necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data can actually be modified in-place, and the changes will be reflected in the data structure. For heterogeneous data (e.g. some of the DataFrame’s columns are not all the same dtype), this will not be the case values attribute itself, unlike the axis labels, cannot be assigned to. Note: When working with heterogeneous data, the dtype of the resulting ndarray will be chosen to accommodate all of the data involved

necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data can actually be modified in-place, and the changes will be reflected in the data structure. For heterogeneous data (e.g. some of the DataFrame’s columns are not all the same dtype), this will not be the case values attribute itself, unlike the axis labels, cannot be assigned to. Note: When working with heterogeneous data, the dtype of the resulting ndarray will be chosen to accommodate all of the data involved

necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data can actually be modified in-place, and the changes will be reflected in the data structure. For heterogeneous data (e.g. some of the DataFrame’s columns are not all the same dtype), this will not be the case values attribute itself, unlike the axis labels, cannot be assigned to. Note: When working with heterogeneous data, the dtype of the resulting ndarray will be chosen to accommodate all of the data involved

necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data draws unnecessary axes when enabling subplots and kind=scatter (GH6951) • Bug in read_csv from a filesystem with non-utf-8 encoding (GH6807) • Bug in iloc when setting / aligning (GH6766) • Bug causing Python data analysis toolkit, Release 0.14.0 Simple Queries with a Timestamp Index Managing heterogeneous data using a linked multiple table hierarchy Merging on-disk tables with millions of rows Deduplicating

necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data rearranged using its transpose method (which does not make a copy by default unless the data are heterogeneous): In [121]: wp.transpose(2, 0, 1) Dimensions: 4 (items) x rearranged using its transpose method (which does not make a copy by default unless the data are heterogeneous): 7.4. Panel4D (Experimental) 129 pandas: powerful Python data analysis toolkit, Release 0.12

necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data draws unnecessary axes when enabling subplots and kind=scatter (GH6951) • Bug in read_csv from a filesystem with non-utf-8 encoding (GH6807) • Bug in iloc when setting / aligning (GH6766) • Bug causing request header 7.9.4 HDFStore The HDFStores docs Simple Queries with a Timestamp Index Managing heterogeneous data using a linked multiple table hierarchy Merging on-disk tables with millions of rows De-duplicating

necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data draws unnecessary axes when enabling subplots and kind=scatter (GH6951) • Bug in read_csv from a filesystem with non-utf-8 encoding (GH6807) • Bug in iloc when setting / aligning (GH6766) • Bug causing request header 7.9.4 HDFStore The HDFStores docs Simple Queries with a Timestamp Index Managing heterogeneous data using a linked multiple table hierarchy Merging on-disk tables with millions of rows De-duplicating

necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data improvement of up to 10x in DataFrame.count and DataFrame.dropna by taking advan- tage of homogeneous/heterogeneous dtypes appropriately (GH9136) • Performance improvement of up to 20x in DataFrame.count when draws unnecessary axes when enabling subplots and kind=scatter (GH6951) • Bug in read_csv from a filesystem with non-utf-8 encoding (GH6807) • Bug in iloc when setting / aligning (GH6766) • Bug causing

necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data request header 7.9.4 HDFStore The HDFStores docs Simple Queries with a Timestamp Index Managing heterogeneous data using a linked multiple table hierarchy Merging on-disk tables with millions of rows Deduplicating rearranged using its transpose method (which does not make a copy by default unless the data are heterogeneous): In [120]: wp.transpose(2, 0, 1) Out[120]: Dimensions: 4 (items)

necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data cannot take string parameters 'rows' or 'columns' (GH14369) • Bug in pd.concat with dataframes heterogeneous in length and tuple keys (GH14438) • Bug in MultiIndex.set_levels where illegal level values improvement of up to 10x in DataFrame.count and DataFrame.dropna by taking advan- tage of homogeneous/heterogeneous dtypes appropriately (GH9136) • Performance improvement of up to 20x in DataFrame.count when