올리비아 코딩스쿨

waffle def create_waffle_chart(categories, values, height, width, colormap, value_sign=''): # compute the proportion of each category with respect to the total total_values = sum(values) category_proportions = [(float(value) / total_values) for value in values] # compute the total number of tiles total_num_tiles = width * height # total number of tiles print ('Total number of tiles is', total_nu..

목차 pie chart pie chart를 만들기 위해서는 split, apply, combine 과정을 거친다. df_continents = df_can.groupby('Continent', axis=0).sum() 파이차트를 효과적으로 만들어줄 것들이 있다. autopct - is a string or function used to label the wedges with their numeric value. The label will be placed inside the wedge. If it is a format string, the label will be fmt%pct. - % 표시 startangle - rotates the start of the pie chart by angle degree..

area plot df.top5.plot(kind='area') df_top5.index = df_top5.index.map(int) df_top5.plot(kind='area', stacked=True, figsize=(20, 10)) # pass a tuple (x, y) size plt.title('Immigration Trend of Top 5 Countries') plt.ylabel('Number of Immigrants') plt.xlabel('Years') plt.show() histogram ### type your answer here df_cof =df_can.loc[['Greece', 'Albania', 'Bulgaria'], years] df_cof=df_cof.transpose()..

목차 why build visuals? -for exploratory data analysis -communicat data clearly -share unbiased representation of data to make a great visual - less is more effective, attractive, impactive! ex) darkhorseanalytics.com https://www.darkhorseanalytics.com/portfolio-all Portfolio — Darkhorse Analytics | Edmonton, AB Humanize Complex Data with Compelling Stories Lead people through complex data landsca..

목차 1. model evaluation in-sample evaluation tells us how well our model will fit the data used to train it. problem is that it's not sure how well the trained model can be worked to predict new data. Therefore we should make data splited, two ways in-sample data(training data) and out-of sample data(test set). training/testing sets train_test_split() x_train1, x_test1, y_train1, y_test1 = train_..