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I met a problem that when I use pandas to read Mysql table, some columns (see 'to_nlc') used to be integer became a float number (automatically add .0 after that). Can anyone figure it out? Or some guessings? Thanks very much!
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read_sql. Read SQL query or database table into a DataFrame. This function is a convenience wrapper around read_sql_table and read_sql_query (for backward compatibility).
Reading SQL queries into Pandas dataframes is a common task, and one that can be very slow. Depending on the database being used, this may be hard to get around, but for those of us using Postgres we can speed this up considerably using the COPY command.
This main difference can mean that the two tools are separate, however, you can also perform several of the same functions in each respective tool, for example, you can create new features from existing columns in pandas, perhaps easier and faster than in SQL.
Problem is your data contains NaN values, so int is automatically cast to float.
I think you can check NA type promotions:
When introducing NAs into an existing Series or DataFrame via reindex or some other means, boolean and integer types will be promoted to a different dtype in order to store the NAs. These are summarized by this table:
Typeclass Promotion dtype for storing NAs
floating no change
object no change
integer cast to float64
boolean cast to object
While this may seem like a heavy trade-off, in practice I have found very few cases where this is an issue in practice. Some explanation for the motivation here in the next section.
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