add cat features

app.py

@@ -13,7 +13,7 @@ def default():
 
 with st.sidebar:
 
-    def_csv = st.selectbox(label='Demo Datasets', options=['spotify.csv', 'heart.csv', 'titanic.csv', 'pokemon.csv'])
+    def_csv = st.selectbox(label='Demo Datasets', options=['penguins.csv', 'spotify.csv', 'titanic.csv', 'pokemon.csv'])
     def_csv = 'datasets/'+def_csv
 
     csv = st.file_uploader(label='or upload a CSV file to analyse')
@@ -22,12 +22,12 @@ with st.sidebar:
 
     corr_type = st.selectbox(label='Correlation Type', options=['kendall', 'pearson', 'spearman'], on_change=default)
 
-    th = st.slider(label='Correlation Threshold', min_value=0., max_value=1., value=0.15, step=0.01, on_change=default)
+    th = st.slider(label='Correlation Threshold', min_value=0., max_value=1., value=0.3, step=0.01, on_change=default)
 
     but = st.button("Display Correlation Graph", on_click=default)
 
 if st.session_state.default:
-    graph_html = display_graph(csv_file='datasets/spotify.csv', threshold=0.15, corr_type='kendall')
+    graph_html = display_graph(csv_file='datasets/penguins.csv', threshold=0.3, corr_type='kendall')
     st.components.v1.html(graph_html, width=None, height=2000)
 
 if but:

datasets/heart.csv

@@ -1,304 +0,0 @@
-age,sex,cp,trtbps,chol,fbs,restecg,thalachh,exng,oldpeak,slp,caa,thall,output
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datasets/penguins.csv

@@ -0,0 +1,345 @@
+species,island,culmen_length_mm,culmen_depth_mm,flipper_length_mm,body_mass_g,sex
+Adelie,Torgersen,39.1,18.7,181,3750,MALE
+Adelie,Torgersen,39.5,17.4,186,3800,FEMALE
+Adelie,Torgersen,40.3,18,195,3250,FEMALE
+Adelie,Torgersen,NA,NA,NA,NA,NA
+Adelie,Torgersen,36.7,19.3,193,3450,FEMALE
+Adelie,Torgersen,39.3,20.6,190,3650,MALE
+Adelie,Torgersen,38.9,17.8,181,3625,FEMALE
+Adelie,Torgersen,39.2,19.6,195,4675,MALE
+Adelie,Torgersen,34.1,18.1,193,3475,NA
+Adelie,Torgersen,42,20.2,190,4250,NA
+Adelie,Torgersen,37.8,17.1,186,3300,NA
+Adelie,Torgersen,37.8,17.3,180,3700,NA
+Adelie,Torgersen,41.1,17.6,182,3200,FEMALE
+Adelie,Torgersen,38.6,21.2,191,3800,MALE
+Adelie,Torgersen,34.6,21.1,198,4400,MALE
+Adelie,Torgersen,36.6,17.8,185,3700,FEMALE
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+Adelie,Torgersen,42.5,20.7,197,4500,MALE
+Adelie,Torgersen,34.4,18.4,184,3325,FEMALE
+Adelie,Torgersen,46,21.5,194,4200,MALE
+Adelie,Biscoe,37.8,18.3,174,3400,FEMALE
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+Adelie,Biscoe,38.8,17.2,180,3800,MALE
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+Adelie,Dream,40.9,18.9,184,3900,MALE
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+Adelie,Dream,36.5,18,182,3150,FEMALE
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+Adelie,Dream,44.1,19.7,196,4400,MALE
+Adelie,Dream,37,16.9,185,3000,FEMALE
+Adelie,Dream,39.6,18.8,190,4600,MALE
+Adelie,Dream,41.1,19,182,3425,MALE
+Adelie,Dream,37.5,18.9,179,2975,NA
+Adelie,Dream,36,17.9,190,3450,FEMALE
+Adelie,Dream,42.3,21.2,191,4150,MALE
+Adelie,Biscoe,39.6,17.7,186,3500,FEMALE
+Adelie,Biscoe,40.1,18.9,188,4300,MALE
+Adelie,Biscoe,35,17.9,190,3450,FEMALE
+Adelie,Biscoe,42,19.5,200,4050,MALE
+Adelie,Biscoe,34.5,18.1,187,2900,FEMALE
+Adelie,Biscoe,41.4,18.6,191,3700,MALE
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+Adelie,Biscoe,36.4,17.1,184,2850,FEMALE
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+Adelie,Biscoe,35.5,16.2,195,3350,FEMALE
+Adelie,Biscoe,41.1,19.1,188,4100,MALE
+Adelie,Torgersen,35.9,16.6,190,3050,FEMALE
+Adelie,Torgersen,41.8,19.4,198,4450,MALE
+Adelie,Torgersen,33.5,19,190,3600,FEMALE
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+Adelie,Torgersen,45.8,18.9,197,4150,MALE
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+Adelie,Torgersen,34.6,17.2,189,3200,FEMALE
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+Adelie,Torgersen,36.7,18.8,187,3800,FEMALE
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+Adelie,Dream,37.3,17.8,191,3350,FEMALE
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+Adelie,Dream,36.9,18.6,189,3500,FEMALE
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+Adelie,Dream,34,17.1,185,3400,FEMALE
+Adelie,Dream,39.6,18.1,186,4450,MALE
+Adelie,Dream,36.2,17.3,187,3300,FEMALE
+Adelie,Dream,40.8,18.9,208,4300,MALE
+Adelie,Dream,38.1,18.6,190,3700,FEMALE
+Adelie,Dream,40.3,18.5,196,4350,MALE
+Adelie,Dream,33.1,16.1,178,2900,FEMALE
+Adelie,Dream,43.2,18.5,192,4100,MALE
+Adelie,Biscoe,35,17.9,192,3725,FEMALE
+Adelie,Biscoe,41,20,203,4725,MALE
+Adelie,Biscoe,37.7,16,183,3075,FEMALE
+Adelie,Biscoe,37.8,20,190,4250,MALE
+Adelie,Biscoe,37.9,18.6,193,2925,FEMALE
+Adelie,Biscoe,39.7,18.9,184,3550,MALE
+Adelie,Biscoe,38.6,17.2,199,3750,FEMALE
+Adelie,Biscoe,38.2,20,190,3900,MALE
+Adelie,Biscoe,38.1,17,181,3175,FEMALE
+Adelie,Biscoe,43.2,19,197,4775,MALE
+Adelie,Biscoe,38.1,16.5,198,3825,FEMALE
+Adelie,Biscoe,45.6,20.3,191,4600,MALE
+Adelie,Biscoe,39.7,17.7,193,3200,FEMALE
+Adelie,Biscoe,42.2,19.5,197,4275,MALE
+Adelie,Biscoe,39.6,20.7,191,3900,FEMALE
+Adelie,Biscoe,42.7,18.3,196,4075,MALE
+Adelie,Torgersen,38.6,17,188,2900,FEMALE
+Adelie,Torgersen,37.3,20.5,199,3775,MALE
+Adelie,Torgersen,35.7,17,189,3350,FEMALE
+Adelie,Torgersen,41.1,18.6,189,3325,MALE
+Adelie,Torgersen,36.2,17.2,187,3150,FEMALE
+Adelie,Torgersen,37.7,19.8,198,3500,MALE
+Adelie,Torgersen,40.2,17,176,3450,FEMALE
+Adelie,Torgersen,41.4,18.5,202,3875,MALE
+Adelie,Torgersen,35.2,15.9,186,3050,FEMALE
+Adelie,Torgersen,40.6,19,199,4000,MALE
+Adelie,Torgersen,38.8,17.6,191,3275,FEMALE
+Adelie,Torgersen,41.5,18.3,195,4300,MALE
+Adelie,Torgersen,39,17.1,191,3050,FEMALE
+Adelie,Torgersen,44.1,18,210,4000,MALE
+Adelie,Torgersen,38.5,17.9,190,3325,FEMALE
+Adelie,Torgersen,43.1,19.2,197,3500,MALE
+Adelie,Dream,36.8,18.5,193,3500,FEMALE
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+Adelie,Dream,38.1,17.6,187,3425,FEMALE
+Adelie,Dream,41.1,17.5,190,3900,MALE
+Adelie,Dream,35.6,17.5,191,3175,FEMALE
+Adelie,Dream,40.2,20.1,200,3975,MALE
+Adelie,Dream,37,16.5,185,3400,FEMALE
+Adelie,Dream,39.7,17.9,193,4250,MALE
+Adelie,Dream,40.2,17.1,193,3400,FEMALE
+Adelie,Dream,40.6,17.2,187,3475,MALE
+Adelie,Dream,32.1,15.5,188,3050,FEMALE
+Adelie,Dream,40.7,17,190,3725,MALE
+Adelie,Dream,37.3,16.8,192,3000,FEMALE
+Adelie,Dream,39,18.7,185,3650,MALE
+Adelie,Dream,39.2,18.6,190,4250,MALE
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+Adelie,Dream,37.8,18.1,193,3750,MALE
+Adelie,Dream,36,17.1,187,3700,FEMALE
+Adelie,Dream,41.5,18.5,201,4000,MALE
+Chinstrap,Dream,46.5,17.9,192,3500,FEMALE
+Chinstrap,Dream,50,19.5,196,3900,MALE
+Chinstrap,Dream,51.3,19.2,193,3650,MALE
+Chinstrap,Dream,45.4,18.7,188,3525,FEMALE
+Chinstrap,Dream,52.7,19.8,197,3725,MALE
+Chinstrap,Dream,45.2,17.8,198,3950,FEMALE
+Chinstrap,Dream,46.1,18.2,178,3250,FEMALE
+Chinstrap,Dream,51.3,18.2,197,3750,MALE
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+Chinstrap,Dream,51.3,19.9,198,3700,MALE
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+Chinstrap,Dream,52,18.1,201,4050,MALE
+Chinstrap,Dream,45.9,17.1,190,3575,FEMALE
+Chinstrap,Dream,50.5,19.6,201,4050,MALE
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+Chinstrap,Dream,58,17.8,181,3700,FEMALE
+Chinstrap,Dream,46.4,18.6,190,3450,FEMALE
+Chinstrap,Dream,49.2,18.2,195,4400,MALE
+Chinstrap,Dream,42.4,17.3,181,3600,FEMALE
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+Chinstrap,Dream,43.2,16.6,187,2900,FEMALE
+Chinstrap,Dream,50.6,19.4,193,3800,MALE
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+Chinstrap,Dream,45.7,17.3,193,3600,FEMALE
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+Chinstrap,Dream,42.5,17.3,187,3350,FEMALE
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+Chinstrap,Dream,55.8,19.8,207,4000,MALE
+Chinstrap,Dream,43.5,18.1,202,3400,FEMALE
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+Gentoo,Biscoe,46.1,13.2,211,4500,FEMALE
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+Gentoo,Biscoe,49,16.1,216,5550,MALE
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+Gentoo,Biscoe,45.8,14.6,210,4200,FEMALE
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+Gentoo,Biscoe,42,13.5,210,4150,FEMALE
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+Gentoo,Biscoe,50.2,14.3,218,5700,MALE
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+Gentoo,Biscoe,46.3,15.8,215,5050,MALE
+Gentoo,Biscoe,42.9,13.1,215,5000,FEMALE
+Gentoo,Biscoe,46.1,15.1,215,5100,MALE
+Gentoo,Biscoe,44.5,14.3,216,4100,NA
+Gentoo,Biscoe,47.8,15,215,5650,MALE
+Gentoo,Biscoe,48.2,14.3,210,4600,FEMALE
+Gentoo,Biscoe,50,15.3,220,5550,MALE
+Gentoo,Biscoe,47.3,15.3,222,5250,MALE
+Gentoo,Biscoe,42.8,14.2,209,4700,FEMALE
+Gentoo,Biscoe,45.1,14.5,207,5050,FEMALE
+Gentoo,Biscoe,59.6,17,230,6050,MALE
+Gentoo,Biscoe,49.1,14.8,220,5150,FEMALE
+Gentoo,Biscoe,48.4,16.3,220,5400,MALE
+Gentoo,Biscoe,42.6,13.7,213,4950,FEMALE
+Gentoo,Biscoe,44.4,17.3,219,5250,MALE
+Gentoo,Biscoe,44,13.6,208,4350,FEMALE
+Gentoo,Biscoe,48.7,15.7,208,5350,MALE
+Gentoo,Biscoe,42.7,13.7,208,3950,FEMALE
+Gentoo,Biscoe,49.6,16,225,5700,MALE
+Gentoo,Biscoe,45.3,13.7,210,4300,FEMALE
+Gentoo,Biscoe,49.6,15,216,4750,MALE
+Gentoo,Biscoe,50.5,15.9,222,5550,MALE
+Gentoo,Biscoe,43.6,13.9,217,4900,FEMALE
+Gentoo,Biscoe,45.5,13.9,210,4200,FEMALE
+Gentoo,Biscoe,50.5,15.9,225,5400,MALE
+Gentoo,Biscoe,44.9,13.3,213,5100,FEMALE
+Gentoo,Biscoe,45.2,15.8,215,5300,MALE
+Gentoo,Biscoe,46.6,14.2,210,4850,FEMALE
+Gentoo,Biscoe,48.5,14.1,220,5300,MALE
+Gentoo,Biscoe,45.1,14.4,210,4400,FEMALE
+Gentoo,Biscoe,50.1,15,225,5000,MALE
+Gentoo,Biscoe,46.5,14.4,217,4900,FEMALE
+Gentoo,Biscoe,45,15.4,220,5050,MALE
+Gentoo,Biscoe,43.8,13.9,208,4300,FEMALE
+Gentoo,Biscoe,45.5,15,220,5000,MALE
+Gentoo,Biscoe,43.2,14.5,208,4450,FEMALE
+Gentoo,Biscoe,50.4,15.3,224,5550,MALE
+Gentoo,Biscoe,45.3,13.8,208,4200,FEMALE
+Gentoo,Biscoe,46.2,14.9,221,5300,MALE
+Gentoo,Biscoe,45.7,13.9,214,4400,FEMALE
+Gentoo,Biscoe,54.3,15.7,231,5650,MALE
+Gentoo,Biscoe,45.8,14.2,219,4700,FEMALE
+Gentoo,Biscoe,49.8,16.8,230,5700,MALE
+Gentoo,Biscoe,46.2,14.4,214,4650,NA
+Gentoo,Biscoe,49.5,16.2,229,5800,MALE
+Gentoo,Biscoe,43.5,14.2,220,4700,FEMALE
+Gentoo,Biscoe,50.7,15,223,5550,MALE
+Gentoo,Biscoe,47.7,15,216,4750,FEMALE
+Gentoo,Biscoe,46.4,15.6,221,5000,MALE
+Gentoo,Biscoe,48.2,15.6,221,5100,MALE
+Gentoo,Biscoe,46.5,14.8,217,5200,FEMALE
+Gentoo,Biscoe,46.4,15,216,4700,FEMALE
+Gentoo,Biscoe,48.6,16,230,5800,MALE
+Gentoo,Biscoe,47.5,14.2,209,4600,FEMALE
+Gentoo,Biscoe,51.1,16.3,220,6000,MALE
+Gentoo,Biscoe,45.2,13.8,215,4750,FEMALE
+Gentoo,Biscoe,45.2,16.4,223,5950,MALE
+Gentoo,Biscoe,49.1,14.5,212,4625,FEMALE
+Gentoo,Biscoe,52.5,15.6,221,5450,MALE
+Gentoo,Biscoe,47.4,14.6,212,4725,FEMALE
+Gentoo,Biscoe,50,15.9,224,5350,MALE
+Gentoo,Biscoe,44.9,13.8,212,4750,FEMALE
+Gentoo,Biscoe,50.8,17.3,228,5600,MALE
+Gentoo,Biscoe,43.4,14.4,218,4600,FEMALE
+Gentoo,Biscoe,51.3,14.2,218,5300,MALE
+Gentoo,Biscoe,47.5,14,212,4875,FEMALE
+Gentoo,Biscoe,52.1,17,230,5550,MALE
+Gentoo,Biscoe,47.5,15,218,4950,FEMALE
+Gentoo,Biscoe,52.2,17.1,228,5400,MALE
+Gentoo,Biscoe,45.5,14.5,212,4750,FEMALE
+Gentoo,Biscoe,49.5,16.1,224,5650,MALE
+Gentoo,Biscoe,44.5,14.7,214,4850,FEMALE
+Gentoo,Biscoe,50.8,15.7,226,5200,MALE
+Gentoo,Biscoe,49.4,15.8,216,4925,MALE
+Gentoo,Biscoe,46.9,14.6,222,4875,FEMALE
+Gentoo,Biscoe,48.4,14.4,203,4625,FEMALE
+Gentoo,Biscoe,51.1,16.5,225,5250,MALE
+Gentoo,Biscoe,48.5,15,219,4850,FEMALE
+Gentoo,Biscoe,55.9,17,228,5600,MALE
+Gentoo,Biscoe,47.2,15.5,215,4975,FEMALE
+Gentoo,Biscoe,49.1,15,228,5500,MALE
+Gentoo,Biscoe,47.3,13.8,216,4725,NA
+Gentoo,Biscoe,46.8,16.1,215,5500,MALE
+Gentoo,Biscoe,41.7,14.7,210,4700,FEMALE
+Gentoo,Biscoe,53.4,15.8,219,5500,MALE
+Gentoo,Biscoe,43.3,14,208,4575,FEMALE
+Gentoo,Biscoe,48.1,15.1,209,5500,MALE
+Gentoo,Biscoe,50.5,15.2,216,5000,FEMALE
+Gentoo,Biscoe,49.8,15.9,229,5950,MALE
+Gentoo,Biscoe,43.5,15.2,213,4650,FEMALE
+Gentoo,Biscoe,51.5,16.3,230,5500,MALE
+Gentoo,Biscoe,46.2,14.1,217,4375,FEMALE
+Gentoo,Biscoe,55.1,16,230,5850,MALE
+Gentoo,Biscoe,44.5,15.7,217,4875,.
+Gentoo,Biscoe,48.8,16.2,222,6000,MALE
+Gentoo,Biscoe,47.2,13.7,214,4925,FEMALE
+Gentoo,Biscoe,NA,NA,NA,NA,NA
+Gentoo,Biscoe,46.8,14.3,215,4850,FEMALE
+Gentoo,Biscoe,50.4,15.7,222,5750,MALE
+Gentoo,Biscoe,45.2,14.8,212,5200,FEMALE
+Gentoo,Biscoe,49.9,16.1,213,5400,MALE

utils.py

@@ -4,15 +4,45 @@ import matplotlib as mpl
 from matplotlib import cm
 import gravis as gv
 import networkx as nx
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.impute import SimpleImputer
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
 
 
+def get_only_features_names(name):
+    return name[5:]
+
 def clean_csv_file(csv_file):
+
     df = pd.read_csv(csv_file)
 
-    df.dropna(inplace=True)
-    df.drop_duplicates(inplace=True)
+    num_cols = df.select_dtypes(include=['float64', 'int64']).columns
+    cat_cols = df.select_dtypes(include=['object']).columns
+
+    numeric_transformer = Pipeline(steps=[
+        ('imputer', SimpleImputer(strategy='mean')),
+        ('scaler', StandardScaler())
+    ])
+
+    categorical_transformer = Pipeline(steps=[
+        ('imputer', SimpleImputer(strategy='most_frequent')),
+        ('onehot', OneHotEncoder(handle_unknown='ignore'))
+    ])
+
+    preprocessor = ColumnTransformer(
+        transformers=[
+            ('num', numeric_transformer, num_cols),
+            ('cat', categorical_transformer, cat_cols)
+            ])
+
+    pipeline = Pipeline(steps=[('preprocessor', preprocessor)])
+
+    transform = pipeline.fit_transform(df)
+
+    df_transform = pd.DataFrame(data=transform, columns=list(map(get_only_features_names, preprocessor.get_feature_names_out())))
 
-    return df
+    return df_transform
 
 
 def build_graph(csv_file, *, threshold, corr_type):