python-snippets

Python for Data sciense and ML(DL)

Usage

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()