syntax

   Backward fill uses method='backfill' or method='bfill' in fillna.

2. Step 2: Check options for correct syntax

   df.fillna(method='backfill') uses method='backfill', which is correct. Others are invalid or incorrect syntax.

3. Final Answer:

   df.fillna(method='backfill') -> Option A

4. Quick Check:

   Backward fill syntax = method='backfill' [OK]

Hint: Use method='backfill' for backward fill in fillna [OK]

Common Mistakes:

• Using method='ffill' for backward fill
• Passing invalid parameters like backward=True
• Confusing method names in fillna

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'A': [1, None, None, 4, None]})
df_filled = df.fillna(method='ffill')

What is the content of df_filled?

medium

A. [1, None, None, 4, 1]

B. [1, 1, 1, 4, None]

C. [1, None, None, 4, None]

D. [1, 1, 1, 4, 4]

Solution

1. Step 1: Understand forward fill on the DataFrame

   Forward fill copies last valid value downwards to fill missing values.

2. Step 2: Apply forward fill step-by-step

   Index 0: 1 (valid)
   Index 1: filled with 1
   Index 2: filled with 1
   Index 3: 4 (valid)
   Index 4: filled with 4

3. Final Answer:

   [1, 1, 1, 4, 4] -> Option D

4. Quick Check:

   Forward fill propagates last valid value forward [OK]

Hint: Forward fill fills missing with last valid above [OK]

Common Mistakes:

• Thinking forward fill leaves trailing NAs unfilled
• Failing to propagate through consecutive NAs
• Confusing forward fill with backward fill

4. Consider this code snippet:

import pandas as pd
df = pd.DataFrame({'B': [None, 2, None, 4]})
df.fillna(method='bfill', inplace=True)

After running this, what is the content of column 'B' in df?

medium

A. [2, 2, 4, 4]

B. [None, 2, None, 4]

C. [2, 2, 2, 4]

D. [None, 2, 4, 4]

Solution

1. Step 1: Understand backward fill with inplace=True

   Backward fill copies next valid value upwards, modifying df directly.

2. Step 2: Apply backward fill to column 'B'

   Index 0: None filled with next valid 2
   Index 1: 2 (valid)
   Index 2: None filled with next valid 4
   Index 3: 4 (valid)

3. Final Answer:

   [2, 2, 4, 4] -> Option A

4. Quick Check:

   Backward fill copies next valid value up [OK]

Hint: Backward fill copies next valid value upward [OK]

Common Mistakes:

• Not using inplace=True and expecting df to change
• Confusing backward fill with forward fill
• Leaving None values unchanged

5. You have a DataFrame df with missing values scattered in multiple columns. You want to fill missing values by first forward filling, then backward filling any remaining missing values. Which code snippet correctly achieves this?

hard

A. df.fillna(method='ffill', method='bfill')

B. df.fillna(method='bfill').fillna(method='ffill')

C. df.fillna(method='ffill').fillna(method='bfill')

D. df.fillna(method=['ffill', 'bfill'])

Solution

1. Step 1: Understand chaining fillna methods

   First forward fill fills missing values from previous valid data, then backward fill fills remaining missing values from next valid data.

2. Step 2: Analyze options for correct chaining

   df.fillna(method='ffill').fillna(method='bfill') chains fillna with method='ffill' then method='bfill', which is correct.
   df.fillna(method='bfill').fillna(method='ffill') reverses order, which may fill differently.
   Options C and D use invalid syntax.

3. Final Answer:

   df.fillna(method='ffill').fillna(method='bfill') -> Option C

4. Quick Check:

   Chain forward then backward fill to cover all missing [OK]

Hint: Chain fillna with ffill then bfill to fill all gaps [OK]

Common Mistakes:

• Using invalid syntax with multiple methods in one fillna
• Reversing order and missing some fills
• Expecting one fillna call to do both fills