Capturing Output from Print Function in a Pandas DataFrame

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As data scientists, we often encounter functions that provide valuable output but are not easily convertible to structured formats. In this article, we will explore an efficient way to capture output from print functions and store it in a pandas DataFrame.

Understanding the Problem

The given function multilabel3_message is used to process data from a dataframe scav_df. The function uses the print statement to display its output values. However, we want to capture these output values into a structured format using a pandas DataFrame.

Solution Overview

Our solution involves replacing the print statements with a return value and utilizing a pandas DataFrame to store the captured data. We will use the pandas.DataFrame constructor to create a new DataFrame from a dictionary of key-value pairs.

Step 1: Replace Print Statement with Return Value

We can replace the print statement with a return value by adding a variable name before the function call and assigning it to a variable. This allows us to capture the output values in a pandas DataFrame.

import pandas as pd

def multilabel3_message(model, scav_df):
    output_values = []
    for ind in scav_df.index: 
        doc= (scav_df['park_data'][ind]) 
        doc_list = sentence_tokenizer(doc)
        y_pred = pipeline4.predict(doc_list)
        # Remove double class predictions
        y_pred = list(set(y_pred))
        tags = [dict_classes[i] for i in y_pred]
        # Remove `misc` tag if the list contains other tags as well.
        if len(tags) > 1 and dict_classes[46] in tags:
            del tags[tags.index(dict_classes[46])]  
        output_values.append({'tag': tags, 'doc': doc})

    return output_values

# Create a pandas DataFrame from the captured data
df = pd.DataFrame(multilabel3_message(pipeline4, scav_df))

Step 2: Creating a Pandas DataFrame from Dictionary

In the modified function, we create a list of dictionaries output_values to store the captured data. Each dictionary contains two keys: ’tag’ and ‘doc’. We use the pandas DataFrame constructor to create a new DataFrame from this dictionary.

Step 3: Utilizing Pandas DataFrame Features

The resulting DataFrame provides various features for data manipulation, analysis, and visualization. We can leverage these features to gain insights into our data.

Example Use Cases

1. Data Analysis:

# Calculate the average number of ratings for each location
avg_ratings = df.groupby('doc')['tag'].apply(lambda x: len(set(x))).mean()
print(avg_ratings)

2. Data Visualization:

import matplotlib.pyplot as plt

# Plot a bar chart to display the distribution of tags
plt.figure(figsize=(10, 6))
df['tag'].value_counts().plot(kind='bar')
plt.title('Distribution of Tags')
plt.xlabel('Tag')
plt.ylabel('Count')
plt.show()

Conclusion

Capturing output from print functions and storing it in a pandas DataFrame can be achieved by replacing the print statement with a return value and utilizing a pandas DataFrame to store the captured data. By leveraging the features of pandas DataFrames, we can perform various data analysis and visualization tasks to gain insights into our data.

Additional Tips

• Always replace print statements with return values when capturing output in functions.
• Utilize pandas DataFrame features for efficient data manipulation and analysis.
• Experiment with different data visualization techniques to effectively communicate your findings.

Last modified on 2024-03-08