Pandas read sql integer became float

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.

As already said the problem is that pandas' integer can not handle NULL/NA value.

You can replace read_sql_table with read_sql and convert NULL to some integer value (for example 0 or -1, something which has NULL sense in your setting):

df = pandas.read_sql("SELECT col1, col2, IFNULL(col3, 0) FROM table", engine)

Here col3 can be NULL in mysql, ifnull will return 0 if it is NULL or col3 value otherwise.

Or same thing with little function helper:

def read_sql_table_with_nullcast(table_name, engine, null_cast={}):
    """
    table_name - table name
    engine - sql engine
    null_cast - dictionary of columns to replace NULL:
           column name as key value to replace with as value.
           for example {'col3':0} will set all NULL in col3 to 0
    """
    import pandas
    cols = pandas.read_sql("SHOW COLUMNS FROM " + table_name, engine)
    cols_call = [c if c not in null_cast else "ifnull(%s,%d) as %s"%(c,null_cast[c],c) for c in cols['Field']]
    sel = ",".join(cols_call)
    return pandas.read_sql("SELECT " + sel + " FROM " + table_name, engine)

read_sql_table_with_nullcast("table", engine, {'col3':0})