python moving average time series
#Creating a 100 day moving average from 'df'
df.rolling(100).mean()python moving average time series
#Creating a 100 day moving average from 'df'
df.rolling(100).mean()moving average numpy
def moving_average(a, n=3) :
ret = np.cumsum(a, dtype=float)
ret[n:] = ret[n:] - ret[:-n]
return ret[n - 1:] / n
>>> a = np.arange(20)
>>> moving_average(a)
array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11.,
12., 13., 14., 15., 16., 17., 18.])
>>> moving_average(a, n=4)
array([ 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5])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 ] """python plotting moving average
#Plot the moving average that is the result of the rolling window
r90 = data.rolling(window=90).mean() #<---- Moving Average
data.join(r90.add_suffix(' MA 90')).plot(figsize=(8,8))
plt.show()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()12 month movinf average in python for dataframe
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
%matplotlib inline
import seaborn as sns
sns.set(style='darkgrid', context='talk', palette='Dark2')
my_year_month_fmt = mdates.DateFormatter('%m/%y')
data = pd.read_pickle('./data.pkl')
data.head(10)Copyright © 2021 Codeinu
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