pd.merge combine two df was so huge!
My Code
```python
quote = pro.daily(ts_code=code, end_date=end_date.strftime("%Y%m%d"))
basic = pro.daily_basic(ts_code=code, end_date=end_date.strftime("%Y%m%d"))
combine_quote = pd.merge(left=quote, right=basic, on='ts_code', how='inner')
```
#### Problem description

I have two dataframe which named quote and basic,let me see two tables detail

```python
quote.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
Index: 2869 entries, 20190315 to 20070427
Data columns (total 10 columns):
ts_code      2869 non-null object
open         2869 non-null float64
high         2869 non-null float64
low          2869 non-null float64
close        2869 non-null float64
pre_close    2869 non-null float64
change       2869 non-null float64
pct_chg      2869 non-null float64
vol          2869 non-null float64
amount       2869 non-null float64
dtypes: float64(9), object(1)
memory usage: 568.8 KB
```

```python
basic.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
Index: 2869 entries, 20190315 to 20070427
Data columns (total 15 columns):
ts_code            2869 non-null object
close              2869 non-null float64
turnover_rate      2869 non-null float64
turnover_rate_f    2869 non-null float64
volume_ratio       2864 non-null float64
pe                 2869 non-null float64
pe_ttm             2869 non-null float64
pb                 2869 non-null float64
ps                 2869 non-null float64
ps_ttm             2869 non-null float64
total_share        2869 non-null float64
float_share        2869 non-null float64
free_share         2869 non-null float64
total_mv           2869 non-null float64
circ_mv            2869 non-null float64
dtypes: float64(14), object(1)
memory usage: 680.8 KB

```

then I will merge two to one use pd.merge,this procedure is not fast,about 1.5-2 second,I was shocked when I check memory usage,see that

```python
combine_quote.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
Int64Index: 8231161 entries, 0 to 8231160
Data columns (total 26 columns):
ts_code            object
trade_date_x       object
open               float64
high               float64
low                float64
close_x            float64
pre_close          float64
change             float64
pct_chg            float64
vol                float64
amount             float64
trade_date_y       object
close_y            float64
turnover_rate      float64
turnover_rate_f    float64
volume_ratio       float64
pe                 float64
pe_ttm             float64
pb                 float64
ps                 float64
ps_ttm             float64
total_share        float64
float_share        float64
free_share         float64
total_mv           float64
circ_mv            float64
dtypes: float64(23), object(3)
memory usage: 3.0 GB
```
why 568KB + 680KB = 3GB?!


but pd.concat was correct
```python
quote = pro.daily(ts_code=code, end_date=end_date.strftime("%Y%m%d"))
quote.set_index("trade_date", inplace=True)
basic = pro.daily_basic(ts_code=code, end_date=end_date.strftime("%Y%m%d"))
basic.set_index("trade_date", inplace=True)
combine2 = pd.concat([quote, basic], axis=1)

combine2.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2869 entries, 0 to 2868
Data columns (total 27 columns):
ts_code            2869 non-null object
trade_date         2869 non-null object
open               2869 non-null float64
high               2869 non-null float64
low                2869 non-null float64
close              2869 non-null float64
pre_close          2869 non-null float64
change             2869 non-null float64
pct_chg            2869 non-null float64
vol                2869 non-null float64
amount             2869 non-null float64
ts_code            2869 non-null object
trade_date         2869 non-null object
close              2869 non-null float64
turnover_rate      2869 non-null float64
turnover_rate_f    2869 non-null float64
volume_ratio       2864 non-null float64
pe                 2869 non-null float64
pe_ttm             2869 non-null float64
pb                 2869 non-null float64
ps                 2869 non-null float64
ps_ttm             2869 non-null float64
total_share        2869 non-null float64
float_share        2869 non-null float64
free_share         2869 non-null float64
total_mv           2869 non-null float64
circ_mv            2869 non-null float64
dtypes: float64(23), object(4)
memory usage: 1.2 MB
```


<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.19.28-1-MANJARO
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.2
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.3.1
pymysql: 0.9.3
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None

</details>
