#Some useful math operations!
##PLEASE NOTE: This is not a stats class (though the CCBB does offer a peer-led stats working group!). We're just giving you tools, it's up to you to ensure that you apply them appropriately. The SSC offers free statistics consulting. If you're unsure, ask for help!
| Function | Use | Example | Need to import |
|---|---|---|---|
| tstat, pval = stats.ttest_ind(list1, list2) | Do a simple t-test and get a p-value | tstat, pval = stats.ttest_ind(subset1, subset2) | from scipy import stats |
| npr.permutation(object) | Randomly permute the data object | npr.permutation(subset2) | from numpy import random as npr |
| object.mean() | Get mean of object | subset2.mean() | import numpy |
| object1.cov(object2) | Estimate coefficient of covariance between two objects | subset1.cov(subset2) | import pandas |
| object1.corr(oject2, method='spearman') | Perform a Kendall, Spearman or Pearson correlation test | subset1.corr(subset2) | import pandas |
| rolling_mean(data frame, window).plot(style='k') | Get and plot a rolling mean in your data frame with window size x. Plot it | rolling_mean(df, 1).plot(style='k') | import pandas |
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