1 files changed, 64 insertions(+), 0 deletions(-)

A tickets-per-day-of-month.py

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+#
+# How many tickets are issued in Boston per day of month?
+#
+import matplotlib.pyplot as plt
+
+import pandas as pd
+import numpy as np
+import utils
+
+data = utils.data
+#data.info()
+
+bdom = data.groupby(pd.Grouper(key="Issued", freq="D"))["Issued"].count()
+fig, ax = plt.subplots(figsize=(10, 4))
+
+# drop day 31 (there are only 7 months)
+b31 = bdom[bdom.index.day == 31]
+bdom.drop(b31.index, inplace=True)
+
+# drop 2020 data
+covid = bdom[bdom.index.year == 2020]
+bdom.drop(covid.index, inplace=True)
+
+# avg, +- std
+med = bdom.groupby(bdom.index.day).median()
+std = bdom.groupby(bdom.index.day).std()
+
+plt.fill_between(med.index, med-std, med+std, facecolor="white")
+# plt.plot(med.index, med, color="white")
+
+# fit trend line
+# fit = np.poly1d(np.polyfit(bdom.index.day, bdom.values, 1))
+# plt.plot(bdom.index.day, fit(bdom.index.day), color='black')
+
+# plot each data point (add in covid)
+plt.scatter(covid.index.day, covid.values, color="tab:red", alpha=0.2)
+plt.scatter(bdom.index.day, bdom.values, color="black", alpha=0.2)
+
+
+plt.xticks(range(1, 31))
+
+ax.set(
+    title="Tickets Issued on Day of Month",
+    ylabel="Tickets Issued"
+)
+
+plt.tight_layout()
+plt.savefig(
+    utils.FIG_DIR / "tickets-by-day-of-month.svg",
+    transparent=True)
+
+
+# Why so many low values?
+low = bdom[bdom.values < 1000]
+
+# faily evenly spaced out between months
+#low.groupby(low.index.month).count()
+
+# and by year
+#low.groupby(low.index.year).count()
+
+# However, day of week shows that 80% 459 of the days
+# fall on a Sunday, when a majority of meters are inactive
+#low.groupby(low.index.dayofweek).count()