columns (akin to SQL’s *). In SQL, you can add a calculated column: SELECT *, tip/total_bill as tip_rate FROM tips; With pandas, you can use the DataFrame.assign() method of a DataFrame to append a new analysis toolkit, Release 1.3.2 In [7]: tips.assign(tip_rate=tips["tip"] / tips["total_bill"]) Out[7]: total_bill tip sex smoker day time size tip_rate 0 16.99 1.01 Female No Sun Dinner 2 0.059447 1 10 data you are working with. • If you are dealing with a time series that is growing at an increasing rate, method='quadratic' may be appropriate. • If you have values approximating a cumulative distribution

SELECT *, tip/total_bill as tip_rate FROM tips; With pandas, you can use the DataFrame.assign() method of a DataFrame to append a new column: In [7]: tips.assign(tip_rate=tips["tip"] / tips["total_bill"]) tips["total_bill"]) Out[7]: total_bill tip sex smoker day time size tip_rate 0 16.99 1.01 Female No Sun Dinner 2 0.059447 1 10.34 1.66 Male No Sun Dinner 3 0.160542 2 21.01 3.50 Male No Sun Dinner 3 0.166587 3 23 data you are working with. • If you are dealing with a time series that is growing at an increasing rate, method='quadratic' may be appro- priate. • If you have values approximating a cumulative distribution

SELECT *, tip/total_bill as tip_rate FROM tips; With pandas, you can use the DataFrame.assign() method of a DataFrame to append a new column: In [7]: tips.assign(tip_rate=tips["tip"] / tips["total_bill"]) tips["total_bill"]) Out[7]: total_bill tip sex smoker day time size tip_rate 0 16.99 1.01 Female No Sun Dinner 2 0.059447 1 10.34 1.66 Male No Sun Dinner 3 0.160542 2 21.01 3.50 Male No Sun Dinner 3 0.166587 3 23 data you are working with. • If you are dealing with a time series that is growing at an increasing rate, method='quadratic' may be appro- priate. • If you have values approximating a cumulative distribution

SELECT *, tip/total_bill as tip_rate FROM tips LIMIT 5; With pandas, you can use the DataFrame.assign() method of a DataFrame to append a new column: In [7]: tips.assign(tip_rate=tips['tip'] / tips['total_bill']) tips['total_bill']).head(5) Out[7]: total_bill tip sex smoker day time size tip_rate 0 16.99 1.01 Female No Sun Dinner 2 0.059447 1 10.34 1.66 Male No Sun Dinner 3 0.160542 2 21.01 3.50 Male No Sun Dinner 3 0 data you are working with. • If you are dealing with a time series that is growing at an increasing rate, method='quadratic' may be appropriate. • If you have values approximating a cumulative distribution

SELECT *, tip/total_bill as tip_rate FROM tips; With pandas, you can use the DataFrame.assign() method of a DataFrame to append a new column: In [7]: tips.assign(tip_rate=tips["tip"] / tips["total_bill"]) tips["total_bill"]) Out[7]: total_bill tip sex smoker day time size tip_rate 0 16.99 1.01 Female No Sun Dinner 2 0.059447 1 10.34 1.66 Male No Sun Dinner 3 0.160542 2 21.01 3.50 Male No Sun Dinner 3 0.166587 3 23 data you are working with. • If you are dealing with a time series that is growing at an increasing rate, method='quadratic' may be appro- priate. • If you have values approximating a cumulative distribution

SELECT *, tip/total_bill as tip_rate FROM tips; With pandas, you can use the DataFrame.assign() method of a DataFrame to append a new column: In [7]: tips.assign(tip_rate=tips["tip"] / tips["total_bill"]) tips["total_bill"]) Out[7]: total_bill tip sex smoker day time size tip_rate 0 16.99 1.01 Female No Sun Dinner 2 0.059447 1 10.34 1.66 Male No Sun Dinner 3 0.160542 2 21.01 3.50 Male No Sun Dinner 3 0.166587 3 23 data you are working with. • If you are dealing with a time series that is growing at an increasing rate, method='quadratic' may be appro- priate. • If you have values approximating a cumulative distribution

SELECT *, tip/total_bill as tip_rate FROM tips LIMIT 5; With pandas, you can use the DataFrame.assign() method of a DataFrame to append a new column: In [7]: tips.assign(tip_rate=tips['tip'] / tips['total_bill']) tips['total_bill']).head(5) Out[7]: total_bill tip sex smoker day time size tip_rate 0 16.99 1.01 Female No Sun Dinner 2 0.059447 1 10.34 1.66 Male No Sun Dinner 3 0.160542 2 21.01 3.50 Male No Sun Dinner 3 0 data you are working with. • If you are dealing with a time series that is growing at an increasing rate, method='quadratic' may be appropriate. • If you have values approximating a cumulative distribution

SELECT *, tip/total_bill as tip_rate FROM tips LIMIT 5; With pandas, you can use the DataFrame.assign() method of a DataFrame to append a new column: In [7]: tips.assign(tip_rate=tips['tip'] / tips['total_bill']) tips['total_bill']).head(5) Out[7]: total_bill tip sex smoker day time size tip_rate 0 16.99 1.01 Female No Sun Dinner 2 0.059447 1 10.34 1.66 Male No Sun Dinner 3 0.160542 2 21.01 3.50 Male No Sun Dinner 3 0 data you are working with. • If you are dealing with a time series that is growing at an increasing rate, method='quadratic' may be appropriate. • If you have values approximating a cumulative distribution

SELECT *, tip/total_bill as tip_rate FROM tips LIMIT 5; With pandas, you can use the DataFrame.assign() method of a DataFrame to append a new column: In [7]: tips.assign(tip_rate=tips['tip'] / tips['total_bill']) tips['total_bill']).head(5) Out[7]: total_bill tip sex smoker day time size tip_rate 0 16.99 1.01 Female No Sun Dinner 2 0.059447 1 10.34 1.66 Male No Sun Dinner 3 0.160542 2 21.01 3.50 Male No Sun Dinner 3 0 analysis toolkit, Release 1.1.1 • If you are dealing with a time series that is growing at an increasing rate, method='quadratic' may be appropriate. • If you have values approximating a cumulative distribution

SELECT *, tip/total_bill as tip_rate FROM tips LIMIT 5; With pandas, you can use the DataFrame.assign() method of a DataFrame to append a new column: In [7]: tips.assign(tip_rate=tips['tip'] / tips['total_bill']) tips['total_bill']).head(5) Out[7]: total_bill tip sex smoker day time size tip_rate 0 16.99 1.01 Female No Sun Dinner 2 0.059447 1 10.34 1.66 Male No Sun Dinner 3 0.160542 2 21.01 3.50 Male No Sun Dinner 3 0 analysis toolkit, Release 1.1.0 • If you are dealing with a time series that is growing at an increasing rate, method='quadratic' may be appropriate. • If you have values approximating a cumulative distribution