Organizing transcribed phone call data

We're almost ready to build a text classifier. But right now, all of our transcribed text data is in two lists, pre_purchase_text and post_purchase_text.

To organize it better for building a text classifier as well as for future use, we'll put it together into a pandas DataFrame.

To start we'll import pandas as pd then we'll create a post purchase dataframe, post_purchase_df using pd.DataFrame().

We'll pass pd.DataFrame() a dictionary containing a "label" key with a value of "post_purchase" and a "text" key with a value of our post_purchase_text list.

We'll do the same for pre_purchase_df except with pre_purchase_text.

To have all the data in one place, we'll use pd.concat() and pass it the pre and post purchase DataFrames.

This exercise is part of the course

Spoken Language Processing in Python

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Exercise instructions

Create post_purchase_df using the post_purchase_text list.
Create pre_purchase_df using the pre_purchase_text list.
Combine the two DataFrames using pd.concat().

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

import pandas as pd

# Make dataframes with the text
post_purchase_df = pd.DataFrame({"label": "post_purchase",
                                 "text": ____})
pre_purchase_df = pd.____({"label": "pre_purchase",
                                "text": ____})

# Combine DataFrames
df = pd.____([post_purchase_df, pre_purchase_df])

# Print the combined DataFrame
print(df.head())

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