The hue parameter allows to compare different categories based on the

6 min readDec 10, 2020

Sometimes my son will be screaming for some toy and I’ll try to seriously talk to him about his attitude. I’ll sternly count to ten and say “you can’t always have what you want.” Trying to mold character. Then my wife comes in. “He’s just tired. Put him to bed.” And she’s right. His want for that particular dinosaur was not his need. He needed rest. I was missing the forest for the screams.

The heights of bars show the mean value for each category. When we are asked the spent amount for a person with no children, we can estimate based on the average value which is 1400. That would be the most likely value. Similarly, for a person with 2 children, the estimation will be around 900.

For the love of God, if you’re sleepy just go to bed. The human brain is useless when tired, you need to turn it off and on again. Tell your partner you love them and go to bed. If you’re the hangry type, eat something.

It seems like there is a positive correlation between the spent amount and salary which is expected. The more money you have, the more you are likely to spend. We also see that married people are likely to earn and spend more money than single people.

2D histogram provides an overview of how two variables change concurrently. The darker regions contain more data points. We can say most people are in the lower region of both ‘AmountSpent’ and ‘Salary’ columns.

The concept of grouping visualizations with similar functionalities make it easier to learn the library. For instance, different visualizations on distributions can be created with the displot functon by changing the kind parameter.

What I learned after having children is that every outburst is not what it seems. Every complaint is not the problem. Sometimes we’re talking about wants but our real problem is needs.

Even among adults, a fight is rarely what it seems. A fight is a feeling, a feeling of not being heard, not being seen, not being tended to. And a feeling comes from a body, a body that may have a lot of other stuff going on. So, like a Doctor checking blood pressure before anything, you should check your inner child first. Are you hungry? Are you sleepy? Need to poo-poo? Then you can move on to the adult.

Kde plots are also used to visualize distributions. Instead of using discrete bins like histograms, kde plots smooth the observations with a Gaussian kernel. As a result, a continuous density estimate is produced.

Early in our marriage (when we fought more) we tried to talk everything out before bed and I’d just end up nodding off in the middle of some serious disclosure. We’d just end up fighting and sleep deprived.
In the previous plot, we saw that married people earn more money than single people in general. Let’s plot the distribution of the “Salary” column for married and single people separately.
Sometimes my daughter will tell me (repeating back what we’ve told her), “babies can’t tell us what they want so they cry.” Are adults so different? We know lots of words, but do we know what we need? Are our needs really so different from a child’s? Don’t we all just need food, love, and care?

The range of spent amount is narrow for young people compared to other age groups. All age groups have outliers on the upper side. The line in the middle of box shows the median (the middle value) of the variable.2D histograms combine 2 different histograms on a grid (x-axis and y-axis). Thus, we are able to visualize the density of overlaps or concurrence. In other words, we visualize the distribution of a pair of variables.The concept of grouping visualizations with similar functionalities make it easier to learn the library. For instance, different visualizations on distributions can be created with the displot functon by changing the kind parameter.

The hue parameter allows to compare different categories based on the

Written by As Salat

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