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

Need a 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

Need a 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

Need a 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

Need a hint?

Use print(filled_temps) to show the DataFrame.