""""""
"""
Here is a bar chart that uses three different randomly generated sets of data:

These three datasets look identical! As far as we can tell, they each have the same mean and similar confidence intervals.

We can get a lot of information from these bar charts, but we can't get everything. 
For example, what are the minimum and maximum values of these datasets? How spread out is this data?

While we may not see this information in our bar chart, these differences might be significant and worth understanding 
better.
"""
"""
You work as a scientist and are measuring the amounts of plastic in different bodies of water. 
You're interested in comparing data collected from different locations.

We've imported four different datasets using NumPy and have combined them into one DataFrame, df.
"""

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns

# Take in the data from the CSVs as NumPy arrays:
set_one = np.genfromtxt("dataset1.csv", delimiter=",")
set_two = np.genfromtxt("dataset2.csv", delimiter=",")
set_three = np.genfromtxt("dataset3.csv", delimiter=",")
set_four = np.genfromtxt("dataset4.csv", delimiter=",")

# Creating a Pandas DataFrame:
n=500
df = pd.DataFrame({
    "label": ["set_one"] * n + ["set_two"] * n + ["set_three"] * n + ["set_four"] * n,
    "value": np.concatenate([set_one, set_two, set_three, set_four])
})

# Setting styles:
sns.set_style("darkgrid")
sns.set_palette("pastel")

# Add your code below:

sns.barplot(data=df, x="label", y="value")
plt.show()


