Review of Python Courses (Part 7)

In Part 6, I summarized my Datacamp courses 16-18. Today I will continue with the next three.

As a reminder, I introduced you to my recent work learning Python here.

My course #19 was Manipulating DataFrames with pandas. This course covers:

• Indexing DataFrames (using square brackets, using .loc, using .iloc, selecting certain columns with [[ ]] )
• Slicing DataFrames (R boundary included with .loc but not .iloc, slicing with one/two brackets gets Series/df)
• Filtering DataFrames
• Transforming DataFrames [vectorized computations in numpy without loops, .map() for index, .apply() for Series]
• Indexed objects and labeled data (name attribute for index and columns attributes)
• Hierarchical indexing (sorting MultiIndex)
• Pivoting DataFrames [.pivot(index= , columns= , values= ) ]
• Stacking and unstacking DataFrames (pivoting doesn’t work well on MultiIndex so unstack to move index to column)
• Melting DataFrames [reverses .pivot() ]
• Pivot tables
• Categoricals and groupby
• Groupby and aggregation/transformation
• Iterating over and filtering groupby object
• Understanding the column labels
• .idxmax() and .idxmin() (row/column label where max/min value located)
• .T attribute (transposes numpy array)
• Reshaping DataFrames for visualization
• Making a histogram (bins, range, normalizing)

My twentieth course was Manipulating Time Series Data in Python. This course has lots of good information for backtesting:

• How to use dates and times with pandas ( [sequences of] timestamp and period objects)
• Indexing and resampling time series [selecting missing ‘price’ values, .asfreq() ]
• Lags, changes, and returns for stock price series [.shift(), n-period % chg, .diff(), .pct_change(), stock price chg in df]
• Compare time series growth rates ( .iloc as abs ref, normalizing series, concat prices and .dropna, perf vs. benchmark)
• Changing the time series frequency: resampling
• Upsampling and interpolation with .resample()
• Downsampling and aggregation (plotting resample data with ax)
• Rolling window functions with pandas (plotting price and moving average, plotting multiple rolling metrics)
• Expanding window functions with pandas (calculating running return, running rate of return)
• Relationships between time series: correlation
• Select index components and import data
• Build a market-cap weighted index
• Evaluate index performance
• Index correlation and exporting to Excel

My course #21 was Working with Dates and Times in Python. This course covers:

• Dates in Python
• Math with dates (time delta)
• Turning dates into strings
• Adding time to the mix
• Printing and parsing datetimes (no time printed from datetime object)
• Working with durations
• UTC offsets
• Time zone database (from dateutil import tz)
• Starting Daylight Saving Time
• Ending Daylight Saving Time [ .datetime_ambiguous() and .enfold() for ambiguous times]
• Reading date and time data in Pandas (loading datetimes with parse_dates [or manually with .to_datetime() ])
• Summarizing datetime data in Pandas (alternative to for loop)
• Additional datetime methods in Pandas
• Index correlation and exporting to Excel