Pandas: Create Empty DataFrame from Another DataFrame

Table of Contents

1. Core Concepts
2. Typical Usage Method
3. Common Practice
4. Best Practices
5. Code Examples
6. Conclusion
7. FAQ
8. References

Core Concepts

DataFrame in Pandas

A DataFrame in Pandas is similar to a table in a relational database or an Excel spreadsheet. It consists of rows and columns, where each column can have a different data type (e.g., integers, floating - point numbers, strings). The structure of a DataFrame is defined by its column names and the data types of those columns.

Creating an Empty DataFrame from Another

When creating an empty DataFrame from another, we are essentially replicating the column names and data types of the original DataFrame while leaving the rows empty. This allows us to have a DataFrame ready to receive data in the same format as the original.

Typical Usage Method

The most straightforward way to create an empty DataFrame from another DataFrame is to use the DataFrame constructor and pass in the column names and data types from the original DataFrame.

import pandas as pd

# Create an original DataFrame
original_df = pd.DataFrame({
    'col1': [1, 2, 3],
    'col2': ['a', 'b', 'c'],
    'col3': [1.1, 2.2, 3.3]
})

# Create an empty DataFrame with the same columns and dtypes
empty_df = pd.DataFrame(columns=original_df.columns, dtype=original_df.dtypes)

In this code, we first create an original DataFrame named original_df. Then, we use the DataFrame constructor to create a new DataFrame named empty_df. We pass the column names of original_df to the columns parameter and the data types of original_df to the dtype parameter.

Common Practice

Appending Data to the Empty DataFrame

Once you have created an empty DataFrame with the same structure as another DataFrame, you might want to append data to it. Here’s an example of how to do this:

import pandas as pd

# Create an original DataFrame
original_df = pd.DataFrame({
    'col1': [1, 2, 3],
    'col2': ['a', 'b', 'c'],
    'col3': [1.1, 2.2, 3.3]
})

# Create an empty DataFrame with the same columns and dtypes
empty_df = pd.DataFrame(columns=original_df.columns, dtype=original_df.dtypes)

# Create a new row of data
new_row = {'col1': 4, 'col2': 'd', 'col3': 4.4}

# Append the new row to the empty DataFrame
empty_df = empty_df.append(new_row, ignore_index=True)

In this code, we first create an empty DataFrame with the same structure as original_df. Then, we create a new row of data as a dictionary. Finally, we use the append method to add the new row to the empty DataFrame. The ignore_index=True parameter is used to reset the index of the DataFrame after appending the new row.

Best Practices

Using pd.concat Instead of append

The append method in Pandas is being deprecated in favor of pd.concat. Here’s how you can use pd.concat to append data to the empty DataFrame:

import pandas as pd

# Create an original DataFrame
original_df = pd.DataFrame({
    'col1': [1, 2, 3],
    'col2': ['a', 'b', 'c'],
    'col3': [1.1, 2.2, 3.3]
})

# Create an empty DataFrame with the same columns and dtypes
empty_df = pd.DataFrame(columns=original_df.columns, dtype=original_df.dtypes)

# Create a new row of data
new_row = pd.DataFrame({'col1': [4], 'col2': ['d'], 'col3': [4.4]})

# Concatenate the new row to the empty DataFrame
empty_df = pd.concat([empty_df, new_row], ignore_index=True)

In this code, we create a new row as a DataFrame instead of a dictionary. Then, we use pd.concat to combine the empty DataFrame and the new row. The ignore_index=True parameter is used to reset the index of the resulting DataFrame.

Code Examples

Full Example with Iterative Data Addition

import pandas as pd

# Create an original DataFrame
original_df = pd.DataFrame({
    'col1': [1, 2, 3],
    'col2': ['a', 'b', 'c'],
    'col3': [1.1, 2.2, 3.3]
})

# Create an empty DataFrame with the same columns and dtypes
empty_df = pd.DataFrame(columns=original_df.columns, dtype=original_df.dtypes)

# Simulate iterative data addition
for i in range(3):
    new_row = pd.DataFrame({
        'col1': [i + 4],
        'col2': [chr(ord('d') + i)],
        'col3': [float(i + 4.4)]
    })
    empty_df = pd.concat([empty_df, new_row], ignore_index=True)

print(empty_df)

In this example, we simulate iterative data addition by using a for loop. In each iteration, we create a new row as a DataFrame and use pd.concat to add it to the empty DataFrame.

Conclusion

Creating an empty DataFrame from another DataFrame in Pandas is a useful technique when you need to work with data that has the same structure. By using the DataFrame constructor and passing in the column names and data types of the original DataFrame, you can easily create an empty DataFrame with the same schema. When appending data to the empty DataFrame, it’s recommended to use pd.concat instead of the deprecated append method.

FAQ

Q: Why do I need to create an empty DataFrame from another DataFrame?

A: You might need to do this when you want to build a new dataset based on the same schema as an existing dataset, or when you need to initialize a DataFrame to hold results from iterative operations.

Q: Is it possible to create an empty DataFrame with a subset of columns from another DataFrame?

A: Yes, you can simply pass a list of the desired column names to the columns parameter of the DataFrame constructor.

Q: What’s the difference between using append and pd.concat?

A: The append method is being deprecated in Pandas. pd.concat is more flexible and can handle multiple DataFrame objects at once. It’s also more efficient when appending multiple rows or DataFrame objects.

References

• Pandas official documentation: https://pandas.pydata.org/docs/
• Python official documentation: https://docs.python.org/3/

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