Forward fill and backward fill in Data Analysis Python - Mini Project: Build & Apply

Forward fill and backward fill

📖 Scenario: Imagine you have a small dataset of daily temperatures, but some days are missing values. You want to fill these missing values to keep your data complete for analysis.

🎯 Goal: You will create a dataset with missing values, set a configuration for filling method, apply forward fill and backward fill to fill missing data, and finally display the filled dataset.

📋 What You'll Learn

Create a pandas DataFrame with specific temperature data including missing values

Create a variable to hold the fill method ('ffill' or 'bfill')

Use the fillna() method with the chosen fill method to fill missing values

Print the resulting DataFrame after filling missing values

💡 Why This Matters

🌍 Real World

Filling missing data is common in real-world datasets like weather records, sales data, or sensor readings to prepare data for analysis.

💼 Career

Data scientists and analysts often need to clean data by filling missing values to build accurate models and reports.

1

Create the temperature DataFrame with missing values

Create a pandas DataFrame called temps with a column 'Temperature' and these exact values: 20.5, NaN, 22.0, NaN, 21.5.

Data Analysis Python

import pandas as pd
import numpy as np
# Create the DataFrame 'temps' with the specified values
# Your code here

Hint

Use pd.DataFrame with a dictionary. Use np.nan for missing values.

2

Set the fill method configuration

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

Data Analysis Python

import pandas as pd
import numpy as np

temps = pd.DataFrame({'Temperature': [20.5, np.nan, 22.0, np.nan, 21.5]})
# Create the variable 'fill_method' and set it to 'ffill'
# Your code here

Hint

Just assign the string 'ffill' to the variable fill_method.

3

Apply forward fill or backward fill to fill missing values

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

Data Analysis Python

import pandas as pd
import numpy as np

temps = pd.DataFrame({'Temperature': [20.5, np.nan, 22.0, np.nan, 21.5]})
fill_method = 'ffill'
# Use fillna() with method=fill_method to create 'filled_temps'
# Your code here

Hint

Use temps.fillna(method=fill_method) to fill missing values.

4

Print the filled DataFrame

Print the DataFrame filled_temps to display the result after filling missing values.

Data Analysis Python

import pandas as pd
import numpy as np

temps = pd.DataFrame({'Temperature': [20.5, np.nan, 22.0, np.nan, 21.5]})
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 forward fill do when handling missing data in a dataset?

easy

A. It removes rows with missing values.

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

C. It replaces missing values with zero.

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

Solution

1. Step 1: Understand forward fill concept

   Forward fill means filling missing data by copying the last known value forward to the next missing spot.

2. Step 2: Compare options with definition

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

3. Final Answer:

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

4. Quick Check:

   Forward fill = copy last known value forward [OK]

Hint: Forward fill copies previous value forward to fill gaps [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 syntax to apply forward fill on a pandas DataFrame df?

easy

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

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

C. df.fillna(forward=True)

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

Solution

1. Step 1: Recall pandas fillna syntax

   In pandas, forward fill is done by fillna(method='ffill').

2. Step 2: Check options for correct syntax

   df.fillna(method='ffill') matches the correct method name 'ffill'. df.fillna(method='bfill') performs backward fill. The remaining options use invalid syntax.

3. Final Answer:

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

4. Quick Check:

   Forward fill method = 'ffill' [OK]

Hint: Use method='ffill' for forward fill in pandas [OK]

Common Mistakes:

• Using 'bfill' instead of 'ffill' for forward fill
• Passing invalid parameters like forward=True
• Confusing method names in fillna

3. Given the DataFrame df below, what will be the result of df.bfill()?

import pandas as pd
df = pd.DataFrame({'A': [1, None, None, 4, None]})
print(df.bfill())

medium

A. A
0 1.0
1 4.0
2 4.0
3 4.0
4 NaN

B. A
0 1.0
1 1.0
2 1.0
3 4.0
4 NaN

C. A
0 1.0
1 NaN
2 NaN
3 4.0
4 NaN

D. A
0 1.0
1 4.0
2 4.0
3 4.0
4 4.0

Solution

1. Step 1: Understand backward fill on given data

   Backward fill copies the next known value backward to fill missing spots. Here, missing values at index 1 and 2 get filled with 4.0 from index 3. The last missing at index 4 has no next value, so remains NaN.

2. Step 2: Match output with options

   A 0 1.0 1 4.0 2 4.0 3 4.0 4 NaN matches this result exactly. A 0 1.0 1 4.0 2 4.0 3 4.0 4 4.0 incorrectly fills last NaN with 4.0, others do not match.

3. Final Answer:

   A 0 1.0 1 4.0 2 4.0 3 4.0 4 NaN -> Option A

4. Quick Check:

   Backward fill fills missing with next known value [OK]

Hint: Backward fill uses next known value to fill missing [OK]

Common Mistakes:

• Assuming last NaN gets filled when no next value exists
• Confusing forward and backward fill results
• Misreading index positions in output

4. You run the code below but get an error. What is the mistake?

import pandas as pd
df = pd.DataFrame({'A': [None, 2, None]})
df.fillna(method='forward')

medium

A. fillna requires inplace=True to work.

B. DataFrame cannot have None values.

C. The method name 'forward' is invalid; should be 'ffill'.

D. fillna does not accept method parameter.

Solution

1. Step 1: Check fillna method parameter

   The valid method names are 'ffill' for forward fill and 'bfill' for backward fill. 'forward' is not valid.

2. Step 2: Identify cause of error

   Using 'forward' causes a ValueError. DataFrames can have None values, fillna does not require inplace=True, and it accepts the method parameter.

3. Final Answer:

   The method name 'forward' is invalid; should be 'ffill'. -> Option C

4. Quick Check:

   Use 'ffill' for forward fill method [OK]

Hint: Use 'ffill' not 'forward' as method name in fillna [OK]

Common Mistakes:

• Using 'forward' instead of 'ffill'
• Thinking inplace=True is mandatory
• Believing fillna can't handle None values

5. You have a DataFrame df with missing values scattered in column 'B'. You want to fill missing values by first forward filling, then backward filling any remaining missing values. Which code correctly does this?

hard

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

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

C. df['B'].fillna(method='bfill').fillna(method='ffill')

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

Solution

1. Step 1: Understand chaining fillna calls

   To fill missing values first forward then backward, chain fillna calls: first with method='ffill', then on result with method='bfill'.

2. Step 2: Evaluate options for correct chaining

   df['B'].fillna(method='ffill').fillna(method='bfill') correctly chains fillna twice. df['B'].fillna(method='bfill').fillna(method='ffill') reverses order. Attempting multiple methods in one call (duplicate kwargs or list) uses invalid syntax.

3. Final Answer:

   df['B'].fillna(method='ffill').fillna(method='bfill') -> Option B

4. Quick Check:

   Chain fillna with 'ffill' then 'bfill' [OK]

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

Common Mistakes:

• Trying to pass multiple methods in one fillna call
• Reversing forward and backward fill order
• Not chaining calls, so some NaNs remain