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‎Default.sublime-commands

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[
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{
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"caption": "Python Data Science Snippets: Open Readme",
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"command": "open_file",
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"args": {
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"file": "${packages}/Python Data Science Snippets/README.md"
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}
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}
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]

‎LICENSE

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MIT License
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Copyright (c) 2021 futureprogrammer360
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md

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# 📊 Python Data Science Snippets
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[Python Data Science Snippets](https://github.com/futureprogrammer360/Python-Data-Science-Snippets) is a collection of [Sublime Text](https://www.sublimetext.com/) snippets for data science and machine learning in Python.
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## 💻 Installation
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The easiest way to install Python Data Science Snippets is through [Package Control](https://packagecontrol.io/packages/Python%20Data%20Science%20Snippets). 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!
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## 📈 Snippets
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* [Imports](#imports)
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* [NumPy](#numpy)
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* [Pandas](#pandas)
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* [Matplotlib](#matplotlib)
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* [Scikit-learn](#scikit-learn)
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* [Keras](#keras)
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### Imports
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Import snippets start with `i` followed by the package's import alias.
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| Trigger | Description |
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|------------|-----------------------------------|
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| `ikeras` | `from tensorflow import keras` |
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| `inp` | `import numpy as np` |
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| `ipd` | `import pandas as pd` |
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| `iplt` | `import matplotlib.pyplot as plt` |
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| `isklearn` | `from sklearn.$1 import $2` |
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| `isns` | `import seaborn as sns` |
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| `itf` | `import tensorflow as tf` |
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| `itorch` | `import torch` |
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### NumPy
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| Trigger | Description |
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|------------|----------------|
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| `arange` | `np.arange` |
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| `array` | `np.array` |
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| `linspace` | `np.linspace` |
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| `logspace` | `np.logspace` |
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| `ones` | `np.ones` |
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| `zeros` | `np.zeros` |
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### Pandas
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| Trigger | Description |
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|------------|----------------|
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| `columns` | `df.columns` |
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| `describe` | `df.describe` |
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| `df` | `pd.DataFrame` |
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| `head` | `df.head` |
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| `read_csv` | `pd.read_csv` |
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| `ser` | `pd.Series` |
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| `tail` | `df.tail` |
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### Matplotlib
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| Trigger | Description |
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|------------|----------------|
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| `bar` | `plt.bar` |
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| `legend` | `plt.legend` |
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| `pie` | `plt.pie` |
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| `plot` | `plt.plot` |
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| `scatter` | `plt.scatter` |
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| `show` | `plt.show` |
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| `subplots` | `plt.subplots` |
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| `title` | `plt.title` |
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| `xlabel` | `plt.xlabel` |
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| `ylabel` | `plt.ylabel` |
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### Scikit-learn
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| Trigger | Description |
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|----------|--------------------------|
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| `knn` | `KNeighborsClassifier` |
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| `linreg` | `LinearRegression` |
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| `logreg` | `LogisticRegression` |
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| `rfc` | `RandomForestClassifier` |
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| `tts` | `train_test_split` |
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### Keras
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| Trigger | Description |
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|--------------|---------------------------|
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| `compile` | `model.compile` |
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| `fit` | `model.fit` |
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| `layer` | `keras.layers.layer` |
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| `load_model` | `keras.models.load_model` |
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| `save` | `model.save` |
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| `sequential` | `keras.Sequential` |
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The snippet files are in the `snippets` folder of [this GitHub repository](https://github.com/futureprogrammer360/Python-Data-Science-Snippets).

‎messages.json

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{
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"install": "messages/install.txt"
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}

‎messages/install.txt

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Hello fellow data scientist!
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Thank you for installing the Python Data Science Snippets package. This is a collection of useful snippets for data science and machine learning in Python.
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To see the snippets available, check out the README.md file via the `Python Data Science Snippets: Open Readme` command in command palette.
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Enjoy!
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GitHub homepage: https://github.com/futureprogrammer360/Python%20Data%20Science%20Snippets

‎snippets/imports/keras.sublime-snippet

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<snippet>
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<content><![CDATA[
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from tensorflow import keras
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]]></content>
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<tabTrigger>ikeras</tabTrigger>
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<scope>source.python</scope>
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<description>from tensorflow import keras</description>
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</snippet>

‎snippets/imports/np.sublime-snippet

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<snippet>
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<content><![CDATA[
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import numpy as np
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]]></content>
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<tabTrigger>inp</tabTrigger>
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<scope>source.python</scope>
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<description>import numpy as np</description>
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</snippet>

‎snippets/imports/pd.sublime-snippet

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<snippet>
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<content><![CDATA[
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import pandas as pd
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]]></content>
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<tabTrigger>ipd</tabTrigger>
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<scope>source.python</scope>
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<description>import pandas as pd</description>
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</snippet>

‎snippets/imports/plt.sublime-snippet

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<snippet>
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<content><![CDATA[
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import matplotlib.pyplot as plt
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]]></content>
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<tabTrigger>iplt</tabTrigger>
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<scope>source.python</scope>
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<description>import matplotlib.pyplot as plt</description>
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</snippet>

‎snippets/imports/sklearn.sublime-snippet

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<snippet>
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<content><![CDATA[
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from sklearn.${1:model_selection} import ${2:train_test_split}
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]]></content>
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<tabTrigger>isklearn</tabTrigger>
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<scope>source.python</scope>
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<description>from sklearn.$1 import $2</description>
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</snippet>

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