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lessons/3_Project.ipynb

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"source": [
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"🔔 **Question**: Let's take a look at the final data frame.\n",
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"\n",
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"1. How many rows and columns are there in the total dataframe?\n",
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"1. <span style=\"color:purple\"> How many rows and columns are there in the concatenated DataFrame?</span>\n",
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"2. How many numeric columns are there in the dataset?\n",
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"3. What data type are the values in the `poverty_level` column?"
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"3. What data type are the values in the `poverty_level` column?\n"
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]
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},
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{
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"## 🥊 Challenge 4: Count Values\n",
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"The first thing we will want to do is count values of some features. \n",
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"1. Run `value_counts()` on the `poverty_level` column. \n",
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"2. Look at the [documentation](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html) to see how you can normalize the output of `value_counts()`."
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"2. <span style=\"color:purple\"> Look through the [documentation](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html) and **normalize** the output of `value_counts()`.</span>"
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]
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"source": [
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"## 🥊 Challenge 8: Bar Plot of Value Counts\n",
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"\n",
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"Finally, let's create our bar plots. Fill in the blanks below, following the steps.\n",
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"Finally, let's create our bar plots. Fill in the blanks below, following the steps. \n",
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"\n",
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"1. Run a **normalized** `value_counts()` on the `general_health` column of `df_above`.\n",
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"2. Run `plot()` on the output of the resulting DataFrame. Enter the values for two arguments: `kind` must be set to `bar`, and `alpha` must be set to `.5`."
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"2. Run `plot()` on the output of the resulting DataFrame. Enter the values for two arguments: `kind` must be set to `bar`, and `alpha` must be set to `.5`.\n"
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]
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{
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"df_below['general_health'].value_counts(normalize=True).plot(kind=..., alpha=...,color='maroon');"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"🔔 <span style=\"color:purple\"> **Question**: What is the `alpha` parameter doing? Read through the [documentation](https://pandas.pydata.org/docs/user_guide/visualization.html) to find out.</span>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},

solutions/3_Project.ipynb

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"## 🥊 Challenge 4: Count Values\n",
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"The first thing we will want to do is count values of some features. \n",
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"1. Run `value_counts()` on the `poverty_level` column. \n",
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"2. Look at the [documentation](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html) to see how you can normalize the output of `value_counts()`."
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"2. Look through the [documentation](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html) and **normalize** the output of `value_counts()`."
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]
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},
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{

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