Creating an extended frequency table using Python

Question

I am trying to create a frequency table based on a data frame with pandasand Python. In fact, this is exactly the same as my previous question that R used .

Let's say that I have a dataframe in pandas that looks like this (actually the frame size is much larger, but for illustrative purposes I have limited the lines):

node    |   precedingWord
-------------------------
A-bom       de
A-bom       die
A-bom       de
A-bom       een
A-bom       n
A-bom       de
acroniem    het
acroniem    t
acroniem    het
acroniem    n
acroniem    een
act         de
act         het
act         die
act         dat
act         t
act         n

node, . : neuter, non-neuter rest. neuter , beforeWord : t, het, dat. non-neuter de die, rest , neuter non-neuter. ( , , , rest - , . - node.)

( , , freqDf, :

node    |   neuter   | nonNeuter   | rest
-----------------------------------------
A-bom       0          4             2
acroniem    3          0             2
act         3          2             1

, . , . , node, - .

- (untested):

def specificFreq(d):  
    for uniqueWord in d['node']
        return pd.Series({'node': uniqueWord ,
            'neuter': sum(d['node' == uniqueWord] & d['precedingWord'] == 't|het|dat'),
            'nonNeuter':  sum(d['node' == uniqueWord] & d['precedingWord'] == 'de|die'),
            'rest': len(uniqueWord) - neuter - nonNeuter}) # Length of rows with the specific word, distracted by neuter and nonneuter values above

df.groupby('node').apply(specificFreq)

, - .

Answer 1

R, , -:

df.loc[df.precedingWord.isin(neuter), "gender"] = "neuter"
df.loc[df.precedingWord.isin(non_neuter), "gender"] = "non_neuter"
df.loc[df.precedingWord.isin(neuter + non_neuter)==0, "gender"] = "rest"
# neuter + non_neuter is the concatenation of both lists.

pd.crosstab(df.node, df.gender)
gender    neuter  non_neuter  rest
node                              
A-bom          0           4     2
acroniem       3           0     2
act            3           2     1

, neuter non_neuter precedingword, KeyError, .

---

, .

, :

ct = pd.crosstab(df.node, df.precedingWord) 

:

pW        dat  de  die  een  het  n  t
node                                  
A-bom       0   3    1    1    0  1  0
acroniem    0   0    0    1    2  1  1
act         1   1    1    0    1  1  1

:

neuter = ["t", "het", "dat"]
non_neuter = ["de","die"]
freqDf = pd.DataFrame()

freqDf["neuter"] = ct[neuter].sum(axis=1)
ct.drop(neuter, axis=1, inplace=1)

freqDf["non_neuter"] = ct[non_neuter].sum(axis=1)
ct.drop(non_neuter, axis=1, inplace=1)

freqDf["rest"] = ct.sum(axis=1)

freqDf:

          neuter  non_neuter  rest
node                              
A-bom          0           4     2
acroniem       3           0     2
act            3           2     1