sheet_name='Sheet2') Note: Wringing a little more performance out of read_excel Internally, Excel stores all numeric data as floats. Because this can produce unexpected behavior when reading in data, pandas in PyTables < 3.2 which may appear when querying stores using an index. If you see a subset of results being returned, upgrade to PyTables >= 3.2. Stores created previously will need to be rewritten using which write the HDF5 to PyTables in a fixed array format, called the fixed format. These types of stores are not appendable once written (though you can simply remove them and rewrite). Nor are they queryable;

’table’, where = [’index>2’]) A B 3 3 3 4 4 4 – provide dotted attribute access to get from stores, e.g. store.df == store[’df’] – new keywords iterator=boolean, and chunksize=number_in_a_chunk are The MultiIndex object is the hierarchical analogue of the standard Index object which typically stores the axis labels in pandas objects. You can think of MultiIndex an array of tuples where each tuple 156479 -1.696377 0.819712 -2.107728 All of the MultiIndex constructors accept a names argument which stores string names for the levels themselves. If no names are provided, None will be assigned: In [179]:

hdf.dropna_table (GH4625) • pass thru store creation arguments; can be used to support in-memory stores 1.2.7 DataFrame repr Changes The HTML and plain text representations of DataFrame now show a truncated Out[53]: A B 3 3 3 4 4 4 [2 rows x 2 columns] – provide dotted attribute access to get from stores, e.g. store.df == store[’df’] – new keywords iterator=boolean, and chunksize=number_in_a_chunk are The MultiIndex object is the hierarchical analogue of the standard Index object which typically stores the axis labels in pandas objects. You can think of MultiIndex an array of tuples where each tuple

hdf.dropna_table (GH4625) • pass thru store creation arguments; can be used to support in-memory stores 1.3.7 DataFrame repr Changes The HTML and plain text representations of DataFrame now show a truncated Out[54]: A B 3 3 3 4 4 4 [2 rows x 2 columns] – provide dotted attribute access to get from stores, e.g. store.df == store[’df’] – new keywords iterator=boolean, and chunksize=number_in_a_chunk are The MultiIndex object is the hierarchical analogue of the standard Index object which typically stores the axis labels in pandas objects. You can think of MultiIndex an array of tuples where each tuple

hdf.dropna_table (GH4625) • pass thru store creation arguments; can be used to support in-memory stores 1.7.7 DataFrame repr Changes The HTML and plain text representations of DataFrame now show a truncated Out[54]: A B 3 3 3 4 4 4 [2 rows x 2 columns] – provide dotted attribute access to get from stores, e.g. store.df == store[’df’] – new keywords iterator=boolean, and chunksize=number_in_a_chunk are The MultiIndex object is the hierarchical analogue of the standard Index object which typically stores the axis labels in pandas objects. You can think of MultiIndex an array of tuples where each tuple

hdf.dropna_table (GH4625) • pass thru store creation arguments; can be used to support in-memory stores 1.6.7 DataFrame repr Changes The HTML and plain text representations of DataFrame now show a truncated Out[54]: A B 3 3 3 4 4 4 [2 rows x 2 columns] – provide dotted attribute access to get from stores, e.g. store.df == store[’df’] – new keywords iterator=boolean, and chunksize=number_in_a_chunk are The MultiIndex object is the hierarchical analogue of the standard Index object which typically stores the axis labels in pandas objects. You can think of MultiIndex an array of tuples where each tuple

(GH10332) • Bug in read_hdf where auto_close could not be passed (GH9327). • Bug in read_hdf where open stores could not be used (GH10330). 1.2. v0.16.2 (June 12, 2015) 31 pandas: powerful Python data analysis hdf.dropna_table (GH4625) • pass thru store creation arguments; can be used to support in-memory stores 1.11.7 DataFrame repr Changes The HTML and plain text representations of DataFrame now show a truncated Out[54]: A B 3 3 3 4 4 4 [2 rows x 2 columns] – provide dotted attribute access to get from stores, e.g. store.df == store[’df’] – new keywords iterator=boolean, and chunksize=number_in_a_chunk are

a concrete example on combining .groupby and .pipe , imagine having a DataFrame with columns for stores, products, revenue and sold quantity. We’d like to do a groupwise calculation of prices (i.e. revenue/quantity) New pandas: powerful Python data analysis toolkit, Release 0.21.1 • Compression defaults in HDF stores now follow pytables standards. Default is no compression and if complib is missing and complevel This has a similar implementation to the python range object (xrange in python 2), in that it only stores the start, stop, and step values for the index. It will transparently interact with the user API

This has a similar implementation to the python range object (xrange in python 2), in that it only stores the start, stop, and step values for the index. It will transparently interact with the user API (GH10332) • Bug in read_hdf where auto_close could not be passed (GH9327). • Bug in read_hdf where open stores could not be used (GH10330). • Bug in adding empty DataFrame``s,now results in a ``DataFrame that hdf.dropna_table (GH4625) • pass thru store creation arguments; can be used to support in-memory stores 242 Chapter 1. What’s New pandas: powerful Python data analysis toolkit, Release 0.19.0 DataFrame

This has a similar implementation to the python range object (xrange in python 2), in that it only stores the start, stop, and step values for the index. It will transparently interact with the user API (GH10332) • Bug in read_hdf where auto_close could not be passed (GH9327). • Bug in read_hdf where open stores could not be used (GH10330). • Bug in adding empty DataFrame``s,now results in a ``DataFrame that hdf.dropna_table (GH4625) • pass thru store creation arguments; can be used to support in-memory stores 1.16. v0.13.0 (January 3, 2014) 243 pandas: powerful Python data analysis toolkit, Release 0.19