indexing, and axis labeling / alignment apply across all of the objects. To get started, import numpy and load pandas into your namespace: In [225]: import numpy as np # will use a lot in examples In [226]: 0.3748041 -1.213427 0.2262565 foo In [150]: df.save(’foo.pickle’) The load function in the pandas namespace can be used to load any pickled pandas object (or any other pickled 6.10. Pickling and serialization serialization 65 pandas: powerful Python data analysis toolkit, Release 0.7.1 object) from file: In [151]: load(’foo.pickle’) Out[151]: a b c d 0 1.159659 -1.706724 1.882596 foo 1 0.03558944 1.100821 0.8058826

indexing, and axis labeling / alignment apply across all of the objects. To get started, import numpy and load pandas into your namespace: In [225]: import numpy as np # will use a lot in examples In [226]: 5 1.577024 -1.523379 1.86923 foo In [150]: df.save(’foo.pickle’) The load function in the pandas namespace can be used to load any pickled pandas object (or any other pickled 6.10. Pickling and serialization serialization 65 pandas: powerful Python data analysis toolkit, Release 0.7.2 object) from file: In [151]: load(’foo.pickle’) Out[151]: a b c d 0 -1.678 0.3162712 -0.7645219 foo 1 -2.408878 0.2149379 -0.1459749

indexing, and axis labeling / alignment apply across all of the objects. To get started, import numpy and load pandas into your namespace: In [225]: import numpy as np # will use a lot in examples In [226]: -0.8191439 -1.939507 -1.617302 foo In [150]: df.save(’foo.pickle’) The load function in the pandas namespace can be used to load any pickled pandas object (or any other pickled 6.10. Pickling and serialization serialization 71 pandas: powerful Python data analysis toolkit, Release 0.7.3 object) from file: In [151]: load(’foo.pickle’) Out[151]: a b c d 0 -0.4554712 0.03155879 -0.09976363 foo 1 -0.7006397 -1.481563

sets/html/00Index.html can now be loaded into Pandas objects import pandas.rpy.common as com com.load_data(’Titanic’) • tz_localize can infer a fall daylight savings transition based on the structure 2013-01-05 0.110977 Freq: D, dtype: float64 • pandas.io.gbq provides a simple way to extract from, and load data into, Google’s BigQuery Data Sets by way of pandas DataFrames. BigQuery is a high performance indexing, and axis labeling / alignment apply across all of the objects. To get started, import numpy and load pandas into your namespace: In [1]: import numpy as np # will use a lot in examples In [2]: randn

sets/html/00Index.html can now be loaded into Pandas objects import pandas.rpy.common as com com.load_data(’Titanic’) • tz_localize can infer a fall daylight savings transition based on the structure 2013-01-05 0.110977 Freq: D, dtype: float64 • pandas.io.gbq provides a simple way to extract from, and load data into, Google’s BigQuery Data Sets by way of pandas DataFrames. BigQuery is a high performance indexing, and axis labeling / alignment apply across all of the objects. To get started, import numpy and load pandas into your namespace: In [1]: import numpy as np # will use a lot in examples In [2]: randn

• you may need to unpickle pandas version < 0.15.0 pickles using pd.read_pickle rather than pickle.load. See pickle docs • when plotting with a PeriodIndex, the matplotlib internal axes will now be arrays sets/html/00Index.html can now be loaded into Pandas objects import pandas.rpy.common as com com.load_data(’Titanic’) • tz_localize can infer a fall daylight savings transition based on the structure 2013-01-05 0.110977 Freq: D, dtype: float64 • pandas.io.gbq provides a simple way to extract from, and load data into, Google’s BigQuery Data Sets by way of pandas DataFrames. BigQuery is a high performance

• you may need to unpickle pandas version < 0.15.0 pickles using pd.read_pickle rather than pickle.load. See pickle docs • when plotting with a PeriodIndex, the matplotlib internal axes will now be arrays sets/html/00Index.html can now be loaded into Pandas objects import pandas.rpy.common as com com.load_data(’Titanic’) • tz_localize can infer a fall daylight savings transition based on the structure 2013-01-05 0.110977 Freq: D, dtype: float64 • pandas.io.gbq provides a simple way to extract from, and load data into, Google’s BigQuery Data Sets by way of pandas DataFrames. BigQuery is a high performance

offset and timeRule keywords from Series.tshift/shift, in favor of freq (GH4853, GH4864) • Remove pd.load/pd.save aliases in favor of pd.to_pickle/pd.read_pickle (GH3787) 1.1.3 Performance Improvements • you may need to unpickle pandas version < 0.15.0 pickles using pd.read_pickle rather than pickle.load. See pickle docs 1.7. v0.15.0 (October 18, 2014) 87 pandas: powerful Python data analysis toolkit Pandas objects # note that pandas.rpy was deprecated in v0.16.0 import pandas.rpy.common as com com.load_data('Titanic') • tz_localize can infer a fall daylight savings transition based on the structure

indexing, and axis labeling / alignment apply across all of the objects. To get started, import numpy and load pandas into your namespace: In [1]: import numpy as np # will use a lot in examples In [2]: randn yourself, please be careful. 9.6 Adding an index to an existing DataFrame Occasionally you will load or create a data set into a DataFrame and want to add an index after you’ve already done so. There In [173]: df.to_pickle(’foo.pkl’) The read_pickle function in the pandas namespace can be used to load any pickled pandas object (or any other pickled object) from file: In [174]: read_pickle(’foo.pkl’)

tz.tzlocal cannot be regarded as valid dtype (GH13583) • Bug in pd.read_hdf() where attempting to load an HDF file with a single dataset, that had one or more categorical columns, failed unless the key returns a Series containing a new datetimelike column (GH11324) • Bug in pandas.json when file to load is big (GH11344) • Bugs in to_excel with duplicate columns (GH11007, GH10982, GH10970) • Fixed a offset and timeRule keywords from Series.tshift/shift, in favor of freq (GH4853, GH4864) • Remove pd.load/pd.save aliases in favor of pd.to_pickle/pd.read_pickle (GH3787) Performance Improvements • Development