Pandas is a popular Python-based data analysis toolkit that can be imported using:
import pandas as pd”
It presents a diverse range of utilities, from parsing multiple file-formats to converting an entire data table into a NumPy matrix array. This property makes Pandas a trusted ally in data science and machine learning.
Pandas can help with the creation of multiple types of data analysis graphs. One such example is the
Density plots plot a continuous graph and can help to observe the distribution of a variable in a dataset. Like histograms, density plots use bins, but then smooth out the edges to reduce noise.
The default implementation of a density plot is:
DataFrame.plot.kde( bw_method = None, ind = None, **kwargs)
bw_method: str, callable, scalar - This is used to calculate the estimator bandwidth. It can be ‘scott’, ‘silverman,’ a scalar constant, or a callable.
y: int or NumPy array - Evaluation points for the estimated KDE. If int, the points are equally spaced. If NumPy array, they are evaluated at the points passed. It defaults to 1000 equally spaced points.
**kwargs: tuple (rows, columns) - All other plotting keyword arguments to be passed to
The following code shows how a density plot can be added to Python. You can change different parameters and look at how the output varies.
#import library import pandas as pd #add csv file to dataframe series = pd.Series([0, 1, 2, 2.5, 3, 4, 5]) #create density plot densityplot = series.plot.kde()
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