Introduction to Python Pandas Plotting

In This Article, You Will Learn About Python Pandas DataFrame Operation.

Python Pandas Plotting – Before moving ahead, let’s know about Python Pandas Correlations

Table of Contents

Pandas Plotting

To create diagrams such as Histogram, Pie-Chart, Bar graph, etc., in Python, Pandas take help from Python Matplotlib library and use its plot() method.

Note: You will learn later in detail about Python Matplotlib Library.

Example – Plotting Pandas Data set in a graph by importing Matplotlib.

				
					import pandas as pd

import matplotlib.pyplot as plt

file = pd.read_csv('file.csv')

file.plot()

plt.show()

				
			
python-pandas-plotting

As a result, it returned a graph of dataset of Pandas.

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Scatter Plot

A Scatter Plot needs to have an x-axis and a y-axis.

The plot has the argument “kind = scatter” to specify Scatter Plot.

Example – Plotting a Scatter graph of Pandas data set.

				
					import pandas as pd

import matplotlib.pyplot as plt

data = pd.read_csv('data.csv')

data.plot(kind = 'scatter', x = 'Dur.', y = 'Cal')

plt.show()

				
			
python-pandas-plotting

As can be seen, it returned a Scatter (graph) of dataset of Pandas.

Example – Plotting a Scatter graph of dataset of Pandas.

				
					import pandas as pd

import matplotlib.pyplot as plt

data = pd.read_csv('data.csv')

data.plot(kind='scatter', x='Dur.', y='X')

plt.show()

				
			
python-pandas-plotting

As a result, it returned a Scatter (graph) of dataset of Pandas.

Histogram

A Histogram needs to have only one column.

The plot argues “kind = hist” to specify a histogram.

A histogram shows the frequency of a given value, e.g., how many quantities of product are sold from Jan to Oct.

Example – Plotting data set into Histogram.

				
					import pandas as pd

import matplotlib.pyplot as plt

data = pd.read_csv('data.csv')

data["Dur."].plot(kind='hist')

plt.show()

				
			
python-pandas-plotting

As shown above, it returned a Histogram (graph) of dataset of Pandas.

If you find anything incorrect in the above-discussed topic and have any further questions, please comment below.

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