In version 0.19.0 you can use parameter dtype=”category” in read_csv: data=”col1,col2,col3\na,b,1\na,b,2\nc,d,3″ df = pd.read_csv(pd.compat.StringIO(data), dtype=”category”) print (df) col1 col2 col3 0 a b 1 1 a b 2 2 c d 3 print (df.dtypes) col1 category col2 category col3 category dtype: object If want specify column for category use dtype with dictionary: df = pd.read_csv(pd.compat.StringIO(data), … Read more
You can using pd.factorize df.apply(lambda x : pd.factorize(x)[0]).corr(method=’pearson’, min_periods=1) Out[32]: a c d a 1.0 1.0 1.0 c 1.0 1.0 1.0 d 1.0 1.0 1.0 Data input df=pd.DataFrame({‘a’:[‘a’,’b’,’c’],’c’:[‘a’,’b’,’c’],’d’:[‘a’,’b’,’c’]}) Update from scipy.stats import chisquare df=df.apply(lambda x : pd.factorize(x)[0])+1 pd.DataFrame([chisquare(df[x].values,f_exp=df.values.T,axis=1)[0] for x in df]) Out[123]: 0 1 2 3 0 0.0 0.0 0.0 0.0 1 0.0 0.0 … Read more
You can generate dictionary: c = language.lang.astype(‘category’) d = dict(enumerate(c.cat.categories)) print (d) {0: ‘english’, 1: ‘spanish’} So then if necessary is possible map: language[‘code’] = language.lang.astype(‘category’).cat.codes language[‘level_back’] = language[‘code’].map(d) print (language) lang level code level_back 0 english intermediate 0 english 1 spanish intermediate 1 spanish 2 spanish basic 1 spanish 3 english basic 0 english … Read more
I believe need Series.cat.categories or unique: np.random.seed(1245) a = [‘No’, ‘Yes’, ‘Maybe’] df = pd.DataFrame(np.random.choice(a, size=(10, 3)), columns=[‘Col1′,’Col2′,’Col3’]) df[‘Col1’] = pd.Categorical(df[‘Col1’]) print (df.dtypes) Col1 category Col2 object Col3 object dtype: object print (df[‘Col1’].cat.categories) Index([‘Maybe’, ‘No’, ‘Yes’], dtype=”object”) print (df[‘Col2’].unique()) [‘Yes’ ‘Maybe’ ‘No’] print (df[‘Col1’].unique()) [Maybe, No, Yes] Categories (3, object): [Maybe, No, Yes]
Just reindex df.reindex(reorderlist) Out[89]: Age G Tm Year id Player Maurice Baker 25 7 VAN 2004 5335 Adrian Caldwell 31 81 DAL 1997 6169 Ratko Varda 22 60 TOT 2001 13950 Ryan Bowen 34 52 OKC 2009 6141 Cedric Hunter 27 6 CHH 1991 2967 Update info you have multiple players with same name out … Read more
If you read the docs for OneHotEncoder you’ll see the input for fit is “Input array of type int”. So you need to do two steps for your one hot encoded data from sklearn import preprocessing cat_features = [‘color’, ‘director_name’, ‘actor_2_name’] enc = preprocessing.LabelEncoder() enc.fit(cat_features) new_cat_features = enc.transform(cat_features) print new_cat_features # [1 2 0] new_cat_features … Read more
I know it’s been a while since this question was asked, but there is (at least now there is) a one-liner that is supported by the documentation: In [4]: df Out[4]: label 0 (a, c, e) 1 (a, d) 2 (b,) 3 (d, e) In [5]: df[‘label’].str.join(sep=’*’).str.get_dummies(sep=’*’) Out[5]: a b c d e 0 1 … Read more
In version 0.19.0 you can use parameter dtype=”category” in read_csv: data=”col1,col2,col3\na,b,1\na,b,2\nc,d,3″ df = pd.read_csv(pd.compat.StringIO(data), dtype=”category”) print (df) col1 col2 col3 0 a b 1 1 a b 2 2 c d 3 print (df.dtypes) col1 category col2 category col3 category dtype: object If want specify column for category use dtype with dictionary: df = pd.read_csv(pd.compat.StringIO(data), … Read more