Scatter plots and line charts based on pandas dataframes and seaborn

Scatter plots:

Similar to previous section that we saw Seaborn combined with Pandas framework made visualization too easy and understandable in comparison to do every details by using matplotlib, in this section, we use the same tips dataset to create scatter plot:

In fact, we are plotting the total bill versus amount of tips and the different colors show that if the customer was a smoker or not and the shape of markers show the time of the meal purchase, which again is pretty self-explanatory. The final scatter plot is shown here:

Line charts:

In comparison to what we saw for scatter plots, line charts emphasize on continuity of the data. Here we use fMRI dataset to show the dependency of signal strength of MRI machine versus timepoints:

Command of 'fmri = sns.load_dataset("fmri")' load the dataset into a Pandas dataframe and 'sns.relplot(x="timepoint", y="signal", kind="line", data=fmri)' plot the dataset for x-axis as timepoint and y-axis as signal strength and the kind of the plot is specified as line.