Week 9 glossary

Week 9 glossary#

Online resources#

Scipy’s documentation on linregress() can be found at https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html.

For a more thorough guide of the “group-by” capability of pandas, see https://pandas.pydata.org/docs/user_guide/groupby.html

Functions from scipy.stats#

  • scipy.stats.linregress(): function for performing linear regression

Pandas group summarization methods#

  • <DataFrame>.groupby(): group rows of a DataFrame for summarization. The result is a pandas DataFrameGroupBy object

  • <DataFrameGroupBy>.count(): count the number of non-NA values in each group

  • <DataFrameGroupBy>.max(): Compute the maximum value in each group

  • <DataFrameGroupBy>.min(): Compute the minimum value in each group

  • <DataFrameGroupBy>.mean(): Compute the mean of each group

  • <DataFrameGroupBy>.median(): Compute the median of each group

  • <DataFrameGroupBy>.nunique(): Compute the number of unique values in each group

  • <DataFrameGroupBy>.quantile(): Compute a given quantile of the values in each group

  • <DataFrameGroupBy>.sem(): Compute the standard error of the mean of the values in each group

  • <DataFrameGroupBy>.std(): Compute the standard deviation of the values in each group

  • <DataFrameGroupBy>.var(): Compute the variance of the values in each group

  • <Series>.astype(): coerce the conversion of data type of a pandas series

Matplotlib methods for multi-panel plot#

  • <Figure>.add_subplot(): add a subplot to a matplotlib Figure instance, possibly creating a multi-panel figure.

  • <Figure>.subplots_adjust(): adjust the spacing between subplots of a figure

  • <Figure>.supxlabel(): add an overall horizontal (x-)axis label to the multi-panel figure

  • <Figure>.supxlabel(): add an overall vertical (y-)axis label to the multi-panel figure

  • <figure>.suptitle(): add an overall title to the multi-panel figure

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