Data Analysis and Wrangling in Pandas

This course covers the essentials of pandas, the base Python library for working with large data sets. The library incorporates features familiar to Excel users and builds on the Numpy library covered in earlier courses in this series.

Suitability

Tech Analysts, S&T Analysts (optionally)

Learning Outcomes

By the end of this course, participants should be able to:rn• Read and write data into / out of pandasrn• deploy pandas’ basic analytical tools,rn• explore how pandas extends NumPy’s analytical functionality,rn• interrogate a simple data set to extract key numbers and create a simple graphical output.

Course Content

• Overview: spreadsheets and data frames: how pandas is the best of excel and Numpy in one package.rn• Loading data into pandasrno performing initial checks on size, shape of dataset.rno Using simple graphing to identify null values in the dataset and taking action with pandas to ‘clean’ the data.rn• Workshop 1: import a csv file of stock prices into pandas and output basic statistical values.rn• From prices to returns – pct_change() and non-stationarity: working with rolling means, variances.rn• Workshop 2: compare performance and risk of 2 securities’ prices.rn• Iterating through rows and columns in a data frame.rn• Workshop 3: find minimum, maximum values for a number of parameters across a portfolio of stocks.

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