Creating a New DataFrame by Slicing Rows from an Existing DataFrame

===========================================================

In this article, we will explore how to create a new DataFrame in Python using the pandas library by slicing rows from an existing DataFrame. This technique allows you to store off rows that throw exceptions into a new DataFrame.

Understanding DataFrames and Row Slicing

A DataFrame is a two-dimensional data structure with columns of potentially different types. It’s similar to an Excel spreadsheet or a table in a relational database. Each row represents a single record, and each column represents a field or attribute of that record.

When you iterate over the rows of a DataFrame using the iterrows() method, you can access specific rows by their index. However, this approach is not ideal for several reasons:

• It’s slow because it involves iterating over all rows and checking if the row matches your condition.
• It doesn’t provide a convenient way to create a new DataFrame with only the desired rows.

A better approach is to use slicing, which allows you to select specific rows based on their index. This technique is faster and more efficient than iterating over all rows.

Building a New DataFrame Using Slicing

To create a new DataFrame by slicing rows from an existing DataFrame, you can use the iloc attribute. The iloc attribute allows you to access rows and columns by their integer positions.

Here’s an example:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'recordID': [1, 2, 3, 4, 5],
    'linkID': [10, 20, 30, 40, 50],
    'date': ['2022-01-01', '2022-01-02', '2022-01-03', '2022-01-04', '2022-01-05']
})

# Create a new DataFrame by slicing rows from the existing DataFrame
df_new = df.iloc[0:3]

print(df_new)

In this example, we create a sample DataFrame df with five rows. We then create a new DataFrame df_new by slicing rows 0 to 2 (inclusive) using iloc[0:3]. The resulting DataFrame contains only the first three rows of the original DataFrame.

Handling Exceptions and Creating a New DataFrame

Now, let’s modify our example to handle exceptions and store off rows that throw exceptions into a new DataFrame.

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'recordID': [1, 2, 3, 4, 5],
    'linkID': [10, 20, 30, 40, 50],
    'date': ['2022-01-01', '2022-01-02', '2022-01-03', '2022-01-04', '2022-01-05']
})

# Create a new DataFrame to store rows that throw exceptions
df_except = pd.DataFrame(columns=['recordID', 'linkID', 'date'])

# Iterate over the rows of the original DataFrame
for index, row in df.iterrows():
    try:
        # Perform some operation on the current row
        updateStatement = """
            EXEC dbo.storedProc
                @recordID = {0},
                @linkID = {1},
                @date = '{2}',
            """.format(row.recordID, row.linkID, row.date)       
        cursor.execute(updateStatement)

    except Exception as e:
        # Store the current row in a new DataFrame if an exception occurs
        lst = ({'recordID':row.recordID,'linkID':row.linkID,'date':row.date})
        df_except = pd.concat([df_except, pd.DataFrame(lst)], ignore_index=True)

print(df_except)

In this modified example, we create a new DataFrame df_except to store rows that throw exceptions. We then iterate over the rows of the original DataFrame and perform some operation on each row using a try-except block. If an exception occurs, we store the current row in df_except.

Conclusion

Creating a new DataFrame by slicing rows from an existing DataFrame is a convenient way to store off rows that throw exceptions into a new DataFrame. By using the iloc attribute and iterating over the rows of the original DataFrame, you can create a new DataFrame with only the desired rows.

We hope this article has provided a comprehensive guide on how to create a new DataFrame by slicing rows from an existing DataFrame in Python using pandas.

Last modified on 2024-04-15