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Welcome back.

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Now that we have seen a little
bit about how Matplotlib works,

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let's talk briefly about
different chart types.

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As we work through
the rest of this course,

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you will get a better sense of how and
when to use each type.

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Charts come in a wide
range of visual styles and

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are used to represent different things.

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Matpotlib supports a lot of options.

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Line, bar, pie, scatter plot,
histogram, box plot,

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heat maps and candlestick charts
are some of the more common charts.

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In this course,
we'll be showcasing scatter, histogram and

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box plots with Matplotlib.

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I'll touch on some of the others as well,
and

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included links in the teacher's notes
to other charts for further reading.

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Let's talk about the when's and
why's of some of these chart types.

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A line chart is used to identify trends or
patterns in data and

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commonly used for
exploring trends over time.

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They can be used to compare multiple
groups by using different lines.

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For example, the total sales of several
products over a period of time.

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A bar chart is most effective when
comparing categories of data.

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They can also be used, like a line chart,
for tracking changes over time.

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When used in this fashion they, are best
applied to large changes in the data.

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A bar chart will have two axes.

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One typically with numerical data and
the other with a category.

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For example,

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the number of different types of apples
sold each week at a farmers' market.

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Or for a time example, the total
population of the world since 1000 BCE.

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A pie chart is best used when comparing
parts of a whole at a snapshot in time,

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instead of a change over time.

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A pie chart would answer questions like,
what is

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the percentage of each type of apples
sold last week at our farmer's market.

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A pie chart will easily show that,
for example, 23% were red delicious,

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18% were golden delicious and
15% were granny Smith.

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Scatter plots are similar to line charts
in that they both have a horizontal and

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vertical axis.

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However, scatter plots are used
to show how much one variable is

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impacting by another or its correlation.

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We use scatter plots to show
relationships between values.

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The plotter points are markers, can be
different sizes to showcase importance,

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and different color to show
specific data buckets.

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It allows us to quickly visualize
the distribution of the data and

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notice any outliers.

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We can see if there's a positive,
negative, or

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non-existent correlation between data
based on the scatter plot results.

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Histograms look like bar charts,
however, looks can be deceiving as they

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are not the same, and
are indeed used for different purposes.

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Histograms are used to show distributions
of variables while bar charts

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are used to compare the variables.

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Unlike a bar chart a histogram
won't have gaps between data.

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Empty values may be possible however
if there are no data points for

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particular value.

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In a histogram chart, the data
are split into different intervals or

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bins, to show the frequency of
distribution of continuous data.

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This allows for the inspection
of the data distribution, and

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will show outliers or skewed data.

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When using a histogram,
choosing an appropriate number of bins and

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their width is important for
meaningful and accurate reporting.

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Box plots, sometimes called box and
whisker plots.

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Combine the functionality,
the bar chart, with a histogram.

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They allow for the quick examination of
and comparison between different sets of

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data while displaying statistical
information about the data.

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It allows for
visualizing the minimum first quartile,

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medium, third quartile, and
maximum values of a data set.

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Wow, that's a lot like a high
school statistics class.

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Put more simply it allows us to
see the overall distribution,

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central value and
variability of a data set.

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Much like a histogram choosing
the number of bins and

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their width is an important
consideration for reporting.

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Finally, I'd also like to briefly touch
on heatmaps and candlestick charts.

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While we won't be using them in this course,

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you're likely to come across them.

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A heatmap is a chart in which the area
inside recognized boundaries is shaded in

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proportion to the data being represented.

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For example you could have a heatmap
representing population density.

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Countries with higher populations will be
represented with different colors than

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countries with lower populations.

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Candlestick charts are heavily
used in a financial sector.

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While they bear resemblance to box charts,
that where their similarities end.

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Each candlestick will typically show
one day of price movement of a stock,

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currency or derivative.

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It's like a combination of a line and
bar chart showing

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an overtime trend while also showing
the daily information for the data.

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Specifically they show the open,
close, high, and low values for

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security and are a cornerstone
of financial technical analysis.

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Wow, that's a lot of charting options, and

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it only scratches the surface of
the charts available in Matplotlib.

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It's also just the beginning of
the when and why to use each chart.

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Choosing the chart type is predominantly
determined by the questions about your

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data and how to best represent your
data for the intended audience.

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In addition to picking
the proper chart type,

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there is one other aspect of reporting
that is important to remember, scale.

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This is the value you mark on the axis
to show the relationship between

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the units that are being measured.

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Often, we may want to utilize multiple
chart types to showcase our data.

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When doing so, we need to keep
scale in mind across our charts, so

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that our data viz efforts
aren’t misleading.

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We’ll examine this a bit more as we look
at more charting options with matplotlib.

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Now is a good time to take a short break.

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Get up and stretch a bit before we
look at a real world dataset and

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see how to visualize it,
using a variety of charts.