How to Save Each DataFrame Globally in a Loop: A Solution for Overwritten DataFrames in Python

Creating a Global DataFrame in a Loop: A Solution to Overwritten DataFrames in Python

In this article, we will explore the issue of overwritten DataFrames when working with multiple DataFrames in a loop. We will examine the provided code and offer a solution that saves each DataFrame globally, allowing for easier access and manipulation outside the loop.

Understanding DataFrames and Loops in Python

Python’s pandas library provides an efficient way to work with structured data, known as DataFrames. A DataFrame is a two-dimensional table of data with rows and columns, similar to an Excel spreadsheet or a SQL database. When working with multiple DataFrames in a loop, it can be challenging to keep track of each DataFrame individually.

In the provided code, we see a list of DataFrames (list_of_df) that are iterated over using a for loop. Inside the loop, we create a temporary copy of each DataFrame and replace empty values with NaN (Not a Number). However, this approach leads to overwritten DataFrames because we’re assigning the updated DataFrame back to the original variable name.

The Problem: Overwritten DataFrames

The issue with the provided code is that the df variable inside the loop is always reassigned to the last updated DataFrame. This means that each iteration overwrites the previous DataFrame, resulting in only the last DataFrame being stored outside the loop.

To illustrate this problem, let’s consider an example:

Suppose we have a list of DataFrames: ['NPSFeedback', 'courses', 'test']. The code replaces empty values with NaN in each DataFrame using the following snippet:

for df in list_of_df:
    temp = pd.read_json(df + '.json')
    temp = temp.replace('', np.nan)
    df = temp.copy()
    del temp
df

If we execute this code, the output will be only the last DataFrame ('test'), because all previous DataFrames are overwritten.

Solution: Saving Each DataFrame Globally

To solve this problem, we can use a dictionary to store each DataFrame globally, allowing us to access and manipulate them outside the loop. The dictionary key can be the name of the original JSON file (e.g., 'NPSFeedback.json').

Here’s the modified code that saves each DataFrame globally:

list_of_df = ['NPSFeedback', 'courses','test'] 
dict_df = {}

for filename in list_of_df :
    df = pd.read_json(filename + ".json")
    df.replace('', np.nan, inplace=True)
    dict_df[filename] = df

print(dict_df['NPSFeedback'])  # Access the original NPSFeedback DataFrame
print(dict_df['courses'])      # Access the original courses DataFrame

By using a dictionary to store each DataFrame globally, we can easily access and manipulate them outside the loop. This approach ensures that all DataFrames are preserved, even if we have multiple iterations.

Additional Tips and Considerations

DataFrame Names: When accessing DataFrames from the dict_df dictionary, make sure to use the original name of the JSON file (e.g., 'NPSFeedback') instead of the temporary variable name (df).
Nested Loops: If you need to perform operations on multiple DataFrames inside a loop, consider using nested loops or dictionaries with conditional statements to manage data storage and retrieval.
Memory Efficiency: When working with large DataFrames, consider using memory-efficient techniques such as chunking or compressing data to reduce storage requirements.

Conclusion

In this article, we explored the issue of overwritten DataFrames when working with multiple DataFrames in a loop. We examined the provided code and offered a solution that saves each DataFrame globally, allowing for easier access and manipulation outside the loop. By using dictionaries to store DataFrames, we can preserve all dataframes even if there are many iterations and avoid having data overwritten.