基于DataFrame其他列数据统计列表中的词汇频率

Hey there! Let's walk through how to calculate the total frequency of specific target words using your provided DataFrame. First, let's recap your dataset clearly:

Step 1: Set up your data and environment #

First, make sure you've imported pandas, then load your data into a DataFrame:

import pandas as pd

# Build your DataFrame from the provided data
data = {
    "Utterance": [
        "Directions to Starbucks",
        "Show me directions to Starbucks",
        "Give me directions to Starbucks",
        "Navigate me to Starbucks",
        "Display navigation to Starbucks",
        "Direct me to Starbucks",
        "Navigate to Starbucks"
    ],
    "Frequency": [1045, 754, 612, 498, 376, 201, 180]
}

df = pd.DataFrame(data)

Step 2: Define your target word list #

List out the specific words you want to calculate frequencies for. For example:

target_words = ["directions", "navigate", "starbucks", "direct"]

Step 3: Write the frequency calculation logic #

We'll loop through each target word, check which utterances contain the word (case-insensitive), then sum up their corresponding frequencies:

# Initialize a dictionary to store our results
word_total_freq = {}

for word in target_words:
    # Sum frequencies where the utterance contains the target word (ignore case)
    total = df[df["Utterance"].str.contains(word, case=False)]["Frequency"].sum()
    word_total_freq[word.lower()] = total

# Convert results to a DataFrame for readability
result_df = pd.DataFrame(word_total_freq.items(), columns=["Word", "Total Frequency"])
print(result_df)

Step 4: Review the output #

Running the code above will give you this result:

A quick note: If you need exact word matches (instead of matching substrings, e.g., avoiding counting "navigation" for the word "navigate"), use a regex with word boundaries:

total = df[df["Utterance"].str.contains(rf'\b{word}\b', case=False)]["Frequency"].sum()

内容的提问来源于stack exchange，提问作者user_seaweed