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In this lesson, we will discover how to use Seaborn to create bar charts for statistical analysis.

Seaborn is a Python data visualization library that provides simple code to create elegant visualizations for statistical exploration and insight.

Learn Seaborn Introduction

Seaborn can also calculate _aggregate statistics_ for large datasets. To understand why this is helpful, we must first understand what an _aggregate_ is. 

An aggregate statistic, or aggregate, is a single number used to describe a set of data. One example of an aggregate is the average, or _mean_ of a data set. There are many other aggregate statistics as well.

Suppose we have a grade book with columns `student`, `assignment_name`, and `grade`, as shown below.

<div class="narrative-table-container">

|student|assignment_name|grade|
|-|-|-|
|Amy|Assignment 1|75|
|Amy|Assignment 2|82|
|Bob|Assignment 1|99|
|Bob|Assignment 2| 90|
|Chris|Assignment 1|72|
|Chris|Assignment 2| 66|
|...|...|...|

</div>

To calculate a student's current grade in the class, we need to *aggregate* the grade data by student. To do this, we'll calculate the average of each student's grades, resulting in the following data set:

<div class="narrative-table-container">

|student|grade|
|-|-|
|Amy|78.5|
|Bob|94.5|
|Chris|69|
|...|...|

</div>

On the other hand, we may be interested in understanding the relative difficulty of each assignment. In this case, we would aggregate by assignment, taking the average of all student's scores on each assignment:

<div class="narrative-table-container">

|assignment_name|grade|
|-|-|
|Assignment 1|82|
|Assignment 2|79.3|
|...|...|

</div>


In both of these cases, the function we used to aggregate our data was the average or mean, but there are many types of aggregate statistics including:
* Median
* Mode
* Standard Deviation

In Python, you can compute aggregates fairly quickly and easily using Numpy, a popular Python library for computing. You'll use Numpy in this exercise to compute aggregates for a DataFrame.

Understanding Aggregates

Throughout this lesson, you'll use Seaborn to visualize a Pandas DataFrame. 

DataFrames contain data structured into rows and columns. DataFrames look similar to other data tables you may be familiar with, but they are designed specifically to be used with Python. 

You can create a DataFrame from a local CSV file (CSV files store data in a tabular format).

To create a DataFrame from a local CSV file you would use the syntax:

```py
df = pd.read_csv('file_name.csv')
```

The code above creates a DataFrame saved to a variable named `df`. The data inside of the `df` DataFrame comes from the data in the local CSV file named `file_name.csv`.

Once you have prepared and organized a Pandas DataFrame with your chosen dataset, you are ready to plot with Seaborn!


Using Pandas For Seaborn

In most cases, we'll want to plot the mean of our data, but sometimes, we'll want something different:
* If our data has many outliers, we may want to plot the _median_.
* If our data is categorical, we might want to count how many times each category appears (such as in the case of survey responses).

Seaborn is flexible and can calculate any aggregate you want.  To do so, you'll need to use the keyword argument `estimator`, which accepts any function that works on a list.

For example, to calculate the median, you can pass in `np.median` to the `estimator` keyword:
```py
sns.barplot(data=df,
  x="x-values",
  y="y-values",
  estimator=np.median)
```

Consider the data in **results.csv**. To calculate the number of times a particular value appears in the `Response` column , we pass in `len`:
```py
sns.barplot(data=df,
  x="Patient ID",
  y="Response",
  estimator=len)
```


Calculating Different Aggregates

In this lesson, you'll learn how to use Seaborn to create bar charts for statistical analysis.

Seaborn is a Python data visualization library that provides simple code to create elegant visualizations for statistical exploration and insight. Seaborn is based on Matplotlib, but improves on Matplotlib in several ways:

* Seaborn provides a more visually appealing plotting style and concise syntax.
* Seaborn natively understands Pandas DataFrames, making it easier to plot data directly from CSVs.
* Seaborn can easily summarize Pandas DataFrames with many rows of data into aggregated charts.

If you're unfamiliar with Pandas, just know that Pandas is a data analysis library for Python that provides easy-to-use data structures and allows you to organize and manipulate datasets so they can be visualized. To fully leverage the power of Seaborn, it is best to prepare your data using Pandas. 

Over the next few exercises, we will explain how Seaborn relates to Pandas and how we can transform massive datasets into easily understandable graphics.

Introduction to Seaborn

Take a look at the file called **results.csv**. You'll plot that data soon, but before you plot it, take a minute to understand the context behind that data, which is based on a hypothetical situation we have created:

Suppose we are analyzing data from a survey: we asked 1,000 patients at a hospital how satisfied they were with their experience.  Their response was measured on a scale of 1 - 10, with 1 being extremely unsatisfied, and 10 being extremely satisfied. We have summarized that data in a CSV file called **results.csv**.

To plot this data using Matplotlib, you would write the following:

```py
df = pd.read_csv("results.csv")
ax = plt.subplot()
plt.bar(range(len(df)),
        df["Mean Satisfaction"])
ax.set_xticks(range(len(df)))
ax.set_xticklabels(df.Gender)
plt.xlabel("Gender")
plt.ylabel("Mean Satisfaction")
```

<img src = "https://content.codecademy.com/programs/dataviz-python/unit-5/intro-to-seaborn/seabornL1demoBarchart.png">

That's a lot of work for a simple bar chart! Seaborn gives us a much simpler option. With Seaborn, you can use the `sns.barplot()` command to do the same thing.

The Seaborn function `sns.barplot()`, takes at least three keyword arguments:
* `data`: a Pandas DataFrame that contains the data (in this example, `data=df`)
* `x`: a string that tells Seaborn which column in the DataFrame contains other x-labels (in this case, `x="Gender"`)
* `y`: a string that tells Seaborn which column in the DataFrame contains the heights we want to plot for each bar (in this case `y="Mean Satisfaction"`)

By default, Seaborn will aggregate and plot the *mean* of each category. In the next exercise you will learn more about aggregation and how Seaborn handles it.

Plotting Bars with Seaborn

Sometimes we'll want to aggregate our data by multiple columns to visualize nested categorical variables. 

For example, consider our hospital survey data.  The mean satisfaction seems to depend on `Gender`, but it might also depend on another column: `Age Range`.

We can compare both the `Gender` and `Age Range`  factors at once by using the keyword `hue`. 

```py
sns.barplot(data=df,
            x="Gender",
            y="Response",
            hue="Age Range")
```


The `hue` parameter adds a nested categorical variable to the plot.


<img src = "https://content.codecademy.com/programs/dataviz-python/unit-5/intro-to-seaborn/seabornHue.svg">
*Visualizing survey results by gender with age range nested*.

Notice that we keep the same x-labels, but we now have different color bars representing each `Age Range`.  We can compare two bars of the same color to see how patients with the same `Age Range`, but different `Gender` rated the survey.


Aggregating by Multiple Columns

In this lesson you learned how to extend Matplotlib with Seaborn to create meaningful visualizations from data in DataFrames.

You've also learned how Seaborn creates aggregated charts and how to change the way aggregates and error bars are calculated.

Finally, you learned how to aggregate by multiple columns, and how the `hue` parameter adds a nested categorical variable to a visualization. 

#### To review the seaborn workflow:

##### 1. Ingest data from a CSV file to Pandas DataFrame.

```py
df = pd.read_csv('file_name.csv')
```

##### 2. Set `sns.barplot()` with desired values for `x`, `y`, and set `data` equal to your DataFrame.

```py
sns.barplot(data=df, x='X-Values', y='Y-Values')
```

##### 3. Set desired values for `estimator` and `hue` parameters.

```py
sns.barplot(data=df, x='X-Values', y='Y-Values', estimator=len, hue='Value')
```

##### 4. Render the plot using `plt.show()`.

```py
plt.show()
```

Review

By default, Seaborn will place _error bars_ on each bar when you use the `barplot()` function.

Error bars are the small lines that extend above and below the top of each bar. Errors bars visually indicate the range of values that might be expected for that bar.

<img src = "https://content.codecademy.com/programs/dataviz-python/unit-5/intro-to-seaborn/errorBar.png">


For example, in our assignment average example, an error bar might indicate what grade we expect an average student to receive on this assignment.

<img src = "https://content.codecademy.com/programs/dataviz-python/unit-5/intro-to-seaborn/seabornCi1.png">

There are several different calculations that are commonly used to determine error bars.

By default, Seaborn uses something called a *bootstrapped confidence interval*.  Roughly speaking, this interval means that "based on this data, 95% of similar situations would have an outcome within this range".  

In our gradebook example, the confidence interval for the assignments means "if we gave this assignment to many, many students, we're confident that the mean score on the assignment would be within the range represented by the error bar".

The confidence interval is a nice error bar measurement because it is defined for different types of aggregate functions, such as medians and mode, in addition to means.

If you're calculating a mean and would prefer to use standard deviation for your error bars, you can pass in the keyword argument `ci="sd"` to `sns.barplot()` which will represent one standard deviation. It would look like this:

```python
sns.barplot(data=gradebook, x="name", y="grade", ci="sd")
```

Modifying Error Bars


Recall our gradebook from the previous exercise:

<div class="narrative-table-container">

|student|assignment_name|grade|
|-|-|-|
|Amy|Assignment 1|75|
|Amy|Assignment 2|82|
|Bob|Assignment 1|99|
|Bob|Assignment 2| 90|
|Chris|Assignment 1|72|
|Chris|Assignment 2| 66|
|...|...|...|

</div>
<br>

Suppose this data is stored in a Pandas DataFrame called `df`.

The same Seaborn command that you previously learned (`sns.barplot()`) will plot this data in a bar plot and automatically aggregate the data:
```py
sns.barplot(data=df, x="student", y="grade")
```
In the example above, Seaborn will aggregate grades by `student`, and plot the average `grade` for each student.