please use python and i will import all csv need files(VERY IMPORTANT)

5. Produce a new data frame (with a correct pandas time series index and the original column names) by applying the specified mathematical transformation to each variable in the data. 6. Standardise each transformed variable from (Q5) above by subtracting its own time series mean and then dividing by its own time series standard deviation. Fill any missing values in the standardised data with a zero. Write this table to file ('transformed_data. csv') with a correct Pandas time series index and using the original variable names as column names. Using the standardised data from (Q6) produce a PCA analysis. Call the function (provided for you in the template.py file) pca_function (), passing in your standardised dataset from Q6. 3. Produce a histogram of the distribution of the factor, and a plot of the time series of the factor (both in one figure) and save this to a pdf file ('factor.pdf'). Note that the command plt.tight_layout () can be called after you have drawn your plot and added a title so as to neatly arrange the axis/ titles so that they do not overlap