Answers for "python rolling average"

rolling average df

df['pandas_SMA_3'] = df.iloc[:,1].rolling(window=3).mean()

python rolling mean

#Create a rolling window of 10 sequential values
data.rolling(window=10).mean()

python moving average of list

import numpy
def running_mean(x, N):
  """ x == an array of data. N == number of samples per average """
    cumsum = numpy.cumsum(numpy.insert(x, 0, 0)) 
    return (cumsum[N:] - cumsum[:-N]) / float(N)
  
val = [-30.45, -2.65, 56.61, 47.13, 47.95, 30.45, 2.65, -28.31, -47.13, -95.89]
print(running_mean(val, 3))
""" [  7.83666667  33.69666667  50.56333333  41.84333333  27.01666667
   1.59666667 -24.26333333 -57.11      ]	"""

moving averages python

# option 1

df['data'].rolling(3).mean()
df['data'].shift(periods=1).rolling(3).mean()

# option 2: compute a 9-day simple moving average with pandas

BTC_USD['SMA_9'] = BTC_USD['Close'].rolling(window=9, min_periods=1).mean()