Seaborn has a built-in function for drawing barplots and countplot with geom_countplot(). These plots are very useful when you’re looking at your data in two dimensions. This blog post walks through the code that generated these figures, as well as how to make them interactive.

Seaborn’s “countplot” is a barplot with a count. It is used to show how many occurrences of each value occur in the data set. The “seaborn catplot” is similar, but instead of bars it shows dots.

Barplots are typically beneficial in a rapid exploratory data analysis to understand the variables in a dataset, whether you like them or not. We’ll see many examples of how to build barplots/countplots using Seaborn’s catplot() method in this article. Seaborn created the catplot() function a few years ago, which offers a consistent foundation for creating most typical plots using category and numerical data.

The catplot() method from Seaborn returns

access to numerous axes-level functions that use one of many visual representations to depict the connection between a number and one or more category variables.

Let’s start by loading the necessary packages.

seaborn as a sns import import matplotlib as pd import pandas plt as pyplot

We’ll utilize the NYC Cab and Limousine Commission (TLC) taxi dataset, which is accessible on Seaborn.

sns.load dataset = taxis (“taxis”)

It’s a large dataset with information on over 6000 rides in New York City.

taxis.columns Index([‘pickup’, ‘dropoff’, ‘passengers’, ‘distance’, ‘fare’, ‘tip’, ‘tolls’, ‘total’, ‘color’, ‘payment’, ‘pickup zone’, ‘dropoff zone’], dtype=”object”) taxis.shape is a word that has a lot of different meanings (6433, 14)

### Seaborn’s catplot is combined with a barplot ()

The link between a numeric and a categorical variable is shown using barplots. Categorical variable values are usually shown on the x-axis, with the bar height representing the numerical value corresponding to each of the categorical variable’s values. As a categorical variable, we’ll utilize the number of passengers that rode in a cab, and the frequency count as a numerical variable.

We may retrieve counts for each unique value of the category variable using Pandas value counts().

taxis (df) [‘passengers’] . value counts(). reset index() [“passengers”, “count”] df.columns =

This is how our data appears.

travelers counted by df 4 6 153 5 4 110 6 0 96 0 1 4678 1 2 876 2 5 277 3 3 243 4 6 153 5 4 110 6 0 96 0 1 4678 1 2 876 2 5 277 3 3 243 4 6 153 5 4 110

To create the barplot, we may use Seaborn’s catplot() method with the kind=”bar” option. Make certain that each argument is described by its name. The height and aspect variables are also used to alter the size of the barplot picture.

sns.catplot(x=”passengers”, y=”count”, kind=”bar”, data=df, height=5, aspect=1.5) sns.catplot(x=”passengers”, y=”count”, kind=”bar”, data=df, height=5, aspect=1.5) plt.xlabel(“Number of Passengers Traveled”, size=14) plt.xlabel(“Number of Passengers Traveled”, size=14) plt.xl plt.ylabel(“Count”, size=14) plt.ylabel(“Count”, size=14) plt.ylabel( size=18, plt.title(“Seaborn Barplot Example”) plt.savefig(“Seaborn barplot with catplot.png”) plt.tight layout()

Here is how the barplot looks like. We can quickly see that, single traveler taxi hire is most common among all the rides. Seaborn Barplot with Catplot

### How to Reorder Bars in a Seaborn Catplot Barplot ()

The “order” option is used to determine whether the bars in the barplot should be ordered ascending or descending. Because our dataframe is already in descending order, we are sorting the bars in decreasing order in this case.

sns.catplot(x=”passengers”, y=”count”, kind=”bar”, order = df[‘passengers’], data=df, height=5, aspect=1.5) plt.xlabel(“Number of Passengers Travelled”, size=14) plt.ylabel(“Count”, size=14) plt.title(“Seaborn Barplot Example: Decending order”, size=18) plt.tight_layout() plt.savefig(“Seaborn_barplot_with_reordering_bars_catplot.png”) How to Reorder Bar in a Barplot made with Seaborn Catplot

We just flip the order argument to sort the bars in ascending order. order=reversed(df[‘passengers’]) in this case.

### Countplot with Seaborn Catplot (Seaborn Countplot with Seaborn Catplot)

In the preceding example, the numbers on the y-axis are counts of the category variable’s values. Another simple way for creating a barplot using counts is to use countplot with just the category variable specified. This eliminates the need to use value counts() to acquire the number of observations for each category variable value. Seaborn’s catplot() method requires kind=”count” and just the x-axis option.

sns.catplot(x=”passengers”, kind=”count”, data=taxis, height=5, aspect=1.5), sns.catplot(x=”passengers”, kind=”count”, data=taxis, height=5, aspect=1.5) plt.xlabel(“Number of Passengers Traveled”, size=14) plt.xlabel(“Number of Passengers Traveled”, size=14) plt.xl plt.ylabel(“Count”, size=14) plt.ylabel(“Count”, size=14) plt.ylabel( size=18, plt.title(“Seaborn Countplot Example”) plt.tight layout() plt.savefig(“Seaborn countplot with catplot.png”)

The same barplot appears as previously.

Count plot using catplot by Seaborn ()

We utilize the order option to rearrange the bars in the countplot, and this time we need to obtain the correct order. The value counts() method is used in this example to retrieve the order of the values in descending order.

sns.catplot(x=”passengers”, kind=”count”, order = taxis[‘passengers’].value_counts().index, data=taxis, height=5, aspect=1.5) plt.xlabel(“Number of Passengers Travelled”, size=14) plt.ylabel(“Count”, size=14) plt.title(“Seaborn Countplot Example”, size=18) plt.tight_layout() plt.savefig(“Seaborn_countplot_with_catplot_reordering_bars_in_decending_order.png”) Seaborn countplot: Reordering bars in descending order

### Watch This Video-

The “seaborn catplot stacked bar” is a visualization that uses the seaborn library to plot data. It can be used in conjunction with the seaborn’s function called “barplot”. The function allows for plotting of categorical variables and counts.

### Frequently Asked Questions

#### How do you plot continuous and categorical variables?

A: You can plot continuous and categorical variables using the boxplot, which is a type of histogram.

#### How do you plot two categorical variables?

A: This is not a question that can be answered with one answer. However, it is possible to plot two categorical variables, such as age and gender. They are both categorical variables because they only have two categories within them.

#### What is the difference between Countplot and Barplot?

A: Countplot is a type of graph where the vertical axis represents frequencies and the horizontal axis represents time. Bar plots are more straightforward in that they show value on both axes at once, with no customisation available.

#### Related Tags

- seaborn countplot percentage
- seaborn barplot show values
- seaborn catplot
- seaborn catplot figure size
- seaborn barplot example