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Official Data

Official Data#

import os
os.chdir("../../")
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

from src.tourism.ts_utils import *

Official and Aviation (Seats Arrivals) from 2019#

Hide code cell source 
def process_data(path):
    df = pd.read_csv(path).drop("Unnamed: 0", axis=1)
    df.columns = [col.lower() for col in df.columns]
    if "date" not in df.columns:
        df["date"] = [
            str(int(y)) + "-" + str(int(m)) for y, m in zip(df.year, df.month)
        ]
    df["date"] = pd.to_datetime(df["date"])

    return df


def draw_plot(df, country, ax):
    temp = df[["date", "total", "seats_arrivals_total"]]
    sns.lineplot(x="date", y="total", data=temp, ax=ax)
    sns.lineplot(x="date", y="seats_arrivals_total", data=temp, ax=ax)
    ax.axvline(pd.Timestamp(2020, 3, 11), color="r", linestyle="--")
    ax.set_title(str(country).upper().replace("_", " "))
    ax.tick_params(axis='x', rotation=45)
    return ax


countries = ["palau", "solomon_islands", "vanuatu", "samoa", "tonga"]
fig, ax = plt.subplots(2, 3, figsize=(12, 9), sharex=True)
for idx, country in enumerate(countries):
    path = os.getcwd() + "/data/tourism/" + str(country) + \
        "/intermediate/" + str(country) + "_merged.csv"
    data = process_data(path)
    draw_plot(data, country, ax[idx // 3, idx % 3])
ax[1, 2].remove()
plt.tight_layout()
plt.show()
../_images/bb81be365ade9bd9e86675f83ba0f408bfff02fef2a95f783cba9cfdcb1a4b6c.png

Stationarity Table for Official Statistics#

Hide code cell source 
adf_dfs = pd.DataFrame()
for country in countries:
    path = os.getcwd() + "/data/tourism/" + str(country) + \
        "/intermediate/" + str(country) + "_monthly_visitor.csv"
    data = process_data(path)
    adf_df = pd.DataFrame(adf_test(data["total"]), columns=[country.upper().replace("_", "")]).T
    adf_dfs = pd.concat([adf_dfs, adf_df], axis=0)
    
display(adf_dfs)
Test Statistic p-value # Lags Used Number of Observations Used Critical Value (1%) Critical Value (5%) Critical Value (10%)
PALAU -1.230261 0.660444 13.0 170.0 -3.469413 -2.878696 -2.575917
SOLOMONISLANDS -2.602577 0.092466 3.0 140.0 -3.477945 -2.882416 -2.577902
VANUATU -2.425354 0.134708 13.0 211.0 -3.461727 -2.875337 -2.574124
SAMOA -3.403589 0.010837 14.0 227.0 -3.459490 -2.874358 -2.573602
TONGA -2.094926 0.246560 14.0 129.0 -3.482088 -2.884219 -2.578864
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