TableProxy equivalent to RowProxies

[sumau]

RowProxy have useful functionality like row.keys() and row['column']

If I use fetchmany/fetchall I return a list of RowProxies. Is the best way of keeping the functionality of RowProxies to convert the list to a pandas data frame? Are there any plans to create "Table"Proxy equivalent to RowProxy?

[Mike Bayer]

On Sun, Oct 27, 2019, at 7:06 AM, sumau wrote:

RowProxy have useful functionality like row.keys() and row['column']

If I use fetchmany/fetchall I return a list of RowProxies. Is the best way of keeping the functionality of RowProxies to convert the list to a pandas data frame? Are there any plans to create "Table"Proxy equivalent to RowProxy?

The "row proxy" is no longer a "proxy" in 1.4, however this is an implementation detail that won't be noticable.

The table equivalent is the Table object.  Just like the Row(Proxy) represents a row in a database fetched over, Table represents the structure of the Table, which can be fetched over if you use Table(..., autoload_with=connection).

What this has to do with a dataframe, not really sure, which suggests this is not really what you're asking, so, would use some more specifics.

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[Soumaya Mauthoor]

With a dataframe you can do df.columns to get the column labels and df[["col1","col2"]] to extract column1 and column2 

rowProxy has similar functionality as explained before

However if I use s= fetchmany() I return a string and it's not possible to do the equivalent s.keys() and s["col1"].

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[Mike Bayer]

On Sun, Oct 27, 2019, at 2:48 PM, Soumaya Mauthoor wrote:

With a dataframe you can do df.columns to get the column labels and df[["col1","col2"]] to extract column1 and column2 

I'm going to guess this means that you're getting the col1/col2 from multiple rows at once.

In python we use a list comprehension to get this:

[(row["col1"], row["col2"]) for row in result.fetchmany()]

also the keys to each row are are present both via row.keys() as well as result.keys().

if you want full matrix operations available to a result set, it's best to load it into a pandas dataframe first, since the "fetch" is an exhaustive operation (e.g. only works once per row).  

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[sumau]

Crystal clear thanks!