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.
Do you want to show your product/service to 25000 data science enthusiasts every month? I am looking for companies which would like to become a partner of this blog.
Are you interested? Is your employer interested? Here are the details of the offer.
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)')
Remember to share on social media! If you like this text, please share it on Facebook/Twitter/LinkedIn/Reddit or other social media.
If you watch programming live streams, check out my YouTube channel.
You can also follow me on Twitter: @mikulskibartosz
If you want to hire me, send me a message on LinkedIn or Twitter.
