@@ -2858,11 +2858,8 @@ def to_sql(
2858
2858
2859
2859
>>> df.to_sql('users', con=engine)
2860
2860
3
2861
- >>> pd.read_sql_query("SELECT * FROM users", con=engine)
2862
- index name
2863
- 0 0 User 1
2864
- 1 1 User 2
2865
- 2 2 User 3
2861
+ >>> engine.execute("SELECT * FROM users").fetchall()
2862
+ [(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]
2866
2863
2867
2864
An `sqlalchemy.engine.Connection` can also be passed to `con`:
2868
2865
@@ -2892,10 +2889,8 @@ def to_sql(
2892
2889
>>> df2.to_sql('users', con=engine, if_exists='replace',
2893
2890
... index_label='id')
2894
2891
2
2895
- >>> pd.read_sql_query("SELECT * FROM users", con=engine)
2896
- id name
2897
- 0 0 User 6
2898
- 1 1 User 7
2892
+ >>> engine.execute("SELECT * FROM users").fetchall()
2893
+ [(0, 'User 6'), (1, 'User 7')]
2899
2894
2900
2895
Specify the dtype (especially useful for integers with missing values).
2901
2896
Notice that while pandas is forced to store the data as floating point,
@@ -2914,11 +2909,8 @@ def to_sql(
2914
2909
... dtype={"A": Integer()})
2915
2910
3
2916
2911
2917
- >>> pd.read_sql("SELECT * FROM integers", con=engine)
2918
- A
2919
- 0 1.0
2920
- 1 NaN
2921
- 2 2.0
2912
+ >>> engine.execute("SELECT * FROM integers").fetchall()
2913
+ [(1,), (None,), (2,)]
2922
2914
""" # noqa:E501
2923
2915
from pandas .io import sql
2924
2916
0 commit comments