Python，Pandas：将数据框过滤为一个子集并就地更新此子集

Question

I have a pandas dataframe that looks like:

cleanText.head()
    source      word    count
0   twain_ess            988
1   twain_ess   works    139
2   twain_ess   short    139
3   twain_ess   complete 139
4   twain_ess   would    98
5   twain_ess   push     94

And a dictionary that contains the total word count for each source:

titles
{'orw_ess': 1729, 'orw_novel': 15534, 'twain_ess': 7680, 'twain_novel': 60004}

My goal is to normalize the word counts for each source by the total number of words in that source, ie turn them into a percentage. This seems like it should be trivial but python seems to make it very difficult (if anyone could explain the rules for inplace operations to me that would be great).

The caveat comes from needing to filter the entries in cleanText to just those from a single source, and then I attempt to inplace divide the counts for this subset by the value in the dictionary.

# Adjust total word counts and normalize
for key, value in titles.items():

    # This corrects the total words for overcounting the '' entries
    overcounted= cleanText[cleanText.iloc[:,0]== key].iloc[0,2]
    titles[key]= titles[key]-overcounted

    # This is where I divide by total words, however it does not save inplace, or at all for that matter
    cleanText[cleanText.iloc[:,0]== key].iloc[:,2]= cleanText[cleanText.iloc[:,0]== key]['count']/titles[key]

If anyone could explain how to alter this division statement so that the output is actually saved in the original column that would be great.

Thanks

Answer 1

If I understand Correctly:

cleanText['count']/cleanText['source'].map(titles)

Which gives you:

0    0.128646
1    0.018099
2    0.018099
3    0.018099
4    0.012760
5    0.012240
dtype: float64

To re-assign these percentage values into your count column, use:

cleanText['count'] = cleanText['count']/cleanText['source'].map(titles)