periods=10)) ....: In [41]: dfq.to_hdf(path,’dfq’,format=’table’,data_columns=True) Use boolean expressions, with in-line function evaluation. In [42]: read_hdf(path,’dfq’, ....: where="index>Timestamp(’20130104’) expression evaluation using numexpr behind the scenes. This results in large speedups for complicated expressions involving large DataFrames/Series. For example, 1.2. v0.13.0 (January 3, 2014) 27 pandas: powerful files, Python 3 support for HDFStore, filtering of groupby expressions via filter, and a revamped replace routine that accepts regular expressions. 1.3.1 API changes • The I/O API is now much more consistent

with a specified frequency (GH6273, GH6274) • Bug in eval where type-promotion failed for large expressions (GH6205) • Bug in interpolate with inplace=True (GH6281) • HDFStore.remove now handles start list-like from 0.12 (GH6394) • Float64Index with nans not comparing correctly (GH6401) • eval/query expressions with strings containing the @ character will now work (GH6366). • Bug in Series.reindex when specifying 32-bit platforms (GH6808) • Bug in setting a tz-aware index directly via .index (GH6785) • Bug in expressions.py where numexpr would try to evaluate arithmetic ops (GH6762). • Bug in Makefile where it didn’t

0.15.2 0 1 1 2 2 3 3 4 dtype: int64 In [48]: s.dt.freq Out[48]: This enables nice expressions like this: In [49]: s[s.dt.day==2] Out[49]: 1 2013-01-02 09:10:12 dtype: datetime64[ns] You with a specified frequency (GH6273, GH6274) • Bug in eval where type-promotion failed for large expressions (GH6205) • Bug in interpolate with inplace=True (GH6281) • HDFStore.remove now handles start list-like from 0.12 (GH6394) • Float64Index with nans not comparing correctly (GH6401) • eval/query expressions with strings containing the @ character will now work (GH6366). • Bug in Series.reindex when specifying

Out[47]: 0 1 1 2 2 3 3 4 dtype: int64 In [48]: s.dt.freq Out[48]: This enables nice expressions like this: In [49]: s[s.dt.day==2] Out[49]: 1 2013-01-02 09:10:12 dtype: datetime64[ns] You with a specified frequency (GH6273, GH6274) • Bug in eval where type-promotion failed for large expressions (GH6205) • Bug in interpolate with inplace=True (GH6281) • HDFStore.remove now handles start list-like from 0.12 (GH6394) • Float64Index with nans not comparing correctly (GH6401) • eval/query expressions with strings containing the @ character will now work (GH6366). • Bug in Series.reindex when specifying

Out[42]: 0 1 1 2 2 3 3 4 dtype: int64 In [43]: s.dt.freq Out[43]: This enables nice expressions like this: In [44]: s[s.dt.day==2] Out[44]: 1 2013-01-02 09:10:12 dtype: datetime64[ns] You with a specified frequency (GH6273, GH6274) • Bug in eval where type-promotion failed for large expressions (GH6205) • Bug in interpolate with inplace=True (GH6281) • HDFStore.remove now handles start list-like from 0.12 (GH6394) • Float64Index with nans not comparing correctly (GH6401) • eval/query expressions with strings containing the @ character will now work (GH6366). • Bug in Series.reindex when specifying

0.0 0 1 2.5 1 2 5.0 2 3 7.5 3 4 10.0 4 In [12]: df.eval('c = a + b') FutureWarning: eval expressions containing an assignment currentlydefault to ˓→operating inplace. This will change in a future updated to allow multi-line expressions for multiple assignments. These expressions will be evaluated one at a time in order. Only assignments are valid for multi-line expressions. 1.3. v0.18.0 (March 13 Out[42]: 0 1 1 2 2 3 3 4 dtype: int64 In [43]: s.dt.freq Out[43]: This enables nice expressions like this: In [44]: s[s.dt.day==2] Out[44]: 1 2013-01-02 09:10:12 dtype: datetime64[ns] You

0.0 0 1 2.5 1 2 5.0 2 3 7.5 3 4 10.0 4 In [12]: df.eval('c = a + b') FutureWarning: eval expressions containing an assignment currentlydefault to ˓→operating inplace. This will change in a future updated to allow multi-line expressions for multiple assignments. These expressions will be evaluated one at a time in order. Only assignments are valid for multi-line expressions. In [104]: df Out[104]: Out[42]: 0 1 1 2 2 3 3 4 dtype: int64 In [43]: s.dt.freq Out[43]: This enables nice expressions like this: In [44]: s[s.dt.day==2] Out[44]: 1 2013-01-02 09:10:12 dtype: datetime64[ns] You

now() – pd.Term, is removed, as it is not applicable to user code. Instead use in-line string expressions in the where clause when searching in HDFStore – pd.Expr, is removed, as it is not applicable sort and order methods (GH10726) • Where clauses in pytables are only accepted as strings and expressions types and not other data-types (GH12027) • DataFrame has dropped the combineAdd and combineMult 0.0 0 1 2.5 1 2 5.0 2 3 7.5 3 4 10.0 4 In [12]: df.eval('c = a + b') FutureWarning: eval expressions containing an assignment currentlydefault to ˓→operating inplace. This will change in a future

ValueError: Cannot assign expression output to target It also used to be possible to evaluate expressions inplace, even if there was no item assignment: In [4]: pd.eval("1 + 2", target=arr, inplace=True) now() – pd.Term, is removed, as it is not applicable to user code. Instead use in-line string expressions in the where clause when searching in HDFStore – pd.Expr, is removed, as it is not applicable sort and order methods (GH10726) • Where clauses in pytables are only accepted as strings and expressions types and not other data-types (GH12027) • DataFrame has dropped the combineAdd and combineMult

now() – pd.Term, is removed, as it is not applicable to user code. Instead use in-line string expressions in the where clause when searching in HDFStore – pd.Expr, is removed, as it is not applicable analysis toolkit, Release 0.20.2 • Where clauses in pytables are only accepted as strings and expressions types and not other data-types (GH12027) • DataFrame has dropped the combineAdd and combineMult Python data analysis toolkit, Release 0.20.2 In [12]: df.eval('c = a + b') FutureWarning: eval expressions containing an assignment currentlydefault to ˓→operating inplace. This will change in a future