statistics, social science, and many areas of engineering. For R users, DataFrame provides everything that Rs data.frame provides and much more. pandas is built on top of NumPy and is intended to integrate well the following can be done: In [202]: rs = s.reindex(df.index) In [203]: rs Out[203]: a -1.577151 b 1.165881 c 0.408422 d 1.733967 dtype: float64 In [204]: rs.index is df.index Out[204]: True This agg({'col1': 'mean'}) summarise(gdf, total=sum(col1)) df.groupby('col1').sum() Base R Slicing with Rs c R makes it easy to access data.frame columns by name df <- data.frame(a=rnorm(5), b=rnorm(5), c=rnorm(5)

objects. So if we have a Series and a DataFrame, the following can be done: In [87]: rs = s.reindex(df.index) In [88]: rs Out[88]: a -1.545498 6.6. Reindexing and altering labels 55 pandas: powerful Python Python data analysis toolkit, Release 0.7.1 b -1.145757 c 1.412967 d 0.179204 In [89]: rs.index is df.index Out[89]: True This means that the reindexed Series’s index is the same Python object as the

objects. So if we have a Series and a DataFrame, the following can be done: In [87]: rs = s.reindex(df.index) In [88]: rs Out[88]: a -0.081110 6.6. Reindexing and altering labels 55 pandas: powerful Python Python data analysis toolkit, Release 0.7.2 b 1.958399 c -0.231891 d 0.822924 In [89]: rs.index is df.index Out[89]: True This means that the reindexed Series’s index is the same Python object as the

objects. So if we have a Series and a DataFrame, the following can be done: In [87]: rs = s.reindex(df.index) In [88]: rs Out[88]: a -0.559624 6.6. Reindexing and altering labels 61 pandas: powerful Python Python data analysis toolkit, Release 0.7.3 b 1.008419 c 1.626928 d 0.692417 In [89]: rs.index is df.index Out[89]: True This means that the reindexed Series’s index is the same Python object as the

DataFrame, the following can be done: In [108]: rs = s.reindex(df.index) In [109]: rs a 1.721293 b 0.355636 c 0.498722 d -0.277859 dtype: float64 In [110]: rs.index is df.index True This means that the

the following can be done: In [149]: rs = s.reindex(df.index) In [150]: rs Out[150]: a 1.112686 b -1.069046 c -1.218080 d -0.944778 dtype: float64 In [151]: rs.index is df.index Out[151]: True This

the following can be done: In [152]: rs = s.reindex(df.index) In [153]: rs Out[153]: a 1.112686 b -1.069046 c -1.218080 d -0.944778 dtype: float64 In [154]: rs.index is df.index Out[154]: True This

the following can be done: In [183]: rs = s.reindex(df.index) In [184]: rs Out[184]: a -1.010924 b -0.672504 c -1.139222 d 0.354653 dtype: float64 In [185]: rs.index is df.index Out[185]: True This nouri • chinskiy • gfyoung • jeps-journal • jonaslb • kotrfa • nileracecrew • onesandzeroes • rs2 • sinhrks • tsdlovell pandas 0.18.0 Release date: (March 13, 2016) This is a major release from

the following can be done: In [183]: rs = s.reindex(df.index) In [184]: rs Out[184]: a -1.010924 b -0.672504 c -1.139222 d 0.354653 dtype: float64 In [185]: rs.index is df.index Out[185]: True This nouri • chinskiy • gfyoung • jeps-journal • jonaslb • kotrfa • nileracecrew • onesandzeroes • rs2 • sinhrks • tsdlovell pandas 0.18.0 Release date: (March 13, 2016) This is a major release from

the following can be done: In [221]: rs = s.reindex(df.index) In [222]: rs Out[222]: a -0.454087 b -0.360309 c -0.951631 d -0.535459 dtype: float64 In [223]: rs.index is df.index \\\\\\\\\\\\\\\\\ Python data analysis toolkit, Release 0.20.3 • jonaslb • kotrfa • nileracecrew • onesandzeroes • rs2 • sinhrks • tsdlovell 36.8 pandas 0.18.0 Release date: (March 13, 2016) This is a major release