# core
# BFrame
------------------------------------------------------------------------
source
### BFrame
``` python
def BFrame(
*values, keys:NoneType=None, names:NoneType=None, **fields
):
```
*A native BPL value with shape, keys and names. An array that holds a
function as a direct item is a `FunctionArray`.*
``` python
test_frame = BFrame(dict(col=[1,2,3]))
test_frame
```
┌→────────────┐
│ col:┌→────┐ │
│ │1 2 3│ │
│ └ₓ────┘ │
└∊────────────┘
``` python
test_eq(BFrame, type(test_frame))
```
## Reading and writing
### CSV
------------------------------------------------------------------------
source
### BFrame.read_csv
``` python
def read_csv(
path, **kwargs
):
```
``` python
bf = BFrame.read_csv("../data/toy.csv", encoding="UTF-8")
bf.df.head(3)
```
|
Number |
City |
Gender |
Age |
Income |
Illness |
| 0 |
1 |
Dallas |
Male |
41 |
40367.0 |
No |
| 1 |
2 |
Dallas |
Male |
54 |
45084.0 |
No |
| 2 |
3 |
Dallas |
Male |
42 |
52483.0 |
No |
------------------------------------------------------------------------
source
### BFrame.save_csv
``` python
def save_csv(
path
):
```
``` python
# bf.save_csv("../data/save_test.csv");
```
## Selection
------------------------------------------------------------------------
source
### BFrame.take
``` python
def take(
n:int
):
```
``` python
head = bf.take(100)
head.take(2).df
```
|
Number |
City |
Gender |
Age |
Income |
Illness |
| 0 |
1 |
Dallas |
Male |
41 |
40367.0 |
No |
| 1 |
2 |
Dallas |
Male |
54 |
45084.0 |
No |
------------------------------------------------------------------------
source
### BFrame.head
``` python
def head(
n:int=5
):
```
``` python
bf.head().df
```
|
Number |
City |
Gender |
Age |
Income |
Illness |
| 0 |
1 |
Dallas |
Male |
41 |
40367.0 |
No |
| 1 |
2 |
Dallas |
Male |
54 |
45084.0 |
No |
| 2 |
3 |
Dallas |
Male |
42 |
52483.0 |
No |
| 3 |
4 |
Dallas |
Male |
40 |
40941.0 |
No |
| 4 |
5 |
Dallas |
Male |
46 |
50289.0 |
No |
------------------------------------------------------------------------
source
### BFrame.tail
``` python
def tail(
n:int=5
):
```
``` python
bf.tail().df
```
|
Number |
City |
Gender |
Age |
Income |
Illness |
| 0 |
149996 |
Austin |
Male |
48 |
93669.0 |
No |
| 1 |
149997 |
Austin |
Male |
25 |
96748.0 |
No |
| 2 |
149998 |
Austin |
Male |
26 |
111885.0 |
No |
| 3 |
149999 |
Austin |
Male |
25 |
111878.0 |
No |
| 4 |
150000 |
Austin |
Female |
37 |
87251.0 |
No |
## Agg
``` python
MEAN = add.reduce_first / tally
MEAN(bf.head(100)["Income"])
```
44362.88
## Groupby
``` python
fanout
```
↣(□, {f←↑⍺⋄f ⍵}⍤[0 ∞]⍤[∞ 2])
------------------------------------------------------------------------
source
### BFrame.matrix
``` python
def matrix(
cols
):
```
------------------------------------------------------------------------
source
### BFrame.groupby
``` python
def groupby(
by, cols, **aggs
):
```
``` python
RMSE = MEAN.under(square)
fn_dict=dict(mean=MEAN, rmse=RMSE)
fn_dict
```
{'mean': +⌿÷≢, 'rmse': (+⌿÷≢)@⊼}
``` python
head.groupby("Gender", "Income", rmse=RMSE)
```
┌→───────────────┐
↓"Male"⌷ │
↓ Income│
│rmse 49632.10639│
│ │
│"Female"⌷ │
│ Income│
│rmse 40738.65853│
└~───────────────┘
``` python
head.groupby("Gender", ["Age", "Income"], **fn_dict)
```
┌→───────────────────────────┐
↓"Male"⌷ │
↓ Age Income│
│mean 44.57407407 48301.42593│
│rmse 45.66970793 49632.10639│
│ │
│"Female"⌷ │
│ Age Income│
│mean 45.30434783 39739.36957│
│rmse 46.73468128 40738.65853│
└~───────────────────────────┘
## Rolling
`windows` docs with arg explanations:
https://answerdotai.github.io/basedpl/glyphs/up-down-arrow.html
------------------------------------------------------------------------
source
### BFrame.rolling
``` python
def rolling(
cols, size, edge:str='fill', step:int=1, fill:int=0, **aggs
):
```
``` python
ts = BFrame(dict(price=[5,4.3,4.1,2,10,20,15,25]))
ts.rolling("price", size=3, mean=MEAN, rmse=RMSE).df
```
|
|
price |
| 0 |
mean |
3.100000 |
| rmse |
3.807449 |
| 1 |
mean |
4.466667 |
| rmse |
4.483302 |
| 2 |
mean |
3.466667 |
| rmse |
3.619392 |
| 3 |
mean |
5.366667 |
| rmse |
6.345865 |
| 4 |
mean |
10.666667 |
| rmse |
12.961481 |
| 5 |
mean |
15.000000 |
| rmse |
15.545632 |
| 6 |
mean |
20.000000 |
| rmse |
20.412415 |
| 7 |
mean |
13.333333 |
| rmse |
16.832508 |
``` python
ts.rolling("price", size=4, step=2, mean=MEAN, rmse=RMSE).df
```
|
|
price |
| 0 |
mean |
3.350000 |
| rmse |
3.882654 |
| 1 |
mean |
5.100000 |
| rmse |
5.901271 |
| 2 |
mean |
11.750000 |
| rmse |
13.500000 |
| 3 |
mean |
15.000000 |
| rmse |
17.677670 |
## Viz
------------------------------------------------------------------------
source
### BFrame.plot
``` python
def plot(
**kwargs
):
```
``` python
BFrame(dict(price=[5,4.3,4.1,2,10,20,15,25])).plot(mark="line", title="Price over time")
```
------------------------------------------------------------------------
source
### BFrame.hist_input
``` python
def hist_input(
n:int
):
```
``` python
income = head["Income"]
income.hist_input(5).df
```
|
x |
count |
| 0 |
21417.9 |
6 |
| 1 |
31917.7 |
21 |
| 2 |
42417.5 |
27 |
| 3 |
52917.3 |
36 |
| 4 |
63417.1 |
10 |
------------------------------------------------------------------------
source
### BFrame.hist
``` python
def hist(
n:int=50, **kwargs
):
```
``` python
bf["Income"].hist()
```
