Solved – Convert Daily Data to Monthly Data in Python : Time Series Analysis

Question

I am new to data analysis with python. I have daily data of flu cases for a five year period which I want to do Time Series Analysis on. Am using the Pandas library. It is easy to plot this data and see the trend over time, however now I want to see seasonality. As it is, the daily data when plotted is too dense (because it's daily) to see seasonality well and I would like to transform/convert the data (pandas DataFrame) into monthly data so I can better see seasonality. Is there an easy way to do this with pandas (or any other python data munging library)?

Answer 1

There are examples of doing what you want in the pandas documentation. In pandas the method is called resample.

monthly_x = x.resample('M')

Or this is an example of a monthly seasonal plot for daily data in statsmodels may be of interest.

import statsmodels.api as sm
import pandas as pd
dta = sm.datasets.elnino.load_pandas().data
dta['YEAR'] = dta.YEAR.astype(int).astype(str)
dta = dta.set_index('YEAR').T.unstack()
dates = map(lambda x : pd.datetools.parse('1 '+' '.join(x)),
                                       dta.index.values)
dta.index = pd.DatetimeIndex(dates, freq='M')
fig = sm.graphics.tsa.month_plot(dta)