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futureprogrammer360futureprogrammer360
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Add some Matplotlib and Pandas snippets
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‎README.md‎

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@@ -23,7 +23,7 @@ The easiest way to install Python Data Science Snippets is through [Package Cont
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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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Import snippets start with `i` followed by the package/module's import alias.
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| Trigger | Description |
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|------------|-------------------------------------------|
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| `head` | `df.head` |
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| `read_csv` | `pd.read_csv` |
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| `rename` | `df.rename` |
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| `reset_index` | `df.reset_index` |
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| `sample` | `df.sample` |
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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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| Trigger | Description |
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|----------------|--------------------|
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| `annotate` | `plt.annotate` |
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| `bar_label` | `plt.bar_label` |
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| `bar` | `plt.bar` |
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| `barh` | `plt.barh` |
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| `fill_between` | `plt.fill_between` |
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| `hist` | `plt.hist` |
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| `imread` | `plt.imread` |
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| `imsave` | `plt.imsave` |
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| `imshow` | `plt.imshow` |
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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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| `savefig` | `plt.savefig` |
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| `scatter` | `plt.scatter` |
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| `show` | `plt.show` |
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| `stackplot` | `plt.stackplot` |
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| `subplot` | `plt.subplot` |
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| `subplots` | `plt.subplots` |
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| `suptitle` | `plt.suptitle` |
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| `text` | `plt.text` |
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| `tight_layout` | `plt.tight_layout` |
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| `title` | `plt.title` |
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| `xlabel` | `plt.xlabel` |
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| `xlim` | `plt.xlim` |
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| `ylabel` | `plt.ylabel` |
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| `ylim` | `plt.ylim` |
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### Scikit-learn
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‎messages.json‎

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{
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"install": "messages/install.txt",
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"1.0.1": "messages/1.0.1.txt",
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"1.0.2": "messages/1.0.2.txt"
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"1.0.2": "messages/1.0.2.txt",
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"1.0.3": "messages/1.0.3.txt"
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}

‎messages/1.0.3.txt‎

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v1.0.3
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New snippets are added for Matplotlib and Pandas.
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To see the snippets, run the `Python Data Science Snippets: Open Readme` command in the command palette.
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Enjoy!

‎snippets/matplotlib/annotate.sublime-snippet‎

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<snippet>
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<content><![CDATA[
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plt.annotate(${1:text}, ${2:xy})
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]]></content>
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<tabTrigger>annotate</tabTrigger>
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<scope>source.python</scope>
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<description>plt.annotate</description>
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</snippet>

‎snippets/matplotlib/bar_label.sublime-snippet‎

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<snippet>
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<content><![CDATA[
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plt.bar_label(${1:container})
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]]></content>
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<tabTrigger>bar_label</tabTrigger>
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<scope>source.python</scope>
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<description>plt.bar_label</description>
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</snippet>

‎snippets/matplotlib/barh.sublime-snippet‎

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<snippet>
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<content><![CDATA[
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plt.barh(${1:y}, ${2:width})
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]]></content>
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<tabTrigger>barh</tabTrigger>
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<scope>source.python</scope>
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<description>plt.barh</description>
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</snippet>

‎snippets/matplotlib/fill_between.sublime-snippet‎

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<snippet>
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<content><![CDATA[
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plt.fill_between(${1:x}, ${2:y1}, ${3:y2})
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]]></content>
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<tabTrigger>fill_between</tabTrigger>
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<scope>source.python</scope>
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<description>plt.fill_between</description>
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</snippet>

‎snippets/matplotlib/hist.sublime-snippet‎

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<snippet>
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<content><![CDATA[
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plt.hist(${1:x}, bins=${2:10})
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]]></content>
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<tabTrigger>hist</tabTrigger>
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<scope>source.python</scope>
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<description>plt.hist</description>
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</snippet>

‎snippets/matplotlib/imread.sublime-snippet‎

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<snippet>
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<content><![CDATA[
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plt.imread(${1:fname})
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]]></content>
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<tabTrigger>imread</tabTrigger>
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<scope>source.python</scope>
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<description>plt.imread</description>
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</snippet>

‎snippets/matplotlib/imsave.sublime-snippet‎

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<snippet>
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<content><![CDATA[
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plt.imsave(${1:fname}, ${2:arr})
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]]></content>
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<tabTrigger>imsave</tabTrigger>
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<scope>source.python</scope>
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<description>plt.imsave</description>
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</snippet>

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