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revise part 1 and part 2
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lessons/01_python_data_visualization.ipynb

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"cell_type": "markdown",
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"# Python Data Visualization\n",
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"# Python Data Visualization: Part 1\n",
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"\n",
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"* * * \n",
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"Syntax: call the **dataframe** (`gm_latest`), followed by the plot type (`.hist()`): `gm_latest.hist()`\n",
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"Let's check out the [documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.hist.html) of `pd.hist()`. The required input values is a column of a dataframe. We no longer need to select the column using square brackets, but simplying passing in the name of the column to the parameter `column`. Now you can see how `pandas` differs from `matplotlib`. The function `pd.hist()` is not meant to be a generic plotting method; it is rather specific to the dataframe where the data comes from. "
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"Let's check out the [documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.hist.html) of `pd.hist()`. The required input value is a column of a dataframe. We no longer need to select the column using square brackets, but simplying passing in the name of the column to the parameter `column`. Now you can see how `pandas` differs from `matplotlib`. The function `pd.hist()` is not meant to be a generic plotting method; it is rather specific to the dataframe where the data comes from. "
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lessons/02_python_data_visualization.ipynb

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"🎬 **Demo**: Showing off something more advanced – so you know what Python can be used for!<br> \n",
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"\n",
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"### Sections\n",
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"6. [Scatter Plot](#section6)\n",
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"7. [Line Plot](#section7)\n",
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"8. [Subplot](#section8)\n",
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"9. [Customization](#section9)\n",
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"7. [Demo: Change Plot Styles](#section10)"
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"7. [Scatter Plot](#section6)\n",
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"8. [Line Plot](#section7)\n",
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"9. [Subplot](#section8)\n",
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"10. [Customization](#section9)\n",
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"11. [Demo: Change Plot Styles](#section10)"
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