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.