单词和组元组之间的映射以获得单词的频率 [英] mapping between words and a group tuple to get frequency of words

问题描述

我有一个如下所示的数据框

I have a dataframe that looks like the following

Utterance                         Frequency   

Directions to Starbucks           1045
Show me directions to Starbucks   754
Give me directions to Starbucks   612
Navigate me to Starbucks          498
Display navigation to Starbucks   376
Direct me to Starbucks            201
Navigate to Starbucks             180

在这里，有些数据显示了人们所说的话，以及人们说出这些话的频率.

Here, there is some data that show utterances made by people, and how frequently these were said.

即说出去星巴克的路线" 1045次，说出给我看去星巴克的路线" 754次，等等.

I.e., "Directions to Starbucks" was uttered 1045 times, "Show me directions to Starbucks" was uttered 754 times, etc.

我能够通过以下方式获得所需的输出:

I was able to get the desired output with the following:

df = (df.set_index('Frequency')['Utterance']
        .str.split(expand=True)
        .stack()
        .reset_index(name='Words')
        .groupby('Words', as_index=False)['Frequency'].sum()
        )

print (df)
         Words  Frequency
0       Direct        201
1   Directions       1045
2      Display        376
3         Give        612
4     Navigate        678
5         Show        754
6    Starbucks       3666
7   directions       1366
8           me       2065
9   navigation        376
10          to       3666

但是，我也在尝试寻找以下输出:

However, I'm also trying to look for the following output:

print (df)
                        Words        Frequency
0                  Directions        2411
1   Give_Show_Direct_Navigate        2245
2                     Display        376
3                   Starbucks        3666
4                          me        2065
5                  navigation        376
6                          to        3666

即，我正在尝试找出一种组合某些短语并获得这些单词出现频率的方法.例如，如果讲话者说"Seattles_Best"或"Tullys"，那么理想情况下，我会将其添加到"Starbucks"，并将其重命名为"coffee_shop"或类似名称.

I.e., I'm trying to figure out a way to combine certain phrases and get the frequency of those words. For example, if the speaker says "Seattles_Best" or "Tullys", then ideally i would add it to "Starbucks" and rename it "coffee_shop" or something like that.

谢谢！

推荐答案

这是坚持上一个问题中的collections.Counter的一种方法.

Here is one way, sticking with collections.Counter from your previous question.

您可以在lst中添加任意数量的元组，以便为​​您选择的组合附加其他结果.

You can add any number of tuples to lst to append additional results for combinations of your choice.

from collections import Counter
import pandas as pd

df = pd.DataFrame([['Directions to Starbucks', 1045],
                   ['Show me directions to Starbucks', 754],
                   ['Give me directions to Starbucks', 612],
                   ['Navigate me to Starbucks', 498],
                   ['Display navigation to Starbucks', 376],
                   ['Direct me to Starbucks', 201],
                   ['Navigate to Starbucks', 180]],
                  columns = ['Utterance', 'Frequency'])

c = Counter()

for row in df.itertuples():
    for i in row[1].split():
        c[i] += row[2]

res = pd.DataFrame.from_dict(c, orient='index')\
                  .rename(columns={0: 'Count'})\
                  .sort_values('Count', ascending=False)

def add_combinations(df, lst):
    for i in lst:
        words = '_'.join(i)
        df.loc[words] = df.loc[df.index.isin(i), 'Count'].sum()
    return df.sort_values('Count', ascending=False)

lst = [('Give', 'Show', 'Navigate', 'Direct')]

res = add_combinations(res, lst)

结果

                           Count
to                          3666
Starbucks                   3666
Give_Show_Navigate_Direct   2245
me                          2065
directions                  1366
Directions                  1045
Show                         754
Navigate                     678
Give                         612
Display                      376
navigation                   376
Direct                       201

这篇关于单词和组元组之间的映射以获得单词的频率的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！