Find the most relevant columns for each single class in pandas
In the end, this is what I've found around the internet. Really helpful

def best_features(df,target):
    features = list(df.columns)
    features.remove(target)

    y = df[target]
    X=df[features]
    #apply SelectKBest class to extract top 10 best features
    bestfeatures = SelectKBest(score_func=chi2, k=15)
    fit = bestfeatures.fit(X,y)
    dfscores = pd.DataFrame(fit.scores_)
    dfcolumns = pd.DataFrame(X.columns)
    #concat two dataframes for better visualization 
    featureScores = pd.concat([dfcolumns,dfscores],axis=1)
    featureScores.columns = ['Specs','Score']  #naming the dataframe columns
    return featureScores.nlargest(15,'Score')  #print 10 best features


def worst_features(df,target):
    features = list(df.columns)
    features.remove(target)

    y = df[target]
    X=df[features]
    #apply SelectKBest class to extract top 10 best features
    bestfeatures = SelectKBest(score_func=chi2, k='all')
    fit = bestfeatures.fit(X,y)
    dfscores = pd.DataFrame(fit.scores_)
    dfcolumns = pd.DataFrame(X.columns)
    #concat two dataframes for better visualization 
    featureScores = pd.concat([dfcolumns,dfscores],axis=1)
    featureScores.columns = ['Specs','Score']  #naming the dataframe columns
    return featureScores.nsmallest(15,'Score')  #print 10 best features

def largest(df,target):
    features = list(df.columns)
    features.remove(target)

    y = df[target]
    X=df[features]
    model = ExtraTreesClassifier()
    model.fit(X,y)
    feat_importances = pd.Series(model.feature_importances_, index=X.columns)
    feat_importances.nlargest(15).plot(kind='barh')
    plt.show()
    print(feat_importances.nlargest(15).index)

def smallest(df,target):
    features = list(df.columns)
    features.remove(target)

    y = df[target]
    X=df[features]
    model = ExtraTreesClassifier()
    model.fit(X,y)
    feat_importances = pd.Series(model.feature_importances_, index=X.columns)
    feat_importances.nsmallest(15).plot(kind='barh')
    plt.show()
    return feat_importances.nsmallest(15).index.to_list()