In pandas, you can use the hist() function for plotting the histogram.
When you plot multiple histograms in a single plot, you may notice that the default spacing between multiple histograms can make your histograms appear cluttered.
You can use figsize and tight_layout functions from the matplotlib to adjust the space between histograms and avoid cluttering.
The following example explains how to adjust the spacing between pandas histograms.
Create a sample DataFrame,

In pandas, you can use the hist() function for plotting the histogram.
Sometimes, it could be tricky to add the global title at the top of the collection of histograms when you plot them in a single plot.
You can use the suptitle() function from matplotlib to add the centered global title to the collection of pandas histogram.
The following example explains how to plot multiple histograms using pandas hist() function and add a global title at the top of these histograms using the suptitle() function.

When you fit a random forest model in Python, it is essential to save the fitted model for future use for predicting the new dataset.
If you save the random forest model (or any other machine learning model) to a file, it will save your time for future use, especially when the model takes significant time or resources to train.
In Python, you can use the dump function from pickle and joblib packages to save the random forest model to file.

A confidence interval provides an estimated range of interval which is likely to include the unknown parameter (such as mean) of a population when you draw samples many times from the population.
In R, you can use the ggplot function from the ggplot2 library to plot the confidence interval.
The following examples explain plotting confidence Intervals using the ggplot2 library.
Plot 95% confidence interval Let’s use an example of built-in mtcars data for plotting a 95% confidence interval,

By default, you can join two pandas DataFrame based on common column name (key column) using the merge function.
If you want to join multiple DataFrames (three or more) based on key column, you can use either the merge or join function.
Method 1: merge function For example, if you have three DataFrames df1, df2, and df3 with a col1 key column among these three DataFrames. You can join these three DataFrames using the merge function as follows:

By default, pandas display only 10 rows (first and last 5 rows and truncate middle section) for large DataFrame.
However, you can use the set_option function from pandas to set the maximum rows to display for a large DataFrame.
The basic syntax for the set_option function is:
Method 1: Display limited number of rows # import package import pandas as pd pd.set_option('display.max_rows', n) Where n is the number of rows that you want to display for pandas DataFrame.