. . . . . . . . . . . . . . . . . . . . . . . 115 10 Group By: split-apply-combine 117 10.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization usually sacrifices performance. So if you focus on iterrows method for efficiently iterating through the rows of a DataFrame • Add DataFrame.to_panel with code adapted from LongPanel.to_long • Add reindex_axis method added to DataFrame • Add level option to

. . . . . . . . . . . . . . . . . . . . . . . 115 10 Group By: split-apply-combine 117 10.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization usually sacrifices performance. So if you focus on iterrows method for efficiently iterating through the rows of a DataFrame • Add DataFrame.to_panel with code adapted from LongPanel.to_long • Add reindex_axis method added to DataFrame • Add level option to

. . . . . . . . . . . . . . . . . . . . . . . 123 10 Group By: split-apply-combine 125 10.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization usually sacrifices performance. So if you focus on iterrows method for efficiently iterating through the rows of a DataFrame • Add DataFrame.to_panel with code adapted from LongPanel.to_long • Add reindex_axis method added to DataFrame • Add level option to

. . . . . . . . . . . . . . . . . . . . . . . 237 12 Group By: split-apply-combine 239 12.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization usually sacrifices performance. So if you focus on 2000-01-05 0 1.096474 -0.362772 0.726118 2000-01-06 0 -0.245540 2.410427 1.338336 # Change your code to In [5]: df.sub(df[0], axis=0) # align on axis 0 (rows) 0 1 2 3 2000-01-01 0 2.234824 1.403111

requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378 3.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 3.3.1 Version . . . . . . . . . . 384 3.5 Contributing to the code base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 3.5.1 Code standards . . . . . . . . . . . . . . . . . . . . Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386 3.5.3 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 3.5.3.1 Writing tests . .

requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376 3.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 3.3.1 Version . . . . . . . . . . 382 3.5 Contributing to the code base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382 3.5.1 Code standards . . . . . . . . . . . . . . . . . . . . Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 3.5.3 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385 3.5.3.1 Writing tests . .

. . . . . . . . . . . . . . . . . . . . . . . 319 13 Group By: split-apply-combine 321 13.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization usually sacrifices performance. So if you focus on set for this query # Your Google BigQuery Project ID # To find this, see your dashboard: # https://code.google.com/apis/console/b/0/?noredirect projectid = xxxxxxxxx; df = gbq.read_gbq(query, project_id

requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 406 3.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 3.3.1 Version . . . . . . . . . . 412 3.5 Contributing to the code base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412 3.5.1 Code standards . . . . . . . . . . . . . . . . . . . . Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414 3.5.3 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415 3.5.3.1 Writing tests . .

Requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 3.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 3.4 Contributing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 3.5 Contributing to the code base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 3.6 Contributing . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 11 Working with Text Data 387 11.1 Splitting and Replacing Strings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . 313 10 Working with Text Data 317 10.1 Splitting and Replacing Strings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 16 Group By: split-apply-combine 435 16.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization usually sacrifices performance. So if you focus on