34.83 5.17 Female No Thur Lunch 4 211 25.89 5.16 Male Yes Sat Dinner 4 Top N rows per group -- Oracle's ROW_NUMBER() analytic function SELECT * FROM ( SELECT t.*, ROW_NUMBER() OVER(PARTITION BY day Dinner 3 2.0 197 43.11 5.00 Female Yes Thur Lunch 4 1.0 142 41.19 5.00 Male No Thur Lunch 5 2.0 -- Oracle's RANK() analytic function SELECT * FROM ( SELECT t.*, RANK() OVER(PARTITION BY sex ORDER BY tip) ') engine = create_engine('mysql+mysqldb://scott:tiger@localhost/foo') engine = create_engine('oracle://scott:tiger@127.0.0.1:1521/sidname') engine = create_engine('mssql+pyodbc://mydsn') # sqlite

GH4163, GH5950, GH6292). All databases supported by SQLAlchemy can be used, such as PostgreSQL, MySQL, Oracle, Microsoft SQL server (see documentation of SQLAlchemy on included dialects). The functionality ') engine = create_engine('mysql+mysqldb://scott:tiger@localhost/foo') engine = create_engine('oracle://scott:tiger@127.0.0.1:1521/sidname') engine = create_engine('mssql+pyodbc://mydsn') # sqlite 34.83 5.17 Female No Thur Lunch 4 211 25.89 5.16 Male Yes Sat Dinner 4 Top N rows per group -- Oracle's ROW_NUMBER() analytic function SELECT * FROM ( SELECT t.*, ROW_NUMBER() OVER(PARTITION BY day

GH4163, GH5950, GH6292). All databases supported by SQLAlchemy can be used, such as PostgreSQL, MySQL, Oracle, Microsoft SQL server (see documentation of SQLAlchemy on included dialects). The functionality ') engine = create_engine('mysql+mysqldb://scott:tiger@localhost/foo') engine = create_engine('oracle://scott:tiger@127.0.0.1:1521/sidname') engine = create_engine('mssql+pyodbc://mydsn') # sqlite 34.83 5.17 Female No Thur Lunch 4 211 25.89 5.16 Male Yes Sat Dinner 4 Top N rows per group -- Oracle's ROW_NUMBER() analytic function SELECT * FROM ( SELECT t.*, ROW_NUMBER() OVER(PARTITION BY day

GH4163, GH5950, GH6292). All databases supported by SQLAlchemy can be used, such as PostgreSQL, MySQL, Oracle, Microsoft SQL server (see documentation of SQLAlchemy on included dialects). The functionality HDF5, ...) pandas: powerful Python data analysis toolkit, Release 0.20.3 engine = create_engine('oracle://scott:tiger@127.0.0.1:1521/sidname') engine = create_engine('mssql+pyodbc://mydsn') # sqlite 17 Female No Thur Lunch 4 211 25.89 5.16 Male Yes Sat Dinner 4 32.6.2 Top N rows per group -- Oracle's ROW_NUMBER() analytic function SELECT * FROM ( SELECT t.*, 32.6. Pandas equivalents for some

GH4163, GH5950, GH6292). All databases supported by SQLAlchemy can be used, such as PostgreSQL, MySQL, Oracle, Microsoft SQL server (see documentation of SQLAlchemy on included dialects). The functionality ') engine = create_engine('mysql+mysqldb://scott:tiger@localhost/foo') engine = create_engine('oracle://scott:tiger@127.0.0.1:1521/sidname') engine = create_engine('mssql+pyodbc://mydsn') # sqlite 17 Female No Thur Lunch 4 211 25.89 5.16 Male Yes Sat Dinner 4 32.6.2 Top N rows per group -- Oracle's ROW_NUMBER() analytic function SELECT * FROM ( SELECT t.*, 32.6. Pandas equivalents for some

GH4163, GH5950, GH6292). All databases supported by SQLAlchemy can be used, such as PostgreSQL, MySQL, Oracle, Microsoft SQL server (see documentation of SQLAlchemy on included dialects). The functionality ') engine = create_engine('mysql+mysqldb://scott:tiger@localhost/foo') engine = create_engine('oracle://scott:tiger@127.0.0.1:1521/sidname') engine = create_engine('mssql+pyodbc://mydsn') # sqlite 17 Female No Thur Lunch 4 211 25.89 5.16 Male Yes Sat Dinner 4 32.6.2 Top N rows per group -- Oracle's ROW_NUMBER() analytic function SELECT * FROM ( SELECT t.*, 32.6. Pandas equivalents for some

34.83 5.17 Female No Thur Lunch 4 211 25.89 5.16 Male Yes Sat Dinner 4 Top N rows per group -- Oracle's ROW_NUMBER() analytic function SELECT * FROM ( SELECT t.*, ROW_NUMBER() OVER(PARTITION BY day Dinner 3 2.0 197 43.11 5.00 Female Yes Thur Lunch 4 1.0 142 41.19 5.00 Male No Thur Lunch 5 2.0 -- Oracle's RANK() analytic function SELECT * FROM ( SELECT t.*, RANK() OVER(PARTITION BY sex ORDER BY tip) Notice that when using rank(method='min') function rnk_min remains the same for the same tip (as Oracle’s RANK() function) In [38]: (tips[tips['tip'] < 2] ....: .assign(rnk_min=tips.groupby(['sex'])['tip']

tools 179 pandas: powerful Python data analysis toolkit, Release 0.25.0 Top N rows per group -- Oracle's ROW_NUMBER() analytic function SELECT * FROM ( SELECT t.*, ROW_NUMBER() OVER(PARTITION BY day Dinner 3 2.0 197 43.11 5.00 Female Yes Thur Lunch 4 1.0 142 41.19 5.00 Male No Thur Lunch 5 2.0 -- Oracle's RANK() analytic function SELECT * FROM ( SELECT t.*, RANK() OVER(PARTITION BY sex ORDER BY tip) Notice that when using rank(method='min') function rnk_min remains the same for the same tip (as Oracle’s RANK() function) In [37]: (tips[tips['tip'] < 2] ....: .assign(rnk_min=tips.groupby(['sex'])['tip']

34.83 5.17 Female No Thur Lunch 4 211 25.89 5.16 Male Yes Sat Dinner 4 Top N rows per group -- Oracle's ROW_NUMBER() analytic function SELECT * FROM ( SELECT t.*, ROW_NUMBER() OVER(PARTITION BY day Dinner 3 2.0 197 43.11 5.00 Female Yes Thur Lunch 4 1.0 142 41.19 5.00 Male No Thur Lunch 5 2.0 -- Oracle's RANK() analytic function SELECT * FROM ( SELECT t.*, RANK() OVER(PARTITION BY sex ORDER BY tip) Notice that when using rank(method='min') function rnk_min remains the same for the same tip (as Oracle’s RANK() function) In [37]: (tips[tips['tip'] < 2] ....: .assign(rnk_min=tips.groupby(['sex'])['tip']

34.83 5.17 Female No Thur Lunch 4 211 25.89 5.16 Male Yes Sat Dinner 4 Top N rows per group -- Oracle's ROW_NUMBER() analytic function SELECT * FROM ( SELECT t.*, ROW_NUMBER() OVER(PARTITION BY day Dinner 3 2.0 197 43.11 5.00 Female Yes Thur Lunch 4 1.0 142 41.19 5.00 Male No Thur Lunch 5 2.0 -- Oracle's RANK() analytic function SELECT * FROM ( SELECT t.*, (continues on next page) 192 Chapter 1 Notice that when using rank(method='min') function rnk_min remains the same for the same tip (as Oracle’s RANK() function) In [38]: (tips[tips['tip'] < 2] ....: .assign(rnk_min=tips.groupby(['sex'])['tip']