qcut. The pandas documentation describes qcut as a “Quantile-based discretization function.” This basically means that qcut tries to divide up the underlying data into equal sized bins. The function defines the bins using percentiles based on the distribution of the data, not the actual numeric edges of the bins. pandas.cut¶ pandas.cut (x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates='raise') [source] ¶ Bin values into discrete intervals. Use cut when you need to segment and sort data values into bins. This function is also useful for going from a continuous variable to a categorical variable.

*qcut. The pandas documentation describes qcut as a “Quantile-based discretization function.” This basically means that qcut tries to divide up the underlying data into equal sized bins. The function defines the bins using percentiles based on the distribution of the data, not the actual numeric edges of the bins.*Most pandas methods return a DataFrame so that another pandas method can be applied to the result. This improves readability of code. df = (pd.melt(df) .rename(columns={ 'variable' : 'var', 'value' : 'val'}) .query('val >= 200') ) df[df.Length > 7] Extract rows that meet logical criteria. qcut. The pandas documentation describes qcut as a “Quantile-based discretization function.” This basically means that qcut tries to divide up the underlying data into equal sized bins. The function defines the bins using percentiles based on the distribution of the data, not the actual numeric edges of the bins. pandas.cut¶ pandas.cut (x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates='raise') [source] ¶ Bin values into discrete intervals. Use cut when you need to segment and sort data values into bins. This function is also useful for going from a continuous variable to a categorical variable.