Turning table data into columns and counting by frequency

问题

I have a dataframe in the following form:

shape is 2326 x 1271

Column names are just serialized from 0-1269 while rows are categories that could repeat like "apple" in the example. The internal data points can represent anything (let's say they represent stores in this example) and I'm trying to convert them into columns and having the data points become the number of times that category shows up in that "store". Visually, here is the table I'm trying to get to:

Note that Apple shows up in AA and RR twice

回答1:

Use stack along with crosstab to compute the frequency counts:

Data:

index= ['Apple', 'Orange', 'Apple', 'Banana', 'Kiwi']
data = [['AA', 'DD', 'RR', ''], ['DD', 'PP', '', ''], 
        ['AA', 'RR', 'TT', 'SS'], ['EE', 'NN', '',''], ['NN', 'WW','', '']]
frame = pd.DataFrame(data, index, columns=np.arange(4))
frame

Operations:

df = frame.stack().reset_index(0, name='values')
df = pd.crosstab(df['level_0'], df['values']).drop('', axis=1).replace(0, '')
df.index.name=None; df.columns.name=None
df

来源：https://stackoverflow.com/questions/39898338/turning-table-data-into-columns-and-counting-by-frequency