file-cache 0.1.1.5

Cache dataframe with local file

# File Case
- This is used to cache the Dataframe result, even there are multiply Dataframe, which can help to reduce the huge time in feature engineering
- It also support to log the function time cost and parameters


## Installation
pip install file_cache

## Sample case


```python
from file_cache.cache import file_cache
import numpy as np
import pandas as pd

@file_cache()
def test_cache_normal(name):
import time
import numpy as np
time.sleep(3)
return pd.DataFrame(data= np.arange(0,10).reshape(2,5))

normal_df = test_cache_normal('Felix')
normal_df.head()
```




<div>

<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>0</th>
<th>1</th>
<th>2</th>
<th>3</th>
<th>4</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0</td>
<td>1</td>
<td>2</td>
<td>3</td>
<td>4</td>
</tr>
<tr>
<th>1</th>
<td>5</td>
<td>6</td>
<td>7</td>
<td>8</td>
<td>9</td>
</tr>
</tbody>
</table>
</div>



## Return mulpiple DF with tuple
Support to cache multiple DF with tuple


```python
import time
from functools import lru_cache

@lru_cache()
@file_cache()
def test_cache_tuple(name):
time.sleep(3)
df0 = pd.DataFrame(data= np.arange(5,15).reshape(2,5))
df1 = pd.DataFrame(data= np.arange(20,30).reshape(2,5))
return df0, df1

df0, df1 = test_cache_tuple('Felix2')
print(df0 , '\n')
print(df1)
```


0 1 2 3 4
0 5 6 7 8 9
1 10 11 12 13 14

0 1 2 3 4
0 20 21 22 23 24
1 25 26 27 28 29


## For the input paras can not be cached
If the input is DF or cannot be hashed, ignore the cache, run the function directly


```python
@file_cache()
def test_cache_ignore(name):
df0 = pd.DataFrame(data= np.arange(5,15).reshape(2,5))
return df0

df = pd.DataFrame(data= np.arange(5,15).reshape(2,5))
ignore = test_cache_ignore(df)

```


## Log the function time and parameter


```python
from file_cache.utils.util_log import *
@timed()
def log_time(arg):
return f'{arg} msg'

print(log_time("hello"))
```

2018-12-26 11:08:52,662 util_log.py[61] DEBUG Start the program at:LALI2-M-G0MD, 127.0.0.1, with:Load module
2018-12-26 11:08:52,665 util_log.py[41] INFO log_time begin with(1 paras) :['hello'], []
2018-12-26 11:08:52,667 util_log.py[49] INFO log_time cost: 0.00 sec:(1 paras)(['hello'], []), return:hello msg, end


hello msg


## Not only support DataFrame, but also support Series


```python
from file_cache.cache import file_cache
@file_cache()
def get_train_data():
from sklearn import datasets
import pandas as pd
import numpy as np
data = datasets.load_boston()
df = pd.DataFrame( data.data , columns=data.feature_names)
df['target'] = data.target
df.head()
return df, df['target']

df, series = get_train_data()
print(type(df), type(series))

df, series = get_train_data()
print(type(df), type(series))

```

<class 'pandas.core.frame.DataFrame'> <class 'pandas.core.series.Series'>
<class 'pandas.core.frame.DataFrame'> <class 'pandas.core.series.Series'>