Often when visualizing time-series data, it makes sense to look not at day-by-day changes, but
rather at a 7 -day rolling average. For example, when looking at Covid-19 cases, there are
reporting fluctuations from day to day, so instead it is usually better to look at the average number
of cases over the previous 7 days. Write a function def rolling( A, windowe7): which takes as input
a one-dimensional numpy array A, and a parameter window whose default value is 7. The function
must retum an array R whose length is shorter than the length of A. The entry R[] should be the
average of A[J]. A[j+1], etc, up to A[j+window1]. Then use this function and matplotib to produce a
plot: a line plot of the array A produced below, and a line plot of the 7 -day rolling average R
produced by your function. These plots should be on the same figure, and clearly labeled. [] " Here
is the code to produce your test array A. II Please use this array in your ploti x _array = np-
arange(3ee) A=5(x_array/200)24 Please use this code to inport anatiotilb. inport natplotilbapyplot
as plt I [ ] Were is a plot of the array, to get you started. plt.plot (xarray,A) [ ].
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
Often when visualizing timeseries data it makes sense to l.pdf
1. Often when visualizing time-series data, it makes sense to look not at day-by-day changes, but
rather at a 7 -day rolling average. For example, when looking at Covid-19 cases, there are
reporting fluctuations from day to day, so instead it is usually better to look at the average number
of cases over the previous 7 days. Write a function def rolling( A, windowe7): which takes as input
a one-dimensional numpy array A, and a parameter window whose default value is 7. The function
must retum an array R whose length is shorter than the length of A. The entry R[] should be the
average of A[J]. A[j+1], etc, up to A[j+window1]. Then use this function and matplotib to produce a
plot: a line plot of the array A produced below, and a line plot of the 7 -day rolling average R
produced by your function. These plots should be on the same figure, and clearly labeled. [] " Here
is the code to produce your test array A. II Please use this array in your ploti x _array = np-
arange(3ee) A=5(x_array/200)24 Please use this code to inport anatiotilb. inport natplotilbapyplot
as plt I [ ] Were is a plot of the array, to get you started. plt.plot (xarray,A) [ ]
![Often when visualizing time-series data, it makes sense to look not at day-by-day changes, but
rather at a 7 -day rolling average. For example, when looking at Covid-19 cases, there are
reporting fluctuations from day to day, so instead it is usually better to look at the average number
of cases over the previous 7 days. Write a function def rolling( A, windowe7): which takes as input
a one-dimensional numpy array A, and a parameter window whose default value is 7. The function
must retum an array R whose length is shorter than the length of A. The entry R[] should be the
average of A[J]. A[j+1], etc, up to A[j+window1]. Then use this function and matplotib to produce a
plot: a line plot of the array A produced below, and a line plot of the 7 -day rolling average R
produced by your function. These plots should be on the same figure, and clearly labeled. [] " Here
is the code to produce your test array A. II Please use this array in your ploti x _array = np-
arange(3ee) A=5(x_array/200)24 Please use this code to inport anatiotilb. inport natplotilbapyplot
as plt I [ ] Were is a plot of the array, to get you started. plt.plot (xarray,A) [ ]](https://image.slidesharecdn.com/oftenwhenvisualizingtimeseriesdataitmakessensetol-230322045912-fb92a805/85/Often-when-visualizing-timeseries-data-it-makes-sense-to-l-pdf-1-320.jpg)
