Followed this Facebook prophet tutorial https://www.digitalocean.com/community/tutorials/a-guide-to-time-series-forecasting-with-prophet-in-python-3 and adopted it slightly for PyCharm

gistfile1.txt

import pandas as pd
from fbprophet import Prophet

import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')

df = pd.read_csv('D:/PyCharmProjects/Prophet/Data/AirPassengers.csv')
df['Month'] = pd.DatetimeIndex(df['Month'])
#Prophet also imposes the strict condition that the input columns be named ds (the time column) and y (the metric column)
df = df.rename(columns={'Month': 'ds',
                        'AirPassengers': 'y'})

print(df.head(5))

print(df.dtypes)

my_model = Prophet()
my_model.fit(df)

future_dates = my_model.make_future_dataframe(periods=36, freq='MS')
future_dates.tail()

forecast = my_model.predict(future_dates)
forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()

my_model.plot(forecast,
              uncertainty=True)
plt.show()