计算 pandas 数据框中单词的出现频率

本文介绍了计算 pandas 数据框中单词的出现频率的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我有一个如下表:

URN Firm_Name 0 104472 R.X. Yah & Co 1 104873 Big Building Society 2 109986 St James's Society 3 114058 The Kensington Society Ltd 4 113438 MMV Oil Associates Ltd

我想计算Firm_Name列中所有单词的出现频率，以获得如下输出:

And I want to count the frequency of all the words within the Firm_Name column, to get an output like below:

我尝试了以下代码:

import pandas as pd import nltk data = pd.read_csv("X:\Firm_Data.csv") top_N = 20 word_dist = nltk.FreqDist(data['Firm_Name']) print('All frequencies') print('='*60) rslt=pd.DataFrame(word_dist.most_common(top_N),columns=['Word','Frequency']) print(rslt) print ('='*60)

但是，以下代码不会产生唯一的字数.

However the following code does not produce a unique word count.

推荐答案

IIUIC，使用value_counts()

In [3361]: df.Firm_Name.str.split(expand=True).stack().value_counts() Out[3361]: Society 3 Ltd 2 James's 1 R.X. 1 Yah 1 Associates 1 St 1 Kensington 1 MMV 1 Big 1 & 1 The 1 Co 1 Oil 1 Building 1 dtype: int64

或者，

Or,

pd.Series(np.concatenate([x.split() for x in df.Firm_Name])).value_counts()

或者，

Or,

pd.Series(' '.join(df.Firm_Name).split()).value_counts()

对于前N个，例如3

For top N, for example 3

In [3379]: pd.Series(' '.join(df.Firm_Name).split()).value_counts()[:3] Out[3379]: Society 3 Ltd 2 James's 1 dtype: int64

详细信息

Details

In [3380]: df Out[3380]: URN Firm_Name 0 104472 R.X. Yah & Co 1 104873 Big Building Society 2 109986 St James's Society 3 114058 The Kensington Society Ltd 4 113438 MMV Oil Associates Ltd