To bin a column with Python Pandas, we can use the cut method.
For instance, we werite
bins = [0, 1, 5, 10, 25, 50, 100]
df['binned'] = pd.cut(df['percentage'], bins)
to add the binned column by calling cut with the data frame df‘s percentage column with the bins.
To sort a dataFrame in Python Pandas by two or more columns, we call sort_values with the ascending argument.
For instance, we write
df.sort_values(['a', 'b'], ascending=[True, False])
to call sort_values on the df data frame with an array of columns to sort as the first argument.
And we set ascending to an array of booleans to sort the columns in the order listed for each column.
To change the order of Python Pandas DataFrame columns, we can pyut the columns in a different order in a brackets.
For instance, we write
df = df[['mean', '0', '1', '2', '3']]
to add an array with the column names we want in the brackets to reorder them as listed.
And then we reassign the returned dataframe to df.
To create a cartesian product in Python Pandas, we call the data frame merge method with how set to 'cross'.
For instance, we write
from pandas import DataFrame
df1 = DataFrame({'col1':[1,2],'col2':[3,4]})
df2 = DataFrame({'col3':[5,6]})
df1.merge(df2, how='cross')
to call df1.merge with df2 and the how argument set to 'cross' to return cartesian product between df1 and df2.
To select multiple columns in a Python Pandas dataframe, we can use df.loc.
For instance, we write
df.loc[:, 'C':'E']
to select all columns between 'C' and 'E' inclusive.