/ loading data from the ultrafast HDF5 format • Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting • 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

/ loading data from the ultrafast HDF5 format • Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting • 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

/ loading data from the ultrafast HDF5 format • Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting • 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

/ loading data from the ultrafast HDF5 format • Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting • 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

/ loading data from the ultrafast HDF5 format • Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting • 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

/ loading data from the ultrafast HDF5 format • Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting • 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

/ loading data from the ultrafast HDF5 format • Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting • 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 called ‘categories’)”. This can lead to subtle bugs. If you use Categorical directly, please audit your code before updating to this pandas version and change it to use the from_codes() constructor. See more

/ loading data from the ultrafast HDF5 format • Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting • 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 called ‘categories’)”. This can lead to subtle bugs. If you use Categorical directly, please audit your code before updating to this pandas version and change it to use the from_codes() constructor. See more

Requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 3.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 3.4 Contributing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 3.5 Contributing to the code base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 3.6 Contributing . . . . . . . . . . . . . . . . . . . . . . . 891 29 rpy2 / R interface 893 29.1 Updating your code to use rpy2 functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 893 29.2 R

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 . .