Smoothing time series in Pandas
To make time series data more smooth in Pandas, we can use the exponentially weighted window functions and calculate the exponentially weighted average.
First, I am going to load a dataset which contains Bitcoin prices recorded every minute.
1 2 3 4 5 6 7 data = pd.read_csv('../input/bitstampUSD_1-min_data_2012-01-01_to_2019-03-13.csv') data['date'] = pd.to_datetime(data['Timestamp'], unit="s") input_data = data[["date", "Close"]] subset = input_data[input_data["date"] >= "2019-01-01"] subset.set_index('date', inplace=True)
I want to plot their daily weighted average, so I must compress 3600 values into one using this function:
1 subset['Close'].ewm(span = 3600).mean()
We see that by default the adjusted version of the weighted average function is used, so the first element of the time series is not 0.
Finally, I can plot the original data and both the smoothed time series:
1 2 3 4 5 6 7 subset['Close'].plot(style = 'r--', label = 'Bitcoin prices') subset['Close'].ewm(span = 3600).mean().plot(style = 'b', label = ' Exponential moving average') plt.legend() plt.title("Bitcoin prices") plt.xlabel('Date') plt.ylabel('Price (USD)')
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