Python Data Science Snippets

by futureprogrammer360 ALL

📊 Collection of Sublime Text snippets for data science and machine learning in Python (Imports, NumPy, Pandas, Matplotlib, Scikit-learn, Keras, PyTorch, etc)

Labels snippets, python, data science, auto-complete, autocomplete, completions

Details

Version 1.0.3
Homepage packagecontrol.io
Issues github.com
Modified 8 months ago
Last Seen 2 hours ago
First Seen 2 years ago

Installs

Total 2K
Win 1K
Mac 293
Linux 351

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Windows	0	2	2	1	1	0	0	1	1	0	0	2	0	1	2	0	0	1	1	1	0	4	1	2	2	2	0	1	1	1	2	0	1	1	0	1	1	2	1	0	0	1	3	1	1	1
Mac	0	2	1	1	0	0	0	0	0	0	0	0	1	0	0	0	0	2	0	2	0	0	0	0	1	1	0	1	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0
Linux	0	0	1	0	1	2	0	0	0	0	0	0	1	0	0	1	0	2	0	1	2	0	0	0	0	1	1	0	1	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0

Readme

Source: raw.githubusercontent.com

📊 Python Data Science Snippets

Python Data Science Snippets is a collection of Sublime Text snippets for data science and machine learning in Python.

💻 Installation

The easiest way to install Python Data Science Snippets is through Package Control. After it is enabled inside Sublime Text, open the command palette and find Package Control: Install Package and press ENTER. Then, find Python Data Science Snippets in the list. Press ENTER again, and this package is installed!

Imports

Import snippets start with i followed by the package/module's import alias.

Trigger	Description
`ikeras`	`from tensorflow import keras`
`inp`	`import numpy as np`
`ipd`	`import pandas as pd`
`iplt`	`import matplotlib.pyplot as plt`
`isklearn`	`from sklearn.$1 import $2`
`isns`	`import seaborn as sns`
`itf`	`import tensorflow as tf`
`itorch`	`import torch`
`inn`	`from torch import nn`
`idl`	`from torch.utils.data import DataLoader`

NumPy

Trigger	Description
`arange`	`np.arange`
`array`	`np.array`
`linspace`	`np.linspace`
`logspace`	`np.logspace`
`ones`	`np.ones`
`zeros`	`np.zeros`

Pandas

Trigger	Description
`apply`	`df.apply`
`columns`	`df.columns`
`describe`	`df.describe`
`df`	`pd.DataFrame`
`dropna`	`df.dropna`
`fillna`	`df.fillna`
`groupby`	`df.groupby`
`head`	`df.head`
`read_csv`	`pd.read_csv`
`rename`	`df.rename`
`reset_index`	`df.reset_index`
`sample`	`df.sample`
`ser`	`pd.Series`
`tail`	`df.tail`
`to_csv`	`df.to_csv`
`to_datetime`	`pd.to_datetime`

Matplotlib

Trigger	Description
`annotate`	`plt.annotate`
`bar_label`	`plt.bar_label`
`bar`	`plt.bar`
`barh`	`plt.barh`
`fill_between`	`plt.fill_between`
`hist`	`plt.hist`
`imread`	`plt.imread`
`imsave`	`plt.imsave`
`imshow`	`plt.imshow`
`legend`	`plt.legend`
`pie`	`plt.pie`
`plot`	`plt.plot`
`savefig`	`plt.savefig`
`scatter`	`plt.scatter`
`show`	`plt.show`
`stackplot`	`plt.stackplot`
`subplot`	`plt.subplot`
`subplots`	`plt.subplots`
`suptitle`	`plt.suptitle`
`text`	`plt.text`
`tight_layout`	`plt.tight_layout`
`title`	`plt.title`
`xlabel`	`plt.xlabel`
`xlim`	`plt.xlim`
`ylabel`	`plt.ylabel`
`ylim`	`plt.ylim`

Scikit-learn

Trigger	Description
`knn`	`KNeighborsClassifier`
`linreg`	`LinearRegression`
`logreg`	`LogisticRegression`
`rfc`	`RandomForestClassifier`
`tts`	`train_test_split`

Keras

Trigger	Description
`compile`	`model.compile`
`evaluate`	`model.evaluate`
`fit`	`model.fit`
`layer`	`keras.layers.layer`
`load_model`	`keras.models.load_model`
`predict`	`model.predict`
`save`	`model.save`
`sequential`	`keras.Sequential`

PyTorch

Trigger	Description
`dataloader`	`torch.utils.data.DataLoader`
`device`	`torch.device (cuda/cpu)`
`module`	`torch.nn.Module`

The snippet files are in the snippets folder of this GitHub repository.