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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 kochbar (7M) Results Analysis - MCMC - 100 iter-5Fold
Step1:  User Rating Average and Standard Deviation Histograms
Step2:  Item Rating Average and Standard Deviation Histograms
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Python Code:
Analyse('kochbar\\Output', ['kochbar_ds.','kochbar_ds2.','kochbar_ds3.','kochbar_ds4.','kochbar_ds5.','kochbar_ds6.','kochbar_ds7.'], 100)
print 'kochbar_ds - Rating + Users + Item\nkochbar_ds2 - Rating + Users + Item + AvgRatUser\nkochbar_ds3 - Rating + Users + Item + AvgRatUser + StdDevUser\nkochbar_ds4 - Rating + Users + Item + AvgRatItem\nkochbar_ds5 - Rating + Users + Item + AvgRatItem + StdDevItem\nkochbar_ds6 - Rating + Users + Item + AvgRatUser + AvgRatItem\nkochbar_ds7 - Rating + Users + Item + AvgRatUser + StdDevUser + AvgRatItem + StdDevItem\n'
Explanation: kochbar (7M) Results Analysis - MCMC - 100 iter-5Fold
End of explanation
user_stats = pd.read_csv('kochbar\\Input\\kochbar_ds.usr.avgstd', usecols=[0,1], sep='\t')
user_stats.hist(alpha=0.5, bins=100)
fig = plt.gcf()
fig.set_size_inches(13,9)
plt.show()
Explanation: User Rating Average and Standard Deviation Histograms
End of explanation
user_stats = pd.read_csv('kochbar\\Input\\kochbar_ds.item.avgstd', usecols=[0,1], sep='\t')
user_stats.hist(alpha=0.5, bins=100)
fig = plt.gcf()
fig.set_size_inches(13,9)
plt.show()
Explanation: Item Rating Average and Standard Deviation Histograms
End of explanation
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