chapter 9: visualization with seaborn

call _______ to display the object figure or graph

plt.show()

what does .histplot() do?

provides frequency counts for continuous data

Seaborn is imported as:

sns

you can use ____________ to create bar charts

sns.barplot()

creates a separate plot for each region within a plot function:

col = 'Region'

matplotlib.pyplot is imported as:

plot

you can use ________ to create a histogram

.histplot()

seaborns ______________ method can be used to make scatter plots of data

.scatterplot()

what does the top and bottom of the dispersion line represent in barplots?

95% confidence interval

in sns.barplot(), by default, seaborn represents the mean of the data as the height of the bar and represents the dispersion of the data with:

a small grey line that crosses though the top of the bar

when making a lineplot, seaborn is acting a lot like a pd.groupby() where it is...

automatically aggregating over groups of data to produce the plots

another way to show both central tendency and dispersion is using:

boxplot

what happens when you add the 'hue' argument in barcharts?

creates grouped bar charts

regression is a technique for fitting a model to your data that can...

help you better understand how two variables in your data set are related and make predictions

using sns.lineplot(), if we hand seaborn data where there are many values for each point, what happens?

it will assume we wan the mean plotted, and automatically handle aggregating, calculating the mean, and then plotting the line

We can make plots of the frequency of categorical data using:

sns.countplot()

example of countplot:

sns.countplot(x = "Region", data = df_stateinfo)

scatter plot with regression line:

sns.lmplot(x = 'Rates.Violent.All', y = 'Rates.Property.All', data = dfbig)

.histplot() uses the argument 'bins=' to

specify the number of bins in the histogram

often when we make a lineplot...

the line is actually representing a summary of lots of data points