pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else values of low and high, based on whether the total_bill is less or more than $10. In spreadsheets, logical comparison can be done with conditional formulas. We’d use a formula of =IF(A2 < 10, "low", "high") where(tips["total_bill"] < 10, "low", "high") In [19]: tips Out[19]: total_bill tip sex smoker day time size bucket 0 14.99 1.01 Female No Sun Dinner 2 high 1 8.34 1.66 Male No Sun Dinner 3 low 2 19.01 3.50 Male

pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else values of low and high, based on whether the total_bill is less or more than $10. In spreadsheets, logical comparison can be done with conditional formulas. We’d use a formula of =IF(A2 < 10, "low", "high") where(tips["total_bill"] < 10, "low", "high") In [19]: tips Out[19]: total_bill tip sex smoker day time size bucket 0 14.99 1.01 Female No Sun Dinner 2 high 1 8.34 1.66 Male No Sun Dinner 3 low 2 19.01 3.50 Male

pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else values of low and high, based on whether the total_bill is less or more than $10. In spreadsheets, logical comparison can be done with conditional formulas. We’d use a formula of =IF(A2 < 10, "low", "high") where(tips["total_bill"] < 10, "low", "high") In [19]: tips Out[19]: total_bill tip sex smoker day time size bucket 0 14.99 1.01 Female No Sun Dinner 2 high 1 8.34 1.66 Male No Sun Dinner 3 low 2 19.01 3.50 Male

pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else values of low and high, based on whether the total_bill is less or more than $10. In spreadsheets, logical comparison can be done with conditional formulas. We’d use a formula of =IF(A2 < 10, "low", "high") where(tips["total_bill"] < 10, "low", "high") In [19]: tips Out[19]: total_bill tip sex smoker day time size bucket 0 14.99 1.01 Female No Sun Dinner 2 high 1 8.34 1.66 Male No Sun Dinner 3 low 2 19.01 3.50 Male

pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else values of low and high, based on whether the total_bill is less or more than $10. In spreadsheets, logical comparison can be done with conditional formulas. We’d use a formula of =IF(A2 < 10, "low", "high") where(tips["total_bill"] < 10, "low", "high") In [19]: tips Out[19]: total_bill tip sex smoker day time size bucket 0 14.99 1.01 Female No Sun Dinner 2 high 1 8.34 1.66 Male No Sun Dinner 3 low 2 19.01 3.50 Male

pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else values of low and high, based on whether the total_bill is less or more than $10. In spreadsheets, logical comparison can be done with conditional formulas. We’d use a formula of =IF(A2 < 10, "low", "high") where(tips["total_bill"] < 10, "low", "high") In [19]: tips Out[19]: total_bill tip sex smoker day time size bucket 0 14.99 1.01 Female No Sun Dinner 2 high 1 8.34 1.66 Male No Sun Dinner 3 low 2 19.01 3.50 Male

Length: 3, dtype: int64 On their own, a PandasArray isn’t a very useful object. But if you need write low-level code that works generically for any ExtensionArray, PandasArray satisfies that need. Notice pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else to create new columns. data tips; set tips; format bucket $4.; if total_bill < 10 then bucket = 'low'; else bucket = 'high'; run; The same operation in pandas can be accomplished using the where method

pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else bucket = 'low'; else bucket = 'high'; run; The same operation in pandas can be accomplished using the where method from numpy. In [12]: tips['bucket'] = np.where(tips['total_bill'] < 10, 'low', 'high') smoker day time size bucket 0 14.99 1.01 Female No Sun Dinner 2 high 1 8.34 1.66 Male No Sun Dinner 3 low 2 19.01 3.50 Male No Sun Dinner 3 high 3 21.68 3.31 Male No Sun Dinner 2 high 4 22.59 3.61 Female

pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else bucket = 'low'; else bucket = 'high'; run; The same operation in pandas can be accomplished using the where method from numpy. In [12]: tips['bucket'] = np.where(tips['total_bill'] < 10, 'low', 'high') smoker day time size bucket 0 14.99 1.01 Female No Sun Dinner 2 high 1 8.34 1.66 Male No Sun Dinner 3 low 2 19.01 3.50 Male No Sun Dinner 3 high 3 21.68 3.31 Male No Sun Dinner 2 high 4 22.59 3.61 Female

pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else bucket = 'low'; else bucket = 'high'; run; The same operation in pandas can be accomplished using the where method from numpy. In [12]: tips['bucket'] = np.where(tips['total_bill'] < 10, 'low', 'high') smoker day time size bucket 0 14.99 1.01 Female No Sun Dinner 2 high 1 8.34 1.66 Male No Sun Dinner 3 low 2 19.01 3.50 Male No Sun Dinner 3 high 3 21.68 3.31 Male No Sun Dinner 2 high 4 22.59 3.61 Female