handling of missing data (represented as NaN) in floating point as well as non-floating point data • Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects • Automatic datetimes 2 DataFrame General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed columns 3 Panel General 3D labeled, also size-mutable array 4.1.1 Why more than 1 data All pandas data structures are value-mutable (the values they contain can be altered) but not always size-mutable. The length of a Series cannot be changed, but, for example, columns can be inserted into

handling of missing data (represented as NaN) in floating point as well as non-floating point data • Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects • Automatic datetimes 2 DataFrame General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed columns 3 Panel General 3D labeled, also size-mutable array 4.1.1 Why more than 1 data All pandas data structures are value-mutable (the values they contain can be altered) but not always size-mutable. The length of a Series cannot be changed, but, for example, columns can be inserted into

handling of missing data (represented as NaN) in floating point as well as non-floating point data • Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects • Automatic datetimes 2 DataFrame General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed columns 3 Panel General 3D labeled, also size-mutable array 4.1.1 Why more than 1 data All pandas data structures are value-mutable (the values they contain can be altered) but not always size-mutable. The length of a Series cannot be changed, but, for example, columns can be inserted into

over one hundred packages and involves downloading the installer which is a few hundred megabytes in size. If you want to have more control on which packages, or have a limited internet bandwidth, then installing handling of missing data (represented as NaN) in floating point as well as non-floating point data • Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects • Automatic Dimensions Name Description 1 Series 1D labeled homogeneously-typed array 2 DataFrame General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed column Why more than one data structure

handling of missing data (represented as NaN) in floating point as well as non-floating point data • Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects • Automatic the .sum() operation. N = 1000000 ngroups = 10 df = DataFrame({'key' : np.random.randint(0,ngroups,size=N), 'data' : np.random.randn(N) }) df.groupby('key')['data'].sum() Releasing of the GIL could benefit ================================ Dep. Variable: hr No. Observations: 68 Model: Poisson Df Residuals: 63 Method: MLE Df Model: 4 Date: Fri, 09 Oct 2015 Pseudo R-squ.: 0.6878 Time: 20:59:49 Log-Likelihood:

over one hundred packages and involves downloading the installer which is a few hundred megabytes in size. If you want to have more control on which packages, or have a limited internet bandwidth, then installing handling of missing data (represented as NaN) in floating point as well as non-floating point data • Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects • Automatic Dimensions Name Description 1 Series 1D labeled homogeneously-typed array 2 DataFrame General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed column Why more than one data structure

over one hundred packages and involves downloading the installer which is a few hundred megabytes in size. If you want to have more control on which packages, or have a limited internet bandwidth, then installing handling of missing data (represented as NaN) in floating point as well as non-floating point data • Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects • Automatic Dimensions Name Description 1 Series 1D labeled homogeneously-typed array 2 DataFrame General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed column Why more than one data structure

over one hundred packages and involves downloading the installer which is a few hundred megabytes in size. If you want to have more control on which packages, or have a limited internet bandwidth, then installing handling of missing data (represented as NaN) in floating point as well as non-floating point data • Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects • Automatic Dimensions Name Description 1 Series 1D labeled homogeneously-typed array 2 DataFrame General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed column Why more than one data structure

over one hundred packages and involves downloading the installer which is a few hundred megabytes in size. If you want to have more control on which packages, or have a limited internet bandwidth, then installing handling of missing data (represented as NaN) in floating point as well as non-floating point data • Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects • Automatic Dimensions Name Description 1 Series 1D labeled homogeneously-typed array 2 DataFrame General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed column Why more than one data structure

over one hundred packages and involves downloading the installer which is a few hundred megabytes in size. If you want to have more control on which packages, or have a limited internet bandwidth, then installing handling of missing data (represented as NaN) in floating point as well as non-floating point data • Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects • Automatic Dimensions Name Description 1 Series 1D labeled homogeneously-typed array 2 DataFrame General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed column Why more than one data structure