<jupyter_start><jupyter_text>Table of ContentsTime series for demand prediction  Time series for demand prediction<jupyter_code>%matplotlib inline
import matplotlib.pyplot as plt

import numpy as np
import seaborn as sns
from h3 import h3

from pylab import rcParams
rcParams['figure.figsize'] = (10, 7)

import pandas as pd
pd.set_option('max_columns', 40)
pd.set_option('max_rows', 20)
pd.set_option('precision', 3)
%%time

data = pd.read_feather('clean_data.feather', columns=[
                       'datetime', 'lon', 'lat', 'h3_res8'])

data = data.sample(100000)

data.head()
from tools import date_counts

# plt.figure(figsize=(15, 5))

pd.options.plotting.backend = 'matplotlib'
vals = date_counts(data, date_index=False)
vals.plot();<jupyter_output><empty_output>
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