from pathlib import Path import numpy as np import pandas as pd from dataset import data import matplotlib.pyplot as plt import seaborn as sns from scipy import stats import matplotlib.style as style %matplotlib inline style.use("fivethirtyeight") style_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']

d = data() d

items_clicked = d.load("Items Clicked") items_on_cart = d.load("Items put on cart")

items_clicked["TIME_DIM_KEY"] = pd.to_datetime(items_clicked["TIME_DIM_KEY"],format="%Y%m%d")

items_clicked

clicks_per_day = items_clicked["TIME_DIM_KEY"].value_counts() clicks_per_day

len(clicks_per_day)

summary_stats = clicks_per_day.describe() summary_stats

fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize=(20,5)) ax1.hist(x=clicks_per_day) sns.boxplot(x=clicks_per_day,ax=ax2) plt.show()

#Sort the number of clicks per day clicks_per_day_sorted = clicks_per_day.sort_values(ascending=False) clicks_per_day_sorted.index= clicks_per_day_sorted.index.map(lambda x: x.strftime('%Y-%m-%d'))

threshold = summary_stats["75%"]+(1.5*stats.iqr(clicks_per_day)) is_outlier = clicks_per_day_sorted>threshold is_outlier.sum()

fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize=(20,5)) is_outlier2 = clicks_per_day>threshold ax1.hist(clicks_per_day[~is_outlier2]) sns.boxplot(x=clicks_per_day[~is_outlier2],ax=ax2) plt.show()

fig, ax = plt.subplots(figsize=(15,10)) ax.bar(height=clicks_per_day_sorted[is_outlier],x=clicks_per_day_sorted[is_outlier].index,label="outlier",color=style_colors[1]) ax.bar(height=clicks_per_day_sorted[~is_outlier],x=clicks_per_day_sorted[~is_outlier].index,color=style_colors[0],label="non outlier") for location in ['left', 'right', 'bottom', 'top']: ax.spines[location].set_visible(False) ax.set_xticks([]) ax.tick_params(pad=5) ax.legend(prop={"size":20}) ax.set_xlabel("Days",fontsize=20,labelpad=30) ax.set_ylabel("Clicks",fontsize=20,labelpad=30) ax.set_title("Days With Most Clicks") plt.show()