Python Pandas: Calculate moving average within group
You can use rolling with transform: df[‘moving’] = df.groupby(‘object’)[‘value’].transform(lambda x: x.rolling(10, 1).mean()) The 1 in rolling is for minimum number of periods.
moving-average
Apache Spark Moving Average
You can use the sliding function from MLLIB which probably does the same thing as Daniel’s answer. You will have to sort the data by time before using the sliding function. import org.apache.spark.mllib.rdd.RDDFunctions._ sc.parallelize(1 to 100, 10) .sliding(3) .map(curSlice => (curSlice.sum / curSlice.size)) .collect()
pyspark: rolling average using timeseries data
I figured out the correct way to calculate a moving/rolling average using this stackoverflow: Spark Window Functions – rangeBetween dates The basic idea is to convert your timestamp column to seconds, and then you can use the rangeBetween function in the pyspark.sql.Window class to include the correct rows in your window. Here’s the solved example: … Read more
Smoothing values over time: moving average or something better?
If you are trying to remove the occasional odd value, a low-pass filter is the best of the three options that you have identified. Low-pass filters allow low-speed changes such as the ones caused by rotating a compass by hand, while rejecting high-speed changes such as the ones caused by bumps on the road, for … Read more
Calculate rolling / moving average in C++
If your needs are simple, you might just try using an exponential moving average. http://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average Put simply, you make an accumulator variable, and as your code looks at each sample, the code updates the accumulator with the new value. You pick a constant “alpha” that is between 0 and 1, and compute this: accumulator = … Read more
How to efficiently compute average on the fly (moving average)?
Your solution is essentially the “standard” optimal online solution for keeping a running track of average without storing big sums and also while running “online”, i.e. you can just process one number at a time without going back to other numbers, and you only use a constant amount of extra memory. If you want a … Read more
Understanding NumPy’s Convolve
Convolution is a mathematical operator primarily used in signal processing. Numpy simply uses this signal processing nomenclature to define it, hence the “signal” references. An array in numpy is a signal. The convolution of two signals is defined as the integral of the first signal, reversed, sweeping over (“convolved onto”) the second signal and multiplied … Read more
Moving Average Pandas
The rolling mean returns a Series you only have to add it as a new column of your DataFrame (MA) as described below. For information, the rolling_mean function has been deprecated in pandas newer versions. I have used the new method in my example, see below a quote from the pandas documentation. Warning Prior to … Read more
How to calculate moving average without keeping the count and data-total?
You can simply do: double approxRollingAverage (double avg, double new_sample) { avg -= avg / N; avg += new_sample / N; return avg; } Where N is the number of samples where you want to average over. Note that this approximation is equivalent to an exponential moving average. See: Calculate rolling / moving average in … Read more
How to calculate rolling / moving average using python + NumPy / SciPy?
If you just want a straightforward non-weighted moving average, you can easily implement it with np.cumsum, which may be is faster than FFT based methods: EDIT Corrected an off-by-one wrong indexing spotted by Bean in the code. EDIT def moving_average(a, n=3) : ret = np.cumsum(a, dtype=float) ret[n:] = ret[n:] – ret[:-n] return ret[n – 1:] … Read more