Use the read_csv() function in the Pandas library to import and read the CSV data. You can print the first few rows using the head() method on the dataframe.

import pandas as pd

# Load the data
df = pd.read_csv('Qualitative_Data.csv')

# Print the first few rows of the dataframe
print(df.head())

Use the join() method on a string that represents the delimiter you want to use to join the strings. For example, if you want to join the strings with a space in between, you would do ' '.join(notes_list).

notes_list = df['Notes From Previous Conversations'].tolist()
notes_string = ' '.join(notes_list)
print(notes_string)

Use the split() method on the string you want to split. If you want to split the string by spaces, you would do notes_string.split(' ').

words_list = notes_string.split(' ')
print(words_list)

Use the replace() method on the string you want to modify. The first argument should be the substring you want to replace, and the second argument should be what you want to replace it with. For example, to remove all occurrences of the word 'banana', you would do example_string.replace('banana', '').

notes_string_no_product = notes_string.replace('product', '')
print(notes_string_no_product)

To import pandas, use the import keyword followed by pandas as pd. Use the pd.read_csv() function to read the csv file and df.head() to display the first few rows of the DataFrame.

import pandas as pd

# Load the dataset
df = pd.read_csv('Qualitative_Data.csv')

# Display the first few rows
df.head()

Use the Series.str.split() function to split the strings into words and then use indexing to get the second word. For example, df['column_name'].str.split().str[1].

second_words = df['Notes From Previous Conversations'].str.split().str[1]
second_words.head()

Use the Series.str.lower() function to lowercase all strings. For example, df['column_name'].str.lower().

df['Notes From Previous Conversations'] = df['Notes From Previous Conversations'].str.lower()
df['Notes From Previous Conversations'].head()

Use the Series.str.split() function to split the strings into words. For example, df['column_name'].str.split().

words = df['Notes From Previous Conversations'].str.split()
print(words)

Use the re.sub() function with the pattern r'\d+' to replace all numbers with an empty string. The \d in the regex pattern represents any digit, and the + sign indicates one or more times. Therefore, \d+ matches one or more digits.

import re

# Assuming 'data' is the provided string with numbers
cleaned_data = re.sub(r'\d+', '', data)
print(cleaned_data)

Use the re.sub() function with the pattern r'product' to replace the substring 'product' with an empty string. The regex pattern here is a direct match for the word 'product' in the data.

# Assuming 'cleaned_data' is the current state of the data
cleaned_data = re.sub(r'product', '', cleaned_data)
print(cleaned_data)

Use the re.sub() function with the pattern r'&' to replace '&' with '&'. This pattern looks for the exact sequence of '&' and replaces it with a single ampersand.

# Assuming 'cleaned_data' is the current state of the data
cleaned_data = re.sub(r'&', '&', cleaned_data)
print(cleaned_data)

Use the re.sub() function with the pattern r'http[s]?://\S+' to match and replace URLs with an empty string. In this pattern, http[s]? matches 'http' followed by an optional 's', and \S+ matches one or more non-whitespace characters, which continues until a space is encountered.

# Assuming 'cleaned_data' is the current state of the data
cleaned_data = re.sub(r'http[s]?://\S+', '', cleaned_data)
print(cleaned_data)

Use the re.sub() function with the pattern r'#\w+' to match and replace hashtags with an empty string. The # character in the regex pattern matches the hashtag symbol, and \w+ matches one or more word characters (letters, digits, or underscores).

# Assuming 'cleaned_data' is the current state of the data
cleaned_data = re.sub(r'#\w+', '', cleaned_data)
print(cleaned_data)

You can use the BeautifulSoup class from the bs4 module to parse the HTML and extract the text. Here's an example of initializing the BeautifulSoup class with your data:

soup = BeautifulSoup(html_string, 'html.parser')

Remember, get_text() will retrieve the text content from the parsed HTML.

from bs4 import BeautifulSoup

# Assuming 'html_string' is the provided string containing HTML
def extract_text(html_string):
    soup = BeautifulSoup(html_string, 'html.parser')
    return soup.get_text()

text_content = extract_text(html_data)
print(text_content)