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Cleaning Empty Cells

Empty cells in datasets often represent missing or incomplete data. Pandas provides various techniques to handle empty cells, ensuring your dataset remains consistent and ready for analysis. This section covers how to identify and clean empty cells effectively.

Identifying Empty Cells

You can use the isnull() function to identify empty cells in your DataFrame. This function returns a Boolean DataFrame indicating True for empty cells and False otherwise. Here’s an example: 

import pandas as pd

# Create a sample DataFrame with empty cells
data = {
    "Name": ["Karthick", "Durai", None, "Praveen"],
    "Age": [25, None, 22, 30],
    "City": ["Chennai", "Coimbatore", None, "Madurai"]
}

df = pd.DataFrame(data)

# Identify empty cells
empty_cells = df.isnull()
print(empty_cells)

Output

Name Age City
False False False
False True False
True False True
False False False

Explanation: The isnull() function identifies empty cells in the DataFrame. The resulting Boolean DataFrame marks True for empty cells and False for non-empty cells.

Removing Empty Cells

To clean empty cells, you can use the dropna() function to remove rows or columns with missing data. This method ensures that your DataFrame contains only complete records. Here’s an example: 

# Remove rows with empty cells
cleaned_df = df.dropna()
print(cleaned_df)

Output

Name Age City
Karthick 25.0 Chennai
Praveen 30.0 Madurai

Explanation: The dropna() function removes rows containing empty cells from the DataFrame. In this example, only rows with complete data are retained.

Filling Empty Cells

Instead of removing rows or columns with empty cells, you can fill them with specific values using fillna(). This is useful for retaining the dataset while addressing missing data. Here’s an example: 

# Fill empty cells in "Age" with the mean
mean_age = df["Age"].mean()
df["Age"] = df["Age"].fillna(mean_age)

# Fill empty cells in "City" with "Unknown"
df["City"] = df["City"].fillna("Unknown")
print(df)

Output

Name Age City
Karthick 25.0 Chennai
Durai 25.666666666666668 Coimbatore
None 22.0 Unknown
Praveen 30.0 Madurai

Explanation: The fillna() function fills missing numeric values in the Age column with the mean and replaces empty cells in the City column with "Unknown." This approach preserves the structure of the dataset while addressing missing values.

Key Takeaways

  • Identifying Empty Cells: Use isnull() to locate missing values in the dataset.
  • Removing Empty Cells: The dropna() method removes rows or columns with empty cells.
  • Filling Empty Cells: Use fillna() to replace empty cells with specific values, such as the mean or a placeholder string.
  • Data Integrity: Cleaning empty cells ensures a consistent dataset, improving analysis quality.
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