分布：hist、kde、box

Before averages, look at the whole spread. A distribution plot shows how often each value appears across a single column.

A histogram drops values into bins and draws a bar for each. Tall bars mark the values that show up most often.

Too few bins hide detail, too many add noise. Adjusting bins tunes how coarse or fine the histogram looks.

A KDE plot draws a smooth curve instead of bars, estimating the density of values. It reveals the overall shape at a glance.

Want both the bars and the smooth line? Add kde=True to a histogram and seaborn overlays the curve for you.

One hump means a single common range, two humps hint at two groups. The number of peaks often signals hidden subgroups in your data.

A box plot squeezes the distribution into five numbers: the median, the middle half, and the reach of the data.

The line inside is the median, the box is the middle 50 percent, and the whiskers stretch toward the typical extremes.

Dots beyond the whiskers are flagged as outliers. Box plots make these unusual values jump right off the screen.

Add a category to x and the box plot splits by group. Now you can compare the spread of income across each team at once.

Reach for a histogram or KDE to see shape, and a box plot to compare groups and catch outliers fast.

You want to compare spread across categories and spot outliers.

You can now read a column three ways: histograms for counts, KDE for smooth shape, and box plots for spread and outliers. 📈