Python for Data sciense and ML(DL)
keywords
Enjoy! Keywords for easy working
immatplotlib => import matplotlib.pyplot as plt
$0
.contourf => .contourf($0)
.xlabel => .xlabel($0)
.ylabel => .ylabel($0)
.show => .show($0)
imnumpy => import numpy as np
.array => .array([$0])
.shape => .shape
.ndim => .ndim
.dtype => .dtype
.size => .size
.arange => .arange($0)
.reshape => .reshape($0)
.linspace => .linspace($0)
.random => .random.random($0)
.sqrt => .sqrt($0)
.sin => .sin($0)
.cos => .cos($0)
.log => .log($0)
.exp => .exp($0)
.randint => .random.randint($0)
.max => .max()
.min => .min()
.mean => .mean()
.sum => .sum()
.std => .std()
.median => .median($0)
.insert => .insert($0)
.sort => .sort($0)
.delete => .delete($0)
.concatenate => .concatenate(($0))
.array_split => .array_split($0)
.resize => .resize($0,())
.zeros => .zeros(($0))
.ones => .ones(($0))
.full => .full(($0),)
.dot => .dot($0)
.trace => .trace($0)
.inv => .linalg.inv($0)
.det => .linalg.det($0)
.eig => .linalg.eig($0)
.percentile => .percentile($0)
l => lambda $1: $0
sig => sig = lambda x: 1/(1+np.exp(-x))
.meshgrid => .meshgrid($0)
.unique => .unique($0)
.ravel => .ravel($0)
.argmax => .argmax($0)
.ravel => .ravel($0)
.average => .average($0)
impandas => import pandas as pd
$0
.read_csv => .read_csv('$0')
.describe => .describe($0)
.head => .head($0)
.info => .info()
.get_dummies => .get_dummies($0)
.corr => .corr($0)
.tail => .tail()
.values => .values
im => import $0
pr => print($0)
ln => len($0)
rn => range($0)
for => for $1 in range($2):
$0
imnb => from numba import jit, njit, prange
njit => @njit(fastmath=True,parallel=True,cache=True)
jit => @jit(nopython=True,cache=True)
r => return $0
t => True
f => False
def => def $1($2):
$0
k => """
$0
"""
init => __init__
defc => def $1(self, $2):
$0
defc_ => def __init__(self, $1):
$0
self => self.$0 = $0
help => help($0)
ima => import numpy as np
import pandas as pd
import seaborn as sns
$0
imseaborn => import seaborn as sns
$0
.pivot_table => .pivot_table($0)
.joinplot => .joinplot(data=$0,)
imsklearn => from sklearn.linear_model import LinearRegression
$0
.fit => .fit($0)
.coef_ => .coef_
.intercept_ => .intercept_
.predict => .predict($0)
train => X_train, X_test, y_train, y_test = train_test_split($0)
train_test => from sklearn.model_selection import train_test_split
imstatsmodels => import statsmodels.api as sn
.add_constant => .add_constant($0)
.summary => .summary()