- According to numba official site, it's high performance
`JIT compiler`

for python. It translates Python code into fast machine code which can be re-run again and again fast once it's compiled and converted to machine code. `Numba`

can only translate a certain subset of python code which involves`loops`

and`code involving numpy`

to faster machine code. Not everything will be running faster using numba. One needs to have basic knowledge of what can be parallelized and what not to make efficient use of numba.`numba`

provides a list of decorators which when applied to functions results in faster versions of that function.- When a function decorated with numba decorators is called, it'll be compiled first to generate faster machine code hence it'll take almost the same time as python code or maybe more in the worst case. Once code is compiled then recalling such function will be way faster because the compiled version will be called subsequently.
- Numba reads python bytecode of function covered with numba decorator, converts it's input arguments and other data used inside function to numba datatypes, optimizes various parts and converts it to machine code using LLVM library.
- If a function is designed to use with various data types (generic functions) then numba will take time to compile function each time function is called with new data type it hasn't seen before. Because it'll be creating a different compilation of the same generic function with different datatypes.
- Numba can help to improve the performance of code running on GPUs as well. It supports
`Nvidia GPUs`

and`AMD ROC GPUs`

. One needs to install Graphics drivers for particular GPUs. It'll also require the`cudatoolkit`

library as well for Nvidia(`conda install cudatoolkit`

). For AMD, it'll require`roctools`

library(`conda install -c numba roctools`

).

**Note:** Python is interpreter based language and numba is JIT compiler of python. Compiler based languages are faster than interpreter based.

- Numba is compatible with
`Python 2.7`

and`3.5 or later`

, and`Numpy versions 1.7 to 1.15`

.

`pip install numba`

`conda install numba`

In [1]:

```
import numpy as np
import matplotlib.pyplot as plt
import numba
import pandas as pd
from numba import jit
%matplotlib inline
```

In [2]:

```
for lib in dir():
lib = eval(lib)
if '__version__' in dir(lib): print(lib.__name__ + ' : '+ lib.__version__)
```

In [3]:

```
def get_squares():
return [ pow(i,2) for i in range(int(1e7))]
%time squares = get_squares()
%time squares = get_squares()
%time squares = get_squares()
```

- Below we have introduced the first decorator of numba
`@jit`

which clearly improves performance compared to normal python code. - If adding numba decorators during testing does not seem to improve performance then it's better to remove
`@jit`

decorator and fall back to using pure python and find out some other ways to improve performance. Because using`@jit`

decorator with functions that can't be converted to numba might result in worsening performance as it'll take time to compile function first time and will result in no performance improvement. Hence time taken first time to compile function to convert will add up overhead. `numba`

generally does not work much faster with list-comprehensions (though below case seems bit exception) and it is suggested to fallback and convert function using comprehensions to loop-based again for faster performance.

In [4]:

```
@jit
def get_squares():
return [ pow(i,2) for i in range(int(1e7))]
%time squares = get_squares()
%time squares = get_squares()
%time squares = get_squares()
```

In [5]:

```
@jit
def get_squares():
squares = []
for i in range(int(1e7)):
squares.append(pow(i,2))
return squares
%time squares = get_squares()
%time squares = get_squares()
%time squares = get_squares()
```

- Below example has a line which uses pandas function
`apply`

which applies a function to all cells of pandas dataframe which if uncommented will result in failure with`@jit`

decorator - Below examples shows that using
`numba`

involving only`pandas`

code will`not`

results in improving performance. It can even backfire and can take time to run the first time as seen below. Because it tried to convert code to numba for improving performance but it failed and fall backed to`pure python`

at last.

In [6]:

```
def work_on_dataframe():
data = {'Col1': range(1000), 'Col2': range(1000), 'Col3': range(1000)}
df = pd.DataFrame(data=data)
#df_square = df.apply(lambda x: x*x*x)
df['Col1'] = (df.Col1 * 100)
df['Col2'] = (df.Col1 * df.Col3)
df = df.where((df > 100) & (df < 10000))
df = df.dropna(how='any')
return df
%time df = work_on_dataframe()
%time df = work_on_dataframe()
%time df = work_on_dataframe()
```

In [7]:

```
@jit
def work_on_dataframe():
data = {'Col1': range(1000), 'Col2': range(1000), 'Col3': range(1000)}
df = pd.DataFrame(data=data)
#df_square = df.apply(lambda x: x*x*x)
df['Col1'] = (df.Col1 * 100)
df['Col2'] = (df.Col1 * df.Col3)
df = df.where((df > 100) & (df < 10000))
df = df.dropna(how='any')
return df
%time df = work_on_dataframe()
%time df = work_on_dataframe()
%time df = work_on_dataframe()
```

In [8]:

```
def calculate_sum():
arr = np.arange(1e7)
s = np.sum(arr)
return s
%time s = calculate_sum()
%time s = calculate_sum()
%time s = calculate_sum()
```

In [9]:

```
@jit
def calculate_sum():
arr = np.arange(1e7)
s = np.sum(arr)
return s
%time s = calculate_sum()
%time s = calculate_sum()
%time s = calculate_sum()
```

In [10]:

```
@jit
def calculate_sum():
s = 0
for i in np.arange(1e7):
s += i
return s
%time s = calculate_sum()
%time s = calculate_sum()
%time s = calculate_sum()
```

`nopython`

attribute to `@jit`

decorator¶`@jit`

compile works in 2 modes.`nopython`

`object`

`nopython mode`

is generally preferred over`object mode`

and way faster than it if it can be used.- If you know that your whole function can be converted using numba then it's preferred to use
`nopython mode`

. - If your function is designed in a way that some parts of it can be converted to numba and some will run in pure python then it's preferred to run in
`object mode`

. - Users can test whether their function can run with
`nopython mode`

first and if it works then use that mode otherwise fall back to`object mode`

.If function fails with`nopython mode`

then it'll require to use`object mode`

.

In [12]:

```
def calculate_all_permutations():
perms = []
for i in range(int(1e4)):
for j in range(int(1e3)):
perms.append((i,j))
%time perms = calculate_all_permutations()
%time perms = calculate_all_permutations()
%time perms = calculate_all_permutations()
```

In [15]:

```
@jit
def calculate_all_permutations():
perms = []
for i in range(int(1e4)):
for j in range(int(1e3)):
perms.append((i,j))
%time perms = calculate_all_permutations()
%time perms = calculate_all_permutations()
%time perms = calculate_all_permutations()
```

In [16]:

```
@jit(nopython=True)
def calculate_all_permutations():
perms = []
for i in range(int(1e4)):
for j in range(int(1e3)):
perms.append((i,j))
%time perms = calculate_all_permutations()
%time perms = calculate_all_permutations()
%time perms = calculate_all_permutations()
```

Sunny Solanki

Learning Numpy - Simple Tutorial For Beginners - NumPy - Array Creation Routines Part 5

Learning Numpy - Simple Tutorial For Beginners - NumPy - Array Attributes Part 4

Learning Numpy - Simple Tutorial For Beginners - NumPy - Data Types Part 3

Learning Numpy - Simple Tutorial For Beginners - NumPy - Ndarray Object Part 2